The global healthcare analytics market was valued at $55.52 billion in 2025 and is on track to exceed $166 billion by 2030, growing at a 24.6% CAGR. Healthcare AI spending by providers alone hit $1.4 billion in 2025, with $600 million directed to ambient clinical documentation and $450 million to coding and billing automation.

Behind these numbers is a sector under immense financial and operational pressure. Clinician burnout is at crisis levels, with physicians spending 13 hours weekly on indirect patient care tasks. Claim denial rates above 10% have surged from 30% of providers in 2022 to 41% in 2025. And payers are now deploying AI to deny claims at a speed and scale that manual workflows cannot match.

The main question is not whether to invest in healthcare analytics, but where to invest to generate the fastest measurable return.

This article examines three high-impact areas where healthcare analytics is delivering proven results: patient outcomes, operational efficiency, and revenue cycle performance. We also outline the data infrastructure required to make analytics work across the enterprise.

Predictive analytics in healthcare: improving patient outcomes

Predictive analytics has become the fastest-growing segment in healthcare analytics, expanding at a 26.5% CAGR through 2030. The core value proposition is: use machine learning models to identify high-risk patients before their conditions escalate, enabling proactive interventions that reduce hospitalizations, readmissions, and mortality.

Early detection and risk stratification

Predictive models achieve substantially better accuracy when they incorporate social determinants of health (SDoH) alongside clinical data. Factors like housing stability, food security, transportation access, and income level have a measurable impact on patient outcomes. 

Roughly half of hospital readmissions are rooted in social determinants, making them more influential than clinical comorbidity factors alone. 

Kaiser Permanente integrated SDoH data into its IBM Watson Health predictive analytics platform alongside EHR and claims data, enabling identification of high-risk patients for targeted care plans that reduced hospitalizations and improved chronic disease management. 

As health systems adopt value-based care models, the ability to layer SDoH data into analytics pipelines is becoming a competitive differentiator for population health management.

NYU Langone developed NYUTron, a large language model that examines physicians’ notes to predict patient outcomes, including 30-day rehospitalization risk with 80% accuracy. 

At Mount Sinai, machine learning models developed during the COVID-19 pandemic analyzed patient history, vital signs, and lab results at admission to predict the likelihood of critical events such as intubation. 

Blue Cross NC deploys ML to proactively identify members at risk of serious health events by analyzing patterns like missed follow-up visits and co-occurring conditions.

Healthcare systems deploying AI-based predictive analytics regularly report 10 to 20% reductions in readmission rates. UnityPoint Health achieved a 25% reduction using an AI clinical decision support tool, while Intermountain Healthcare documented a 15-20% reduction across units using AI-triggered intervention pathways. With unplanned readmissions costing the U.S. healthcare system roughly $26 billion annually, even a modest reduction translates to significant savings per institution.

Personalized treatment plans

Beyond risk stratification, machine learning models are enabling precision treatment planning by analyzing individual genetic profiles, clinical biomarkers, environmental factors, and lifestyle data to match patients with the therapies most likely to succeed.

In oncology, Tempus AI has built one of the largest multimodal clinical datasets in the industry, connecting molecular sequencing data with clinical records from more than 50% of U.S. oncologists. 

The company’s PurIST algorithm helps clinicians select between first-line chemotherapy regimens for advanced pancreatic cancer based on tumor subtyping. Northwestern Medicine became the first health system to integrate Tempus’ generative AI clinical copilot directly into its EHR, enabling real-time, AI-driven treatment insights at the point of care. 

In cardiovascular care, predictive models analyze individual risk factors against networks of similar patients to produce personalized risk projections, helping cardiologists tailor prevention strategies to each patient’s profile.

The shift toward AI-assisted treatment selection is significant because it addresses a core limitation of traditional evidence-based medicine: population-level trial data doesn’t account for individual patient variability. 

Healthcare analytics for operational efficiency

Operational inefficiencies cost the U.S. healthcare system over $250 billion annually in administrative complexity alone. Healthcare analytics targets the highest-waste areas: clinical documentation, staffing, scheduling, and resource allocation.

Ambient clinical documentation

Ambient AI scribes represent healthcare AI’s first breakout category. The segment generated $600 million in revenue in 2025, growing 2.4x year over year. 100% of health systems now report some usage of ambient clinical documentation tools, making it the most universally adopted AI use case.

The clinical evidence is compelling. A randomized trial conducted by UW Health and published in NEJM AI demonstrated that ambient AI notetaking reduced documentation time by 30 minutes per day per provider, improved diagnosis billing accuracy, and produced a clinically meaningful reduction in burnout scores. After the trial (August 2024 through March 2025), UW Health rolled out the system across clinics and hospitals in Wisconsin and Illinois.

Workforce optimization and resource allocation

AI-powered scheduling and staffing tools analyze historical patient flow data, seasonality, and real-time variables to optimize workforce deployment. Hospitals using these systems report improved bed turnover rates and staffing efficiency, directly reducing operational costs while maintaining care quality. Combined with data pipeline engineering that connects EHR systems, scheduling platforms, and staffing databases, health systems can create real-time visibility into operational bottlenecks.

Patient flow optimization, powered by real-time monitoring and predictive modeling, addresses one of the most persistent challenges in emergency departments. 

AI systems can:

• Predict patient census levels
• Recommend optimal staffing ratios
• Identify discharge readiness

This reduces wait times and improves throughput. Hospitals deploying AI-driven patient flow tools can expect a 4% to 10% improvement in avoidable days and a 10% to 20% increase in resource utilization.

HonorHealth, for example, implemented a care operations automation system that achieved an 86% early discharge plan rate and saved $62 million by reducing excess patient days.

AI-powered revenue cycle management

Revenue cycle management is where healthcare analytics delivers the most immediate and quantifiable financial return. Health systems spend over $140 billion annually on revenue cycle operations. Nearly 20% of claims are denied on average, and as many as 60% of those denials are never appealed, representing millions in lost revenue per health system.

In 2025, more than 30% of providers prioritized AI and automation implementation for seven specific revenue cycle use cases, up from four to five use cases in previous years. 63% of providers have introduced AI into their RCM workflows, and according to a 2025 industry survey, 72% of executives report that their highest priority for revenue cycle investment is technology such as automation and AI.

Autonomous medical coding

Autonomous coding is one of the most mature AI applications in revenue cycle management. Natural language processing translates clinical notes into medical codes with high accuracy, reducing manual effort and coding errors. 

Inova Health System: After implementing Nym’s autonomous medical coding engine, Inova achieved $1.3M in annual savings, a 50% decrease in weekly revenue sitting in DNFB, and a 10% increase in average charges per ED encounter. 

Cleveland Clinic: The AI coding assistant can read a clinical document in less than two seconds and process more than 100 documents in 1.5 minutes. 

Denial management in healthcare: prevention and recovery

AI-driven denial prevention uses predictive models trained on historical claims data and payer behavior to flag high-risk claims before submission. 

Thoughtful AI: Healthcare organizations working with Thoughtful AI typically experience a 75% reduction in preventable denials, 80% decrease in operational costs, and up to 95% less manual work for RCM staff. AI Agents consistently perform at 95%+ accuracy. 

Auburn Community Hospital, an independent 99-bed rural access hospital, uses robotic process automation, natural language processing, and machine learning in revenue cycle management. Over the years, Auburn has seen a 50% decrease in discharged-not-final-billed cases, a more than 40% improvement in coder productivity, and a 4.6% increase in case mix index. The overall impact on its bottom line was a little over $1 million, more than 10 times its investment. 

Banner Health automated much of its insurance coverage discovery using a combination of a service that finds each patient’s coverage and a bot that adds that coverage to the patient’s account in various financial systems. A different bot handles insurance company requests for additional information. The health system also uses a bot to automatically generate appeal letters based on certain denial codes. Banner also developed its own predictive model that determines whether a write-off is warranted based on certain denial codes and the low probability of payment. 

Healthcare financial analytics at scale

The healthcare financial analytics market grew from $9.74 billion in 2025 to $11.42 billion in 2026, at a 17.2% CAGR. This growth reflects the shift from treating the revenue cycle as a back-office function to managing it as a strategic asset. 

AI-enhanced revenue operations create unified views of patient journeys from initial contact through billing and follow-up care, enabling organizations to identify bottlenecks, optimize conversion rates, and align clinical and business operations.

90% of healthcare leaders report that revenue cycle labor challenges exacerbate operations, with increasing denials costing hospitals over $20 billion annually and manual processes driving an average $25 rework cost per denied claim. 

At scale, organizations that unify financial analytics across coding, billing, collections, and patient payments gain the ability to forecast cash flow, benchmark payer performance, and quantify the ROI of individual AI investments across the revenue cycle.

Healthcare analytics applications by category, maturity, and expected ROI

How to build a healthcare data analytics platform

Deploying healthcare analytics at scale requires more than selecting AI tools. It demands a coherent data engineering strategy that addresses the unique challenges of healthcare data: strict regulatory requirements (HIPAA, GDPR, EU AI Act), diverse data types (structured EHR records, unstructured clinical notes, imaging data), and legacy system dependencies.

Healthcare data integration and interoperability

The foundation of any healthcare analytics initiative is a unified data layer. Health systems typically operate dozens of disconnected systems: EHRs, lab information systems, billing platforms, scheduling tools, and claims management software. Building interoperable data pipelines that connect these sources, using standards like HL7 FHIR, enables the cross-functional data access that analytics requires.

As Xenoss engineers have observed across enterprise implementations, 80% of success in AI projects comes from proper problem analysis and domain understanding. In healthcare, this means mapping clinical workflows, identifying where data quality breaks down, and building governance frameworks before deploying models. A rushed AI deployment on top of fragmented, low-quality data will fail, regardless of model sophistication.

Healthcare compliance and data security architecture

Healthcare data breaches cost an average of $7.42 million per incident in 2025, and organizations take 279 days to detect and contain one. Shadow AI, the use of unauthorized AI tools by staff without IT approval, is now present in 40% of hospitals. These risks make compliance-first architecture design a prerequisite for any analytics initiative.

A strong infrastructure includes encrypted data pipelines, role-based access controls, continuous monitoring, and audit trails that satisfy HIPAA and emerging AI-specific regulations. For organizations processing data across jurisdictions, the architecture must also address data residency requirements.

Technology stack decisions

Enterprise health systems face a critical choice between building custom analytics infrastructure and integrating vendor solutions. In Xenoss experience delivering custom AI solutions for Fortune 500 organizations, the most effective approach is typically hybrid: standardize on a core data platform for integration and governance, layer in best-of-breed AI models for specific use cases, and maintain the flexibility to swap components as the market evolves.

Key infrastructure components include a centralized data lake or lakehouse architecture, real-time streaming capabilities for clinical monitoring, robust MLOps processes for model training, deployment, and monitoring, and API-driven integration layers that connect analytics outputs to clinical workflows and EHR systems.

Risks and tradeoffs

Healthcare analytics adoption carries specific risks that organizations should evaluate before committing resources.

• Data quality dependency. AI models are only as reliable as the data they process. In healthcare, where clinical notes are often inconsistent, and EHR data is fragmented across systems, poor data quality leads to inaccurate predictions. Organizations should invest in data governance and quality monitoring before scaling analytics.
• Model generalizability. 60% of studies faced challenges with generalizability across diverse patient populations. A model trained on one hospital’s data may perform poorly at another due to differences in patient demographics, clinical practices, and coding conventions. Poor cross-site generalizability remains a major barrier to clinical deployment.
• Regulatory uncertainty. Healthcare AI governance is still evolving. The EU AI Act, FDA guidance on AI/ML-based software, and state-level AI privacy laws create a complex compliance landscape. Organizations need dedicated governance structures to keep pace.
• Change management. Technology adoption without cultural buy-in produces limited results. RCM leaders emphasize investing in change readiness, communication, and leadership capability alongside system deployments. 86% of revenue cycle leaders see value in AI, but only 44% of corporate leaders do, highlighting the internal alignment gap.
• Cost and timeline. Enterprise-grade healthcare analytics infrastructure requires significant upfront investment in data integration, compliance, and talent. Organizations should plan for 12 to 18-month implementation timelines for end-to-end analytics platforms, with a phased rollout starting from the highest-ROI use cases.

Bottom line

Organizations integrating AI across clinical workflows, revenue operations, and patient engagement are seeing measurable results: 30% efficiency gains, reduced readmissions, and millions recovered in previously lost revenue. The healthcare predictive analytics market alone is projected to reach $140 billion by 2035, driven by EHR volumes, value-based care mandates, and the growing sophistication of AI/ML models.

For health system leaders evaluating analytics investments, the clearest path to ROI starts with the use cases that address the most acute operational pain: ambient clinical documentation, autonomous coding, and denial prevention. From there, building toward a unified data engineering foundation enables the expansion into predictive patient analytics, population health management, and precision treatment planning.

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And long-term value is exactly what businesses will need to succeed with digital transformation this year. Most innovations will revolve around AI (generative and agentic), cloud computing optimization, and data governance.

This may seem similar to what’s been relevant for the past few years, but now CIOs and VPs of Digital Transformation feel even more pressure to step beyond experiments and justify each technological decision with clear business value. AI ROI will become the most important factor in whether AI projects succeed or stall, with 54% of executives expecting ROI within six months or less.

John Roese, Chief Technology Officer and Chief AI Officer at Dell Technologies, admits in his interview with Deloitte, the importance of ROI in any technical initiative at their company:

In the front end of our process, we require material ROI signed off by the finance partner and the head of that business unit. That discipline has kept experiments as experiments, and production only happens if there is solid ROI.

In this digital consulting guide, we’ll analyze the modern digital transformation trends, identify why businesses fail with their DT initiatives, and develop a remediation strategy to survive the booming digital market and remain afloat.

The core question we’ll answer is: “How do you stop fearing digital transformation failure and which steps to take to lay the foundation for success from the get-go?” Digital transformation is more than replacing digital technologies or improving existing software (it’s modernization). Digital transformation services are about changing how your business works.

The 2026 digital transformation agenda: Agentic AI, data readiness, and intelligent operations

This year will mark a new era in artificial intelligence and machine learning, as businesses stop chasing the AI bubble and choose well-tested AI solutions, extensively trained on their enterprise and customer data, rather than overhyped one-off experiments that only burn budgets without delivering measurable results.

This shift is reflected in recent executive sentiment. A KPMG study surveying more than 2,500 global executives found that 68% of organizations plan to scale AI use cases in production in 2026, up from just 24% in 2025. 

Joe Depa, a Global Chief Innovation Officer at EY, supports this point of view:

Last year felt like testing the waters with pilots and proofs of concept. This year is different. It is about going all in on AI and doing it with speed and responsibility.

We’re also witnessing a shift from generative to agentic AI, with 88% of organizations already investing in building AI agents to improve operational efficiency and automate the most time- and effort-consuming workflows. This, however, doesn’t mean companies are abandoning Gen AI; it’s just that they’re seeing the first benefits from generative AI systems and seeking new opportunities.

But for agentic AI and other AI and automation technology solutions to work, businesses have to consider their all-time favourite asset, data, which won’t lose its relevance, neither in 2026, nor in the years to come.

Data readiness, storage, governance, and management practices will define the ROI speed and long-term value of digital transformation initiatives. Business leaders will increase their technology investments in data infrastructure, with priorities distributed as follows: 

We’ll also see an increase in data lakehouse adoption, enabling businesses to store large volumes of structured and unstructured data while maintaining the performance and ACID compliance of a data warehouse. Data will become the backbone of AI infrastructure reliability, differentiating high-performing digital leaders from laggards.

When AI models, data platforms, legacy systems, and third-party tools collide in production, organizations are tested for resilience, digital maturity, and change capacity. Bottlenecks rarely appear where teams expect them. They surface in legacy integrations, brittle data pipelines, regulatory constraints, and employee resistance to new ways of working.

Therefore, the purpose of a successful digital transformation strategy is to precisely determine the steps needed to embed new technologies into your current operations. That’s why digital transformation consulting services will also focus on organizational changes rather than solely on AI and data engineering.

Why digital transformations fail and how to flip the odds

57% of business leaders say the pace of digital innovation at their companies is slow due to foundational issues in their technology stacks. For 50% of the other survey respondents, it’s data quality. But eventually, each business faces distinct challenges in undertaking a time-consuming endeavor such as digital transformation. Next, we analyze why large organizations fail at their DT programs and define what we can learn from their example.

Starbucks: From digital transformation leader to weak financial growth

The era of AI and automation proved more difficult for Starbucks than expected. Several high-profile initiatives aimed at modernizing store operations, supply chain management, and planning stalled, creating friction instead of efficiency. Automation intended to speed up service and improve availability ended up hurting store execution and customer experience, contributing to uneven performance and slowing growth.

After an unsuccessful launch of the demand planning and forecasting software Siren Systems, Starbucks struggled with inaccurate inventory visibility and unreliable stock replenishment. AI-driven tools failed to account for fragmented supplier data, legacy systems, and the real-world complexity of stores. At the same time, labor reductions made in anticipation of automation gains worsened service quality, forcing leadership to pause, reassess, and partially roll back its automation-first strategy.

Lessons learned: Starbucks’ case shows that digital transformation fails when technology is expected to compensate for weak data foundations, complex operations, and human workflows. 

AI and automation deliver value only when they are layered on top of resilient processes, integrated systems, and a change management strategy that treats technology as an enabler.

UK supermarket, Asda, recovers from a failed £1 billion IT overhaul

Asda’s long-planned digital transformation, aimed at replacing Walmart-owned systems with a new independent IT stack, turned into a major operational setback. What was intended to modernize the retailer instead led to shelf shortages, payroll errors, online order failures, lost sales, and customer dissatisfaction, directly impacting day-to-day operations across stores and e-commerce.

During the planning and execution of the migration, costs escalated to £1 billion, far exceeding initial expectations. The scale and complexity of decoupling from Walmart systems exposed deep integration challenges across the supply chain, finance, and people management. 

Executive chairman Allan Leighton later pointed to “poor integration, insufficient end-to-end testing, and inadequate capacity planning” as the core reasons the transformation failed. Stabilizing the business and returning to previous sales targets was expected to take around six months, into the second half of 2026.

Lessons learned: Asda’s case shows that large-scale digital transformations fail when core systems are replaced faster than the organization’s operational readiness. Modern digital products cannot compensate for weak integration, limited real-world testing, and governance that allows risk to accumulate unnoticed.

Successful transformation requires phased execution, realistic capacity planning, and the discipline to slow or stop change before disruption reaches customers and frontline employees. 

Jaguar Land Rover: Cyberattack halts production and exposes digital risk

In late August 2025, Jaguar Land Rover (JLR) suffered a major cyberattack that forced the company to shut down most of its global IT systems, halting vehicle production at its factories in the UK, Slovakia, Brazil, and India. The company proactively took systems offline to contain the breach, but the impact was severe: production lines stopped, design and engineering software went dark, and tens of thousands of employees were told not to report to work.

JLR’s digital environment had been deeply outsourced and connected, including cybersecurity oversight under an £800m contract with Tata Consultancy Services, aimed at modernizing and managing its IT infrastructure. When hackers breached those systems, JLR had little ability to isolate individual plants or functions, leaving the attack to trigger a near-complete operational standstill. 

The disruption rippled through its extensive supply chain of hundreds of component makers, threatening supplier viability and wider economic effects; the incident has been described as one of the most costly cyberattacks in UK history, with estimated economic losses of up to £1.9 billion.

Lessons learned: Jaguar Land Rover’s digital experiences show that highly connected digital ecosystems can become single points of failure when resilience and segmentation are weak. Outsourcing critical functions (especially cybersecurity) without robust oversight, threat modeling, and isolation controls leaves the gains from transformation vulnerable to disruption. 

In practice, transformation programs must embed cyber risk as a strategic risk constraint, building strong incident response, segmented architecture, and continuity plans that prevent localized breaches from collapsing entire operational systems.

Selecting a digital transformation consulting partner: Decision framework

A digital transformation consulting partner is a worthy investment if you realize that the consequences of potential risks and issues far outweigh the cost of hiring a digital transformation consultant. But beware of impostors. As, for instance, this Reddit user expresses an opinion on hiring consultants for agentic AI implementation: 

The consultant shake-out is real. There’s a huge gap between people who’ve built production agent systems and people who’ve watched demos. That gap is about to become very obvious.

We’ve prepared a comprehensive evaluation framework that can help you choose the best-fit digital transformation consultants.

Your digital strategy consulting partner should be well-versed in your industry to understand the intricacies, regulatory compliance requirements, and overall business specifics. This knowledge will make the team more proactive in suggesting workarounds if your DT strategy needs to change during execution. A proactive digital strategy consultant is more willing to go the extra mile and deliver beyond your expectations.

Building the business case: ROI benchmarks and success metrics in digital transformation strategy consulting

When setting KPIs and success metrics for the digital transformation strategy, it’s important to remember that DT is a long-term undertaking. Often, businesses focus only on short-term goals, but true transformation comes from aligning operational, strategic, and tactical goals.

Leslie Willcocks, professor at the London School of Economics and Political Science and co-author of 75 tech books, names seven capabilities that define digital transformation success:

This [digital leadership] requires being very good at seven core capabilities, namely strategy, integrated planning, embedded culture, program governance, digital platform, change management, and navigation capabilities.

To achieve this seven-fold success, set feasible KPIs on the macro and micro business levels. Below are potential examples:

Macro-level KPIs (strategic impact):

• Revenue growth or margin improvement that can be attributed to digital initiatives
• Time-to-market reduction for new products or services
• Cost-to-serve reduction across core processes
• Percentage of core workflows digitally enabled or automated
• Customer experience metrics (CSAT, NPS, churn) linked to digital changes
• Risk reduction indicators (compliance incidents, downtime, security exposure)

Micro-level KPIs (execution and adoption):

• User adoption rates of new platforms and tools
• Process cycle-time improvements at the operational level
• Data quality and availability metrics (freshness, completeness, accuracy)
• Model or automation reliability (error rates, override frequency)
• Change readiness indicators (training completion, usage depth, feedback loops)
• Delivery health metrics (on-time releases, rollback frequency, defect rates)

The key is not to maximize every metric at once, but to sequence them intentionally. Early digital transformation phases should emphasize adoption, stability, and data readiness; later phases should increasingly weigh revenue impact, scalability, and competitive differentiation.

Technical ROI benchmarks from KPMG vary depending on company size and current team strategy.

The ROI benchmarks show that digital transformation returns are driven less by how much enterprises spend and more by how effectively they execute. Smaller and early-adopting organizations outperform because they move faster, learn sooner, and operate with fewer integration and governance bottlenecks, while transformation-focused companies benefit from compounding returns over time. 

Takeaway: ROI increases when leaders simplify architectures, strengthen data foundations, clarify ownership, and protect transformation investments from short-term cost pressures, treating digital transformation as a long-term operating system change rather than a collection of isolated projects.

Change management: The human dimension of digital transformation

55% of employees report transformation fatigue from the rapid pace and intense pressure of the modern digital transformation programs. Alex Adamopoulos, Chairman and CEO at Emergn, explains this term as follows:

Transformation fatigue isn’t burnout; it’s when teams stop adapting. The best product-led organizations don’t let that happen. They build environments where people can learn fast, adjust, and keep moving. That’s how you win at continuous change.

