Picking the right data management platform means balancing three capabilities: 

1. Governance (who can use what data and how)
2. Quality (can we trust the data)
3. Integration (how the data moves between systems). 

Some platforms, like Informatica, span all three. Others specialize in one and do it well. A poor match leads to fragmented pipelines, compliance gaps, and AI models trained on unreliable inputs.

This comparison covers 10 leading platforms and introduces what Xenoss data engineers call the Govern-Integrate-Trust (GIT) Maturity Model: a framework for matching platform choices to your organization’s data readiness level.

Summary

• Governance-first platforms (Collibra, Informatica, Atlan) suit regulated enterprises that need auditable lineage, policy enforcement, and compliance workflows.
• Integration-first platforms (Fivetran, Talend) suit teams that need reliable data movement from dozens of sources into analytics-ready warehouses.
• Analytics and AI platforms (Snowflake, Databricks) suit data science teams that need unified compute, storage, and ML capabilities at scale.
• Tool selection depends on maturity, not budget alone. The Govern-Integrate-Trust framework helps map your current readiness to the right platform tier.

Three pillars of data management

Data management tools fall into three categories. 

1. Data governance covers cataloging, lineage tracking, access policies, and compliance. 
2. Data quality handles profiling, validation, anomaly detection, and monitoring. 
3. Data integration moves and transforms data between systems, from sources to warehouses to the analytics layer.

The right choice depends on whether your organization needs depth in one pillar or breadth across all three.

What’s at stake without a data management platform?

47% of CDOs say attracting talent with advanced data skills is now a top challenge, up from 32% in 2023. When skilled people are hard to find, tooling decisions carry even more weight. The wrong platform creates a compounding burden: data engineers spend time fixing pipelines instead of building new capabilities, analytics teams produce conflicting reports from conflicting datasets, and AI models trained on incomplete data deliver inaccurate predictions.

Compliance exposure grows in parallel. Organizations in finance, healthcare, and government without governance automation face regulatory penalties that can reach hundreds of millions of dollars. According to Gartner, 80% of governance initiatives will fail by 2027 if they lack clear business outcomes or urgency.

Why this matters: Choosing tools is a risk and capacity decision. The platforms you pick determine how fast your team can move and how much governance overhead they carry.

Comparative overview: Top 10 data management platforms

The table below summarizes core characteristics. Detailed assessments for each platform follow.

1. Informatica Intelligent Data Management Cloud (IDMC)

Informatica maintains its position as a governance leader through comprehensive capabilities spanning cataloging, lineage, and compliance automation.

Key features:

• AI-powered metadata enrichment and classification
• Automated data quality profiling and monitoring
• Multi-cloud and hybrid environment support
• Advanced policy enforcement and workflow automation

User perspective: According to Gartner reviews, customers consistently highlight strong performance and support, earning Informatica recognition as a leader in data governance platforms.

Limitations:

• Complex setup requiring dedicated resources
• Higher total cost of ownership for smaller organizations
• Steeper learning curve compared to modern alternatives

Best use case: Organizations with distributed data across multiple clouds requiring enterprise-grade governance at scale.

2. Collibra Data Intelligence Platform

Founded in 2008, Collibra pioneered comprehensive data governance and remains the go-to platform for highly regulated industries.

Key features:

• Comprehensive data cataloging with automated discovery
• Workflow automation for data stewardship
• Policy management and compliance tracking
• Graph-based metadata management

Governance strengths: Collibra excels in creating auditable data usage trails and centralized governance structures. The platform enforces policies across thousands of data sources, making it ideal for organizations with strict regulatory requirements.

User feedback: While Collibra offers robust features, user comparisons note that users often struggle with its confusing UI, and implementation can take over a year.

Limitations:

• Heavily manual processes requiring data stewards
• Complex initial setup (12+ months for full deployment)
• Higher cost structure for large-scale deployments

Best use case: Financial institutions, healthcare systems, and heavily regulated enterprises requiring stringent compliance frameworks.

3. Alation Data Intelligence Platform

Alation, founded in 2012, helped define modern data catalogs with its unique behavioral intelligence approach.

Key features:

• AI-powered data discovery with behavioral learning
• Natural language search capabilities
• Collaborative features, including annotations and discussions
• Column-level lineage tracking
• Deep BI tool integration (Tableau, Power BI, Looker)

Collaboration edge: Alation’s platform is often described as “Google for enterprise data.” The gamified adoption features and popularity rankings encourage organic user engagement, driving higher adoption rates than traditional governance tools.

User insights: Reviews indicate Alation leads in the data catalog space, though users note the cost can be prohibitive for smaller companies.

Limitations:

• Higher pricing compared to some alternatives
• Limited customization options in the interface
• Requires additional fees for some third-party integrations

Best use case: Mid-to-large organizations prioritizing data literacy, self-service analytics, and collaborative data culture.

4. Atlan

Atlan positions itself as a next-generation data collaboration platform with strong AI governance capabilities.

Key features:

• Active metadata-driven automation
• Automated column-level lineage via out-of-the-box connectors
• AI governance features for ML model tracking
• Customizable personas and access controls
• Modern, intuitive user interface

Modern approach: Gartner recognized Atlan as a Visionary in 2025. The platform emphasizes fast deployment and minimal configuration, with some organizations achieving value within weeks rather than months.

Comparative advantages: A detailed comparison highlights that while Alation has a clunky interface and Collibra requires extensive manual processes, Atlan offers a user-friendly setup with flexible metadata capture and open architecture for modern data sources.

Best use case: Cloud-native organizations with modern data stacks seeking rapid deployment and AI-ready governance.

Data quality and integration platforms

5. Snowflake

Snowflake became a top player in cloud data warehousing with its unique architecture separating compute and storage.

Key features:

• Elastic, independent scaling of compute and storage
• Native support for semi-structured data (JSON, Parquet, Avro)
• Data sharing capabilities across organizations
• Time-travel and zero-copy cloning
• Native integration with major BI and analytics tools

Integration capabilities: Snowflake’s architecture enables seamless data consolidation from diverse sources. 

Limitations:

• Usage-based pricing can become expensive at scale
• Limited native ETL capabilities (requires third-party tools)
• Vendor lock-in concerns

Best use case: Organizations building centralized analytics platforms requiring flexibility and scalability.

6. Databricks Lakehouse Platform

Databricks pioneered the lakehouse architecture, unifying data lakes and data warehouses.

Key features:

• Delta Lake for ACID transactions on data lakes
• Unified batch and streaming data processing
• Built-in ML and data science workflows
• Support for multiple programming languages (Python, R, Scala, SQL)
• Delta Sharing for secure data sharing

AI and analytics excellence: Databricks excels at supporting complex data science and machine learning workflows. The platform combines the flexibility of data lakes with the management capabilities of data warehouses.

Industry position: Featured prominently in 2025 data management tool rankings, Databricks is recommended for organizations prioritizing AI-driven automation and real-time processing.

Best use case: Data science teams requiring unified analytics and ML capabilities on large-scale data.

7. Talend Data Fabric

Talend provides comprehensive data integration, quality, and governance capabilities, with machine learning.

Key features:

• Open-source foundation with enterprise features
• ML-powered data profiling and anomaly detection
• Real-time and batch data integration
• Data quality management and validation
• GDPR, HIPAA, and CCPA compliance features

Quality focus: According to user reviews, Talend excels at identifying quality issues, uncovering hidden patterns, and spotting anomalies using its ML capabilities.

Security certifications: Talend maintains strong data confidentiality through adherence to multiple industry standards, making it suitable for organizations with stringent security requirements.

Limitations:

• Can be complex for non-technical users
• Requires training for optimal utilization
• Some features require additional licensing

Best use case: Mid-to-large enterprises needing comprehensive data quality and compliance management.

8. IBM InfoSphere Master Data Management

IBM InfoSphere focuses on enterprise-grade master data management across multiple domains.

Key features:

• Multi-domain MDM (customer, product, supplier, location)
• Data consolidation and hierarchy management
• Robust data integration via ETL pipelines
• SQL modeling and incremental batch updates
• Scalable architecture for growing organizations

Pricing structure:

• Small: $31,000/month
• Medium: $51,000/month
• Large: $80,000/month

Limitations:

• High cost barrier for smaller organizations
• Complex implementation requiring specialized expertise
• Primarily suited for large enterprise environments

Best use case: Large enterprises managing complex master data across multiple business domains.

9. Microsoft Purview

Microsoft Purview integrates cataloging, governance, and compliance specifically for Azure ecosystems.

Key features:

• Automated scanning of Azure resources
• AI-driven search and classification
• Native Azure service integration
• Data lineage tracking across Microsoft services
• Unified compliance management

Azure advantage: For organizations heavily invested in Azure, Purview offers seamless integration. It provides cataloging, governance, and compliance in a single pane of glass.

Limitations:

• Primarily Azure-focused (limited to multi-cloud)
• Best value only for Microsoft-centric environments
• Some features require additional Azure services

Best use case: Organizations operating primarily on Azure infrastructure.

10. Fivetran

Fivetran leads automated ELT with managed, reliable data pipelines.

Key features:

• 500+ pre-built, maintained connectors
• Automated schema change handling
• Real-time and batch synchronization
• Data transformation via dbt integration
• Usage-based pricing model

Automation excellence: According to platform comparisons, Fivetran is a market leader in automated data movement, offering fully managed services.

Integration strengths: Fivetran eliminates the need for teams to build and maintain custom connectors. The platform automatically detects and adapts to schema changes, reducing pipeline maintenance overhead.

Limitations:

• Limited data transformation capabilities (requires dbt)
• Can become expensive at high data volumes
• Less suitable for complex transformation logic

Best use case: Analytics teams requiring reliable, low-maintenance data pipelines from diverse sources to cloud warehouses.

Selection framework: The Govern-Integrate-Trust maturity model

Choosing data management tools by feature list alone ignores the most important variable: where your organization stands in its data maturity. What Xenoss data engineers call the Govern-Integrate-Trust (GIT) Maturity Model maps platform choices to three readiness levels.

The GIT model reflects a principle Xenoss engineers see consistently across client engagements: organizations that try to implement Level 3 tooling (enterprise governance platforms with 12-month deployment cycles) before establishing Level 1 foundations (reliable data movement and a basic catalog) burn budget and team capacity without delivering value. The sequence matters as much as the selection.

Hidden cost factors most comparisons miss

Vendor pricing tells only part of the story. Based on Xenoss data engineering experience across Fortune 500 engagements, the following cost multipliers consistently surprise organizations during implementation:

Why this matters: A platform with a lower sticker price can cost more over three years when implementation, training, and infrastructure are factored in. Xenoss engineers recommend modeling the total cost of ownership across a three-year horizon before shortlisting vendors.

Bottom line

The best data management tool depends entirely on organizational context: maturity level, regulatory requirements, AI ambitions, and existing infrastructure.

For regulated enterprises, Informatica IDMC or Collibra provides the compliance frameworks that finance and healthcare organizations need. For analytics-driven teams, Alation, combined with Snowflake or Databricks, balances governance with performance. For cloud-native organizations that need to move fast, Atlan’s active metadata approach delivers value in weeks. For integration-heavy environments, Fivetran’s automation reduces pipeline maintenance to near zero.

Regardless of which platform you choose, the Govern-Integrate-Trust Maturity Model applies: match the tool tier to your data readiness level. Organizations that implement enterprise governance before establishing reliable integration waste both budget and team capacity. Start with the foundation, build trust through quality monitoring, and scale governance as your AI workloads grow.

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Businesses report up to 16% revenue growth after implementing AI-based dynamic pricing. Buyers are also adapting to the new pricing reality as they begin to see personal benefits, such as usage-based pricing for software and technology, which offers much more flexibility than fixed pricing, allowing users to pay for APIs, specific features, or outcomes.

This guide covers how the artificial intelligence algorithms work, which industries benefit most, and how to implement a system that captures value without triggering price wars or regulatory headaches.

How AI dynamic pricing algorithms drive revenue growth

AI helps businesses increase revenue by performing the following:

• Price elasticity optimization: AI calculates the precise point where volume multiplied by margin reaches its maximum. For products with flexible demand, that might mean holding prices higher during periods of higher interest rates. For price-sensitive items, it means finding the floor that still moves inventory levels.
• Demand-supply matching: Algorithms prevent the two most common pricing mistakes: leaving money on the table during high demand and decreasing sales velocity during slow periods.
• Competitive positioning: Rather than blindly matching competitor prices, AI determines when to undercut, when to hold premium positioning, and when price isn’t the deciding factor at all.

Which algorithms to choose depends on the use case and industry. For instance, reinforcement learning machine learning algorithms work well for real-time optimization, where the system learns from each transaction. Time series models are effective for demand forecasting. And regression models can calculate price elasticity across diverse customer segments.

Industries that benefit most from dynamic pricing with AI

Both B2C and B2B industries can benefit equally from AI-driven pricing strategies. Below, we examine different industries and real-life examples of AI implementation to identify what they share and how their approaches differ.

Retail and e-commerce

82% of retail executives consider AI adoption the biggest competitive advantage in the coming years. For instance, such retail giants as Amazon reportedly change prices on millions of items multiple times per day. For mid-market retailers, AI pricing can level the playing field and help them target the same customers as Amazon or Walmart.

Example: 

AS Watson Group has implemented AI to enable dynamic pricing and ensure steady sales growth.

Dr. Malina Ngai, Group CEO at AS Watson Group, reflects on the results of AI adoption at their company: 

We’re using AI for personalized promotions and dynamic pricing. Our recommendation engines suggest products based on customer behavior, which lifts basket size and conversion rates. Hyper-personalization is key. AI curates skincare regimens, sends replenishment reminders, and powers virtual assistants that make online shopping seamless.

Personalized pricing and promotions reinforce one another, as both rely on shared customer insights that businesses can use to enhance the overall shopping experience. In retail environments, AI delivers the greatest value when applied across various touchpoints to improve end-to-end customer engagement and service quality.