People are central to a digital transformation strategy. If you’re not considering how they work, what they need, and how to improve their lives, your DT project won’t yield the promised results. Here are a few time-tested recommendations from our digital consulting firm on the change management process:

• Assemble a centralized digital transformation team led by a VP of Digital Transformation. You can also assign a Chief AI Officer who will oversee how AI, data management, and data analytics workloads intersect, affect one another, and impact long-established business processes.
• Develop a blueprint for every system, process, or workflow change, define what will change, who it affects, how it will be rolled out, and what risks it introduces. The goal is to understand the ripple effects in advance and implement changes in a controlled way, with clear success criteria and rollback options.
• Apply project management practices to digital transformation, only on a larger scale. Develop project charts to track key milestones, using a RACI (responsible, accountable, consulted, and informed) matrix to always know which stakeholders to involve in key decisions.
• Conflict management and resolution are another crucial aspect of the change management strategy, as they are bound to arise with large-scale initiatives like DTs. Seek common ground in every situation and treat each employee as an important contributor to the digital transformation’s success.

Final thoughts

Digital transformation isn’t a set-in-stone strategy that should deliver results simply because a company invested a large budget and assembled a huge team of the best software engineers. It’s a subtle, ever-evolving process that should be tailored to each company. 

If, for instance, your systems are tightly interconnected so that even a minor disruption can completely stall your business operations, consider this in advance to avoid unpleasant surprises. A digital transformation roadmap should support business models and improve their operations, not disrupt them unnecessarily.

Xenoss brings extensive experience delivering digital transformation strategy consulting across industries and geographies, helping organizations identify risks early and translate them into stronger execution and governance.

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Meanwhile, companies are doubling down. Corporate AI spending will hit approximately 1.7% of revenues in 2026, more than double last year’s allocation.

So what separates the 12% of organizations achieving both revenue growth and cost savings from AI from the majority spinning their wheels?

PwC’s global chairman, Mohamed Kande, put it bluntly:

People forgot the basics.

The companies seeing results focused on clean data, well-defined processes, and strong governance before deploying AI. Everyone else rushed to adopt the technology without the foundation to support it.

Key takeaways

• 56% of companies report no meaningful gains from AI investments, while only 12% have achieved both revenue growth and cost savings
• Unplanned downtime costs Fortune 500 manufacturers $1.4 trillion annually (11% of revenue), with predictive maintenance reducing these costs by 25-40%.
• 53% of bankers rank fraud detection as their top AI use case for 2026, ahead of back-office automation (39%) and customer service (39%)
• Gartner predicts 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. However, over 40% of agentic AI projects will be canceled by 2027 due to escalating costs or unclear business value
• High-performing organizations are three times more likely to successfully scale AI agents than their peers, with the key differentiator being workflow redesign rather than technology sophistication

This piece breaks down the current state of enterprise AI adoption, explores proven applications in predictive maintenance and fraud detection, and outlines practical strategies for achieving ROI from custom AI implementations.

Enterprise AI adoption in 2026: The gap between spending and results

Three years after generative AI tools entered mainstream business use, adoption rates have stabilized at a high level. 

The BCG AI Radar report, which surveyed 2,360 executives, found that 72% of CEOs now serve as their organization’s primary decision-maker on AI, twice the share from the previous year. 

The gap between “using AI” and “getting value from AI” keeps growing. And it explains why so many executives are frustrated. Half of them believe their job security depends on successfully implementing AI strategies.

The financial commitment reflects this urgency. Companies plan to spend approximately 1.7% of revenues on AI in 2026, more than double the 0.8% allocation in 2025. Technology and financial services firms lead this investment, with both sectors planning to allocate roughly 2% of revenues to AI initiatives.

The gap between AI experimentation and scaled production

The oft-cited statistic that “95% of AI projects fail” from MIT requires context. Most pilots stall due to organizational factors: unclear success metrics, weak executive sponsorship, skills gap, cultural resistance, rather than technical limitations. 

Technology alone doesn’t separate AI winners from the rest. Only 6% of organizations qualify as “AI high performers,” meaning they attribute 5% or more of EBIT to AI initiatives. 

The defining factor is workflow redesign. High performers are nearly three times more likely to have fundamentally restructured processes around AI capabilities (55% compared to 20% for everyone else). 

They also put real money behind it: over 20% of digital spend goes to AI, versus just 7% at average organizations. Perhaps more telling, these companies are 3.6 times more likely to pursue enterprise-wide transformation, targeting growth and innovation rather than settling for isolated efficiency wins. 

Agentic AI adoption: Enterprise projections and market realities

AI agents, autonomous systems capable of planning and executing multi-step tasks without continuous human prompting, represent the next frontier of enterprise automation. 

40% of enterprise applications will incorporate task-specific AI agents by the end of 2026, up from less than 5% in 2025. In its best-case scenario, agentic AI could generate approximately 30% of enterprise application software revenue by 2035, exceeding $450 billion.

There is a warning worth heeding: over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. 

About 130 of the thousands of vendors claiming “agentic AI” capabilities offer genuine agent technology, with many engaging in “agent washing” by rebranding existing products such as chatbots and RPA tools.

AI ROI benchmarks for custom AI solutions

AI investment keeps climbing, with Gartner projecting enterprise AI software spend to nearly triple to $270 billion this year. Only about one-third of enterprises have seen tangible cost reduction or revenue increase from AI in the past 12 months. 

Matt Marze, Vice President at New York Life Insurance Company, told CIO magazine that his team approaches AI investments “the same way we think about all our investments,” evaluating each project against operating expense reduction, margin improvement, and revenue growth. 

Banking use cases: AI-powered customer service and fraud detection

Bank of America’s Erica virtual assistant has surpassed 3 billion client interactions since its 2018 launch, now serving nearly 50 million users and averaging 58 million interactions per month. 

The bank reports that 98% of users find the information they need through Erica, significantly reducing call center volume. 

On the employee side, over 90% of Bank of America’s workforce uses Erica for Employees, which has reduced IT service desk calls by more than 50%. 

According to Holly O’Neill, president of consumer, retail, and preferred lines of business, the two million daily consumer interactions with Erica save the bank the equivalent of 11,000 staffers’ daily work.

On the fraud side, the UK government’s Cabinet Office reported that AI-powered detection tools helped recover £480 million between April 2024 and April 2025, the highest amount ever recovered by government anti-fraud teams in a single year. The Fraud Risk Assessment Accelerator, developed internally, cross-references data across government departments to identify vulnerabilities before they are exploited.

Manufacturing use cases: Predictive maintenance and quality inspection

Shell’s predictive maintenance platform, built with C3 AI, now monitors over 10,000 pieces of critical equipment across its global operations, ingesting 20 billion rows of data weekly from more than 3 million sensors. The system identified two critical equipment failures in advance, allowing preventive maintenance that saved approximately $2 million and “substantially improved operational reliability.”

In automotive, Siemens and Audi deployed AI-powered visual inspection in Audi’s car body shops, where 5 million welds are made daily. According to NVIDIA, integrating the models with Siemens’ Industrial AI Suite helped Audi achieve up to 25x faster inference directly on the shop floor, where defects can be addressed in real time. A separate Siemens deployment documented in R&D World showed an automotive OEM reducing unplanned downtime by 12% within 12 weeks of connecting more than 10,000 assets across four continents using Senseye Predictive Maintenance.

The predictive maintenance market is projected to grow from $10.93 billion in 2024 to over $70 billion by 2032, reflecting a compound annual growth rate exceeding 26%.

Industrial AI implementations must account for the specific demands of manufacturing environments. Edge deployment capabilities become critical for operations in remote locations or facilities with limited connectivity. Systems must integrate with existing PLCs, SCADA infrastructure, and ERP platforms while meeting regulatory and safety requirements. 

Custom solutions developed with industrial data integration expertise address these technical constraints while delivering production-ready analytics.

What defines a custom AI solution for enterprise automation

To be effective in enterprise automation, AI must be purpose-engineered, designed with the organization’s data, workflows, controls, and compliance frameworks embedded from the outset.

1. Domain-specific AI models

Custom solutions often extend or fine-tune large foundational models with proprietary data and business logic to ensure accuracy and relevance in domain tasks. This goes beyond generic training to include task-specific reasoning, industry taxonomies, and operational constraints.

2. Workflow orchestration

AI must do more than generate outputs. It must execute multi-step workflows:

• Automate decisions where business rules match data evidence
• Trigger human review loops when confidence is low
• Ensure audit trails and accountability by design

This orchestration layer serves as the navigator between AI predictions and enterprise systems.

3. Integration with core systems

Integrations with CRM, ERP, document repositories, compliance systems, and analytics platforms are central to delivering ROI and closing the loop between AI automation and existing enterprise processes.

4. Governance, security, and compliance

Custom solutions embed governance by default, including role-based access, explainability logs, policy controls, and anomaly reporting, to meet regulatory and risk standards.

5. Outcome-driven KPIs

The shift from experimentation to performance mandates operational KPIs rather than model metrics:

• cycle time reduction
  
• cost per transaction
  
• error rates and exception volume
  
• compliance pass rates
  
• real ROI dashboards monitored by business owner

Strategic recommendations for scaling enterprise AI

For manufacturing organizations:

1. Prioritize predictive maintenance: Focus initial AI investments on reducing the $2.8 billion annual downtime costs
2. Implement Edge Computing: Deploy AI systems capable of operating in remote manufacturing locations
3. Develop visual inspection capabilities: Leverage computer vision for real-time quality control
4. Create multi-agent systems: Design collaborative agent networks for complex production optimization

For financial services:

1. Enhance fraud detection: Invest in real-time transaction monitoring and pattern recognition
2. Deploy customer service agents: Implement virtual assistants to handle routine inquiries and reduce call center volume
3. Automate compliance processes: Use AI for KYC verification, AML surveillance, and regulatory reporting
4. Focus on identity management: Develop robust systems for managing both human and AI agent identities

Universal success factors:

1. Adopt the 10-20-70 framework: Invest 70% of resources in people and process transformation
2. Implement strong governance: Establish AI firewalls and security frameworks before scaling
3. Measure outcome-driven KPIs: Focus on operational metrics rather than model performance alone
4. Plan for multi-agent orchestration: Design systems that can evolve from single agents to collaborative networks

Conclusion: AI that drives enterprise value

The era of AI experimentation is giving way to performance-aligned custom solutions. CIOs and business leaders are moving beyond proof-of-concept to enterprise-grade deployment by engineering AI into business processes with governance, integration, and measurable outcomes at the core.

Custom AI solutions perform best when they address specific business problems with domain expertise, embed governance from the start, integrate with existing systems, and measure real operational outcomes. Whether the application is predictive maintenance, reducing million-dollar downtime incidents, or fraud detection protecting billions in transactions, the pattern is consistent: foundation first, technology second.

In 2026 and beyond, success will be determined not by how many AI tools you deploy, but by how your AI delivers measurable impact on business outcomes across the organization.

The post Custom AI solutions for enterprise automation: ROI benchmarks, use cases, and adoption trends appeared first on Xenoss - AI and Data Software Development Company.

People mostly use free AI versions (97% of US consumers), which are enough to simplify their everyday routines. Businesses, on the contrary, need more niche AI solutions that help them achieve measurable business outcomes: enhanced product throughput, increased revenue, or improved suppliers’ verification procedures. That’s why 96% of industrial organizations plan to increase their manufacturing AI investments by 2030.

While consumers treat AI as a new “Google” (only with clear instructions), businesses perceive it more as an asset that requires continuous harnessing to produce continuous results.

We’ve prepared this analysis based on our experience delivering end-to-end AI and data services to businesses operating across different industries and countries. You’ll get clear insights into how consumer and enterprise artificial intelligence differ, why this distinction matters to modern businesses, and how companies can benefit from enterprise and industrial AI.

Enterprise AI vs. consumer AI: Retrospective analysis, definitions, and industry leaders’ views

The rise of consumer AI began in 2022, with the public announcement of ChatGPT. This was a breakthrough for large language models (LLMs). Everyone got agitated that a generative AI application had finally arrived and would take our jobs in a snap. At that time, both businesses and consumers were largely on the same page, as AI tools were free to test. Business benefits weren’t yet clearly visible because generative AI alone did not address enterprise requirements such as workflow integration, permissions, auditability, or domain accuracy.  

We were all at the point of “Innovation Trigger” on the Gartner AI hype curve. Then we passed the peak of “Inflated expectations” and stepped into a long stage of “Trough of Disillusionment”, which some claim will soon be over. A CDO at Profisee, Malcolm Hawker, mentioned in his most recent podcast episode that in 2026, businesses will slowly begin to climb the “Slope of Enlightenment”, making confident steps towards a “Plateau of Productivity”.

There is now a clear distinction between enterprise vs. consumer AI. As businesses see much more potential benefit from this technology than consumers do.

What are enterprise AI and consumer AI?

Enterprise AI is the process of implementing machine learning and generative, agentic, predictive AI, or computer vision into business operations to solve specific problems or help achieve goals. This form of AI requires up-to-date business data that must be thoroughly prepared for AI use. 

Consumer AI are publicly available generative AI services, such as ChatGPT, Gemini, Claude, DeepSeek, Grok, and Perplexity. People use them to make personal or professional queries for individual benefit only. 

To compare these notions in greater detail, see the table below.

What do experts say?

In the LinkedIn post on the difference between consumer and enterprise AI, Rodney W. Zemmel, a Global Head of Blackstone Operating Team and a former Global Leader of AI transformation at McKinsey, gives an interesting analogy:

Consumer adoption has far outpaced enterprise adoption. Why? Because the two are fundamentally different challenges. Consumer AI is a Swiss Army knife — one adaptable tool for many tasks. You might use the scissors one day, the tweezers the next, even the obscure tool for removing stones from horse hooves.

Enterprise AI, by contrast, must be a precision machine tool — reliable, repeatable, and tuned for demanding, high-stakes work. Turning a general-purpose model into something enterprise-grade requires “hardening”: infusing it with your proprietary data and context, embedding it in workflows, and adding human-in-the-loop checks.

Without these additional workarounds, AI in the enterprise won’t function properly and will pose more of a threat to the business than an actual benefit.

A Chief Revenue Officer at Typeface, Jamie Garverick, shares a complementary view: 

Consumers can experiment freely. If something’s off, they move on. Enterprises don’t have that luxury. Brand, trust, accuracy, and consistency matter every time something goes out the door.

Where does industrial AI fit into the narrative?

Amod Satarkar gives a full definition in his post by comparing industrial artificial intelligence with general (consumer) AI: 

General AI is about learning patterns from data to make predictions or decisions. Think Netflix suggestions or ChatGPT. Industrial AI, on the other hand, is built for complex, high-stakes environments like factories, energy grids, aerospace systems, or predictive maintenance of equipment. Here, the AI doesn’t just need to be smart—it must be accurate, explainable, fast, and reliable.

This means that industrial AI is a practical application of enterprise AI in manufacturing, oil and gas, energy, and logistics industries.

Neil C. Hughes, a famous author and podcast host, wrote an extensive LinkedIn article on the results of the Industrial X Unleashed  event, where he mentioned an insight into the importance of industrial AI from one of the keynote speakers:

A technician standing two hundred feet above the ground in freezing conditions cannot rely on a generic chatbot to solve a safety-critical problem. The problem is that consumer AI tools lack awareness of the context, environment, regulatory pressures, and consequences of a wrong decision.

For that reason, industrial AI system focuses more on algorithms that understand the physical world than on those that understand human language. 

Consumer AI is too general and simplistic to cover all the enterprise leaders’ needs; it can even be dangerous if relied on too heavily in industrial settings.

Key specifics of enterprise AI: What businesses need to know

After defining enterprise and industrial AI, we can focus on their core characteristics.

Proprietary data ingestion

Enterprise AI solutions produce the best results when they can query real-time business data via enterprise knowledge bases built on retrieval-augmented generation (RAG). RAG-based systems provide more accurate outputs, as they can use data beyond their training set. Vector databases are another architectural layer necessary for fast and reliable retrieval of unstructured data.

Chetan Gupta, PhD, Head of AI at Hitachi Global Research, explained the specifics of industrial data: 

Industrial data is inherently multimodal—ranging from text in manuals and logs, to video from worksites, time-series sensor data from equipment, and discrete event data from operations. In practice, effective solutions often require models that operate across one or more of these modalities.

Such peculiarities of industrial data mean that, in the case of enterprise AI, data engineers not only prepare business data for AI use but also ensure that the model architecture matches the data modalities, selecting multimodal models when data spans text, video, sensor readings, and operational logs.

Infrastructure dependence

Enterprise AI requires deep integration into the company’s workflows, which is why companies need to build a strong AI infrastructure to support model training, inference, and maintenance. This process may involve purchasing hardware and software components and defining a deployment environment (on-premises, cloud, or edge).

Chetan Gupta, for instance, emphasizes that for industrial companies, edge AI deployment is the most effective way to achieve true IT/OT convergence:

Many industrial use cases require not only on-prem solutions, but true edge deployment to meet stringent latency, reliability, and data-sovereignty requirements.

Accuracy, customization, and model fit

In enterprise settings, hallucinations are a risk. When AI influences procurement decisions, safety checks, inventory planning, predictive maintenance, or compliance reporting, even a small error can cascade into downtime or financial loss.

That’s why enterprises must invest in:

• model selection (fit-for-purpose)
• prompt and workflow engineering
• evaluation harnesses and test suites
• constraint-based outputs
• escalation and human-in-the-loop routing

Scale and integration requirements

Industrial AI should integrate not only with enterprise software (ERP/CRM/data platforms), but also with operational legacy systems such as MES, SCADA, PLCs, asset management enterprise tools, and IoT devices.

This is where enterprise AI differs most from consumer AI types: it must behave as a reliable component inside a distributed system. That’s why scaling different AI platforms can become difficult.

However, McKinsey points out that as of the end of 2025, enterprises are plucking up the courage to move beyond pilot and AI experimentation stages to scale their AI initiatives and garner enterprise-wide benefits.

Governance and compliance

Enterprise AI cannot scale without governance. Data security, data privacy, access permissions, traceability, and auditability become mandatory, especially in regulated industries and safety-critical operations.

Frank Antonysamy, Chief Growth Officer for Hitachi Digital, explains the mindset difference between industrial AI and consumer AI in this respect:

For each industry, we must understand the compliance requirements and ensure 100% adherence. There’s no choice if you want to deploy at scale in these environments.

One way in which we achieve this is through extensive simulation. We simulate millions of real-world scenarios using synthetic data. Only when we’re confident these models will behave predictably across every situation do we put them into production. It’s the opposite of the “release and refine” approach that’s common with consumer AI because in our world, you can’t afford to learn from failure in production.

AI providers increasingly recognize the importance of data security for their business clients. For example, Anthropic obtained HIPAA compliance for their Claude for Healthcare product, while OpenAI has expanded ChatGPT Enterprise with enhanced data protection and compliance features specifically designed for regulated industries.

Real-life success stories: How industrial companies benefit from enterprise AI

Enterprise AI implementation won’t happen overnight, but the investment of time and budget is well-justified, as the following examples of artificial intelligence in industrial automation prove.

Digital twin optimization at the Siemens Electronics Factory, Erlangen 

The Siemens Electronics Factory in Germany demonstrates how production digital twins can replicate physical production lines and optimize operations through AI-powered simulation. By collecting real-time data directly from machines on the factory floor and feeding it into the digital twin via IT/OT applications, the facility achieved remarkable results.

The factory reduced material circulation by 40% and energy usage by 70% through simulation-driven optimization. For automatic guided vehicle (AGV) systems, measuring data directly from the vehicles and running it through the digital twin increased simulation accuracy by more than 10%, enabling better factory floor layout decisions and smoother material flow.

ENEOS Materials: ChatGPT Enterprise at manufacturing scale

ENEOS Materials, a Japanese chemical company specializing in synthetic rubber and thermoplastic elastomers, was among the first companies in Japan to adopt ChatGPT Enterprise. Their deployment strategy offers a blueprint for enterprise AI implementation in manufacturing environments.

The results are compelling:

• More than 90% weekly active usage across the organization, with over 80% of employees reporting significant workflow gains
• 90% reduction in data aggregation and analysis time for the HR department
• Months-to-minutes compression for complex investigations using deep research capabilities
• Over 1,000 custom GPTs created across the company to address specific operational needs

Taku Ichibayashi, Manager at ENEOS Materials’ R&D Department, notes: 

To maximize our business results with AI, ensuring a secure environment for handling proprietary information was essential. ChatGPT Enterprise met our internal cybersecurity requirements and provided the output accuracy we required.

How to maximize ROI with AI for enterprise

We’ve compiled a range of best practices that can help organizations ensure enterprise AI ROI more effectively:

• Start with a data architecture assessment. Define areas for optimization (e.g., data quality issues, limited on-premises data storage, or siloed data with restricted cross-company accessibility) and establish a data strategy, which is necessary for AI implementation and use.
• Focus on clear business problems or goals. Rather than pursuing AI for its own sake, identify specific operational challenges where AI can deliver measurable improvements. Set clear KPIs to measure outcomes and tie AI initiatives to business objectives from the outset.
• Differentiate between ROI types. This means setting different expectations for enterprise AI adoption. ROI focuses on financial returns, ROE on employee productivity, and ROF on the outcomes of AI research and development initiatives.
• Redesign workflows around AI capabilities. The companies capturing the most value from AI aren’t simply deploying models on existing processes. They’re fundamentally rethinking how work gets done. McKinsey identifies this “transformation mindset” as a key differentiator between the 6% of high performers and the remaining organizations still stuck in the pilot mode.

Takeaway: With realistic expectations and a structured rollout, enterprise AI can deliver measurable results quickly. However, pursuing implementation without a clear roadmap, AI-ready data foundations, and organization-wide change management often leads to wasted spend and limited business impact.

What’s next for enterprise AI and consumer AI

More is yet to come in the enterprise AI development services. For instance, Sam Altman, CEO of OpenAI, has announced that 2026 will be the year of enterprise AI at OpenAI, which means more capabilities for organizations to adopt AI in an easy, personalized way.

Gartner’s 2025 AI Hype Cycle suggests key enabling technologies like ModelOps and AI Engineering are approaching the “Plateau of Productivity”, signaling that the infrastructure for scalable enterprise AI is maturing. 

Meanwhile, emerging capabilities like agentic AI (systems capable of autonomous multi-step workflows) are beginning to move from experimentation to early production deployment, with McKinsey reporting that 23% of organizations now scale agentic AI within their enterprises.

When it comes to AI consumer products, Foundation Capital predicts that in 2026, the focus will shift to e-commerce, with AI agents making purchases instead of humans. Already, 70% of shoppers have used AI tools in their purchasing journeys, and industry analysts expect consumers to increasingly delegate shopping, calendar management, and routine decision-making to AI assistants, which creates an entirely new category of “life manager” services or personalized virtual assistants.