Travel and hospitality

An empty hotel room or unsold airline seat means losing revenue for travel and hospitality companies. With the help of AI, these industries optimize booking and increase reservations. For instance, hotels report 20% better forecast accuracy and a 15% revenue uplift after implementing AI-driven pricing strategies.

Example: airBaltic implemented an AI-powered dynamic pricing system to optimize seat assignment fees, replacing static, rule-based pricing with real-time price recommendations driven by customer demand and booking behavior. The airline deployed reinforcement learning models that continuously adjusted prices and were validated through controlled A/B testing against traditional pricing methods.

Within just two months of going live, airBaltic achieved a 6% increase in seat reservation revenue per passenger, surpassing an initial target of 2–3%, while significantly reducing manual pricing effort through automation. The approach enabled more personalized seat offers aligned with traveler preferences, improving both ancillary revenue performance and the customer purchasing experience.

SaaS businesses

In the SaaS industry, AI can optimize pricing tiers, identify behavioral signals of upgrade readiness, and reduce churn by ensuring pricing aligns with perceived value. The recurring revenue model makes even small improvements highly valuable over the customer lifetime.

Example: Zendesk shifted from charging customers for software access or interaction volume to charging only when an AI agent successfully resolves a customer issue without human intervention. Pricing is therefore tied directly to measurable business outcomes rather than system usage, aligning vendor revenue with customer success. Prices begin around $1.50 per successfully resolved interaction, reinforcing the direct link between cost and delivered value. As a result, Zendesk ensured:

• Transition from seat-based SaaS monetization to value-based pricing
• Clearer ROI visibility for enterprise buyers
• Reduced risk perception when adopting AI automation
• Pricing scalability aligned with automation performance

Manufacturing and distribution

B2B pricing in the manufacturing industry involves complex matrices, customer-specific terms, volume discounts, and contract negotiations. AI can optimize quotes for sales teams and manage pricing across thousands of SKU-customer combinations that would be impossible to handle manually.

Example: Global logistics and distribution provider UPS has introduced AI into their B2B pricing operations to address the complexity of contract-based shipping services. Instead of relying on manual pricing decisions, UPS implemented an AI-enabled pricing platform that analyzes historical transaction data, customer segments, and past deal outcomes to recommend optimal prices during negotiations.

The company’s AI-enabled Deal Manager platform recommends prices during negotiations, helping sales representatives identify competitive rates while protecting margins. Following implementation, UPS reported a 22 percentage point improvement in win rates in the U.S., alongside stronger revenue quality driven by reduced over-discounting.

Best AI tools for predicting optimal price points

The market demand for AI pricing tools spans several categories, each suited to different organizational needs.

• End-to-end pricing platforms: Enterprise suites like PROS, Pricefx, and Zilliant offer built-in AI with broad functionality. They work well for organizations that want packaged solutions.
• Cloud ML services: AWS SageMaker, Google Vertex AI, and Azure ML provide infrastructure for building custom pricing models from scratch. They require more technical capability but offer maximum flexibility.
• Specialized pricing engines: Solutions like Competera and Intelligence Node focus on specific verticals, often retail. They bring domain expertise but may not fit other industries.
• Custom-built systems: When off-the-shelf tools can’t handle proprietary business logic, complex integration requirements, or unique competitive dynamics, custom development becomes the path forward.

For enterprises with high load, real-time requirements, and complex data environments, custom solutions often outperform packaged alternatives, particularly when pricing logic includes specific business rules and exception handling.

AI tools and approaches for predicting optimal price points

Based on how your current pricing strategy impacts revenue and profitability, choose the appropriate tool or solution. For example, if your margins have consistently fallen below target for several months, investing in custom development may introduce unnecessary risk. However, if budget capacity exists and the projected ROI justifies the investment, custom development can deliver long-term advantages. But you still need to continuously validate the process through structured measurement and controlled experimentation.

Challenges of AI dynamic pricing and how to overcome them

Based on our year-long experience delivering custom AI solutions, we’ve outlined the four challenges below as the most impactful ones in AI infrastructure development.

Data quality and availability

Common data quality and management issues include incomplete transaction histories, inconsistent product categorization, and missing competitor or market data. Mitigation approaches include data enrichment services and, in some cases, synthetic data generation to fill gaps.

Model explainability and trust

Business stakeholders often resist “black box” recommendations. Using interpretable AI techniques and providing transparent pricing logic that explains why the system recommended a specific price builds the confidence needed for adoption.

Integration complexity

Legacy ERP and e-commerce systems weren’t designed for real-time pricing feeds. Modern solutions use middleware, APIs, and event-driven architectures to bridge the gap, but integration work often consumes more project time than model development.

Organizational change management

Pricing teams may view AI as a threat rather than a tool. Training, clear communication about how roles will evolve, and phased rollouts that demonstrate value before full deployment help manage cultural resistance.

The ethical and regulatory landscape of AI pricing

AI-powered dynamic pricing must align with region- and industry-specific regulations, consumer protection laws, and brand risk management practices.

Regulatory momentum in the European Union

In July 2025, the European Commission launched a public consultation under the Digital Fairness Act (DFA), explicitly identifying dynamic pricing as an area requiring stronger consumer safeguards. The commission paid particular attention to practices in which companies advertise attractive entry prices, while algorithms later apply real-time price increases during the purchasing process.

Regulatory expectations became more concrete following the Court of Justice of the European Union’s October 2024 ruling in the Aldi Süd case. After that, the court confirmed that advertised discounts must be calculated against the lowest price offered within the previous 30 days, effectively classifying artificial price increases prior to promotions as a legal risk. As a result, algorithmic pricing systems now fall directly within consumer protection and compliance oversight.

Regulatory developments in the United States

U.S. regulators are focusing primarily on competition and data usage. In July 2024, the Federal Trade Commission (FTC) initiated a Section 6(b) investigation into so-called surveillance pricing, examining how companies use personal and behavioral data to influence prices. This continued in March 2025 when the Department of Justice Antitrust Division submitted a statement of interest addressing risks of algorithmic collusion.

Legislative proposals are also emerging. Senator Amy Klobuchar reintroduced the Preventing Algorithmic Collusion Act in January 2025, seeking amendments to the Sherman Act that would restrict pricing algorithms trained on nonpublic competitor data. At the state level, New York’s S 3008 law, effective July 2025, requires businesses to disclose when algorithmic systems use personal data to determine prices.

The reputational dimension: Transparency over price

Regulation is only one side of the equation. Customer feedback increasingly determines whether dynamic pricing succeeds or fails. The widely criticized Oasis/Ticketmaster ticket pricing episode in 2024, where tickets initially priced at £148 surged to nearly £355, demonstrated that consumer backlash is rarely about price increases alone. The central issue was opacity.

Consumers generally accept surge pricing models, such as those on ride-hailing platforms, because pricing mechanisms are transparent and alternatives are clear. Hidden algorithmic repricing and price gouging, by contrast, create a perception of manipulation, triggering long-term brand damage.

A practical compliance framework for revenue leaders

Successful AI pricing programs share three governance principles:

1. Transparency by design. Clearly disclose when and why dynamic pricing is applied.
2. Pricing guardrails. Implement hard price floors and ceilings, and require human approval for significant adjustments.
3. Data governance and auditability. Maintain traceable records of pricing decisions, particularly when personal or behavioral data informs segmentation.

Responsible implementation is no longer a differentiator but a prerequisite for sustainable AI-driven revenue and price optimization.

How to measure revenue impact from AI pricing

Proving ROI requires controlled experiments and clear attribution. The metrics that you should measure include:

• Revenue per transaction. Track changes in average order value. Even small improvements compound across high transaction volumes.
• Sales growth. Measure whether optimized pricing increases conversion rates or expands demand without relying on aggressive discounting. Sustained growth indicates that pricing better aligns with customer willingness to pay.
• Margin contribution. Measure gross margin improvement. This helps confirm that revenue gains come from smarter pricing decisions rather than higher sales volume alone.
• Price realization rate. Compare actual prices achieved to list prices. Improvements typically signal reduced discount leakage and stronger pricing discipline across sales teams or automated channels.
• Win rate (B2B). Track quote-to-close conversion. Higher win rates combined with stable margins indicate pricing competitiveness without sacrificing profitability.
• Inventory turnover. Measure how pricing affects sell-through and the age of inventory. Faster turnover often reflects better synchronization between demand signals and pricing decisions.
• Cost-to-serve reductions. Evaluate whether pricing helps prioritize profitable customers, products, or delivery conditions. AI pricing can reduce operational inefficiencies tied to low-margin transactions.

Without well-established controls, organizations cannot reliably separate AI impact from broader market conditions. For instance, A/B testing against control groups provides the cleanest measurement.

AI-powered dynamic pricing: Implementation takeaways

AI pricing rarely produces dramatic overnight results, and that’s precisely the point. Its value lies in systematically removing revenue leakage that organizations have historically accepted as unavoidable. Over time, better pricing decisions compound into stronger margins, more predictable revenue, and improved operational efficiency.

At Xenoss, we help companies design and implement AI pricing systems that integrate directly into existing sales, data, and operational workflows, ensuring measurable ROI.

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72% of B2B buyers expect one-on-one consultations and personalized, high-touch support. Plus, 67% of sales professionals say that personalization is more important to customers than last year.

But sellers still spend almost 60% of their time on non-selling tasks, which prevent them from actively engaging with clients. AI-powered sales automation software can free up sales teams for building stronger human relationships with customers. Here are some proofs:

• 94% of sales managers admit that AI agents help them with a better understanding of customers’ needs, and 92% use AI for automating prospecting
• 64% of Chief Revenue Officers (CROs) plan on integrating AI to automate manual sales tasks
• Sales representatives report a 30% increase in win rates thanks to using AI
• 85% of SDRs use AI to free time for more value-adding work, and 84% apply sales AI tools for training and acquiring new skills

A user on Reddit shares similar excitement for using AI in optimizing the sales cycle and building customer trust:

Where I think AI can make a huge difference is in areas like:

• Forecasting and deal health
• Analyzing calls and meetings to surface action items, objections, and sentiment
• Sales training and roleplaying to get reps ready for real conversations

I don’t think AI will replace sales pros, but I am bullish on AI-augmented reps beating everyone else.

How exactly you can use AI for sales at your company depends on the strengths and weaknesses of your sales team, current revenue goals, and sales pipeline management practices. In this deep-dive analysis, we’ll help you decide on the right AI use cases and implementation patterns, supported by real-life examples and ROI metrics.

AI sales automation: Why revenue leaders need it

Adoption of revenue-specific AI solutions directly correlates with a 13% increase in revenue growth and a 85% higher commercial impact, according to a survey of more than 3,000 revenue and sales leaders.

These gains rarely come from automation alone. Instead, AI strengthens the underlying revenue system by improving signal quality, surfacing deal risk earlier, increasing selling time per rep, and tightening alignment between sales, finance, and RevOps teams.

Justin Shreiber, the CEO and Founder of Terret, offers his point of view on the purpose of AI in the modern sales management process:

AI isn’t replacing sales. It’s forcing revenue teams to become systems thinkers and doubling the value of real human trust.

An AI-driven sales automation tool forces revenue teams to operate like engineered systems rather than collections of individual sellers. Once AI starts:

• scoring leads,
• flagging deal risk,
• automating follow-ups,
• predicting outcomes,

any weakness in data quality, process design, or handoffs becomes visible immediately. This pushes CROs to design sales as a repeatable, measurable system. And once these processes start to work like clockwork, real human judgment and credibility don’t disappear, but become twice as valuable, as only people can interpret nuance and build trust in ambiguous situations.

Chris Clement, VP of Sales at EPIC Insights, highlights in his post that this year, CROs will be expected to deliver far beyond monetary value:

In 2026, great CROs are judged on more than numbers:

• Sales productivity per head.
• Cross-functional alignment with RGM, Finance, and Insights.
• Retailer and customer NPS as a measure of partnership quality.

Together, they show three dimensions of modern revenue leadership:

Rather than fearing that AI can replace SDRs or erode human trust, you can leverage AI as an advanced automation tool to increase productivity, help your sales teams engage in valuable conversations with customers, and, consequently, increase revenue. Put simply, AI removes the pressure of managing sales numbers manually and allows sales teams to focus on what drives those numbers: trust, relevance, and real customer interactions.

Sales automation use cases: Lead scoring, forecasting & CRM

96% of revenue teams plan to actively use AI in 2026, and their top priority will be increasing sales reps’ productivity through a number of strategic use cases illustrated below. 

We’ve grouped those use cases into three categories, and we’ll analyze their impact on SDR productivity and company revenue growth through real-life examples.

AI-powered lead scoring

Traditional CRM sales automation was designed to enforce consistency. It applies predefined rules and workflows to move leads through the sales funnel. For example:

• If a lead downloads a whitepaper, add ten points.
• If they belong to a target industry, add five more.
• If they don’t respond after three emails, mark them as cold.

These systems help standardize processes, but they don’t improve themselves. They rely on assumptions created upfront and rarely adapt to changing customer behavior.

AI-driven lead scoring works differently. Instead of following static logic, it learns from historical outcomes: which leads converted, which deals stalled, which behaviors correlated with closed revenue, and continuously adjusts recommendations based on live data.

Example: Grammarly’s implementation of AI lead scoring solution increased premium plan conversions by roughly 80%, while a machine learning model deployed at a price comparison service drove a 20% jump in lead-to-opportunity conversions.

Sales forecasting models

Only 7% of sales teams achieve at least 90% accuracy in sales forecasting, and 69% of respondents say forecasting has gotten much harder than it was three years ago. 

AI-based automated sales tools can be a viable alternative to manual and time-consuming forecasting. Machine learning solutions can provide high predictive accuracy at high speed. For instance, here’s the flow of how AI models can predict the deal win probability by evaluating multiple parameter categories:

• Deal-specific factors: Deal size, sales stage, time in stage, discount level, contract terms.
• Engagement signals: Email opens, meeting frequency, stakeholder count, response latency.
• Customer attributes: Company size, industry, past purchase history, tech stack fit.
• External conditions: Budget cycle timing, competitive pressure, and economic indicators.