Final takeaway

The gap between consumer and enterprise AI will continue to widen as businesses recognize that specialized, integrated, governance-compliant AI enterprise systems deliver fundamentally different value than general-purpose chatbots. While consumer AI products have democratized access to generative AI capabilities, enterprise AI represents the true frontier for productivity and competitive advantage.

Organizations that move beyond AI experimentation to scalable integration into enterprise workflows (by addressing data architecture, infrastructure readiness, and change management alongside model selection) will capture measurable ROI during this transition. 

The key differentiators: proprietary data ingestion through RAG architectures, deep integration with operational systems, governance frameworks that satisfy regulators, and accuracy standards that eliminate costly hallucinations.

The Xenoss team helps industrial companies make AI an integral part of their operations by building AI-ready data foundations, integrating models into real workflows and systems, and putting the right governance and monitoring in place.

The post Enterprise AI vs. consumer AI: Why industrial AI requires a different approach appeared first on Xenoss - AI and Data Software Development Company.

In regulated industries, organizations process up to 250 million documents each year across claims, underwriting, onboarding, and invoicing workflows. Documentation gaps in any of these workflows become compliance risks that surface later as audit findings, denied claims, and abandoned applications.

Automation has helped with throughput, but regulators now expect evidence-grade data: proof that every decision ties back to the correct source, that nothing critical is missing, and that extracted data is accurate. That’s a higher bar than most document capture systems were built to clear.

Therefore, investments shift from throughput-focused automation toward compliance-driven document processing, where document intelligence validates completeness, checks consistency, and flags problems before they propagate into core systems. 

The sections below break down how this works across insurance claims, underwriting, banking onboarding, and manufacturing invoicing, with practical benchmarks for evaluating compliance-ready initiatives.

Document capture vs. intelligent document processing 

Standard document capture focuses on field-level extraction. However, regulated workflows require traceability to the right source document. 

A claim file missing an adjuster note or a valuation report with mismatched fields might pass through a capture pipeline without a “system error,” but fail an audit. That same mismatch can route a claim straight to adjudication, only to trigger a denial-and-appeal cycle that costs more to resolve than the original claim.

In health insurance alone, 19% of in-network and 37% of out-of-network claims were denied in 2023, with documentation gaps cited as a leading cause.

Document processing for regulated industries reduces these risks by:

• Validating packet completeness upfront
• Checking document consistency
• Flagging missing or outdated evidence before a case reaches a core system.

Core components of document intelligence for regulatory compliance

Document intelligence relies on a defined set of components designed to meet regulatory expectations for accuracy, traceability, and control.

Data extraction with confidence scoring

Every extracted field carries a confidence score, typically expressed as a probability between 0 and 1.

A service date pulled cleanly from a structured form might score 0.98; the same field handwritten on a faxed document might score 0.62. That score determines what happens next: high-confidence values move straight through, while low-confidence values route to human review.

Rossum’s automation framework, for example, uses a default threshold of 0.975, meaning documents are auto-exported only when the system is at least 97.5% confident in each extracted field.

Confidence also rolls up to the packet level. If three of twelve documents in a claim file have low extraction confidence, the system flags the entire submission for intake review before it enters adjudication.

Document governance across ingestion, review, and release

Governance defines what happens before data reaches core systems. 

Validated ingestion channels ensure documents enter through approved sources. File-type and format checks reject submissions that don’t meet requirements. Role-based review enforces segregation of duties, so the same person can’t submit and approve a case.

Override controls matter here, too. When a reviewer changes an extracted value, the system requires a rationale, logs the change, and locks the record against silent edits.

Accuracy metrics for regulated document workflows

Overall accuracy numbers can be misleading. A system might report 96% extraction accuracy, but if errors concentrate in high-impact fields like claim amounts or policy dates, the operational risk is much higher than that number suggests.

In ML terms, precision measures the proportion of correct positive predictions, while recall measures the proportion of positives the model identified. The F1 score balances these two metrics into a single number.

In document processing terms:

• Precision: How often the system’s extracted values are correct.
• Recall: Did we find all the values we were supposed to find?

Mature programs track this specifically in critical fields, calibrating the trade-off between false acceptance (bad data that gets through) and false rejection (good data that gets flagged unnecessarily).

End-to-end visibility across document packets

Regulated decisions rarely depend on a single document. 

A claim file might include bills, clinical notes, adjuster reports, and policy documents. An underwriting submission might combine inspection reports, loss histories, and financial statements.

Packet-level visibility ensures completeness across all required documents, checks that identifiers align (same claimant, same policy, same dates), and surfaces inconsistencies before the case moves downstream.

Document intelligence for insurance claims

Insurance claims operations run on documentation: itemized bills, clinical notes, adjuster reports, policy records, and supporting evidence that arrives in dozens of formats. Small inconsistencies in any of these documents can determine whether a claim moves straight through to payment or falls into an exception queue that takes weeks to resolve.

Nearly 15% of all claims submitted to payers for reimbursement are initially denied, and 77% of those denials stem from paperwork or plan design rather than medical judgment. Administrative issues account for 18% of in-network claim denials.

Estimates show that hospitals and health systems spend $19.7 billion annually on fighting denied claims, at an average cost of $47.77 per claim. More than half of those denials (51.7%) are eventually overturned and paid, meaning billions go toward resolving claims that should have been approved in the first place.

Document intelligence addresses this by catching problems before they trigger denials.

Claim packet assembly and completeness validation

Each line of business carries its own documentation requirements. A workers’ compensation claim needs different evidence than a health insurance claim; an inpatient stay requires a discharge summary that an outpatient procedure wouldn’t.

Document intelligence models these requirements as a library of expected and conditional documents. 

When a claim arrives, the system classifies each document, attaches it to the correct record, and evaluates the packet against completion rules. If an inpatient claim is missing a discharge summary, it flags immediately rather than waiting for a reviewer to notice.

This produces a packet-level completeness score. High-completeness claims flow straight into adjudication. Low-completeness claims route to intake review with specific prompts: “missing wage statement” or “adjuster note not attached.” 

Since 45% of providers cite missing or inaccurate data as their top cause of denials, upfront completeness checks are among the highest-leverage interventions available.

Data lineage and versioning for audit trails

Regulators expect every decision to be reproducible. Data lineage tracks three things

Origin: Where did the value come from?

Transformation: How was the data cleaned or mapped?

Human intervention: Who approved an override and why?

This makes outcomes auditable. When a reviewer modifies an extracted field, the system logs the original value, the correction, the rationale, and the reviewer ID.

Banks that have adopted modern data lineage tools report 57% faster audit preparation and roughly 40% gains in engineering productivity. 

Extraction accuracy and claims adjudication alignment

Even minor extraction errors can alter outcomes. For example, a misread service date can shift a claim outside the coverage window or trigger the wrong prior-authorization rule.

Up to 49% of claims are affected by routine coding and documentation issues. Document intelligence reduces this by cross-checking extracted values against other evidence in the packet: 

• Service dates validated across clinical notes and billing records
• Billed amounts reconciled against itemized line items
• Procedure codes checked against supporting clinical documentation

When values don’t match, the system routes the claim for review with a clear explanation rather than allowing it to proceed to adjudication, where it’s more likely to be denied.

As a result, claim files remain fully reproducible during audits, with complete version histories and transformation logs.

Document intelligence for underwriting

Underwriting depends on interpreting evidence (inspection reports, valuations, financial statements, etc.) and turning that evidence into consistent, defensible risk assessments. Unlike claims, underwriting is not about adjudicating a past event, but about predicting future loss. That prediction is only as reliable as the documentation behind it.

Manual document review remains a bottleneck. Industry data shows that underwriters spend up to 40% of their time on administrative tasks, including gathering and verifying supporting documents. For commercial lines, turnaround times for standard policies have dropped from 3-5 days to as little as 12.4 minutes with AI-assisted processing, provided extraction and validation are tightly integrated.

Extracting structured insights from supporting evidence

Underwriters rely on diverse documents, such as loss histories or property photographs, that were never designed for automated processing. Underwriting document automation transforms these materials into structured inputs by classifying each document, extracting key attributes, and validating them against business rules. For example, inspection reports are parsed for building characteristics, and financial statements yield revenue, debt, and liquidity indicators.

Building reliable documentation chains

Underwriting files must show how a conclusion was reached. Document intelligence links extracted values to their source pages, maintains version histories, and records reviewer adjustments. During peer review or audit, underwriters can replay the file to see which evidence supported a pricing decision and why conflicting information was resolved a certain way.

Reducing decision variability across underwriters

Variation between underwriters is a well-known source of pricing inconsistency. By standardizing document classification, completeness checks, and extraction logic, document intelligence ensures that every submission enters review with the same normalized evidence set.

Document intelligence for banking onboarding and KYC automation

In the 2025 Fenergo global survey, 70% of institutions lost clients in the past year due to inefficient onboarding, with abandonment rates averaging around 10%.

Corporate client onboarding is particularly slow: full KYC reviews take an average of 95 days. Document intelligence helps reduce this drop-off by standardizing how evidence is captured, checked, and reconciled across the KYC process.

Structured validation of ID documents and supporting materials

Document intelligence classifies each document, extracts regulated fields, and validates them against jurisdiction-specific rules: 

• ID expiration dates
• MRZ consistency
• Issuer authenticity
• Address-issuer validity
• Income-document coherence
• Ownership declarations 

that must match corporate registries or supporting evidence.

These checks reveal issues that basic extraction misses: outdated IDs, incomplete address proofs, or income statements that contradict declared information.

Mismatch detection and risk signaling

Extracted fields from IDs, proofs of address, income statements, and declarations are cross-checked against each other and against external records. When values diverge (name variations, addresses that don’t match public records, ownership that conflicts with company filings), the system raises a structured alert and routes the case for additional review.

Audit-ready onboarding trails

What makes onboarding audits difficult is showing exactly which version was used when a decision was made, how discrepancies were resolved, and why a reviewer cleared a risk flag.

Document intelligence creates an audit-ready onboarding record by preserving every document and decision in a reproducible, time-indexed chain. Each upload becomes an immutable version with provenance metadata; extracted fields are stored alongside the model version and validation rules used to generate them; and every reviewer action is time-stamped and linked to an individual.

Document intelligence for invoice automation in manufacturing

Manufacturing invoicing looks structured on paper, but in practice, formats vary by vendor, plant, and region. Quantity and price discrepancies, missing receipts, and incorrect cost centers all create exceptions that finance and plant teams must resolve manually.

Across industries, accounts payable teams still spend about $9–10 to process a single invoice, with cycle times averaging 9.2 days and invoice exception rates around 22%.

Line-item validation and structured reconciliation

Document intelligence normalizes vendor-specific invoice layouts into a consistent schema, then applies PO-based and three-way matching rules at the line level. 

Quantities, unit prices, tax amounts, and freight charges are checked against purchase orders and goods receipts within defined tolerances. This catches issues such as overbilled units, duplicate freight, or misapplied discounts before the invoice is posted.

Cross-system lineage for financial accuracy

Each line item that passes validation ultimately feeds ERP, AP, inventory, and forecasting systems. Document intelligence maintains lineage from invoice line to PO line, receipt, and GL posting, so controllers and auditors can see precisely how a billed amount flowed into COGS, accruals, or capital projects.

Discrepancy tracking and exception clustering

Not all issues are one-off errors. Some vendors systematically overinvoice freight,  certain plants may miscode cost centers, and specific product lines may have recurring mismatches between shipping documents and invoices.

By aggregating and clustering exceptions, document intelligence highlights these patterns: which vendors generate the most mismatches, and which plants or buyers approve out-of-tolerance invoices.

Document intelligence benchmarks: accuracy and efficiency gains by workflow

The table below summarizes typical performance bands used in enterprise evaluations of document intelligence programs.

Architectural requirements for audit-ready document intelligence

Architecture determines whether extracted data holds up under regulatory scrutiny months or years after a decision. Three layers matter most.

Controlled ingestion and extraction

Documents entered through validated channels are checked for format and integrity, and the system rejects or flags inputs that fail prerequisites. 

At the extraction layer, every transformation is logged and versioned. Each field ties back to a source page, extraction logic, model version, and timestamp. Reprocessing the same document under the same configuration must yield identical results.

Governance and lineage

The governance layer maintains end-to-end traceability from source document to decision input, records reviewer actions and overrides, and enforces segregation of duties. Overrides require justification, approval, and permanent audit trails.

Ongoing accuracy monitoring

Document formats change, vendors update templates, and rules evolve. Mature programs track discrepancy rates on high-impact fields (amounts, dates, identifiers) rather than headline accuracy alone. A rise in discrepancies signals degradation before overall metrics show it. Override patterns, such as frequent fixes to the same fields or document types, identify gaps in extraction logic. Model updates undergo formal retraining cycles, are tested on validation sets, and are versioned for auditability.

Conclusion: Document intelligence as a compliance multiplier

In regulated industries, document processing is a foundation for defensible decision-making. Accuracy, completeness, and traceability now determine whether claims are paid correctly, risks are priced consistently, clients are onboarded compliantly, and invoices are approved without downstream disputes.

Document intelligence reframes automation around these requirements. By combining field-level accuracy metrics, document lineage, and embedded governance controls, organizations can drive cycle-time reduction in document workflows while limiting downstream rework.

As regulatory scrutiny increases, this compliance-first approach turns document processing from a source of risk into a measurable, scalable advantage across claims, underwriting, onboarding, and invoicing.

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What draws brands to programmatic DOOH? 

In short, advertisers are interested in high-precision targeting and clear-cut ROI for a broadcast reach of digital out-of-home. For years, teams struggled to measure the effectiveness of out-of-home ads and attribute positive lifts in key metrics to such campaigns. 

Programmatic DOOH solutions solve this problem by bringing the advertising experience closer to audience-driven buying of digital ads.

In this post, we unpack:

• DOOH meaning for the advertising industry (and the big hopes behind it!)
• How programmatic DOOH works and what features DOOH systems have
• Why now is the right time to develop programmatic DOOH products
• Unique tech challenges AdTechs have to account for
• Latest market trends and developments in the DOOH industry

What is DOOH?

Digital out-of-home advertising (DOOH) combines hardware and software technologies for displaying dynamic ads in public spaces.

Think your average billboard, but on an HD digital screen and updated in real-time, based on real-world conditions such as weather or audience demographics.

Digital OOH can be highly contextual and creative. You can run short video reels, create interactive consumer experiences, or personalize the ad based on current events — sports scores, traffic conditions, or even passing planes. DOOH ads can also be configured to generate collect customer data, measure viewer sentiment regarding your brand, or generate leads on the spot.

This translates to higher view rates, better brand recall, and follow-up actions.

I think marketers see digital OOH as a great alternative to reach people in the same hyper-relevant way as with digital, but in a channel that can’t be skipped or blocked.

Lauren Sak, Senior Marketing Director at Intersection

Due to the novelty of DOOH ads, consumers are more likely to engage with them.

In fact, 76% of DOOH viewers take action (watching videos, visiting promoted stores or restaurants) after interacting with the digital billboard.  

Finally, DOOH can be programmatic. Innovative digital out-of-home advertising companies like Lamar and Broadsign allow brands to purchase out-of-home ads at selected locations and run them at fixed times. New market entrants are sizing up custom DOOH platform development, too.

Demand for programmatic DOOH is also on the rise. 32% of US advertisers rely on a combination of programmatic and manual buying, and 28% of surveyed respondents rely exclusively on programmatic campaigns.

Other benefits of programmatic DOOH include:

• Ability to run trigger-based buying campaigns
• Innovative ways to target consumers
• Higher brand recall and awareness
• A wider audience reach a lower cost

How programmatic DOOH works: Technical architecture overview

DOOH systems have two key elements:

• Connected hardware, often equipped with cameras and sensors.
• Software backend, featuring a combination of modules for dynamic ad displays, data capture, and subsequent data analysis.

A simple DOOH system can have these components:

Such a device can be connected to a supply-side platform (SSP). The DOOH SSP, in turn, proposes the available inventory to a demand-side platform (DSP), where advertisers can place real-time bids on available inventory. Essentially, you get the same programmatic ad buying experience as for digital ads — but you purchase placements in the physical world.

The latest versions of DOOH devices also come with extra capabilities.

Environment recognition

A DOOH device can be equipped with multiple sensors:

• Temperature gauges
• Accelerometers
• Air quality sensors
• Motion sensors

These sensors can be used to create contextual ads and trigger-based buying campaigns, which fuse physical and digital realms.

For instance, as part of the “Magic of Flying” campaign, British Airways installed a digital billboard in London, equipped with an ADSB antenna. Each time a BA plane flew over the area, the billboard automatically displayed an ad, synchronized to the flight path of the plane. Such creative dynamic content significantly enhanced viewer engagement with the ad and improved brand recall.

Measuring foot traffic

Lack of measurability often deters advertisers from OOH. Programmatic DOOH changes that. You can know how many people had the potential to view your ad. You can also analyze how popular each area is to estimate the possible ad impression count.

There are different methods for measuring foot traffic next to DOOH devices:

• Smartphone counts
• Infrared (IR) sensor counts
• Using pressure sensors
• By combining sensing technology with computer vision

CityTraffic, a creation of The Netherlands company Bureau RMC, conducted foot traffic measurements in some 620 European cities, across 600 shopping streets and 110 events with high precision and with all privacy considerations.

They use a combination of the stereoscopy-based scanner, infrared sensors, mobile device MAC addresses sensor, and a mobility viewer device equipped with computer vision. This combo allows them to measure unique footfall at different locations. Many DOOH inventory providers rely on a similar approach for foot traffic measurement.

Motion and gesture detection

The latest DOOH systems include a camera connected to a computer vision system. Such a setup lets you collect non-personally identifiable audience data such as age, gender, or facial expression attributes. You can also use motion detection systems to active ad showings and deliver an immersive brand experience.

British energy company E.ON used Ocean Outdoor’s network of digital out-of-home screens in Manchester and Birmingham to create a socially conscious “Let’s clean the air campaign.”

Each screen live-streamed the person within the detection range and the amount of pollution they were breathing in at the moment (using real-time data). Messaging changed depending on the pollution levels. The campaign attracted over 2,500 U.K. residents in one weekend and drove a positive lift in brand perception.

Geo-targeting and retargeting capabilities

Sensor-based DOOH systems can also process location data for retargeting. For example, you can track the number of Bluetooth-enabled devices in the area or tag users by their phone’s MAC address. Then supply this data to advertisers for optimized targeting.

Hivestack — a full-stack programmatic digital out-of-home platform — helped Mazda create a high-precision geo campaign built around custom audiences. Using available geofencing and mobile IDs data collected by DOOH devices, Hivestack pointed Mazda towards the optimal DOOH locations for running their ads. Then the Mazda team programmatically bid on open RTB ad impressions from DOOH SSPs, buying inventory that meets their custom audience criteria.

As a result of this campaign Mazda enjoyed a:

• 21% lift in aided ad recall
• 24% lift in brand perception
• 3% lift in brand behavior

Interactive elements

DOOH systems are more than “big screens.” They have connected devices with computing and data processing capabilities. Therefore, advertisers can easily integrate third-party data into their campaigns to make them more interactive and personalized.

DOOH software platforms can process:

• Point of sale data
• Social media feeds
• Weather data
• Sports scores
• Pollution levels
• Traffic data

…and other third-party insights, obtained from data brokers.

In a recent DOOH campaign, Skoda used location and live traffic data to show passersby how long it would take them to drive to one of the U.K.’s beautiful holiday destinations. For an automotive company, that was a refreshing take on advertising. Instead of promoting the technical characteristics of their new SUV, Skoda chose to focus on the “lifestyle aspect” of car ownership. And that landed well with their target audience — families.

Interactivity also lends extra engagement to DOOH ads. An Ultraleap study found that compared to static DOOH, dynamic DOOH ads have 21% longer dwell time and result in 2X more conversions. Also, viewers spend 50% more time viewing the ad, and they are 52% more effective in increasing brand awareness.

Why invest in the development of programmatic DOOH products

Brands are intrigued with the new omnichannel customer targeting possibilities of DOOH.

According to an Alfi study, 96% of senior advertising executives believe DOOH data can improve campaign creativity and allow brands to leverage even more granular targeting.

Not only are brands now able to utilize the same audience data across channels for targeting and activation, but the increased flexibility means that mid-campaign optimization can now be applied to DOOH. For example, the best locations for driving in-store traffic or mobile downloads can be upweighted at the click of a button, and advertisers can see the impact of each media within the campaign mix and adjust accordingly.

Helen Miall, CMO of  VIOOH 

Here are five solid reasons to add programmatic DOOH to your AdTech software development roadmap. Larger advertisers are looking for high-precision targeting, transparent reporting,  and creative campaign styles. Programmatic DOOH ticks all of these boxes — and lets you optimize your operating margins too.

Advanced attribution

Programmatic DOOH lets you match device-collected data with audience insights from third-party attribution vendors to provide more precise targeting. A comprehensive data ecosystem allows advertisers to run high-performance omnichannel campaigns with DOOH in the mix.

Pepsi Max recently hosted a series of tasting challenges in malls. To retarget those prospects, they logged a unique ID of each participant using beacon technology. Then when one of the tasters entered a mall, Pepsi automatically triggered programmatic DOOH ads on screens. Clever and effective.

Data-rich inventory

DOOH can provide media buyers with rich data on each inventory asset — from average foot traffic to average viewability or ad interaction rates. This makes inventory more appealing to brands — and more profitable for DOOH system owners.

With DOOH, advertisers can purchase ad units in locations most popular with their target audience, perform advanced segmentation, or run sequential ad campaigns across channels. For example, target transit passengers with mobile ads first. Then retarget them with a related ad on a digital screen at their final destination.

Nestlé Purina, for example, leveraged data from Otto Retail to target the audience of cat owners. Based on this first-party data, they’ve selected optimal DOOH ad placements and the best time to display them. Simultaneously, they targeted this audience via online radio channels. The campaign was executed programmatically, which allowed Nestle to boost impression count by 13% without increasing the budget.

Predictive modeling

AdOps teams can further increase the precision with which DOOH captures customers at the point of maximum possible engagement once they embed predictive analytics into the DOOH stack.

Identifying engagement patterns helps media buyers estimate:

• Which locations will yield higher engagement
• What time is optimal for capturing more ready-to-buy passerbyers
• Which ad spend should the team allocate to the campaign

For DOOH vendors, expanding their offerings with predictive analytics helps retain partners and scale their impact in the client’s ad spend.

For example, after a successful DOOH campaign for Anytime Fitness, Vistar Media successfully used collected data to plan the second flight that captured a higher number of relevant venues and generated a 15% increase in sign-up intent compared to the first campaign.

Better ad experience

“Banner blindness” and high usage of ad-blocking software render digital ads less effective. Likewise, many standard digital ad formats don’t allow creating immersive viewing experiences (except for in-game advertising and native ad placements).

DOOH ads, on the other hand, can bridge the physical and digital worlds. The ad creative can be updated dynamically to be more personalized and memorable. DOOH can tie ad messaging to real-time events — weather conditions, the latest game scores, or the number of cars in the area.

For instance, ​​Sea-Doo managed to get an 80% lift in purchase intent after running a weather-based DOOH campaign. Using Foursquare’s audience and POI targeting, the watercraft seller ran ads across several key US locations with dynamic messaging, suggesting that a cloudy day shouldn’t deter you from taking a boat ride.

Easier ad rotation

Unlike standard OOH, you don’t need to change any marketing collateral once the campaign period expires. Programmatic execution lets you rapidly switch between campaigns moments after the impressions were delivered, lowering your management costs and increasing profit margins.

Better pricing dynamics

Instead of entering fixed-price agreements with advertisers, you can run real-time auctions based on OpenRTB standards. Brands can bid on available DOOH inventory and snatch the best deals for the lowest price. Or settle for the next-best option.

This allows you to adjust pricing to the current supply and demand dynamically. At the same time, DOOH owners will get higher fill rates. You can also set up your AdTech platform to support programmatic guaranteed deals or private marketplace advertising deals to retain loyal brands.

Synthetic audiences

Now that the regulations around collecting deterministic user-level data are getting tighter, brands and AdTech vendors are increasingly tapping into synthetic data capabilities.

With generative AI and predictive analytics, DOOH vendors can build audience segments that match the age, income, interests, habits, and movement patterns of real-world audiences. Media buying teams can use their understanding of this traffic to plan campaigns, collect data, and onboard new screens more effectively.

MOVE, an Australia-based DOOH audience measurement company, is already tapping synthetic audiences to help brands better understand local consumers. Its AI-augmented dataset accurately represents 2 million Australians over 14 years old, which is approximately 10% of the country’s population. Based on audience data, MOVE helps brands simulate the moving patterns of target customers and build detailed demographic profiles.

The company’s data modeling technologies reliably support DOOH market leaders, including JCDecaux, Metrospance Outdoor Advertising, APN Outdoor, QMS, and many others.

Hence, for an AdTech vendor, rolling out proprietary synthetic data capabilities can become a powerful differentiation point that helps both attract brand demand and build industry partnerships.

Tech challenges of DOOH

DOOH advertising solutions development requires knowledge of both hardware and software components of the ecosystem. Hardware market fragmentation alone can pose major roadblocks.

Since it’s a new channel, DOOH also has fewer technological standards for programmatic ad serving. At the same time, you also must account for new data types and unique creative formats.

But these shouldn’t phase you, especially if you are working with an experienced AdTech development partner.

Measuring ad viewability

To deliver effective measurement, DOOH devices have to be equipped with HD cameras and robust computer vision systems (which are complex to develop in the first place). This tech combo ensures proper rendering of the environment and ad viewability measurement.

But here’s where things get tricky: You also have physical device constraints. DOOH ads may not be easily visible from every angle. Likewise, it would be best if you minimized passerby double-counts.

OAAA attempts to address DOOH ad measurability issues with set guidelines and best practices. To accurately calculate viewability, they suggest factoring in:

• Distance between the user and DOOH display (varies by venue and device type)
• Latitude and longitude coordinates of the screen
• Average dwell time, based on the consumer’s proximity to the screen
• Cardinal direction that a screen faces

To deliver accurate reporting to buyers, you must capture and analyze a host of new input variables for different types of inventory. The Frankfurt airport employs an innovative DOOH measurement solution, designed by leading global specialists from JCDecaux and Veltys specifically for airports worldwide:

Nonstandard creative formats

DOOH screens come in different shapes and sizes. If you plan to add a DOOH asset to your inventory, you should verify that it can serve ads in adaptive HTML5 format. HTML5 allows advertisers to quickly adapt their content from other channels (mobile, web) to DOOH campaigns.

If you plan to sell video DOOH ads, pay attention to content length. The creative has to be short, 5-10 seconds long. Serving video DOOH ads of widely different lengths can mess up your broadcast scheduling. Some DOOH devices can incorrectly display too short or too long playouts.

The Digital Media Institute (DMI) released recommended specs for video and visual DOOH campaigns. You can (and should) make these part of your requirements for content.

Real-time data

The ability to tailor creatives to real-time traffic, weather, or sensor data is a major part of the DOOH appeal, and it is a non-trivial technical challenge.

Brands and DOOH companies also have to carefully navigate privacy challenges around real-time data collection.

30Seconds Group, a UK-based digital billboard company, faced backlash for using face-tracking cameras to monitor how apartment block residents respond to ads. One of such residents voiced concerns about an AdTech company spying on him in a comment for The Guardian.

RMG says I’m not being spied on, but there are cameras in the devices; you can see them. Even if it was at zero cost to residents, I would still fight these tooth and nail, nobody wants to be spied on by 6ft garbage adverts in their own building.

To avoid public scrutiny, DOOH vendors need to look for alternative data collection tools – live feeds, mobile SDK location data, on-site sensors, QR codes, or Bluetooth.

But, even with a pool of reliable data sources, building a data pipeline that will both display a personalized creative in under 100 milliseconds and scale to serve millions of impressions (this is the scale at which market leaders like Vistar operate) requires strong in-house data engineering capabilities.

When committing to building a pDOOH platform, make sure to select vendors with a proven track record in four areas.

• Designing a pipeline that supports both batch and streaming processing
• Enforcing data quality gates to prevent false or irrelevant data from triggering ad display
• Setting up low-latency integrations with other AdTech intermediaries (DSPs, SSPs, CDPs, and CMPs)
• Building an error-proof creative optimization and content delivery engine.

Working with a team that understands the nuances of low-latency, high-scale architecture of pDOOH solutions will help protect data security and avoid display errors that dissipate brands’ ad spend.

Hardware limitations

DOOH systems have become more advanced. But there are still some inherent hardware limitations. By design, not all systems allow establishing proper programmatic ad serving. You will need to create a prescreening mechanism for media owners to accept only suitable suppliers to your ecosystem.

For example, the DOOH device must have sufficient CPU, GPU, RAM, and storage to display HD content correctly. Also, it should support video codecs your platform can process and have all the needed connectivity options — WiFi, Bluetooth, 4G/5G, etc.

The digital screens market is highly fragmented, so it’s best to decide on some limitations instead of trying to optimize your platform for every type of device.

Proper targeting and attribution

Interactive DOOH systems process data in multiple formats — camera video, sensor data, Bluetooth-enabled devices capture, and data from third-party providers. These data points are necessary for high-precision targeting and attribution.

To ensure proper tracking and analytics, you need to develop a secure, high-load data management platform. Any glitches or inconsistencies can undermine the credibility of your DOOH measurement and reporting. Rapid data matching and processing are also crucial to avoiding lags in ad delivery and targeting efficiency.

Dmitry Sverdlik, CEO at Xenoss

Integrating DOOH inventory into programmatic platforms

The advertising industry has yet to rule on clear-cut standards around movement, ad play, and venue data, which are necessary to establish ad viewability.

The data variables themselves can tell conflicting stories. For example, the direction that the outdoor screen is facing can help validate the travel direction of a mobile device. But it’s a less relevant metric for indoor displays as passersby can pedal back to look at the ad. But not all DOOH hardware can provide this information.

When it comes to programmatic DOOH buying, there’s also no consensus on which data points DSPs and SSPs should exchange. Many platforms fail to factor in the unique characteristics of place-based advertising, such as:

• One-to-many vs. one-to-one impression delivery
• Extra latency in ad delivery for larger creatives
• Pixels for video tracking won’t work as an accurate measurement

The Digital Place-Based Advertising Association (DPAA) developed a framework for programmatic DOOH based on the OpenRTB 2.5 protocols. But with adjustments, accounting for the unique requirements of DOOH ads.

Privacy considerations

DOOH devices can collect more user data — from location to demographics. But requirements around user consent for such data collection vary by country.

Consumer privacy regulations such as GDPR and CCPA set rigid standards for collecting, storing, processing, and disclosing customer information in the EU and the US. Because of these, DOOH providers cannot transfer live video from camera systems — and process only text-based attributes. That’s called anonymous video analytics.

Computer vision-based DOOH devices can only perform facial detection, not facial recognition. The device can scan the consumer’s expression, age, or gender but not directly ID them based on unique facial features. In fact, 54% of US consumers are opposed to advertisers using facial detection technology to measure their reactions to public ad displays.

But the sentiment is different in the East. China, for example, has more relaxed privacy regulations. Back in 2015, China’s postal service did a multi-city DOOH campaign, using displays that tracked the viewers’ eye movements and dwell time of each glance while also factoring in the “biometric signature of each individual.” The country also largely normalized the use of facial recognition technology in “cashless” stores and hotels where customers can check out using their faces.

Ubiquitous connectivity, a wide network of CCTVs, and the newest digital screen models have made China a booming DOOH market with advanced targeting options.

The state of DOOH market

In the US, DOOH ad spending is projected to reach $2.87 billion by 2027.

Much of the industry growth will come from a rapid programmatic DOOH expansion with RTB opportunities now becoming available via mainstream DSPs.

In 2018, JCDecaux — a global leader in outdoor advertising – launched a programmatic out-of-home trading platform (VIOOH). Since then, they’ve been adding thousands of new DOOH devices to their global network. VIOOH recently added Frankfurt Airport to its media portfolio. The fourth busiest airport in Europe implemented a DOOH system across 23km2 of its area.

Through VIOOH, advertisers can now access over 800 panels of Frankfurt Airport in 34 DSP via PMP. JCDecaux (VIOOH’s parent company) currently provides airports DOOH inventory programmatically across the US, EMEA, Asia, and Australia.

AdTech startups are also expanding into programmatic DOOH with the help of venture capital. In 2021, Place Exchange, a DOOH SSP platform, closed a $20 million Series A round. Vistar Media, an end-to-end programmatic platform, secured $30 million in a Series B the same year.

Overall, the DOOH market is merely entering the growth stage. Over the next two years, 95% of advertising executives expect the DOOH market to grow significantly and surpass $50-$55 billion by 2026.

Programmatic DOOH marketplaces

Entering the DOOH market now can still give you the “first mover” advantage and the ability to secure contracts with large brands before they select an alternative provider.

But you must move fast, as other AdTech players are already carving their initials in the markets.

DOOH DSPs

The following companies specialize exclusively in DOOH inventory or have extensive access to it:

DOOH SSPs

The following companies allow digital out-of-home media owners to list their inventory or leverage their own inventory:

Final thoughts

Programmatic DOOH is an uncharted new territory to conquer. It comes with a host of obstacles, mainly around data processing, ad viewability measurement, and low-latency ad creative processing. But those that resolve these issues will be well-positioned for upcoming growth.

Advertisers are looking to scale beyond private marketplace deals and programmatic guaranteed ad placements. Many also want to run dynamic, data-rich campaigns in locations frequented by their ideal targets. But few players deliver that type of end-to-end buying experience. Your company can fill in this gap.

Xenoss can help you with DOOH integration to your AdTech platform or develop a new DOOH DSP/SSP platform. Contact us to discuss your project.

The post Digital Out-Of-Home advertising: Benefits and challenges of implementing programmatic DOOH appeared first on Xenoss - AI and Data Software Development Company.

That’s precisely the aim of hyperautomation. It combines multiple automation tools and AI systems into a unified operational engine, where you can combine them to achieve maximum efficiency and enable end-to-end operational management.

This article gets into what hyperautomation entails, how to identify high-impact use cases, the technology stack required for success, and practical examples of hyperautomation implementation across the manufacturing, insurance, and banking industries.

Why implement hyperautomation in operations

Traditional automation using RPA and BPM tools isn’t new to businesses. For a period, they offered temporary operational relief, but these tools address isolated tasks rather than transform end-to-end business processes. They’re helpful if a company is still at the data-naive maturity level (i.e., reactive decision-making based on siloed data), as illustrated below. 

Shifting towards hyperautomation means becoming a data-driven business, which, in turn, means you not only know where your data is stored and who uses it (data-aware maturity level), but can also use these datasets to your advantage. In other words, hyperautomation helps you turn data into revenue, decrease operational expenses, and gain a competitive advantage. But that’s a double-edged sword; without a well-organized data infrastructure, hyperautomation won’t work, and without hyperautomation, your data won’t bring expected value.

As operations become more complex, companies increasingly use multiple tools simultaneously to reduce costs, effort, and risks. But this distorted use of technology only adds to an already overgrown operational complexity. 

Modern companies deal with almost 50 endpoints in their current automation processes, which include separate use of legacy enterprise systems, RPA tools, AI chatbots, and APIs. That’s why 85% of businesses say they need end-to-end hyperautomation solutions to orchestrate automation and gain visible operational improvements. 

Hyperautomation solutions amplify and organize existing automation technologies to reduce operational and tool management complexity.

Power of process mining: Uncovering hidden automation opportunities

Process mining is essential at the hyperautomation discovery phase. By analyzing event logs from your existing IT systems (e.g., ERPs, CRMs, and BPM systems), process mining creates a dynamic, visual representation of your business processes. It provides an objective, data-backed view of which processes are the most time-consuming, error-prone, or costly, highlighting them as candidates for automation.

Where to start for maximum operational value

A simple and effective process mining model assesses opportunities along two key axes: potential business impact and implementation complexity.

• High-value, low-complexity. These are the “quick wins.” They often involve rule-based, high-volume, and repetitive tasks that can be addressed with technologies like RPA. Examples include invoice processing or employee onboarding paperwork. But hyperautomation can also be efficient when you need to extract insights from large-scale business processes.
• High-value, high-complexity. These are transformative projects that are well-suited to hyperautomation. They need a larger investment but offer the greatest long-term ROI. For instance, end-to-end supply chain optimization or dynamic fraud detection.
• Low-value, low-complexity. These can be automated, but should be lower on the priority list.
• Low-value, high-complexity. These should generally be avoided, as the effort outweighs the potential reward.

Focus first on high-impact, low-complexity tasks and processes, as they deliver the quickest ROI. Then proceed to high-impact and high-complexity tasks to scale hyperautomation initiatives and sustain long-term business value.

Hyperautomation technology stack

Each hyperautomation technology plays a distinct role in creating a cohesive, intelligent system. Understanding these roles will help you develop a custom hyperautomation architecture that fits into your IT infrastructure.

According to Gartner, a hyperautomation tech stack is defined by the scope and level of automation. It encompasses the whole spectrum: from automating simple tasks to automating entire value chains.

Task and process automation: RPA and workflow automation

RPA is ideal for automating repetitive, rule-based tasks that mimic human interaction with digital systems. RPA bots can log into applications, copy-paste data, fill in forms, and extract information from documents.

Workflow automation, often managed through BPM platforms, goes a step further by orchestrating entire sequences of tasks that may involve multiple systems and human decision points.

Business operation and value chain automation: AI, ML, and agentic AI

This layer provides the “brains” of the hyperautomation stack, elevating it from simple task execution to intelligent decision-making across business operations and value chains. AI and ML algorithms analyze historical data to identify patterns, make predictions, and adapt processes over time. They bring human-like capabilities to automation, including:

• Natural language processing (NLP). Understanding and interpreting human language from emails, support tickets, and chat logs.
• Computer vision. Analyzing images and videos for tasks like quality control inspection in manufacturing or damage assessment from photos in insurance claims.
• Intelligent document processing (IDP). Moving beyond simple OCR to extract, classify, validate, analyze, and make recommendations based on unstructured data from complex documents like invoices, contracts, and medical records.
• AI agents and multi-agent solutions. Extending RPA by combining task execution with reasoning. They can: interpret instructions, decide which action to take next, verify outputs, escalate edge cases, and collaborate with other agents. A multi-agentic system, in turn, is a whole “team” of agents responsible for executing diverse tasks. These systems have an orchestrator as their “manager,” which monitors their performance and reports back to humans.  

Connecting the dots: Data fabric, integration platforms as a service (iPaaS), and data analytics

For hyperautomation to work, you should ensure a connection between data and systems. A modern data fabric architecture provides a unified data management layer, ensuring that all automation tools have consistent, secure, and governed access to the right data at the right time, regardless of where it resides.

iPaaS (e.g., Zapier, SnapLogic, Informatica) providers offer pre-built connectors and APIs to seamlessly connect disparate cloud and on-premises applications, breaking down critical data silos. In the hyperautomation context, these systems ensure that RPA and AI agents or models can exchange data between each other and enterprise systems.

iPaaS providers also often offer low-code/no-code tools to build customized drag-and-drop applications that enable business users and operations specialists to monitor hyperautomation workflows.

These applications include data analytics modules with dashboards that track operational KPIs and feed insights back into enterprise software for workflow optimization, or into AI/ML models for continuous improvement.

Emerging operational enablers: Internet of Things (IoT) and digital twins

In many industries, IoT and digital twin technologies are pushing the envelope in advanced hyperautomation. IoT provides a stream of real-time data from sensors installed on machinery, vehicles, and in facilities. Sensor data triggers automated workflows, such as scheduling predictive maintenance when a machine shows signs of wear. 

A digital twin, a virtual model of a physical process or asset, uses IoT data to simulate operations. This allows businesses to test process changes, predict outcomes, and optimize performance in a risk-free virtual environment before implementing changes in the real world.

IoT data and digital twins can provide extra context for AI/ML solutions and close the loop in automating processes across the physical and digital worlds

Real-life operational workflows using hyperautomation technologies

Discover how companies in manufacturing, banking, and insurance tied together different automation technologies with the help of AI.

Siemens automated industrial operations with AI agents

Siemens shifted from using AI assistants that answer simple queries to autonomous AI agents that execute entire industrial workflows without human intervention. The company developed an entire Industrial Copilot hub comprising separate copilots that perform different automated tasks, including design, planning, engineering, operations, and services. Their agentic AI architecture includes a central orchestrator that manages these specialized copilots. 

The agents continuously learn, improve, and communicate with each other and enterprise systems to develop a sophisticated multi-agentic network for automating industrial operations.

As to results and future objectives, Rainer Brehm, CEO Factory Automation at Siemens Digital Industries, says:

By automating automation itself, we envision productivity increases of up to 50% for our customers – fundamentally changing what’s possible in industrial operations.

Bank of Montreal (BMO) implements AI as a strategic asset

82% of banks are integrating AI with additional technologies to maximize impact, such as RPA, and 84% are adopting autonomous agentic AI solutions. As one of the largest banks in North America, BMO belongs to both groups.

The bank combines RPA, agentic AI, and ML to foster digital and human collaboration at the company. Their first attempts to automate financial processes with RPA have generated millions of dollars in value. Implementing AI is an even more promising area. 

For instance, the company uses ML-powered optical character recognition (OCR) in combination with RPA to extract insights from scanned financial documents. Such a workflow helps BMO improve document processing accuracy with ML while also automating data entry with RPA. 

To ensure efficient AI adoption and use within the organization, BMO has established an AI center of excellence (CoE). The AI CoE also helps the company to manage risks and support continuous ingestion of high-quality data. 

To measure AI performance, BMO focuses on return on employee (ROE), aiming to enhance human work with AI rather than replace people. Here’s how Kristin Milchanowski, Chief AI and Data Officer at the BMO Financial Group, puts this:

We’re thinking about the ROI that every agent we deploy will generate and tying it to the balance sheet up front. We believe that scaling technology with value is how we are going to win at this.

For the strategic AI implementation, the BMO group has already been ranked #1 globally in AI talent development.

Allianz uses AI agents for claims processing

An insurance company, Allianz, has implemented an agentic AI system to automate claims processing for food spoilage. The “Nemo” project employs seven task-specific AI agents to reduce processing time from days to hours. The agents handle all work, such as validating coverage and detecting fraud, while the final payout decision is made by human workers using all previously gathered data.

The company launched “Nemo” in 100 days and aims to implement this solution globally to achieve seamless agent-human collaboration for the sake of better and fairer insurance services.

Maria Janssen, Chief Transformation Officer at Allianz Services, is fascinated by the results:

With ‘Project Nemo’ as our first integrated agentic AI solution, we’re achieving an impressive 80% reduction in claim processing and settlement time. This does not only boost productivity in our claims departments but also significantly enhances insurance customer satisfaction.

Janssen also added that:

This initiative not only demonstrates the power of hyperautomation but also sets the foundation for integrating AI across Allianz’s operations—enhancing both efficiency and decision-making processes.

This solution has helped the company focus on more complex claims and deliver much better service to customers, especially in high-stress situations such as natural disasters. 

Each of the companies we discuss prioritizes their employees and customers when implementing hyperautomation solutions. They’re basically asking themselves a question: “How can we deliver better, quicker, and more efficient services to our customers while ensuring that our workers aren’t swamped with routine tasks?”

This question sets the tone for responsible adoption. It reflects a shift from automation for cost-cutting to automation for capability-building, where technology amplifies human expertise, strengthens customer experience, and supports sustainable operational growth. This is precisely the mindset when adopting hyperautomation.

Operational hyperautomation roadmap: From pilot to a company-wide scale

A phased approach to implementing hyperautomation enables you to measure ROI and scale with value rather than accumulate technical and process debt in a rushed rollout.

Phase 1: Proof of concept (PoC)

Begin with a small, focused project designed to prove the value and feasibility of hyperautomation within your organization.

• Select the right use case. Choose a high-impact, low-complexity process identified through your prioritization framework. It should have clear, measurable outcomes, such as reducing processing time by X% or cutting error rates by Y%.
• Define success metrics. Establish clear KPIs (e.g., process cycle time, throughout, first-time-right (FTR) rate, customer satisfaction, net promoter score) before you start. This is crucial for evaluating the PoC and building a business case for further investment.
• Assemble a cross-functional team. Include representatives from operations, IT, and the specific business units. This way, you ensure alignment and incorporate subject matter expertise to fine-tune the automation processes.
• Execute and measure. Implement the automation solution on a small scale. Meticulously track the “before” and “after” metrics to quantify the impact. The goal is to create a clear success story.

Phase 2: Building a hyperautomation CoE

The next step is to establish the governance, standards, and expertise needed to scale effectively. This is the role of an automation CoE. The CoE is a team responsible for:

• Governance and best practices. Defining the standards for developing, deploying, and managing automations to ensure quality, security, and consistency.
• Technology and vendor management. Evaluating and selecting the right hyperautomation tools for the enterprise technology stack.
• Opportunity pipeline management. Creating a systematic process for identifying, evaluating, and prioritizing new automation opportunities from across the business.
• Training and enablement. Providing training and support across business units to foster adoption and use.

Phase 3: Scaling hyperautomation across operations

Once your CoE is established and you have a repeatable model for success, you can begin scaling your hyperautomation initiatives across departments and business processes.

• Federated development model. While the CoE provides central governance, you should encourage individual business units to develop their own automations within the established guidelines.
• Focus on end-to-end processes. Move from automating individual tasks to re-engineering entire value streams, such as the complete procure-to-pay or order-to-cash cycles.
• Invest in reusable components. Develop a library of reusable automation components (e.g., a bot for logging into SAP) that can be easily deployed in multiple workflows, accelerating future development.

Phase 4: Continuous monitoring

Hyperautomation is an ongoing discipline with a continuous cycle of improvement fueled by data and analytics.

• Monitor performance. Use analytics dashboards to continuously evaluate your automated processes against defined KPIs.
• Identify optimization opportunities. Analyze performance data to identify new bottlenecks or areas for improvement. AI and ML models can learn from this data to automatically suggest process optimizations.
• Refine and redeploy. Regularly update and refine your automations to improve their efficiency, resilience, and business impact. This creates a virtuous cycle where your operational processes become progressively more intelligent and efficient over time.