Example: A leading European food distributor struggled with inaccurate manual sales forecasts, resulting in overstocking, spoilage of perishable goods, and lost revenue during seasonal peaks. To fix this, they developed a custom machine learning forecasting platform that consolidated historical ERP sales data, real-time orders, seasonality trend analysis, and external variables into a centralized predictive analytics model. 

The system generated SKU-level demand forecasts and early risk alerts, enabling procurement teams to proactively adjust orders. As a result, the company reduced inventory waste by 34%, improved demand planning accuracy by 29%, and strengthened supplier negotiations while maintaining high product availability during peak demand periods such as Easter and Christmas.

CRM automation and sales activity tracking

CRM data entry is the largest time sink (~8-12 hours weekly) for frontline sales workers who manually log calls, update opportunity stages, and sync calendar activities. Conversation intelligence platforms auto-populate CRM fields by transcribing calls, extracting action items, identifying mentioned competitors, and updating deal stages based on conversation content.

A sales rep on Reddit emphasizes what’s particularly draining for them when working with CRMs: 

The biggest time drain isn’t what most people think. Data entry gets all the attention, but the real killer is context switching between CRM tabs to piece together account history before calls. Sales reps spend 12-18 minutes per call just clicking through activity logs, emails, and notes to prep. That’s where automation actually saves hours, not in field updates.

Automate these first for maximum time recovery: Pre-call briefing summaries that pull recent activities into one view, automatic activity logging from email and calendar so reps never manually log touchpoints, and deal stage progression triggers that update fields when specific actions occur. These three alone typically reclaim 6-8 hours per rep per week because they eliminate repetitive navigation and clicks.

Scheduling and preparing for meetings is another tiresome task for salespeople. AI scheduling assistants (e.g., Calendly, Chili Piper integrated with Salesforce) can help sales managers by offering real-time availability, automatically handling time zone conversions, sending prep materials, and rescheduling.

Sales territory planning can be time-consuming when done manually, but it’s crucial for optimizing market coverage.

Noah Berliner, General Manager, Global Head of Sales at Moody’s Analytics, in his interview with Gartner, shares their company’s approach to using generative AI in sales territory planning:

Internally, we built a sales recon tool that provides sellers with all the information they need about their territory. It pulls data from Salesforce, our news data, and company data, showing which products companies in their territory are not buying and what news and sentiment suggest they should buy. It builds a whole territory plan in 10 minutes, something that used to take several weeks.

When choosing appropriate use cases for AI adoption, analyze which sales processes take the most time and effort but yield zero (or almost zero) efficiency for the team. For instance, meeting with clients or visiting them in person can also be time-consuming yet highly efficient. By contrast, daily entering repetitive data in CRMs is both time-consuming and inefficient.

Sales automation ROI: Win rates, cycle time & forecast accuracy

ZoomInfo survey revealed the following outcomes from using AI on a daily basis:

Plus, 76% of respondents improved their win rates, and 78% decreased their sales cycles. This proves that the best results come from deeply integrated AI systems and their consistent daily use. Infrequent use may not yield the expected results, only discrediting the AI’s value to stakeholders.

Below is a table showing the improvements you can expect from AI sales solutions compared to traditional sales tools and practices.

However, to achieve positive outcomes from AI implementation in sales cycles, you’ll need to consider many factors. In the next section, we explain how to get started and finish your AI for sales enablement project efficiently.

B2B sales automation implementation: 6 best practices

After building AI sales systems for B2B organizations across diverse industries, including manufacturing, healthcare, AdTech, and MarTech, we’ve learned what successful AI implementation requires.

1. Audit CRM data quality and sales processes

We start every engagement with a data quality audit and workflow analysis. Our team examines CRM hygiene across multiple criteria: field completion rates (targeting 95% for critical fields like deal stage, close date, and contact roles), stage definition consistency, duplicate record prevalence, and historical data depth. In our experience, organizations typically discover that 30-40% of their CRM records contain incomplete or inconsistent data, undermining model accuracy.

The audit also maps manual bottlenecks, where reps spend time on manual data entry, research, or administrative tasks that AI could handle. For instance, one enterprise client had seven different definitions of “qualified lead” across regional teams. Standardizing that taxonomy before model training prevented garbage-in-garbage-out scenarios.

2. Set sales forecasting accuracy targets

We work with sales leadership to establish specific, measurable AI objectives tied to business outcomes. Rather than vague goals like “improve forecasting,” define success: reducing forecast error from 25% to 10%, increasing pipeline coverage visibility by 30 days, or improving deal win probability accuracy by 15%.

Our standard metrics framework includes forecast accuracy (weighted pipeline vs. actual bookings), monthly mean absolute percentage error (MAPE), pipeline coverage ratios by stage, and changes in deal velocity. We also establish baseline measurements before implementation, so improvement is quantifiable. For one client, we tracked that their manual forecast process had a 32% MAPE. After six months of using a custom AI system, that number dropped to 14%.

3. Select AI models and tools

The build-vs-buy decision depends on the complexity of the sales motion and the uniqueness of the data. We guide clients through this evaluation by analyzing deal-cycle characteristics, product-portfolio complexity, and integration requirements. Off-the-shelf platforms work well for transactional sales with standard motions, short cycles, single-product focus, and straightforward buyer journeys.

Complex selling environments (e.g., multiple products with different sales cycles, enterprise deals with 6-12 month timelines, multi-stakeholder buying committees, or highly customized solutions) typically require custom models trained on proprietary data. The investment in custom development pays off when forecast accuracy directly impacts revenue planning and resource allocation decisions.

4. Integrate with CRM and data infrastructure

Our integration approach connects AI models to the full data infrastructure. We build pipelines that pull from Salesforce, HubSpot, or Microsoft Dynamics, then enrich with marketing automation data (Marketo, Pardot), customer success platforms (Gainsight, ChurnZero), product usage analytics, and finance systems for revenue recognition.

We also implement reverse ETL patterns to sync predictions back to operational systems, ensure deal scores appear in Salesforce opportunity records, recommended actions surface in rep dashboards, and forecast adjustments flow to financial planning tools. One of our manufacturing clients required integration with their ERP system to factor production capacity into deal probability. That bidirectional sync between the data warehouse and six operational systems took three weeks but delivered forecasts that aligned with fulfillment reality.

5. Sales team AI training and governance

AI model accuracy means nothing if reps don’t trust or act on AI recommendations. 85% of sales reps haven’t received any formal training on using AI, yet 78% admit they would like it. 

Training programs explain how models generate predictions, what signals drive scores, and when to override AI guidance based on context the model can’t see. You can run workshops where sales managers review deals alongside AI predictions to build intuition about model behavior.

Establish governance frameworks which cover data access controls (who can see which predictions), model update cadences (typically monthly retraining with weekly scoring refreshes), forecast review processes (weekly pipeline reviews with AI-flagged deals), and escalation paths when predictions seem wrong.

You can also implement feedback loops that allow reps to flag incorrect predictions. This human-in-the-loop input improves model accuracy over time. Without change management and clear governance, even accurate AI predictions get ignored.