These phases offer a generalized approach to hyperautomation deployment, but you can change tack and skip or substitute certain phases with more applicable ones. For instance, if scaling won’t be your priority for the next six months or a year, you can skip phase 3 and move to phase 4 after establishing CoE. 

Also, if the CoE building contradicts your company culture, consider passing responsibility for hyperautomation management to your AI and data governance team (if you have one) or to an external, experienced vendor with an established governance department.

How to measure hyperautomation ROI in operations

One of the most efficient ways to measure hyperautomation ROI in constantly changing business operations is through real-time analytics dashboards. They provide a consolidated view of all key operational KPIs. 

As illustrated in the example below, you can compare operational performance before and after hyperautomation. This way, you’ll gain visibility and will be able to justify further investment in scaling hyperautomation initiatives.

Hyperautomation benefits compound over time, creating a stronger foundation for growth and innovation. Therefore, consider evaluating the first benefits after 6 to 8 weeks of implementation.

Takeaways

When traditional automation reaches its limits, hyperautomation becomes the architecture that connects data, systems, and intelligent agents into a unified operational engine. It enables the business to grow without the operational complexity and overhead that often accompany scale.

Key takeaways you can garner from this guide:

• Process mining is essential for identifying high-impact automation opportunities, ensuring teams focus on what delivers meaningful ROI.
• Success depends on clean data, strong governance, and the right mix of AI, RPA, workflow tools, and integration platforms.
• Hyperautomation is a continuous project that requires ongoing monitoring, optimization, and reinvestment to maintain long-term value.
• Identify early hyperautomation adopters and champions within the business. Publicly celebrate their successes and the positive impact of pilot projects to build momentum and inspire others.
• Governance is the invisible backbone of scalable hyperautomation. Without clear standards, automation grows faster than the organization can safely manage.

Xenoss can help you start a hyperautomation project with a well-organized data infrastructure, high-impact operational use cases, and established AI and data governance, delivering quick ROI and laying the groundwork for long-term business value.

The post Hyperautomation for operations: Blueprint for measurable ROI and efficiency gains appeared first on Xenoss - AI and Data Software Development Company.

In October 2025, a consortium of AdTech companies led by Scope3, Optable, Swivel, Yahoo!, PubMatic, and Triton Digital launched AdCP, an open protocol that connects AI agents to advertising platforms. 

The new protocol sparked discussions about the future of agentic AdTech. As Emma Newman, CRO of EMEA for PubMatic, put it, the industry is at the dawn of “ agentic era, creating a common language for AI systems to collaborate across planning, optimisation and measurement”. 

If AdCP takes off, it could solve many industry challenges. Data silos and bloated supply chains waste up to 55% of total programmatic spending. 

The potential of AI agents goes beyond replacing OpenRTB. AI agents can successfully automate data analysis, creative production, audience targeting, and retail media campaign management. 

In this post, we’ll explore successful advertising AI agent pilots. Publishers, brands, agencies, and AdTech vendors have all put these into action.

What is agentic advertising? 

The key difference between AI agents and generative AI (GAI) products like ChatGPT lies in agents’ ability to act proactively (whereas LLMs typically respond to user queries). Besides, AI agents follow a multi-step process to complete tasks autonomously. 

1. Goal interpretation: The system receives objectives through natural language input. It determines the necessary steps to achieve them and plans its approach proactively.

2. Tool and data access: The agent connects to relevant platforms, software systems, and data sources. It gathers the capabilities needed for execution, where the Model Context Protocol (MCP) becomes critical for standardized access.

3. Autonomous execution: The agent acts independently. It adapts behavior based on real-time feedback and changing conditions without requiring human intervention.

4. Output generation: The agent uses GAI to produce the required deliverables. This includes text, images, code, or video as part of completing its assigned tasks.

In AdTech, AI agents can help AdOps teams automate routine tasks such as data analysis, media buying, and creative A/B testing. 

Although the range of applications for agentic advertising is still evolving, four distinct agent categories are emerging. 

Campaign monitoring agents track bidding activity, budget pacing, and campaign metrics across programmatic platforms in real time.

Creative optimization agents generate, evaluate, and expand variations of creative assets aligned with specific audience characteristics and contextual signals.

Targeting agents refine audience definitions and maintain unified identity resolution across advertising channels to reach optimal consumer segments.

Measurement agents consolidate attribution data across connected TV, programmatic advertising, and retail media networks to deliver comprehensive performance insights.

Now let’s examine how publishers, brands, agencies, and technology vendors apply agentic advertising technology to solve day-to-day operational challenges. 

Publishers

Publishers are adopting generative AI and AI agents to handle the repetitive AdOps work that eats up their teams’ time. The demand for agentic AI comes from media leaders wanting to run leaner operations. They need to stay competitive against platforms that have bigger budgets and more automation.

AI agents in sell-side AdOps have many uses. They manage yield optimization by checking fill rates and eCPMs with demand partners. They also handle inventory packaging by creating and updating programmatic deals based on advertiser needs. Additionally, AI agents navigate negotiations with direct clients and generate performance reports for advertisers.

#1. Negotiations with buy-side partners: The Sun

An average publisher relies on SSP partners to access diverse sources of demand. An SSP connection, in turn, opens the door to hundreds of demand partners and increases the workload on a publisher’s AdOps team. 

DoubleVerify estimates that a publisher making $100 million in direct-sold revenue needs to spend $13.7 million each year. And also run a dedicated team of over 140 professionals. 

How The Sun uses agentic media buying to solve this problem

To reduce the strain of programmatic media buying, The Sun created an AI agent that automatically connects and communicates with the tabloid’s buy-side partners. 

Dominic Carter, EVP, Publisher at The Sun, told Digiday that the publisher is building an AI agent that will autonomously communicate with buy-side AI agents. The Sun’s team wants the agent to respond to buyer needs quickly. It will match brands with available inventory, negotiate terms, and close deals. 

Under the hood, The Sun’s agent is powered by the Ad Context Protocol, making the company one of the protocol’s first adopters. 

To support agentic advertising, The Sun improved its supply chain and created direct links with key partners – The Trade Desk and Ozone. 

What this means for the industry

The Sun’s early adoption of AI agents via the Ad Context Protocol makes it clear: publishers are ready to commit to agentic media buying. 

Following The Sun’s playbook, publishers who want to explore agentic media buying must modernize their infrastructure and build a leaner supply chain to ensure reliable, straightforward machine-to-machine programmatic negotiations.

#2 Automating deal management and negotiation: Hearst

For larger publishers, manually managing direct client negotiations across an extensive product portfolio creates inefficiencies and shifts sales teams’ focus away from revenue-generating activities. 

At Hearst Communications, a media holding headquartered in New York City,  account research would take employees 40 minutes per account on average. 

Onboarding sales talent came with its own challenges. New reps had to deeply understand each of the 30+ products in Hearst’s portfolio. 

Michael McCarthy, the company’s Senior Director of AI, Sales, and Business Solutions, found traditional methods too slow and ineffective. He claims standard approaches don’t help sales reps with no media experience get up to speed. 

How agentic AI improves publisher productivity in deal management

McCarthy calls Hearst’s advertising agent a ‘computer use agent.’ Because it can navigate systems, open apps, browse the web, input data, and manage files all on its own.  

Sales reps only guide the agent with commands like “research sales accounts for me” and log it on LinkedIn. 

Hearst’s agent works by searching the publisher’s internal databases. It finds client info, pricing details, audience insights, past campaign data, and budget rules. The tool uses this information to generate media plans, complete CRM updates, conduct account research, perform pre-call planning, and create media proposals. 

Hearst has also built an agentic knowledge base for training and onboarding. New sales reps can consult this platform to get personalized answers about products and processes. 

Although Hearst’s agentic AI pilot is recent, AdOps, sales, and business departments are already seeing ROI uplifts both in employee productivity and deal negotiations. The time needed for account research dropped from 40 minutes per task to 2 minutes per task when supported by the AI agent. 

Sales executives report that AI agents help them “show up better to customers and answer their objections more effectively”. Hearst documented a 153% increase in average sale value since implementation. Multiple sales reps have credited the AI assistant with helping them close six-figure deals by enabling data-informed conversations with advertisers.

What this means for the industry

Hearst’s successful pilot shows that AI agents provide quick, clear ROI. They do more than automate tasks; they also help boost revenue. In Hearst’s case, AI agents support sales teams and improve client negotiation by providing SDRs with up-to-date prospect research and relevant offerings. 

#3. Improving cross-department workflows: DPG Media

Cross-border publishers, like DPG Media, a European media group active in Belgium and the Netherlands, face significant challenges. The companies struggle with a gap between regional teams and fragmented workflows.  

DPG Media executives told Digiday that editorial teams struggled to coordinate tasks and access real-time information across the publisher’s newspaper, magazine, TV, and podcast portfolio.

Sales reps faced issues with disconnected systems. They had to switch manually between their order management system, ad server, and email clients to talk to prospects. 

Using a mix of different tools slowed response times. It also raised the chance of mistakes when sharing campaign details. 

DPG has operations in many countries, product lines, and departments. So, the company needed a single solution. The one that would help share information and automate everyday tasks.

How AI agents helped DPG Media connect 3000+ employees

As a solution, the publisher deployed an internal AI assistant, ChatDPG. The tool allows employees across all departments to build custom AI agents and query them for information or task execution. 

As of October 2025, over 3,000 DPG Media employees are creating and using custom AI agents, and 1,500 interact with them daily. 

The system connects with DPG’s order management and ad server. As a result, sales reps can create client emails using the latest campaign data. 

The agent cut out the need to switch between platforms. It also automated workflows that once needed manual coordination. 

What this means for the industry

ChatDPG is yet another promising use case. It showed how AI agents in AdTech help connect the global publisher team effectively. McKinsey reports that, on average, employees waste 20% of their time searching for information across disconnected systems. 

DPG Media’s agentic pilot proves that agents can solve this problem. AI agents automate workflows, connect disparate databases, and break down operational silos. 

#4. Reducing campaign reporting time: LG Ad Solutions 

Campaign reporting bottlenecks are an industry-wide productivity drain in adtech and programmatic advertising. 

Teams on all sides of the pipeline—media, brands, or agencies—report spending up to 40 hours on monthly reporting, rather than allocating that time to strategic campaign optimization and nurturing client relationships. 

Dave Rudnick, CTO at LG Ad Solutions, shared that compiling reports for advertisers would take the company’s AdOps team an average of 2 full business days. 

With no automated system in place, teams had to manually pull data from multiple advertising platforms, consolidate metrics across channels, run calculations, and create visualizations. 

As a result, advertisers faced delays in getting performance insights. This made it hard for them to optimize in real-time.

They risked wasting budgets on weak campaigns or missing opportunities to build successful strategies. 

How AI agents helped LG Ads Solutions cut reporting time

To make reporting easier, LG Ad Solutions launched Agentiv. It is an agentic AI platform that coordinates up to 30 specialized agents. These collect and organize data from brands, agencies, and adtech partners. 

As a founding member of the AdCP protocol, LG designed the system to enable interoperability between agents from different supply chain partners. 

The platform has already delivered tangible productivity gains for LG Ads Solutions. Campaign reporting AI agents reduced the time needed to prepare a report from 16 hours to 5 hours. 

The publisher is now discussing integration opportunities with agency holding companies and brands to enhance their agentic media-buying operations through partner DSPs.

What this means for the industry

The agents at LG Ads Solutions are proving reliable enough to produce complex client reports. The company is a frontrunner in agentic advertising in the CTV space, and other major publishers are likely to follow LG’s efforts in the coming months. 

Advertisers

Advertisers are deploying AI agents across their marketing operations to cut campaign management costs and timelines. 

Large FMCG brands with global operations use agents to support localized campaign execution that their regional teams lack resources for, while smaller companies and startups deploy agents to access enterprise-level capabilities without committing to expensive agency services. 

#1. Automating personalized cross-platform: Coca-Cola

Setting up targeting campaigns without agentic automation requires heavy involvement from marketing teams. 

According to a DoubleVerify study, marketers spend 10 hours per week on manual campaign tasks, and this time increases exponentially as targeting criteria become more granular and campaigns go cross-platform. 

Coca-Cola had to grapple with a labor-intensive campaign setup when it planned to target fast-food fans across Saudi Arabia on seven social media platforms and deliver personalized coupons through third-party mobile apps. 

The campaign required monitoring each platform to spot people posting about fast food, matching those users to their mobile advertising IDs, and serving them targeted coupons on partner apps. 

Executing this manually at scale—hundreds of thousands of coupons — would have required weeks of human effort. 

How an AI agent helped  Coca-Cola automate hyperpersonalized targeting

To monitor social media activity 24/7 and identify frequent fast-food posters, Coca-Cola built an AI agent that analyzed user posts across LinkedIn, X, Reddit, Tumblr, TikTok, YouTube, and Pinterest and identified fast-food chain goers. 

The agent extracted user geographic and demographic data from each platform’s API and matched this information to mobile advertising IDs provided by ad tech partners, Index Exchange and Sharethrough. 

Users identified by the agent would receive personalized meal coupons through third-party apps like the Huffington Post. 

The agent autonomously ran the campaign for over 2 months,  executed 8 million actions, and delivered 828,000 coupon ads to a highly personalized audience. 

#2. Automating cross-platform creative optimization: L’Oreal

Industry research shows that personalized creatives drive a 50% higher brand lift compared to generic messaging. 

However, 70% of marketers struggle to create more compelling DCO units to personalize their creatives better. 

The traditional approach to creative optimization is manual and time-intensive. AdOps teams have to create several asset versions, run multiple rounds of test campaigns, export, organize, and compare results in spreadsheets without a unified source of truth. 

For brands with global reach like L’Oreal, the bottlenecks of DCO are even more apparent. 

To create messages that resonate with L’Oreal’s 1 billion users, the brand is under constant pressure to adapt campaigns to diverse cultural contexts (e.g., Japanese gardens or the streets of Paris). 

Working with agencies to create localized creatives led to longer revision cycles. It kept L’Oreal from achieving the speed and personalization needed to capture TikTok and Instagram users in multiple regions. 

How AI agents helped L’Oreal personalize creatives

L’Oréal addressed dynamic creative optimization bottlenecks by deploying an intelligent agent that automatically generates and localizes creative assets. 

The company’s engineers used Google’s Imagen 3 and Gemini models through its CREAITECH lab to generate localized visuals and campaign assets from text prompts. 

L’Oréal instantly generates photorealistic, localized shots of a product in culturally relevant settings across 20 EMEA markets. 

The company is on track to integrate the AI agent, including Google’s Veo 2. Google’s state-of-the-art video generation model will enable the brand to convert static images into 8-second animated video clips with audio elements, trained on brand-specific styles. 

The agent also uses Tidal to automate paid media buying across platforms. 

This integrated agentic workflow brought L’Oreal 22% higher media efficiency and a 14% increase in campaign conversions in Nordic markets.

What this means for the industry

With AI agents directly involved in creative production and testing, advertising is moving from the automation of isolated tasks to end-to-end autonomous campaign execution. In this model, agents handle everything from asset generation to localization to media placement with minimal human intervention. 

AI agents support brands in achieving personalization at scale that was previously impossible, but simultaneously threaten traditional agency models built on manual creative production and media planning. 

#3. Orchestrating disconnected MarTech tools: Bayer

Industry surveys show that marketing teams allocate up to 30% of their marketing budget to inefficiencies caused by siloed tools. 

Even AI agents, when disconnected from each other, fail to deliver the automation benefits that team leaders hoped to harness. 

For Bayer, disconnected AI tools added layers of complexity instead of simplifying marketing operations. 

Instead of successfully slashing repetitive work and enabling increased scale, the company struggled to manage a patchwork of disconnected systems. AI-assisted campaigns demanded extensive manual effort and human oversight across every stage. 

Since Bayer operates in a regulated industry with strict privacy regulations and brand guidelines that must be embedded throughout the advertising process, the lack of control and fragmentation was particularly problematic. 

How Bayer uses intelligent orchestration to manage AI agents

Bayer tapped Innovid Orchestrator’s orchestration layer to coordinate specialized AI agents across the advertising lifecycle

The pharmaceutical company uses Innovid’s platform to coordinate AI agents that automate basic advertising activities, including ad creation, delivery, measurement, and optimization.

Advertisers using the platform can create detailed guidelines for each step of campaign setup and specify exactly how AI agents should interact with each other and with third-party systems. 

This connected framework delivers what Bayer prioritizes: faster insights, improved automation, and built-in compliance and governance.

What this means for the industry

Orchestration tools help advertisers move from deploying isolated AI agents to creating interconnected systems that work together across the entire advertising lifecycle.

As the AdTech industry begins to explore AI agents as helpful automation tools rather than a novel emerging trend, these orchestration layers will separate companies that achieve quantifiable efficiency gains from those stuck with fragmented systems.

Agencies 

When it comes to AI agent adoption, holding companies are leading the race. 

Basis Technologies reports that, in 2025, agencies have been more advanced than advertisers in using AI to reach audiences, from segment definition to campaign personalization. 

However, this rapid adoption has outpaced governance and safeguards: over 70% of marketers have experienced AI-related incidents, including hallucinations, bias, and off-brand content, yet fewer than 35% plan to increase investment in AI governance or brand integrity oversight. 

Even amidst these challenges, the financial pressure to adopt is intense. 50% of agencies worry that brands will bring AI capabilities in-house and reduce their reliance on partners. 

#1. Campaign planning automation: Omnicom

 67% of CMOs acknowledge they are overwhelmed with data. 

This was the case for Omnicom teams as well. Every day, they have to track up to 10,000 data attributes across 2,000 individual client accounts, and as Jonathan Nelson, CEO at Omnicom Digital, put it for AdExchanger,  “give up in frustration” after 30 minutes. 

Manual audience research required teams to brainstorm marketing objectives from scratch and spend hours sifting through spreadsheet cells to identify relevant audience segments. 

How agentic AI helped Omnicom automate campaign management workflows

Since 2024, Omnicom has supported campaign managers with AI agents that analyze 10,000 data attributes from sources like Experian and PlaceIQ and automate specific agency tasks. 

In a new workflow, a “chief strategist” agent generates marketing objectives, an “audience intelligence” agent creates audience segments and profiles, and targeting agents recommend influencers and social platforms based on audience behavior. 

Each agent is customized with brand-specific guidelines and matched to the right knowledge base, requiring strategists to craft precise prompts that define the persona, context, and desired output format. 

The system allows users to verify agent-generated insights by downloading underlying data into Excel and automating the time-consuming task of sifting through spreadsheet rows that would otherwise require hours of manual analysis.

Omnicom piloted its in-house AI agents for the “Ah, Nuts!” campaign for The Planters, automating influencer matching and enabling teams to prioritize strategic tasks. 

What this means for the industry

In the past, AdOps and data analytics teams had to manually coordinate to process vast volumes of audience and campaign information. AI agents are rewriting the playbook by becoming on-demand data analysts for AdOps teams. 

Fully agentic workflows allow teams like Omnicom to instantly synthesize insights from thousands of attributes and shift from data processors to prompt engineers who guide automated intelligence.

#2. Building self-service agentic solutions for clients: WPP

Historically, agents have struggled to find offerings that meet the needs of two underserved market segments that couldn’t access full-scale agency services. 

One of those is global brands whose local marketing teams lack resources to manage complete campaigns. 

The others are growing businesses, like tech startups, with small teams that are not ready to commit to large marketing departments. 

Large agencies charge monthly retainer fees ranging from $1,000 to $10,000, averaging around $2,500 per month. At the same time, 45% of small businesses spend less than $1,000 annually on marketing due to limited financial resources. 

Up-and-running businesses need affordable access to enterprise-level AI tools, data capabilities, and campaign execution without the commitment and overhead costs of engaging a full-service agency. 

Creating WPP Open Pro, an agentic self-service offering, enabled WPP to tap into these segments without jeopardizing profitability.

How agentic AI enables WPP’s self-service capabilities

WPP launched WPP Open Pro, an agentic AI platform that centralizes campaign workflow in a single interface. 

The agency’s engineers leveraged WPP’s $400 million investment in Google’s AI models, Gemini, and Veo. This tech empowers platform users to create and localize campaign creatives with minimal involvement from the agency. 

WPP Open Pro operates on a “pay for what you use” pricing model rather than traditional agency retainers, which average $2,500 per month. 

WPP positions Open Pro as both a client-acquisition tool for businesses constrained by small marketing budgets and a way to extract value from its existing AI infrastructure investments. 

What this means for the industry

Now that clients are seeking greater autonomy and flexible pricing, agencies are exploring “Agency as a Service” models. 

In the future, holding companies are likely to commoditize campaign execution through self-serve platforms. 

On the one hand, platforms like WPP Open Pro allow agencies to tap into underserved market segments. On the other hand, agencies will have to go the extra mile to retain high-grossing accounts and focus heavily on strategic tasks such as brand positioning and customer experience design. 

#3. Cutting programmatic waste: Butler/Till

Due to the mechanics of the programmatic marketplace, agencies typically have to accept the misalignment between their programmatic buying platforms and campaign performance goals. 

DSPs are engineered to surface bid requests for impressions that agencies are most likely to purchase, rather than for impressions that will actually convert or perform well for the campaign. 

Legacy algorithms also tend to be biased toward cheap reach and direct budgets toward MFA sites. Conversion data coming from these placements is unreliable because users are more likely to accidentally click on an excessive number of ads on the page.

Butler/Till realized the magnitude of this inefficiency when analyzing performance metrics. One of the agency’s campaigns was running across a bloated inventory that included 52% more domains than necessary, many of which were nonperforming or made-for-advertising (MFA) sites. 

Without more sophisticated tools to identify truly performant inventory, Butler/Till lacked meaningful control over how client budgets were allocated. 

AI agents helped Butler/Till prioritize performance over the number of impressions

Butler/Till deployed the SCaLE (Smart Curation and Learning Engine) optimization tool by SWYM.ai to create a programmatic buying system aligned with actual campaign performance rather than platform bidding incentives. 

The AI agent operates by analyzing both sell-side optimization signals from Index Exchange and buy-side signals from Google DV360. It will simultaneously pull attribution data from Google Marketing Platform’s Floodlight conversion and event tracking to understand which impressions drove conversions. 

Butler/Till’s agent identified the bid request characteristics that correlate with strong performance: domains, geos, ad sizes, device types, and channels. The platform used this data to curate private marketplaces that contained only impressions from purchase-ready users. Through an API integration with the supply-side platform, the AI agent dynamically packages lookalike impressions and updates the curated PMPs daily, continuously refining its selection based on real-time optimization signals. 

Since introducing the agent in early 2025, Butler/Till saw a 56% increase in conversion rate and a 26% decrease in cost per conversion compared to the control portion of the campaign that didn’t use the tool. 

The agent reduced the number of domains the campaign ran on by 52% by eliminating nonperforming inventory and avoiding MFA placements.

What this means for the industry

AI agents can help agencies break free from the fundamental conflict of interest in programmatic platforms, where DSPs prioritize impressions that buyers will purchase rather than those that convert. 

Intelligent bid managers can create custom algorithms tailored to campaign goals and independent of legacy systems biased toward cheap reach and MFA placements. 

AdTech vendors

#1. Optimizing retail media budgets: Skai

EMarketer’s data shows that marketers going all-in on retail media are struggling to manage reports from over 200 retail media networks (RMNs). 

Each RMN partner requires marketers to allocate up to a day of their time for analysis and optimization.  

This pain point is becoming more critical as US commerce media ad spending surges 21.8% this year, pressuring marketers to optimize significantly larger budgets across an expanding number of data sources. 

Skai addresses RMN data fragmentation with AI agents

Skai’s new agentic AI solution, Celeste, is marketed as an around-the-clock analyst that analyzes performance data from over 200 retail media partners. 

The tool brings in competitive intelligence and historical cross-channel insights to deliver strategic recommendations and automated reporting. 

Early adopters of Celeste are reporting performance improvements of 30% to 50%.

Skai’s President, Gil Sadeh, also believes the agent can accomplish tasks that took marketing teams “an entire day” in “less than a minute.” 

#2. Enabling interoperability between AI agents: AdCP by a consortium of AdTech vendors

Once AI agents become more widespread in the AdTech ecosystem, the industry will have to face the challenge of integrating them. 

At the moment, AI agents are predominantly developed in isolation by different teams using disparate frameworks and deployed across varied infrastructures. 

In a few years, this may create a digital “Tower of Babel” in which hundreds or thousands of industry agents are siloed, unable to communicate, collaborate, or share knowledge effectively.

How AdCP helps solve AdTech AI agent fragmentation 

AdCP (Ad Campaign Protocol) is an open standard that enables AI agents from different advertising platforms to communicate and coordinate through standardized message formats and interoperability frameworks. 

Developed as a joint initiative by Yahoo, Optable, PubMatic, Scope3, Swivel, and Triton Digital, AdCP establishes common methods for agents to discover each other’s capabilities, exchange campaign data, delegate tasks, and collaborate on media buying workflows. 

At the time of writing, the AdCP protocol is still new with no published performance metrics or adoption rates. However, by the end of 2026, the founding members plan to accelerate adoption and add capabilities for creative generation and performance attribution. 

What this means for the industry

AdCP steps in to address the risk of AI agent fragmentation that already plagues traditional martech stacks.

If the protocol gains broad adoption, it can help publishers and advertisers power away from walled gardens and intermediary-heavy programmatic infrastructure toward direct agent-to-agent transactions between buyers and sellers and slash the  “AdTech tax”.

Bottom line

The advertising industry is already seeing tangible wins from AI agent adoption across all stakeholder groups. 

Brands are using agents to enforce global creative consistency and accelerate asset production at scale. At the same time, agencies leverage them for audience segmentation, competitive intelligence, and keyword strategy that once required days of manual analysis. 

Publishers have deployed agents that transform first-party data access for sales teams, enabling real-time responses to client briefs. AdTech vendors are embedding specialized agents for media mix modeling, forecasting, and performance optimization directly into data environments. 

Early adopters report cost reductions and efficiency gains in campaign execution and faster decision-making, proving that AI agents deliver a measurable operational and financial impact when properly implemented.

However, these wins remain fragile without addressing three critical caveats that threaten to derail broader adoption. 

Interoperability stands as the most pressing challenge: the majority of AI agents operate in isolation, unable to communicate across platforms or share context between creative, media, and analytics systems. 

While agentic AdTech is capable of delivering results to everyone in the pipeline, without focus and investment in orchestration layers, governance frameworks, and compliance safeguards,  early wins could give way to fragmented chaos and regulatory backlash that stifles the technology’s potential.

The post How AdTech used AI agents in 2025: 12 real-world examples from publishers, brands, agencies, and tech vendors appeared first on Xenoss - AI and Data Software Development Company.

However, finding a capable AI vendor doesn’t guarantee a project’s success. As a company leader, your role in shaping the process and setting the right success criteria is just as important as the vendor’s technical expertise.

Vendor evaluations often follow a similar flawed pattern: impressive demos, technical feature comparisons, and contract negotiations that overlook the critical elements determining long-term success. Organizations need a systematic approach that links AI investments to tangible business value.

This guide outlines a 7-step framework for evaluating and partnering with AI and data engineering vendors. You’ll learn how to align project objectives, validate technical depth, calculate TCO, and build sustainable partnerships that drive measurable ROI.

Understanding AI vendor management fundamentals

AI software development vendors offer comprehensive AI and machine learning development services, encompassing custom generative AI and conversational AI solutions, computer vision software, predictive modeling systems, deep learning, and neural network solutions. 

Experienced AI service providers know how to select fitting off-the-shelf models from Anthropic, OpenAI, Mistral, and DeepSeek. They also offer custom and domain-specific model training, development, and inference services.

Besides developing AI solutions, AI vendors help businesses prepare their infrastructure for seamless AI use by:

• ingesting company data from disparate systems
• enabling automated data cleansing
• integrating AI with internal software via custom APIs
• ensuring continuous model improvement with new data
• establishing machine learning operations (MLOps) for seamless model deployment and maintenance

Trusted vendors also monitor AI legislation across various countries and domains, including model clauses in the EU AI Act and data protection rules for AI software development, as outlined in the GDPR, HIPAA, and PCI DSS. Unlike traditional software procurement, AI project procurement requires organizations to incorporate risk management, data governance, human oversight, transparency, and accountability as core principles from the outset.

The vendor relationship management process should follow a structured, step-by-step path: prepare internally, define your goals, screen vendors, validate technical capabilities, negotiate contracts, and manage ongoing collaboration.

Step 1. Prepare internally before engaging vendors

Before diving into an AI vendor search, assemble an internal team of AI advocates who will secure financial support for AI development, explain technical nuances, and ensure security and compliance standards. 

You can always opt for artificial intelligence consulting services if you lack some specialists in-house.

Role	Primary responsibilities	Why it matters
Executive sponsor (C-suite leader)	Secures funding, ensures alignment with company strategy, and collaborates with the vendor to oversee project direction.	Strong executive support ensures that AI initiatives remain strategic, well-funded, and aligned with business priorities.
Technical lead/Data architect	Evaluates architecture, integration feasibility, and oversees technical execution.	Ensures the vendor’s solution fits your infrastructure and scalability needs.
Business stakeholder/Product owner	Defines KPIs and translates business goals into measurable outcomes.	Provides vendors with clarity on the business value they’re expected to deliver.
Procurement representative	Manages vendor selection, contracting, and compliance.	Balances innovation with governance and risk control.
Security & compliance specialist	Reviews data handling, access control, and regulatory adherence.	Protects sensitive data and ensures compliance across all systems.

With due support from your internal team, define your AI maturity level to set the right expectations for collaboration with an AI vendor. 

A thorough data infrastructure audit will give you most of the answers. Examine where your data is stored, who has access to it, how it’s used, your data analytics approaches, core data challenges, data silos, and which tools your data engineers employ.

With a general understanding of your data management practices, you’ll know which data engineering services you’ll need before AI strategy development and can plan your budget efficiently.

Step 2. Define business outcomes

If you jump on the AI bandwagon driven only by model development hype without connecting technical benefits to business objectives, your AI initiatives risk stalling halfway through or even stopping at the ideation phase.

People, organizational structures, and processes account for 70% of all challenges with AI workflows, while technology and algorithms are responsible for only 30% of implementation difficulties.

Map AI and data initiatives to revenue and efficiency metrics

Tools, model selection, architecture patterns, and data pipeline configurations become relevant after establishing clear objectives. Begin by setting a clear goal that follows the SMART formula.

Think about a specific business problem to solve with AI, then attach measurable KPIs that will help you monitor the project’s success. Ensure that your AI initiatives are achievable and relevant to your current business needs. And finally, make your goals time-bound to maintain focus, and give your team and the vendor a clear accountability window for delivering measurable results.

Here are some examples of SMART goals:

• Increased revenue: “Increase average order value by 15% through personalized recommendation engines in six to eight months.”
• Improved employee productivity: “Reduce invoice processing time from 5 days to 2 hours via automated reconciliation in the first month post-rollout.”
• Customer retention: “Improve customer satisfaction scores from 7.2 to 8.5 within a year through predictive support routing.”

For instance, an e-commerce company might set an objective to “reduce website load time by 30% through predictive server allocation algorithms”. This specificity helps vendors understand exactly what business problem they’re solving and work toward achieving the 30% improvement target.

Pro tip from Xenoss CEO:

Dmitry Sverdlik, a technical expert himself with more than 15 years of experience, also highlights the importance of aligning AI powers with business needs:

Document constraints and non-negotiables early

Apart from setting  clear, attainable goals, provide your AI and data engineering services partner with well-documented project constraints and non-negotiable requirements, such as:

• Technical requirements: Legacy system integration capabilities, data architecture compatibility.
• Regulatory compliance: GDPR, SOC 2, HIPAA requirements that cannot be compromised.
• Timeline expectations: Proof of concept to production deployment windows.
• Budget boundaries: Total cost of ownership beyond initial licensing fees.

This step proves essential for any software initiative and becomes especially crucial for AI projects. While traditional development follows predictable patterns with identifiable bugs, AI projects operate in a more fluid and uncertain space.

Model behavior isn’t always deterministic. Resolving issues like persistent hallucinations can take longer than expected. That’s why anything that can be clarified or structured upfront should be carefully documented. It brings stability to what is otherwise an inherently experimental process.

For instance, a global insurer implementing AI-driven claims processing could specify that its 15-year-old policy system must remain operational. Additionally, all AI services should connect only through existing APIs, and no mainframe code can be altered due to compliance rules. These constraints can help the vendor design around reality instead of proposing disruptive upgrades that would’ve delayed ROI and delivered little value.

Here’s what Eric Helmer, CTO at Rimini Street, said on the matter: 

Companies who have a lot of legacy applications may find themselves on an AI journey they didn’t ask for. You may go through the evolution of these very disruptive upgrades, only to find out the functionality you got will never be of use.

The more clearly you define your constraints, the more effectively your AI partner can focus on solving the right problems without over-engineering the solution.

Step 3. Vendor research and screening

Look beyond vendor websites and polished case studies. Explore independent analyst reports (Gartner, Forrester, IDC), cloud partner directories (AWS, GCP, Azure), trusted websites like Clutch or G2, and open-source communities where experienced engineering teams often share project insights.

When reading reviews, verify vendors’ years in operation and project completion rates to ensure maturity. Confirm that vendors were engaged in AI and data projects before the 2022 ChatGPT boom. This demonstrates long-established AI and data expertise and capability to handle projects of varying complexity.

Next, screen mature vendors with proven expertise against these factors:

Financial health. Review the funding history or revenue growth of your potential vendors to ensure they have the financial capacity to provide a modern tech stack and experienced AI engineers.

Delivery model. Understand how the vendor collaborates. Some operate as external consultants who hand off deliverables, while others embed directly into your teams and co-own outcomes.

Reference quality. Request contacts from similar-scale implementations, not just success stories, to speak directly with clients and verify vendor reliability. Reference interview questions can include: 

• “How did the vendor handle challenges or scope changes during the project? Were they transparent and solution-oriented?” 

• “How responsive were they to issues or optimization requests after deployment?”

• “Would you work with this vendor again, and if not, what would you do differently next time?”

Exit strategy. Understand data portability, contract termination terms, and transition support to secure your business in case you need to halt cooperation.

Red flags include vendors who can’t provide recent customer references, refuse to discuss their financial backing, or have significant gaps in their project history.

Evaluation matrix: A structured approach to comparing options

Use an evaluation matrix to objectively score vendors. Assign weights to key categories, such as technical expertise (40%), business alignment (25%), delivery track record (20%), and partnership model (15%). This structured approach helps create a clear, data-driven vendor shortlist for deeper evaluation.

For example, a manufacturing company deploying predictive maintenance across multiple plants might assign greater weight to data infrastructure and integration expertise, as sensor data from legacy SCADA systems must be ingested and analyzed in real time.

A factory automation project using computer vision for quality control may prioritize model accuracy and edge deployment capabilities, as performance directly impacts production throughput.

Meanwhile, a supply chain optimization initiative might emphasize business alignment and delivery track record, ensuring the vendor understands logistics workflows and can deliver measurable ROI within tight operational windows.

Basic screening helps filter out immature candidates lacking AI experience, while the evaluation matrix enables you to sift through potential vendors with even greater scrutiny. 

Step 4. Evaluate technical depth over marketing claims

Marketing materials often reflect only surface-level engineering capabilities and may be helpful in the initial shortlisting of potential partners. However, they rarely reveal the deep technical expertise needed for complex AI implementations.

To determine that, you’ll need to conduct an interview and ask the relevant questions. Here’s a list of typical questions that our engineers answer during initial consultations with clients.

Questions to ask AI vendors to evaluate technical depth

Verify AI and data infrastructure expertise

The success of your AI project depends on the vendor’s data engineering expertise. 79% of organizations cite a lack of expertise in managing unstructured data as the biggest challenge hindering the value of AI. 

Many enterprises still rely on legacy software and data warehousing solutions, whereas modern AI workflows often require access to unstructured data via vector databases or a data lakehouse.

Ask potential vendors about their hands-on experience with:

• Overall AI infrastructure optimization strategies
• Batch processing for periodic high-volume data ingestion using AWS Batch or Apache Airflow
• Real-time data streaming using Apache Kafka, Apache Flink, or Apache Spark
• Multi-cloud engineering across AWS, Azure, and GCP for hosting AI solutions or LLM self-hosting with orchestration frameworks
• Retrieval-augmented generation (RAG) techniques to build enterprise knowledge bases

The critical consideration isn’t so much experience with specific data and AI infrastructure tools as an AI partner’s ability to compose a flexible AI architecture (with optimization in mind). The ideal approach doesn’t require immediate shift to a data lakehouse or heavy investment in on-premises data centers and GPUs.

Instead, your AI ecosystem should extend your current data infrastructure through modular integrations and API-driven interoperability, ensuring scalability without unnecessary complexity.

Assess GenAI and ML integration capabilities

Experienced vendors should be able to manage the full AI/ML lifecycle, from data ingestion and model training to validation, deployment, and inference, within a single, unified environment. Such vendors show expertise in:

• Seamless ingestion of structured and unstructured data (e.g., via integration platforms as a service (iPaaS), natural language processing (NLP) engines for processing unstructured data)
• Domain-specific LLM fine-tuning experience to train AI models with as much company data as possible while minimizing GPU memory requirements and model training costs
• Production-grade model serving and deployment capabilities with AI observability, audit trails, and fallback logic
• Model-agnostic architecture preventing vendor lock-in

AI solutions operate as continuous pipelines where data flows in, outputs serve production needs, and feedback loops enable model retraining. Each stage requires automation, observability, and scalability. Vendors should clearly explain their approach to this process and provide monitoring tools to facilitate continuous improvement of the AI model.

Step 5. AI vendor evaluation in the field: RFP and PoC development

To avoid misunderstandings down the road, write a comprehensive request for proposal (RFP) with detailed specifications. 

For initial RFPs, focus on the first four points and address the others during a consultation call.

Core RFP components

Proof of concept testing 

Another way to validate  vendor trustworthiness without incurring significant risks is through proof of concept (PoC) development. It typically takes between four and six weeks. Besides validating the vendor’s capabilities, a PoC is also helpful in measuring initial AI ROI and convincing executives to increase investments following successful results.. 

Provide vendors with actual business data (anonymized if necessary) rather than synthetic datasets and create user stories that represent your most critical workflows and edge cases. Document current pain points and their impact on the business, and test whether the vendor’s solution effectively addresses these specific issues.

A good PoC should reveal:

• How quickly the vendor can adapt to your environment.
• Whether the proposed architecture performs as expected.
• How well they document and explain results.
• The level of collaboration and transparency under real project pressure.

Pro tip: Treat the PoC as a rehearsal for partnership. The vendor who listens, experiments responsibly, and iterates fast is often the one who’ll perform best in full-scale implementation.

Step 6. Navigate pricing and contractual terms strategically

85% of organizations exceed their AI cost forecasts by 10%. Data platforms and networking charges are the top sources of unexpected AI costs.

When estimating AI project costs, organizations should look beyond LLM token costs and CPU/GPU expenses.

Calculate the total cost of ownership of AI systems

Hidden expenses accumulate quickly across multiple categories that vendors rarely disclose upfront. A comprehensive TCO framework should account for:

• Software licensing and subscriptions for AI models and tools
• On-premises infrastructure vs. cloud costs (difference in capital and operational expenses)
• Ongoing maintenance and support
• Data preparation and integration processes 
• Legacy system integration costs
• Knowledge transfer and team training investments
• Scaling costs as data volumes grow

A report on hidden AI costs reveals that model development and training, in terms of human capital and computing resources, account for the largest share of the AI bill (28.2%). However, less obvious aspects, such as data preparation, workforce training, model refinement and fine-tuning, should also be considered at the outset of your collaboration with the AI vendor.

Skilled vendors have experience building comprehensive TCO models for AI project development, deployment, and cross-company implementation. They realize that planning for implementation complexity upfront prevents expensive surprises later.

Negotiate service-level agreements (SLAs) that protect business continuity

Traditional SLAs are often focused on uptime guarantees and application response times. However, with AI’s non-deterministic nature added to the mix, these agreements require more scrupulous attention from the client and the vendor.

When an AI chatbot maintains 100% availability but provides biased answers in 35% of cases, the solution fails to deliver business value.

Beyond basic system response and availability requirements, AI SLAs should include output quality metrics, such as the percentage of accurate outputs and user satisfaction scores, and model performance metrics, including precision, recall, and F1 scores.

The SLA maturity pyramid below illustrates that technical availability and performance are at levels 1 and 2 of AI SLAs, forming the foundation for AI projects.

AI governance, business impact, and value partnership are at the top of the pyramid, indicating that greater AI maturity shifts the focus from uptime metrics to shared business outcomes, ethical standards, and innovation partnerships.

Depending on the level of business maturity and strategic importance of the AI solution, the complexity of AI project SLAs increases and should be thoroughly documented in vendor contracts.

Step 7. Partnership viability beyond technical capabilities

Organizations can waste millions on vendors who deliver solid code but fail at collaboration, communication, and long-term partnership requirements. 

Like traditional software development projects, collaboration with an AI and data engineering company requires well-established communication strategies and post-implementation support services.

Cultural fit and communication effectiveness

Poor cultural alignment and communication barriers lead to disengagement and project delays. Evaluate these partnership indicators during your AI vendor selection process:

• Collaboration approach. Observe how vendors handle cross-functional meetings. Do they ask clarifying questions about your business context or immediately jump to technical solutions?
• Communication strategy. Track response times during the evaluation phase to ensure timely communication. When deep in project development, seasoned AI vendors should also understand that enterprise clients value efficiency above all else. They may skip daily check-ins, but expect detailed monthly reports that show results validated against initial objectives.
• Problem-solving methodology. Present vendors with a realistic challenge scenario. Watch whether they seek to understand root causes or propose generic solutions.

Knowledge transfer and ongoing support

Effective knowledge transfer determines whether your team can maintain and evolve AI systems after they are implemented. Without proper transition planning, organizations risk creating vendor dependencies that limit future flexibility.. Consider the following:

• Documentation standards. Request examples of technical documentation from previous projects. Look for comprehensive architecture guides, troubleshooting procedures, and operational runbooks.
• Training methodology. Verify that vendors can provide hands-on training for your in-house team beyond document sharing. If an AI partner also offers training materials for AI software end-users, it’s a huge plus and a time-saver for your change management team.
• Support structure. Understand their ongoing support model, response time commitments, and escalation procedures for different severity levels.

The investment in proper knowledge transition pays off in reduced vendor dependency and improved AI solution maintainability. 

Xenoss, for instance, integrates seamlessly into your in-house team from day one, facilitating knowledge and skill transfer as smoothly as possible. Additionally, our team prepares thorough project documentation and user manuals to help AI users quickly get started with the system.

Common pitfalls in AI vendor relationships and how to avoid them

For first-time AI buyers, the biggest challenge is realistically assessing infrastructure readiness for AI adoption. Organizations often overestimate their data quality and system integration capabilities, making them vulnerable to vendors who oversell unnecessary services.

If you’re just stepping into the AI market, request AI consulting services and look out for vendors who are honest with you rather than overpromising. Also, be aware of these common pitfalls that can arise in AI vendor relationships.

• Expectation gap. When investing in progressive technologies like AI, businesses can have too high expectations. But AI is only transformative to the extent that your current infrastructure and data readiness permit it. To minimize issues and conflicts, openly communicate your concerns, constraints, and expectations early on, and allocate time for regular mid-project check-ins to ensure you’re on the same page with your vendor.

• Underestimating data readiness. Organizations consistently overestimate their data quality and accessibility. They often discover, months into projects, that significant data cleansing and integration work is required. Conduct a thorough data audit before vendor selection, be transparent about data limitations during the RFP process, and budget up to 20% of resources for data preparation work.

• Unclear data ownership and compliance. Contracts that do not define who controls data, trained models, or derived insights can lead to serious disputes later. You should clarify intellectual property and data governance from the outset and pay close attention to contract and SLA development.

Every partnership, even the unsuccessful ones, moves you forward. Each project gives you a clearer picture of your AI goals, your team’s strengths, and what it takes to build a more effective collaboration next time.

Final thoughts

How you approach AI vendor selection and management determines whether your AI investment delivers business value or becomes expensive operational overhead.

First, look for teams with domain-specific data infrastructure experience, proven production-scale implementations, and a solid presence in the AI and data engineering market. Vendors who understand data foundations and AI workflows deliver solutions that integrate well across your organization and scale effectively over time.

Once you find the right partner, align their technical expertise with your business goals, discuss budgets and TCO early on, and develop comprehensive SLAs aligned with long-term business strategy. 

Xenoss guarantees all of the above and stays with you through it all: from promising proofs of concept to demanding production rollouts that require persistence and methodical execution.

The post How to work with AI and data engineering vendors: A 7-step AI vendor management framework appeared first on Xenoss - AI and Data Software Development Company.

A 2025 study found that multiple dimensions of data quality strongly influence performance across 19 ML algorithms, proving that fixes to inconsistency, noise, and imbalance translate into real gains. 

Large-scale LM work shows the same pattern. Meta, for one, attributes major quality jumps in Llama 3 to aggressive data curation and multi-round QA.

Where should engineering teams source data for curation? 

Peer-reviewed scientific content brings rigor, provenance, and easier compliance, but narrower coverage of real-world language patterns. 

User-generated content (UGC) captures diverse linguistic variations and edge cases at scale,  but the data can get messy and biased. 

In this article, we’ll map the benefits, trade-offs, concrete patterns, and real-life success stories for combining the two to build accurate, compliant enterprise models. 

How data curation frameworks improve AI model performance

Data curation turns raw data into a carefully reviewed dataset. Implementing a data curation framework helps engineering teams enhance AI performance and reduce compute usage.

DBS Bank proved this by replacing bulk data collection with a systematic six-step curation framework. Their engineering team now cleans, contextualizes, and enriches every input before training, turning their data pipeline into a competitive advantage.