6. Monitor AI model performance and retrain

Build dashboards to monitor model performance and track it against real-time outcomes. Key metrics include prediction accuracy by deal stage, calibration curves showing whether 70% probability deals close as predicted, and drift detection that identifies when model accuracy degrades due to market changes or process shifts.

For instance, our standard practice includes monthly performance reviews and quarterly model retraining cycles.

Final takeaway

AI in B2B sales doesn’t change the goal. Revenue teams still need to hit targets, shorten cycles, and earn customer trust. The change is in how those results are achieved.

When AI is woven into daily sales workflows, the effects become visible quickly. Reps spend less time navigating CRMs and more time preparing for meaningful conversations. Managers spot risks earlier, rather than reacting at the end of the quarter. Forecasts become clearer, which improves planning across finance, marketing, and operations. The improvements in win rates and cycle time are a natural outcome of that clarity. 

The Xenoss team helps you select top sales automation tools. We also design and integrate AI algorithms for lead scoring, forecasting, and sales execution directly into your CRM and data infrastructure, ensuring predictions are reliable, explainable, and aligned with real business metrics.

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Download the full report to read insights from all eight featured companies.

Below, we share highlights from our contribution.

The real shift in enterprise software

The past decade transformed who builds software and why. Organizations that once outsourced development now treat software capability as a competitive weapon. Manufacturing, banking, healthcare, logistics, and energy companies all compete on their ability to ship software that works.

This shift forced a reckoning with data. Companies discovered that cleaning and organizing data consumed 80% of their AI efforts. The result was massive investment in data mesh architectures, DataOps practices, and multi-cloud pipelines. These foundations make today’s AI capabilities possible.

At the same time, AI tools democratized who could build intelligent systems. Data scientists no longer hold exclusive domain over machine learning. Software engineers now work directly with AI frameworks. Business analysts build predictive models on no-code platforms. This expansion brought new quality control challenges that the industry continues to address.

4 trends reshaping enterprise AI

Agentic AI moves from demos to operations. Single-purpose models are giving way to multi-agent systems that coordinate, delegate, and iterate on their own. By 2027, enterprises will architect software assuming AI agents work alongside humans rather than just responding to prompts.

Domain-specific AI outperforms general-purpose models. The push for massive, all-knowing systems hasn’t delivered the expected ROI. Enterprises are shifting toward specialized agents trained on industry data and optimized for specific workflows.

Governance becomes infrastructure, not an afterthought. AI now generates code, documentation, and decisions at scale. Automated provenance controls, audit trails, and validation mechanisms are becoming table stakes.

Validation overtakes generation as the bottleneck. Research indicates 48% of AI-generated code contains potential flaws. Organizations adopting AI coding assistants without rigorous review processes risk introducing vulnerabilities at scale.

What sets Xenoss apart

We bring over 10 years of pre-ChatGPT AI experience. Our engineers built real-time bidding prediction models processing 400,000 queries per second, computer vision systems for automated ad creative production, and user behavior prediction mechanisms for mobile DSPs years before generative AI went mainstream. We’ve delivered AI-powered platforms now used by brands like Nestlé, Adidas, and Uber.

Our domain-first methodology starts from a simple observation: 80% of AI project success comes from properly understanding the business problem. We’ve watched too many organizations waste millions on sophisticated models that solve the wrong problem. Deep domain and business analysis comes before any model development.

We’ve built our reputation serving Fortune 500 clients including Microsoft/Activision Blizzard, Toshiba, AstraZeneca, and Verve Group. We integrate AI into existing enterprise systems like SCADA, IoT, and ERP platforms while meeting regulatory requirements across banking, pharma, energy, and other industries.

AI’s impact on software development today

By late 2025, roughly 85% of developers regularly used AI tools. Approximately 41% of all code involves some AI assistance. GitHub reports developers accept 37-50% of AI suggestions, with 43 million merged pull requests monthly.

The most striking example comes from Anthropic: Boris Cherny, creator of Claude Code, confirmed that 100% of his code contributions over the past 30 days were written by Claude Code. He runs multiple AI instances in parallel, operating with the output capacity of a small engineering department. Anthropic reports productivity per engineer has grown by nearly 70%.

For complex business logic, domain-specific systems, and architectural decisions, human judgment remains essential. The engineers who succeed view AI as leverage, not replacement. They multiply their impact while developing judgment, creativity, and systems thinking that AI cannot replicate.

How we accelerate enterprise AI

As a service company, we build tailored AI systems for every client. We’ve also developed internal accelerators that dramatically reduce implementation timelines while maintaining flexibility.

Our approach centers on meeting clients where they are. Many Fortune 500 companies run critical operations on legacy systems never designed for AI integration. Rather than forcing disruptive replacements, we’ve built middleware and modular microservices that enhance existing stacks. This practical integration work often delivers the fastest ROI because it builds on proven infrastructure.

Our multi-agent orchestration framework coordinates specialized AI components, from LLMs and NER/OCR agents to RPA and decision systems, within unified workflows. For complex business processes, this approach outperforms single-model solutions by over 40% because it matches the right tool to each task.

We’ve invested heavily in edge AI for industrial environments. Oil and gas operations, manufacturing plants, and maritime vessels operate in locations with limited connectivity and harsh conditions. Our solutions support on-device inference for predictive maintenance, where reliability matters more than having the newest model.

Our hybrid AI/physics modeling approach combines domain physics knowledge with ML for equipment virtualization in oil and gas. This produces more reliable predictions than pure ML systems and requires less training data. The best AI solutions often blend multiple methodologies rather than betting everything on a single approach.

Production-ready results

We don’t build proofs-of-concept that sit on a shelf. Every engagement targets specific ROI metrics, and we stay involved until those numbers show up in our clients’ P&L.

Recent outcomes include:

A credit scoring solution for a U.S. bank expanding into India delivered a 1.8-point Gini uplift through a unified multi-modal neural network, significantly improving default risk assessment in a market with limited historical credit data and translating to millions in reduced risk exposure annually.

A fraud detection platform helped a global financial institution reduce false positives by over 30% while maintaining catch rates, directly improving customer experience while protecting against losses.

Predictive maintenance systems for industrial clients prevent equipment failures worth millions. One oil and gas implementation reduced unplanned downtime by identifying failure patterns weeks before critical issues emerged.

AI-powered accounting automation delivered 55% cost reduction for an enterprise client, saving $3.2M annually through intelligent document processing and workflow automation.

AI-optimized advertising achieved 27% CPC reduction with 18% CTR increase for a digital marketplace, demonstrating our approach translates across very different business contexts.

Looking ahead

Enterprise AI is shifting from experimentation to execution. Agentic systems and domain-specific AI are becoming embedded across core workflows.

The limiting factor for most enterprises isn’t the technology itself. It’s readiness to adopt at scale: infrastructure, integration, and change management. Organizations with the right processes and governance frameworks are seeing exponential returns. Those still treating AI as isolated experiments will fall further behind.

Download the full AI Magazine 2026 Industry Report →

Read insights from Xenoss and seven other companies leading enterprise AI transformation.