Outcome: the new approach slashed data preparation time by over 50%, accelerated model development cycles, and ensured regulatory compliance with GDPR and industry mandates.

What makes data curation different from raw data collection

Raw data collection aims to get as much data as possible.

Data curation helps boost the value of raw data through organized preparation. Here are the steps AI engineering teams take to transform raw data into a dataset fit for training algorithms. 

1. Deduplication and validation: Getting rid of duplicate records and dealing with missing values
2. Format standardization: Fixing inconsistencies in data formats and units
3. Normalization: making numerical data uniform across sources 
4. Variable encoding: Coding categorical variables in the right way 
5. Noise reduction: Getting rid of outliers when it makes sense 

Curated datasets trade size for traceability and balance across domains. That’s why engineering teams enrich datasets by combining them and bringing in outside context.

In 2023, a CleverX study revealed that 78% of AI project failures were directly linked to inadequate labeling practices. Even sophisticated machine learning systems used in the research depended heavily on accurately annotated datasets to build relationships between data points.

On the other hand, organizations that implemented robust labeling workflows, like schema-first approaches, pilot testing, and inter-annotator agreement protocols, achieved 64% higher model performance scores across diverse applications from healthcare diagnostics to natural language processing.

Data quality failures: Why volume without curation degrades AI systems 

Zillow’s iBuying division is a poster example of poor data quality, costing the company millions in the long run. The startup relied on machine learning models to predict resale prices and streamline home purchases across several U.S. markets. 

Slow, mixed-up data feeds, fuzzy labels, and shaky features caused the training and serving distributions to diverge. 

The faulty model forced Zillow to take write-downs of hundreds of millions, and cut about 25% of staff linked to the unit. The failure demonstrates that sophisticated algorithms cannot compensate for fundamental deficiencies in data quality.

Costs go up 

Unstructured datasets need more computing power and take longer to train. Data cleaning and proper curation reduce both training time and infrastructure costs.

Gartner estimates that enterprise organizations lose nearly $13 million per year on average due to poor data quality.

Accuracy hits a wall

Mislabeled examples, duplicate records, and irrelevant samples increase gradient variance. 

They make training slower and cause models to focus on random noise instead of useful patterns. Teams add extra data and computing power to fix this, but results improve as training time grows longer and infrastructure costs climb, while validation metrics stop improving. 

Clean datasets, free from duplicates, balanced to cover a range of cases, and verified for accurate labels, bring back efficiency. They help models generalize well without needing massive amounts of data.

Bias gets worse

Biased sampling and labeling lead to higher false-positive rates for protected groups, unstable calibration across segments, and fragile performance when data distributions change.

This creates real risks, like exposure to adverse actions, problems found in audits, and expensive human interventions when models don’t work well for minority groups. 

Selecting data through stratified sampling, removing duplicates, using proxies for sensitive attributes, and adding counterfactual examples stabilizes machine learning models. The SlimPajama-DC study showed that after eliminating low-quality and duplicate text, a 49.6% smaller corpus (627B vs 1.2T tokens) outperformed the larger, noisier dataset. 

Improvements slow down

Once the data threshold reaches a certain size, piling on more of the same messy or repetitive data doesn’t help much. The noise in the gradients goes up, and the test loss stops improving. Careful data selection by finding edge cases, choosing what to learn from, and removing duplicates makes each example more significant. 

It focuses on learning what matters. In real-world situations, these hand-picked batches can enhance your validation scores when simply adding more data is no longer effective. This makes your learning more efficient and helps it work better in new situations.

Scientific content curation: Strengths and limitations

Scientific content builds trust through careful validation. Understanding its structural limitations helps design effective data integration strategies that compensate for inherent gaps in coverage and linguistic diversity. 

Why is scientific data dependable?

Peer review lies at the core of scientific data dependability. Researchers review each other’s work to ensure the accuracy and value of the method before it is published.

This review process serves as a gatekeeper, allowing credible studies to be included in the research archives while excluding flawed ones.

Advantages of using peer-reviewed content in AI training

Using peer-reviewed material provides three significant benefits to advance AI systems.

Language and coverage limits bring big challenges

Organizing scientific data comes with several main hurdles that guide how AI training is shaped.

Approximately 98% of scientific publications appear in English, despite English representing only 18% of global language speakers. This language divide leaves huge gaps in knowledge in non-English AI settings.

Stanford researchers discovered that large language models work well for English speakers but struggle with languages that are less common. The main issue is the lack of good-quality data in those languages.

Even databases like Dimensions, which include 98 million publications, are small when compared to the wide range of data found online. 

Case study: Elsevier Scopus AI

Scopus AI is an example of a powerful machine model trained on scientific content. It understands natural language queries and creates evidence-based summaries using Scopus-reviewed materials. Each summary includes references to specific sources and shows how confident the system is in its answers.

The “Deep Research” tool on the platform relies on agentic AI to create research plans, search through literature, and tweak strategies as new insights appear. 

If it cannot locate enough evidence, the system mentions this gap instead of fabricating believable but false answers. This cuts down the chances of errors caused by hallucinations.

User-generated content (UGC): Value and risks

User-generated content is the fastest-growing data source for AI training, capturing language patterns that scientific literature misses entirely. 

However, UGC deployment requires careful risk management to prevent model degradation and regulatory exposure.

What enterprises gain from UGC integration

UGC encompasses social media posts, forum discussions, reviews, videos, blogs, and community-generated materials that reflect how users actually interact with technology. 

Unlike scientific content, UGC captures emerging slang, regional dialects, and problem-solving approaches that users employ in real conversations.

This linguistic authenticity translates into measurable business advantages. AI systems trained on UGC respond more naturally to customer queries, reducing support escalation rates and improving user satisfaction scores. 

UGC provides real-time signals about market trends, product issues, and customer sentiment. These signals typically precede formal documentation by months or years, enabling organizations to detect emerging patterns and respond to user needs before competitors identify the same opportunities.

How UGC improves AI model performance

Scientific content supports AI models with accurate facts and explainable ideas, but it underrepresents edge cases, human error, and real-world changes to the target domain. 

That’s where user-generated content is helpful in providing a testing ground that challenges models with scenarios they’ll face when deployed. 

Integrating user-generated content into model training yields three important benefits. 

Case study: OpenAI tapped into StackOverflow’s UGC to improve the quality of AI coding

OpenAI needed high-quality, human-authored programming data with governance guarantees to fine-tune and ground coding assistants. Stack Overflow, on the other hand, needed a sustainable, attribution-preserving way to license its community knowledge. 

Solution: The companies formed an API licensing deal that allowed OpenAI to consume validated Q&A via OverflowAPI and return attribution/links in ChatGPT. 

Stack Overflow, in turn, enforced quality through reputation-weighted moderation and formal terms that cover provenance and permitted use. This structure couples community validation with contractual compliance and technical integration paths (RAG and fine-tuning). 

Technical risks and mitigation strategies for UGC integration

Despite its utility in expanding data coverage and offering a real-life picture of the problem, UGC integration introduces operational risks that can damage model performance and expose organizations to regulatory scrutiny.

Synthetic-drift (model collapse)

Training on AI-generated UGC shrinks support and skews token frequency, lowering entropy and hurting OOD generalisation. 

Solution: Implement a mixture-of-data weighting approach that anchors on human-authored corpora, synthetic content filters, KL divergence regularization to a reference model, and strict deduplication and freshness gates.

Bias amplification at training time

UGC often has label/representation imbalance and toxicity that drive slice-wise calibration errors. 

Solution: Use stratified sampling, group-aware reweighting, counterfactual augmentation, and constraint-based objectives (e.g., equalised odds, group calibration). Validate with slice/equity evals and per-segment robustness tests.

Provenance, consent, and enforceability

Beyond policy, you need technical guarantees: content-addressable storage with licenses/consent as immutable metadata, lineage tracking, DLP/PII redaction, license-aware data loaders that block at batch-build time, and periodic re-scans. 

Solution: Add differential privacy where required and version datasets to audit exactly which model is being trained.

Case study: Reddit’s licensing agreement with Google triggered an FTC investigation into UGC commercialization

Challenge: Reddit’s $60M licensing agreement with Google caught the FTC’s attention, prompting an investigation into how platforms make money from user-generated content to train AI models.

Takeaway: Regulators are keeping a closer eye on whether users receive clear information about AI training uses, what they get in return, and how their data is handled from start to finish. Be ready to show proof: license agreements, permission wording, data tracking on how opt-outs work, and ways to enforce the rules.

Combining scientific and UGC data: What works best

Scientific sources and user-generated content solve different problems.

Scientific data offers reliability but fails to capture everyday language patterns. UGC reflects real-world usage but adds noise and bias.

Clever integration tactics allow you to reap the advantages of both methods while reducing their separate flaws.

Citation handling based on polarity

Citation polarity analysis examines whether sources back up, disagree with, or mention specific claims. This new NLP research field helps assess a paper’s true impact beyond basic citation counts. 

Today’s AI systems gain from understanding these sentiment patterns. Positive citations acknowledge strengths, negative ones point out limitations, and neutral citations just compare findings.

Giving AI systems the ability to handle citations with polarity awareness improves their accuracy in presenting scientific debates and promotes a neutral stance.

This becomes essential when training models for fields where scientific knowledge keeps changing, like climate science or medical research.

Case study: Karger Publishers

In 2024, Karger Publishers teamed up with Scite to add “Smart Citations” to all its journals. This move brought in badges that show how each reference is used (supporting, disagreeing, mentioning) for over 1.3 billion cited statements. 

This change gives readers more details about claims right when they need them and makes finding information easier. 

Evidence-first generation with an abstain policy

Policy-makers face an “evidence dilemma”: act with limited proof or wait for stronger evidence while risking harm. AI systems run into similar issues when they create responses based on incomplete information.

Evidence-first approaches create responses when there’s enough proof and hold back when information is lacking. This method cuts down on the risk of making things up while staying trustworthy,which is key for business uses where being right matters more than giving a full answer.

Case study: Lexis+AI

Lexis+AI, a legal assistant, launched with an evidence-first design that offered linked citations to underlying authorities and citation-validation checks that ground responses in vetted sources. 

The model offered auditable, source-backed outputs. A study by Stanford’s HAI Center benchmarked legal RAG tools like Lexis+ AI against general models and found that they had lower hallucination rates.

Expert validation workflows for user-generated content quality control

Professional moderation has an impact on user-generated content quality control before it becomes part of training datasets. Experts review to catch mistakes, take out harmful material, and check accuracy while keeping the real-world language patterns that make UGC valuable.

This human-in-the-loop approach maintains data quality standards while capturing emerging topics and contemporary usage patterns not yet documented in peer-reviewed literature.

Case study: StarCoder2 improved the accuracy of AI coding with expert-reviewed UGC

Coding LLMs that train on raw GitHub user-generated content run the risk of leaking secrets using code with license restrictions and learning from poor-quality snippets. This can harm both model safety and performance in the long run.

To address this problem, the BigCode program (a team-up between ServiceNow and Hugging Face) set up community review sessions, checked licenses and where code came from, got rid of duplicates, and removed personal info and secrets. 

The dataset they used, The Stack v2, helped create a higher-performing model, StarCoder2. StarCoder2-15B matched or outperformed larger benchmarks on MBPP and HumanEval.

Real-world data enrichment

Specific data enrichment plans tackle biased training data distribution by creating samples that show underrepresented areas. This method of semi-supervised learning fills gaps with useful content instead of just gathering more data.

Amazon’s study shows this method cuts down error rates by up to 4.6% across fields. This approach works by spotting where your training data falls short and tackling those gaps with chosen examples instead of random extra data.

Case study: Meta’s Massively Multilingual Speech (MMS)

Many speech models underperform on low-resource languages because the training data tends to skew toward a few well-represented languages. Meta’s Massively Multilingual Speech (MMS) program targeted those gaps by mining real-world speech–text for 1,100+ underserved languages and leveraging self-supervised pretraining to enrich precisely the underrepresented slices. 

Outcome. The resulting models expanded coverage ~10× and more than halved word-error rate versus Whisper on 54 FLEURS languages.

Split roles for copilots to give feedback and manage oversight

Splitting AI system oversight between different roles boosts both performance and accountability. Tech experts handle data sources and quality while domain experts give feedback on what the system produces.

This setup establishes clear lines of responsibility while promoting ongoing improvements through well-organized feedback systems. 

Tech teams zero in on data quality and how well the system runs, while experts in specific areas check if the outputs are on point and accurate in their fields.

Case study: Morgan Stanley’s wealth management assistant

To provide personalized wealth management to its clients and maintain compliance in a regulated market, Morgan Stanley needed trustworthy, auditable AI while steering clear of hallucinations in advisor workflows.

Solution: Morgan Stanley’s machine learning team divided data oversight. Technical teams took charge of retrieving evaluation datasets and conducting daily regression tests, while financial advisors reviewed outputs and provided domain insights before launch—an evaluation-driven process with humans in the loop.

Outcome:The assistant gained firmwide acceptance (98% of advisor teams used it), improved document access from 20% to 80%, and shortened turnaround from days to hours for client follow-ups. 

For Morgan Stanley, joint responsibility between ML engineers and domain experts helped boost both copilot performance and confidence.

Strategic integration of scientific and user-generated training data

The choice between scientific content and user-generated content isn’t binary. Scientific sources provide the reliability enterprises need for specialized domains, but miss the language patterns users actually employ. UGC captures real-world diversity but requires expert filtering to prevent bias and noise from degrading model performance.

Successful AI training programs combine both approaches strategically. 

Polarity-aware citation handling prevents false consensus when scientific debates exist. Evidence-first generation policies reduce hallucinations by requiring sufficient supporting data before generating responses. Expert-moderated UGC loops maintain quality while preserving authentic language patterns.

Companies implementing these hybrid strategies gain competitive advantages that extend beyond accuracy metrics. Their AI systems cost less to train, consume fewer computational resources, and earn user trust through consistent performance. 

The post Scientific content curation vs UGC: How to optimize AI model training data? appeared first on Xenoss - AI and Data Software Development Company.

Menlo Ventures’ survey found that 30% of enterprises consider easily quantifiable ROI as the primary criterion for selecting generative AI tools. But then 46% cite disappointment in their AI ROI. 

IBM surveyed CEOs to discover that only 25% of their AI initiatives delivered ROI, and 16% of them scaled enterprise-wide. And if we consider the famous MIT study, which found that 95% of companies investing in AI fail to achieve ROI, the pattern gets even clearer.

Ensuring predictable and stable AI ROI is challenging, and enterprises often feel frustrated when trying to determine whether their AI initiatives prove worthy of the time, money, and effort invested.

This article will explain:

• Specifics of AI ROI compared to other digital solutions
• Six reasons why enterprise AI projects fail to deliver ROI and how to avoid them
• Lessons from AI leaders on maximizing ROI   

We backed up our research with hands-on experience, as Xenoss consultants help companies organize their AI budgets and build a customized roadmap to measure the ROI of each specific AI project.

How measuring ROI on AI investments differs from other software solutions 

Businesses often apply identical ROI formulas and expectations to AI as they do for traditional software. Traditional software investments pursue clear functional goals through a linear process: problem – digital solution – implementation – result. 

For instance, you implement a SaaS HR system for efficient people management. Monthly costs are transparent, usage metrics are trackable, and you get a clear ROI of increased efficiency of the HR department. A classic ROI formula looks like this:

To give a clear financial example, implementing an ERP system with a TCO of $200,000, which allows the company to earn $100,000 in net profit, would mean a positive ROI of 50%.

By contrast, AI investments follow a fundamentally different decision-making process: hypothesis – experimentation – adoption – evolving outcomes. It’s more complex and requires patience.

For instance, you adopt an AI sales assistant to help your sales team quickly close deals. With the help of the assistant, sellers close some deals faster, others not at all, and in some instances, they may even need to double-check or override the AI’s suggestions. 

The ROI is no longer a simple equation of hours saved. It depends on the accuracy of the recommendations and adoption rates across the team. 

In other words, traditional ROI is deterministic. The savings and efficiencies map neatly to business outcomes. AI ROI is probabilistic. It emerges only when models perform reliably, employees trust and adopt them, and the organization adapts processes to capture the new value.

Given this complexity, companies need to set the right lens for measuring the value of AI. Instead of relying on a single ROI formula, they should frame AI outcomes across multiple goal-oriented dimensions.

Approach AI projects with a goal-driven mindset

According to Gartner, depending on the goal you want to achieve with AI, the focus may be on different business outcomes, such as classic ROI, return on employee (ROE), or return on the future (ROF).

If the goal is increased employee productivity, then your go-to business outcome is ROE, which shows the impact of AI on employee productivity. This business outcome is measured by employee engagement and well-being, as well as the time saved and increased task velocity. 

Workflow efficiency projects utilizing LLMs, agents, assistants, and copilots warrant traditional ROI evaluation. These initiatives focus on quantifiable financial gains through cost reduction and revenue generation.

For ambitious AI projects that pursue competitiveness at the core, the ROF would be a suitable measure of success. It means you invest in a few experimental AI projects (e.g., five different R&D initiatives) at scale, presuming that if at least one project is successful, it’ll pay for the previous failures.

Gartner suggests balancing all three business outcomes to get the most comprehensive assessment of AI benefits for your business. Financial gains aren’t the only thing you can achieve with AI, and you shouldn’t limit yourself to it.

In theory, enterprises may understand that AI ROI isn’t a straightforward path. However, in practice, they often make hasty decisions that prevent them from realizing tangible AI ROI.

Six reasons enterprise AI projects miss ROI expectations 

As an AI and data engineering company, we provide enterprises with AI investment consulting services. From this experience, we have identified six common reasons why AI ROI expectations and actual ROI often differ.

#1. Hype AI adoption with never-ending experiments

Big tech companies heavily invest in AI to win a fierce competition. The byproduct of these tech games is increased AI hype and the FOMO effect among smaller companies, which they attempt to counter by hastily investing in AI without a clear ROI strategy in place or by running many chaotic AI experiments.

A Harvard Business Review article warns companies against the “AI experimentation trap”, as never-ending AI experiments can burn resources, overwhelm teams, and never scale into production.

Instead of running several hyped AI experiments without a clear goal, SMBs and large enterprises alike should focus on solving pressing business and customer problems and defining use cases where AI could bring the most value.

#2. High expectations without measurable KPIs

Businesses set high hopes for AI, giving it almost magic wand powers. Gartner revealed that 74% of CEOs expect AI to be the most transformative technology of all for their businesses. But AI won’t work by itself. It needs solid infrastructure, active cross-company adoption, and clear ROI metrics by which you define its success. 

And here’s the trick. A McKinsey study finds that only 18% of large organizations have well-defined KPIs to track the efficiency of gen AI solutions. If the goal and expected outcomes help you choose the right direction, KPIs serve as your map, helping you stay on track.

Gen AI KPIs can span different areas:

• Reliability and responsiveness metrics, including model latency, error rate, drift, and uptime, are used to evaluate the overall performance of gen AI.
• Model quality metrics, including coherence of the output, instruction following, text quality, and verbosity, help fine-tune the model’s accuracy to ensure it generates high-quality content.
• Business function metrics, such as customer churn, average handle time for customer service, click-through rate, time on site, or revenue per visit for product, marketing, and service use cases.
• Adoption metrics, including adoption rate, frequency of use, and session length, help evaluate the usability and accessibility of the AI solution.
• Business value metrics, including cost savings, revenue generated, and customer experience, are used to evaluate the outcome of the AI project. 

Choose specific KPIs for each AI use case. For instance, if your sales or marketing team uses an AI chatbot for content generation, such as writing emails, sales decks, or marketing reports, then metrics that help evaluate content quality as well as model reliability would be necessary.

Brainstorm and identify the key metrics that are most important to your team. You can then redistribute the gen AI efficiency measurement among different team members. 

For example, entrust the IT or R&D departments with tracking technical metrics using tools like Grafana and OpenTelemetry, while delegating the measurement of business metrics to internal or external business analysts via business intelligence (BI) tools like Tableau and Looker.

#3. Limited data infrastructure readiness

AI implementation requires preparation. Organizations can’t expect AI solutions to provide valuable results when data is siloed, processes aren’t documented, and employees switch between several systems that aren’t interconnected. Although it’s possible to integrate AI with legacy systems, these integrations still require thorough preparation of the data infrastructure.

Building data pipelines that fetch relevant and high-quality data from centralized data storage, including both structured and unstructured datasets, is the first step in implementing AI. Because without this foundation, your project will inevitably fail at the production stage. 

The best way to achieve a high level of AI system accuracy is to build enterprise knowledge bases based on retrieval-augmented generation (RAG) with real-time access to all internal documentation and feed AI solutions with continuous company data.

When real-time enterprise data becomes the lifeblood of your AI system, it produces reliable outputs that bring tangible value to your business, including faster decision-making, reduced operational costs, improved customer experiences, and new revenue opportunities.

#4. Lack of in-house capacity to maintain AI systems

A shortage of AI engineers who can implement, maintain, and fine-tune gen AI solutions can lead to stalled projects, slower adoption across business units, and ultimately, failure to realize the promised ROI.

When facing AI skills shortages, 49% of enterprises are investing in upskilling or reskilling their in-house employees, while 46% of companies are cooperating with external IT integrators and consultants to bridge the gaps. The choice depends on the budget and time-to-market requirements. 

Cultivating internal AI skills can yield better long-term results if you’re planning on more AI projects in the future. However, partnering with expert AI and data engineers can also prove effective, as you pay for the AI project during development and then shift to an on-demand payment for system maintenance and support. You get to tap into vast AI knowledge and expertise without any extra expenses on maintaining an internal AI department.

#5. Ineffective change management practices or their absence

Without strategic change management and AI adoption strategies (e.g., clear communication, phased rollouts, executive buy-in, employee training, and feedback loops), AI experimentation as well as enterprise-wide AI adoption can be catastrophic.

Prioritize solving specific problems for users or employees and introduce AI as a solution and enabler. Comprehensive training programs and security guidelines build user trust, accelerate adoption rates, and encourage consistent usage patterns that deliver faster business benefits. 

Business unit leaders, HR, and learning and development specialists can support your mission by managing and facilitating the adoption of AI.

#6. Complex TCO of AI projects

Similar to ROI difference, traditional IT costs (maintenance and service fees) are mostly predictable, whereas gen AI costs are unpredictable and volatile. That’s why initial investment during experimentation can differ from the costs necessary to launch AI in production and maintain it.

Gen AI models continuously learn and can drift over time if not adequately monitored and managed. Thus, maintenance costs for AI software can vary depending on the level of fine-tuning efforts. 

AI volatility can also increase AI infrastructure costs, as different computational, training, and inference tasks put varying pressures on hardware and software AI components. In this respect, the decision to run AI software in the cloud or on-premises is crucial. While cloud deployment allows you to benefit from cloud FinOps for efficient cost tracking, an on-premises AI rollout provides more control over your infrastructure. 

To optimize performance, ensure flexibility, and reduce GPU usage costs, Toyota has adopted a hybrid approach to launch their AI platform. They reduced the number of on-premises servers to one and use it for normal operations, while scaling to the cloud environment for peak demand. With the hybrid approach, the Toyota team reduces the current TCO while future-proofing software for scaled demand.