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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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The average number of apps deployed per company has now topped 100, growing 9% year over year. 

Meanwhile, 62% of IT leaders say their data systems aren’t configured to fully leverage AI. This gap holds organizations back from fully operationalizing machine learning and generative AI.

The result is a growing demand for platforms that reliably unify data across an increasingly fragmented technology landscape. 

In this post, we’ll break down what data integration platforms do, compare leading solutions, and outline the key criteria for choosing the right approach for your organization.

Why do you need a data integration platform? 

Enterprise data lives everywhere, scattered across SaaS tools, cloud warehouses, legacy systems, and partner feeds. Stitching it together manually is slow, fragile, and a drain on engineering resources.

Data integration platforms solve this problem by handling ingestion, transformation, and sync in one place. They support engineers with reliable, near-real-time data flows and help teams focus on analytics and AI rather than firefighting broken pipelines.

Must-have features for data integration platforms

Support for both batch and streaming data processing

Why it is important: Modern data workloads aren’t one-size-fits-all. Some use cases demand real-time data movement; others are better served by scheduled batch jobs. 

A data integration platform that supports both streaming and batch processing lets teams balance latency, cost, and reliability without juggling separate tools or architectures.

Questions to ask vendors

Question: Does the platform natively support both streaming and batch pipelines within a single orchestration layer?

What to look for in the answer: Strong platforms offer first-class support for both modes, with shared monitoring, governance, and the flexibility to switch or combine processing types without rebuilding pipelines.

Question: How does the platform handle late-arriving, out-of-order, or replayed events in streaming workflows?

What to look for in the answer: Look for built-in mechanisms for event-time processing, deduplication, and replay without data loss or manual intervention.

Data governance and lineage tools 

Why this is important: As data volumes and stakeholders grow, teams need clear visibility into where data originates, how it’s transformed, and who can access it. 

Strong governance and lineage capabilities reduce compliance risk, build trust in analytics, and make it far easier to diagnose issues when pipelines break or upstream data changes. 

Without these frameworks, even well-built pipelines become operationally fragile.

Questions to ask vendors

Question: How are access controls, masking, and compliance policies enforced across integrated data?

What to look for in the answer: Look for centralized policy management that applies consistently across ingestion, transformation, and delivery.

Question: Can lineage and governance metadata integrate with existing catalogs or security tools?

What to look for in the answer: Check for native integrations or open APIs that allow governance data to flow into enterprise catalogs, IAM systems, and audit tools.

A connector library (with the ability to build custom connectors)

Most organizations run fragmented stacks, with data spread across SaaS applications, databases, APIs, and internal systems. 

A broad connector library accelerates integration, and the ability to build custom integrations gives teams the flexibility to integrate internal tools, legacy systems, or proprietary data sources.

Questions to ask vendors

Question: How extensive and actively maintained is the native connector library?

What to look for in the answer: Ensure the library covers modern SaaS applications, databases, and cloud platforms, with frequent updates and clear SLAs for connector reliability.

Question: Can teams build, deploy, and maintain custom connectors without vendor involvement?

What to look for in the answer: Look for a documented SDK or framework that treats authentication, schema evolution, and error handling as first-class features. Custom and native connectors should share the same monitoring, alerting, versioning, and security controls.

Data catalog and metadata management

Why it is important: As data ecosystems scale, teams need a shared understanding of what data exists, what it means, and how it should be used. 

Data catalogs and metadata management help turn raw tables and fields into discoverable assets and reduce confusion and duplicated effort. 

Without this layer, valuable data often goes underutilized or is misinterpreted.

Questions to ask vendors

Question: Is metadata captured automatically across ingestion, transformation, and delivery?

What to look for in the answer: Ensure the vendor offers automated harvesting of technical and business metadata without requiring manual tagging.

Question: Does the catalog support business-friendly documentation and ownership models?

What to look for in the answer: Look for support for descriptions, glossary terms, owners, and stewardship workflows that are accessible to non-technical users.

Top data integration platforms

1. Fivetran

Fivetran is a fully managed, cloud-native data integration platform built around automated, reliable data ingestion from an extensive library of prebuilt connectors. 

It prioritizes low-maintenance pipelines and consistent schema management over complex, custom transformations and is helpful for teams that want fast time to value with minimal operational overhead.

Why teams choose Fivetran

Fivetran stands out for teams that want data pipelines to simply work with minimal ongoing effort. 

The platform manages infrastructure, scaling, and schema changes, so engineers spend far less time maintaining connectors or fixing broken syncs. 

Fivetran’s extensive, production-ready connector library also makes it easy to centralize data from common SaaS tools, databases, and cloud platforms quickly.

For analytics-driven teams that prioritize speed, stability, and low operational overhead over deep customization, Fivetran significantly shortens time to insight and reduces the day-to-day burden of running data integration.

Fivetran is quite pricey, but it will handle all data replication from, for example, Salesforce to whatever warehouse you use. To answer your question, you can configure it to handle updates and deletes depending on your use-case.

A data engineer on the benefits of using Fivetran for data integration

Challenges teams face with Fivetran

Fivetran may feel limiting to teams that need granular control over extraction, transformation, or optimization due to the limited customization of its pipelines. 

While the platform reduces operational burden through abstraction, complex business logic often requires pairing Fivetran with additional transformation or orchestration tools. 

Its consumption-based pricing becomes expensive at scale, particularly for high-volume or high-frequency sources, making cost predictability a concern as data workloads grow.

I really think Fivetran was supposed to be a tool to use when you didn’t have any data engineers. It feels like it’s now supporting use cases far larger than it was really meant to support.

A Reddit comment highlights Fivetran’s limited scalability

Fivetran pricing model

Fivetran uses a usage-based pricing model centered on Monthly Active Rows (MAR), the unique rows inserted, updated, or deleted in your destination each calendar month after the initial sync. Infrastructure costs scale with activity and volume, with each connection metered separately.

A base minimum applies for low-usage connections (for example, $5 for connections generating up to 1 million MAR on paid plans), and unit costs per million rows decline as volume increases. Note that following the 2026 pricing update, billing is applied at the connection level, so total spend grows significantly as the number of connectors increases.

2. Airbyte 

Airbyte is an open-source data integration platform that gives teams extensive control and transparency over how data pipelines are built, customized, and operated. 

It’s well-suited for engineering-led organizations that need the flexibility to create custom connectors, manage transformations closely, and avoid vendor lock-in while scaling ingestion across diverse sources.

Why teams choose Airbyte

Airbyte works well for teams dealing with non-standard data sources or fast-changing APIs who can’t wait for a vendor to ship new connectors. 

Its connector framework makes it practical to extend or modify integrations in-house, so that teams can ingest data from internal tools, SaaS products, or partner systems. 

Because pricing isn’t tied to per-row usage, Airbyte offers more predictable cost control as volumes scale, so it is a solid choice for organizations expecting high throughput and willing to trade operational simplicity for flexibility and ownership.

Airbyte is an open-source data movement platform and one of the fastest growing ETL solutions because of its big community. Cheaper than Fivetran and a good alternative. I like their new AI-assisted connector builder feature.