To implement AI for ROI and drive transformative enterprise value, ensure you:

• Have a clear business goal with a focus on real business or customer problems (rather than hype or the FOMO effect)
• Continuously measure adoption and implementation results with business-specific KPIs
• Feed your AI system with high-quality proprietary data in real time
• Onboard skilled specialists and foster AI adoption with clear-cut change management strategies
• Compose a well-planned AI budget to avoid over- or underspending and ensure the successful launch of your AI project in production, as well as its gradual scaling

These steps can bring you closer to ROI-positive AI projects, but to truly understand what works in practice, it’s worth looking at how leading enterprises succeed with AI. 

Breaking the missed-ROI pattern: Lessons from gen AI leaders

The Google Cloud survey on gen AI ROI discovered that companies leading in AI initiatives have four or more AI projects in production and have invested more than 15% of their operating expenses in AI. These strategic investments generate higher and faster ROI across multiple use cases compared to organizations with less strategic AI use.

But high investments and scaled AI use are already the characteristics of them as leaders, and here are three decisions that helped them become those leaders:

• Clear vision for future growth. Among business goals, they prioritize AI adoption for improved customer experience and the development of new products and services, rather than optimizing only current operational needs.

• Aligned technology and business objectives. They have a clear understanding of how the technological benefits of AI tie to their business strategy. 

• Dedicated gen AI teams. Leaders in gen AI projects prioritize building specialized AI teams that drive technological improvements but also foster cross-company adoption.

In line with our conclusion in the section on reasons for missed AI ROI, Google’s report confirms that core drivers of AI success are teams with a clear vision of AI benefits, not only today but also in the future.

The McKinsey survey yields similar findings on what differentiates leaders in AI initiatives from those who are still figuring out how to derive value from this breakthrough technology. Here’s what Bryce Hall, Associate Partner at McKinsey, said on the matter:

We’re now far enough into the gen AI era to see patterns among companies that are capturing value. One significant difference is that these companies focus as much on driving adoption and scaling as they do on the up-front technology development. 

This is not just hand-waving. Instead, they are following specific management practices that enable them to be successful—such as developing a clear roadmap for scaling, establishing and tracking KPIs, and driving change management by ensuring senior leaders are actively engaged in driving gen AI adoption.

How do real-life enterprises adopt AI and ensure ROI with it?

Walmart invested in gen AI training before it got mainstream 

When generative AI emerged, the Walmart AI/ML team began training open-source large language models (LLMs) to match their business specifics and those of the retail industry in general. This decision enabled them to experiment with AI sooner than most of their competitors and implement it across the entire company.

But AI experimentation wasn’t random. They set five objectives: improve customer experience, developer productivity, operations, and generate content. To measure their results and correct the direction if necessary, Walmart has established specific checkpoints for measuring AI efficiency. They focus on model quality evaluation, A/B tests, and human feedback to keep AI experimentation and production-ready models under control.

When their product catalog became much bigger as more and more retailers were getting on the platform, offering an online shopping experience, the company implemented a gen AI solution with multiple LLMs to create, clean, and improve 850 million data elements. To do the same task manually, Walmart would’ve required nearly 100 times their current workforce.

With an improved catalog, Walmart gathered valuable insights into customer shopping habits. By introducing AI-powered search and sales assistants, the company also saw an increase in sales, as customers could quickly find what they needed. As a payoff, in Q2 2024, they achieved 4.8% revenue growth and 21% growth in the e-commerce function. Such results they mainly attributed to generative AI initiatives. 

The example of Walmart demonstrates that to succeed with AI and achieve a high ROI, you should have a specific goal for your AI initiatives, measure its impact at set milestones, and train generative AI solutions with custom data to match your business’s specific needs.

Sentara Health sees 4 times ROI from the pilot gen AI program

Sentara Health has adopted gen AI technology to facilitate quick and efficient chart reviews in the electronic health record (EHR) system, providing a draft assessment of the patient and saving clinicians’ time while increasing documentation accuracy. 

What takes clinicians hours of manual search, an AI system performs in seconds, and most importantly, it retrieves the most comprehensive information on the patient, which clinicians could overlook after repeating this process for multiple patients in a day. Such accuracy and attention to detail are what particularly convinced clinicians to use AI after they tried it during the pilot program. 

However, to ensure active adoption and use, Sentara Health has also identified AI champions among physicians to serve as informal leaders who can demonstrate to their colleagues the efficacy of AI.  They also established an AI oversight program to validate AI solutions, check for drift, and ensure their security and proper integration into the hospital workflow.

Already at the pilot stage, the company could secure 2-4 times ROI per clinician. Such a success of AI implementation at the administrative level in one hospital prompted scaling AI use to all 12 hospitals. 

Here is how the Chief Health Information Officer at Sentara Health, Joe Evans, explains their success:  

So, from the view of our CFO and hospital operations leaders, the pitch to them is to be able to show the hard ROI and the benefit of capturing the CCS and MCCs [Complications or Comorbidities and Major Complications or Comorbidities] to help with DRG [Diagnosis-Related Groups] upgrades, which helps with hospital reimbursement. 

And it’s easy to map out to them, and that’s what we did after the pilot. We could say this is what we spent on this solution, and this was our hard return on investment. And those results are what helped us be able to spread it through all 12 hospitals.

Sentara Health’s success hinges on a clear problem, effective adoption strategies, and a pilot program designed to measure its impact, even on a small scale. As a result, what started with simple experiments and AI implementation in one hospital, yielding a clear ROI, now extends to more facilities and medical departments, promising even higher ROI, as more clinicians use AI in their workflows.

Bottom line

When investing in AI solutions, you’re investing in the future. AI implementation requires significant preparation, including setting up infrastructure, establishing data pipelines, and enabling the team to work effectively. 

For these efforts to pay off, you need time and enough resources to support the volatile nature of AI initiatives. But once you pass these initial stages of AI experimentation, prototyping, A/B testing, and feedback loops, you’ll gain confidence to invest more lavishly into your AI projects and scale them across business functions to become a gen AI leader in your industry.

Xenoss can be by your side the whole time, from AI feasibility study and data infrastructure assessment to team training and comprehensive AI ROI measurements.

The post Gen AI budget reality: Why enterprise investments miss their AI ROI targets appeared first on Xenoss - AI and Data Software Development Company.

A Salesforce survey discovered that sales representatives spend 70% of their workday on non-selling tasks (administrative work, meeting preparations), with 30% left for closing deals and engaging with prospects. On a global scale, the employee engagement rate at work reached 21%, resulting in a $438 billion loss in productivity for the world economy. 

The question is: how do you flip the ratio? How do you give employees 70% of their day back for meaningful, high-value work, while limiting the routine overhead to 30%?

Through the strategic adoption of digital productivity tools, including conversational AI technology and copilots. A study by OpenAI indicates that 62% of AI value can be realized in core business functions. These solutions help increase productivity by enabling employees to:

• resolve issues on the spot
• find answers when needed most
• continuously learn (e.g., AI sales coaches collect data on calls with prospects to highlight areas that need improvement). 

Unlike traditional automation software, such as robotic process automation (RPA), AI productivity tools do much more than simply entering data. For instance, apart from automatically sending emails, AI can first draft these emails by analyzing previous client calls and interactions.

Read on to explore how to choose, implement, adopt, and measure AI copilots and conversational AI to achieve the maximum productivity levels at your company based on AI in workplace examples from real-world sales, marketing, and customer support teams.

The Xenoss team knows firsthand how to successfully implement AI, as you need to ensure that these tools integrate with your infrastructure, don’t diminish the value of existing automation tools, and, most importantly, don’t create an additional burden for your team. 

The employee inefficiency problem 

Regardless of the company size and org chart complexity, your teams may struggle with daily inefficiencies and operational challenges. Instead of focusing on their direct duties, employees become distracted and overwhelmed by administrative and low-value tasks. 

A startup may have fewer employees, but each employee often has to wear multiple hats and juggle tasks that extend beyond their scope of responsibilities. 

Mid-sized companies may struggle with task prioritization, as they already have larger teams but lack effective methods for distributing a growing workload among their employees efficiently. 

Large companies often become bogged down in bureaucracy and complex processes that consume time and energy, hindering their ability to operate efficiently.

In fact, employee inefficiency tends to increase as your business grows and headcount expands. Today’s decisions regarding employees’ operations define tomorrow’s business performance and profitability.

Why choose AI to tackle operational challenges, or how to convince your board to invest

AI won’t solve every employee productivity issue, but it can be effective in saving employees’ time, improving decision-making accuracy, increasing sales efficiency, and reducing error rates. Below is a breakdown.

Increased profitability through automation 

When trained on high-quality internal datasets, AI systems can be highly effective in analyzing vast amounts of data, supporting informed decision-making, and identifying errors. By automating multiple areas employees juggle during a workday, AI promises to enhance the quality and speed of work, resulting in increased business efficiency and profitability. According to a recent PwC study, companies that adopt AI tools experience three times higher revenue growth per employee compared to those that resist using AI in the workplace.

Reduced errors and improved quality 

Tedious work processes, frustrations, and frequent interruptions negatively impact employees’ productivity levels, leading to burnout and increased error rates. The Microsoft Work Trend Index 2025 reveals that 82% of global employees admit they lack the time and energy to perform their job effectively, as they face up to 275 interruptions a day. Among common examples are:

• manual searches for information
• dealing with incomplete data
• administrative tasks
• taking meeting notes
• last-minute edits

Employers in the US are losing approximately $300 billion due to stressed employees. Automating and streamlining repetitive duties with AI in workplace can help teams reduce interruptions and errors, allowing them to stay focused on what matters (service quality, customer engagement, and team collaboration) and improve work quality.

Make HR processes human again

AI-powered communication tools and copilots can automate onboarding, training, mentorship, and performance evaluations. Thus, AI can help businesses accelerate time to productivity (TTP) for new hires and enable long-term employees to continuously assess their achievements, thereby boosting morale and motivation. Whereas HRs will be able to devote more time to genuine one-on-one communication, conflict resolution, and maintaining a healthy working environment. 

Bring AI from the shadows into the light 

A McKinsey study discovered that employees use AI in the workplace more than their leaders expect them to. Some employees use AI tools in a disorganized (and probably not secure) manner, while others may be reluctant to use them at all. So if a significant number of workers already use AI tools for productivity privately, wouldn’t it be wiser to implement them as a cross-company strategy and achieve consistent productivity improvements? Survey your employees to discover which issues they primarily solve with AI and how these tools save them time and effort on a daily basis. These findings will help you evaluate the relevance and feasibility of AI adoption in your organization. 

Cross-team alignment

AI copilots and conversation tools can serve as a single knowledge hub, enabling marketing, sales, and support teams to access the same insights, reducing miscommunication and duplicated effort, which can often result in missed business opportunities. Team misalignment leads to disengagement and a fragmented focus, where everyone thinks of completing the task rather than delivering value.

Identify the real-life issues that drain your employees the most and base your AI initiatives on those findings. As our CRO, Mariia Novikova, puts it: 

Copilots and conversational AI solutions: Types, pricing, implementation, and ROI

Conversational AI and productivity copilots are based on natural language processing (NLP) and natural language understanding (NLU) technology. Thus, they quickly understand and process human language, mimic real-life and real-time human conversations, and produce useful outputs in multiple formats (text, audio, video). These solutions become versatile productivity tools as they address multiple needs at once, such as communication, search, and content creation. 

AI copilots for instant collaboration

An AI-powered solution designed to collaborate with human users, seamlessly embedded within business tools and workflows to automate routine tasks and provide context-aware insights. They simplify the search for information across systems and files, help employees compile relevant data for easy access, and can generate insights and projections based on files and documents (e.g., analysis of Excel spreadsheets).

Conversational AI solutions to provide answers to every question

Conversational AI falls into several categories, such as AI chatbots, LLMs for content generation, and AI virtual assistants. 

AI chatbots for cross-company real-time messaging

As the name suggests, chatbots simulate human communication and provide relevant information in real time in either audio or text format. Through a branded interface and fed with your internal data, they can become an advanced search engine that quickly retrieves necessary data and explains to your employees how to apply it efficiently. Chatbots can also be user-facing and unburden your customer support team by processing a part of customer requests.

RAG-enhanced LLMs for custom content generation

You can also develop large language models (LLMs) that source data from your proprietary knowledge bases to create context-rich content that directly matches your audience’s pain points and aligns with the brand strategy.

AI virtual assistants to support when it’s needed the most

Virtual assistants are similar to copilots, but their primary aim is to support and streamline workflows rather than generate insights or enhance decision-making. AI productivity assistants can be text- or voice-based to manage calendars, summarize meetings, prioritize tasks, and facilitate cross-tool coordination, helping employees regain focus time and cut through organizational noise.

Which AI productivity tool to choose

Select a suitable copilot or conversational AI solution based on the specific daily pain points your employees face. Which issues take up the most of their time and cost your company the most? That’s your starting point. The first thing Estée Lauder did before implementing LLMs was to ask their employees directly how they would like to use them. Be open to conversation with your employees; they might already have the answers as to which tool they need to increase productivity, but aren’t confident enough to voice their needs.

Plus, consider your AI maturity level. If you’re new to AI solutions and haven’t yet tested them in production, it might not be wise to invest in 240 custom LLMs like Estée Lauder did. Instead, you could start with a simple chatbot to help your marketing team simplify data search and create reports with speed. Based on the above examples, AI chatbots offer the highest ROI, with a relatively low price and implementation simplicity.

Scale with time as more teams see the value of AI implementation for their teams.

From Xenoss experts: Infrastructure and tech stack to increase time-to-value

Your data infrastructure layer is the foundation for AI systems, as they require around-the-clock access to high-quality and reliable datasets to deliver accurate outputs. Collecting, storing, cleaning, and processing data from multiple sources is the first task for your internal or external engineering teams before AI adoption. 

The next step would be to integrate AI with your proprietary systems. This process also requires engineering input to ensure data consistency, as legacy systems and AI may not be readily compatible with each other. 

Dmitry Sverdlik, CEO at Xenoss, gives a possible solution to this issue:

To accelerate time-to-value when implementing AI systems, many teams leverage low-code or no-code integrations embedded in third-party AI tools. They enable seamless integration into existing workflows with minimal disruption. Adding a human-in-the-loop layer is equally important for oversight, ensuring that outputs for client-facing, compliance-sensitive, or financial tasks get reviewed before execution.

Apart from that, copilots and communication AI tools require a layered governance model with robust access controls, transparent audit logs, and compliance with industry-specific regulations such as GDPR, HIPAA, and PCI DSS.

Implementation timeline and associated costs

As for implementation, the cost varies by scope, but quick wins often start with AI tools embedded into a single function (e.g., sales or customer support) before being scaled company-wide. 

A pilot AI solution can be integrated in a few weeks, especially with low-code tools, while full enterprise rollouts may take months. Internal expertise in data engineering and IT is useful but not always required upfront, and collaboration with external partners can accelerate deployment. 

The highest-priority elements are clean data pipelines and integration into core business systems, since these directly determine whether copilots and conversational AI systems deliver measurable productivity fast.

Xenoss experience

We built a conversational AI chatbot for a global on-demand delivery service. Their support teams were overwhelmed due to rapid business expansion and needed an efficient solution to handle routine inquiries, process them in multiple languages, and maintain brand consistency. 

Our AI and data engineering team developed a real-time NLP pipeline integrated with backend systems for up-to-date information retrieval. We combined several classification, recognition, and predictive models to recognize and handle user intents in different languages. For cross-channel use and simplified access, we deployed a chatbot on the company’s website, mobile apps, and across messaging channels.

This solution resolves 40% of inquiries, freeing up support agents from operational burden.

ROI-proven implementations across business functions and industries

The following real-life adoption examples demonstrate how the implementation of conversational AI and copilots enables teams to manage daily challenges.

Case #1: Sales and customer support assistance

Verizon deployed an AI assistant to reduce call time for its 28,000 customer service representatives, freeing them up to sell products to customers. Verizon’s new internal software was created by feeding a version of Google’s language model, Gemini, with nearly 15,000 internal documents. As a result, Verizon reduced call times and improved cross-sell and up-sell interactions, leading to a 40% increase in sales. Enhanced employee efficiency led to measurable business improvements.

Case #2: Advertising and marketing

A metasearch engine for comparing hotel and accommodation prices, trivago, needed a quick solution to unburden their marketing team from manually creating targeted TV ads for over 30 markets. With AI video generation technology HeyGen, the company reduced post-production time, saving their marketing teams an average of 3-4 months of work. Text-to-speech technology enables trivago to produce TV ads in multiple languages, targeting global markets. They successfully localized advertisements in 15 locations in three months. And in less than a year, trivago has created TV ads for all 30 regions.

Case #3: Healthcare support system

With the help of an AI-powered appointment scheduling and confirmation system, Allure Medical increased its appointment confirmation rate by 25% and automated 3,500 calls per month, eliminating the need for manual, time-consuming calls and freeing up administrative staff for more meaningful interactions with patients.

Case #4: Root cause analysis in financial services

Australia’s Bank of Queensland has more than 3,000 employees and nearly 1.4 million customers. They identify overlooked risks with root cause analysis. A/B testing revealed that data analysts using Microsoft Copilot were able to determine the root cause 51.8% faster than those who did not use it. Even the fastest analysts who didn’t use AI couldn’t beat this speed level. The bank concluded that integrating Copilot for 1,000 employees is equivalent to adding 120 new employees in terms of productivity.

These were positive outcomes of AI adoption, but how do you encourage people to use these tools as you intend them to?

Driving adoption: change management and employee enablement

No matter how beneficial new technology is, it’s still new, and some level of resistance to change is inevitable. Transparency and direct communication are key from the outset. Well-prepared AI adoption can take weeks instead of months, which is often the case in environments that already have to put out numerous fires during the post-implementation period. 

Simply introducing AI as a new innovative solution won’t make it. Harvard professor John Kotter has developed an eight-step framework for managing change within an organization. He suggests building a coalition of volunteers at your company who are willing to facilitate change and, most importantly, remove barriers to AI adoption. If you integrate AI but your teams still struggle with legacy bureaucratic processes or systems, AI will feel like an additional burden. 

Clear change management and leadership strategies encourage AI adoption. According to a Gallup report, employees state that the biggest AI adoption challenges are unclear use cases or value propositions, a lack of established guidance and policies, and a lack of comprehensive training programs. The same report also reveals that employees are three times more likely to use AI when leadership communicates a clear plan. 

Here are a few AI adoption best practices we’ve accumulated over the years on AI projects of diverse complexity: 

• Start with pilot programs, which can take up to eight weeks, including project ideation, pilot development and testing, and preparing the scope for future scaling.
• Invite the most skeptical employees to participate in pilot projects, allowing them to voice their concerns and opinions before the rollout goes live.
• Directly communicate security and data policy concerns and establish trust with your employees. Be clear that AI tools integration is for employees’ benefit only, and it’s not meant to spy on them and gather sensitive information.
• Create interactive and straightforward training materials for different roles that encourage employees to learn quickly, rather than breeding frustration with overly complicated terms or processes.
• Establish ownership during the AI adoption process by engaging HR leaders to address frustrations and fears, IT security teams to handle security concerns, and external AI product owners to continuously communicate the value of AI and facilitate training programs.
• Assign department managers to foster and monitor AI adoption across their teams as an additional effective solution to AI resistance.

If AI resistance persists, gather employee feedback to learn the exact workflows or processes that discourage your employees from using AI. You may need to fine-tune your solution or research new tools that would prove more effective.

To sustain AI use, measure its impact over time. It’s beneficial to the business and helps employees feel more motivated to use AI.

Measuring productivity impact to prove AI efficiency

Just as change management requires assigning a responsible person, you should do the same for AI efficiency tracking. Business intelligence and data analytics experts are the most obvious choice, but department managers should also be involved, as they’re directly responsible for employee performance and productivity. 

To define where your AI productivity tools increase productivity rather than add operational burden for your teams, get back to your expectations and issues that you wanted to solve with AI in the first place. If, after a month of AI use, your marketing teams spend even more time on creating marketing campaigns, as they now need to devote time to correcting AI mistakes, then you would need to redefine your initial AI strategy.

To increase confidence in AI efficiency, compare your findings against industry benchmarks. For instance, a Microsoft study discovered baseline productivity improvements of artificial intelligence in the workplace for corporate tasks:

• 10-13% increase in document editing efficiency
• 11% decrease in email processing time
• 39% increase in response times to customers
• 25% improvement in the accuracy of processing customer requests

These benchmarks are average across industries and teams, but they demonstrate that even a 10% or 15% improvement in employee productivity is progress worth investing in AI. 

But remember that AI may require some time to yield first results. Allow time for training, onboarding, and getting accustomed to the new technology. Early wins for employee productivity can be visible after one or two months, but the impact on business may take up to 12 months.

What metrics to measure

As for metrics you can track, choose qualitative and quantitative metrics that were important for defining employee productivity before the AI adoption; this way, changes will be more visible. To achieve the most reliable results, track each department’s performance separately and define core metrics for each (e.g., the number of closed deals per month for sales and the number of closed inquiries per hour for customer support). 

Such general financial metrics as revenue per employee and cost per output (measured by the number of tasks completed) bridge business and productivity gains.

Measure AI efficiency on a month-over-month (MoM) basis to gather sufficient data for comprehensive analysis. It’s also crucial to collect employee feedback on how AI tools affect workload, stress, and job satisfaction. Increased morale often correlates with sustainable productivity gains.

Tracking tools and reporting formats

You can use tools like Hubstaff, ActivTrak, and Worklytics to track productivity gains. These tools collect data from your proprietary systems and communication channels to provide insights into employee performance through interactive dashboards. However, they’re subscription-based and can have limited customization capabilities.

To achieve higher customization and embed analytics into your AI pipelines, you can integrate data analytics services like Amazon SageMaker, which provides branded trend analysis reports and dashboards. 

Smart BI tools, such as Tableau and Power BI, can also be your go-to options. For instance, Tableau Pulse integrates with messaging channels (Slack, Outlook) to provide reports as on-demand messages in a clear and concise format. This is particularly useful for executives who need a quick recap of business performance but don’t have time for full-fledged analytics dashboards.

Xenoss AI engineers and data analysts can set up a custom analytics environment tailored to your busy schedule, budget constraints, and data infrastructure capacity.

What is AI without human oversight? Limitations and lessons learned to bear in mind

Despite promising gains, AI tools still struggle with tasks requiring deep business context, chain-of-thought reasoning, or nuanced judgment, highlighting the persistent need for human review.

Overreliance on AI can reduce employee skills and make them reluctant to verify outputs. AI often makes confident but incorrect claims, and it’s crucial to include clear instructions in your AI use policy on the importance of fact-checking and human judgment. It’s particularly necessary in high-stakes situations, when brand reputation and security are at risk. 

Support and foster human relationships at all levels, as it’s only with a human-first approach that you can yield true productivity gains. 

Xenoss can help you build top AI productivity tools that value human time and effort, and create space for creativity, genuine communication, and team support. Our services strike a balance between human and business value, ensuring that AI tools enhance work culture and drive measurable business outcomes.

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