A data engineer explains the benefits of Airbyte

Challenges teams face with Airbyte 

Airbyte is challenging for teams not prepared to operate and maintain data infrastructure themselves because scaling and monitoring integrations built on the platform require hands-on engineering effort. 

Connector quality and stability vary, particularly for community-maintained integrations, so teams may need to allocate time to debugging sync failures or handling schema changes. 

For organizations that prioritize low operational overhead and guaranteed SLAs over flexibility and control, Airbyte may not be the best fit. 

Airbyte pricing model

Airbyte offers a flexible pricing model ranging from free open-source to cloud-hosted and capacity-based managed plans.

For self-hosted deployments, there’s no license cost – organizations only pay for their own infrastructure. 

Airbyte Cloud starts with a volume- and credit-based model: a low monthly minimum (around $10, including initial credits) covers basic usage, with additional credits consumed based on data volume (approximately $15 per million rows or $10 per GB).

Larger teams can opt for capacity-based pricing using “Data Workers,” a compute-oriented metric that decouples billing from raw data volume for more predictable costs. 

Enterprise customers have access to custom agreements that include SLAs and advanced governance features. This range of options lets teams choose between simple pay-as-you-go billing and predictable capacity-based plans as their needs evolve.

3. DLT

DLT is an open-source data loading framework that lets teams build ingestion pipelines directly in Python, treating data integration as code rather than a black-box platform. 

It’s well-suited for engineering teams that are looking for lightweight, transparent ingestion with full control over logic and deployment without adopting a full-featured ETL platform.

Why data engineering teams choose DLT

DLT is particularly effective for teams that want full transparency and control over data ingestion without the overhead of running a dedicated integration platform. 

Because pipelines are written in plain Python, engineers get to reuse existing code, apply custom logic at ingestion time, and version pipelines alongside application code. 

This makes DLT a strong fit for lean teams that need to integrate APIs, files, or internal services quickly, prefer predictable infrastructure costs, and value debuggability and ownership over out-of-the-box automation.

Interestingly, dlt is the one that is natively programmatic (pip installable library) and code-based, which makes it the most friendly for LLMs as they are great for code generation. Plus the fact that itis  highly flexible, so you can easily cover everything.

Reddit comment explaining why engineers prefer DLT for its flexibility

Challenges teams face with DLT

DLT places most of the responsibility for reliability and scale on the team, adding more burden on engineers as pipelines grow beyond a handful of sources. 

There’s no native UI for monitoring data freshness, diagnosing failures, or managing dependencies, so teams have to build or integrate their own observability, alerting, and orchestration layers. 

Because connectors are implemented as code rather than maintained services, handling API rate limits, authentication changes, backfills, and schema drift requires ongoing engineering work. 

This maintenance overhead makes DLT difficult to sustain for organizations running dozens of integrations or requiring strong operational guarantees.

DLT pricing model 

DLT is open-source and free to use, with no licensing or subscription fees. Teams pay only for the infrastructure they deploy it on (compute, storage, and networking) and any auxiliary services they integrate for orchestration, monitoring, or logging. 

Total deployment costs will therefore vary based on workload scale and the operational tooling required to support production-grade pipelines.

4. dbt 

dbt plays a complementary role in data integration, focusing on transforming and modeling data after it’s been ingested into a warehouse or lakehouse. 

While it doesn’t move data itself, dbt enables teams to standardize and test document data to turn raw inputs from multiple sources into analytics-ready datasets.

Why data engineering teams use dbt in data integration workflows

dbt brings structure and reliability to data integration workflows by making transformations explicit, version-controlled, and testable once data lands in the warehouse. 

Treating transformations as code lets teams apply software engineering best practices, like code reviews, CI, and documentation, to keep integrated data consistent as sources evolve. 

This approach reduces downstream data quality issues, improves trust in shared metrics, and allows ingestion tools to focus on moving data while dbt handles the business logic that turns it into usable datasets.

For transforms and infra, our engs always put dbt first, then airflow dagster or prefect to run things, and great expectations monte carlo or faddom for dq and lineage.

An engineer explains how dbt fits into the data integration flow

Challenges teams face with dbt

dbt often exposes gaps in data integration rather than solving them. 

If upstream pipelines are late, inconsistent, or failing, dbt models will break or produce incomplete outputs. 

As projects scale, teams commonly struggle with slow runs caused by long dependency chains, repeated full refreshes, and inefficient model design that increases warehouse compute costs.

Dbt pricing considerations 

dbt’s open-source core framework is free to use. The managed offering, dbt Cloud, is priced based on developer seats and usage metrics like successful model runs and queried metrics. 

Paid plans start at $100 per developer per month, with overage charges of around $0.01 per additional model run beyond included quotas. 

5. Informatica

Informatica is an enterprise-grade data integration and management platform built for complex, large-scale environments spanning cloud, on-premises, and hybrid systems. 

Why data engineering teams choose Informatica 

Informatica is most valuable in organizations where data integration goes beyond moving data and requires enforcing standards across hundreds of pipelines and teams. 

The platform provides deep, centralized controls for data quality rules, lineage, impact analysis, and access policies, allowing enterprises to understand how a metric was produced, what systems it touches, and what will break if a schema changes. 

These strict controls prevent downstream incidents, enable smoother audits, and enable the ability to scale data operations across business units, reinventing integration logic or governance.

Still huge for large enterprise. Remember, the bigger you are the more things like privacy, compliance, security, SLAs etc. matter. Tools that can run unmanaged code, e.g., Spark, take extra scrutiny – especially for things like data exfiltration. Honestly, it’s a solid product but it’s completely lost its value prop due to a high price tag and because DE is becoming more commoditized.

In a Reddit comment, a data engineer points out that Informatica is still the go-to for enterprise but no longer has competitive pricing

Challenges teams face with Informatica 

Informatica is challenging due to its complexity, cost, and operational overhead. 

For smaller or fast-moving teams, the licensing model and heavyweight governance features may feel disproportionate to their needs, leading to underutilization or parallel “shadow” integration tools emerging outside the central system.

Informatica pricing considerations

Informatica’s cloud platform (Intelligent Data Management Cloud, or IDMC) uses a consumption-based pricing model built around Informatica Processing Units (IPUs). 

This structure gives customers access to a broad set of integrated cloud services without paying for each component separately, with consumption tracked across metrics like data volume and processing activity. 

The platform does not share pricing information publicly – it is typically negotiated based on usage patterns, enterprise size, and required services.

Which data integration platform to choose? 

Building your own data integration platform

Off-the-shelf integration platforms help effectively manage common SaaS sources, standard schemas, and predictable volumes. 

The real challenges emerge where data gets most valuable: high-change operational tables, proprietary internal systems, and cross-domain workflows requiring strict controls.

At this level, teams run into three recurring business constraints.

• Cost unpredictability. Usage pricing (for example, per-connector consumption models) turns incremental growth into surprise spend because every upstream change (updates or deletes, re-syncs, new connectors after an acquisition) increases billable activity.

• Time to change: When a connector breaks due to an API change or schema drift, organizations pay twice, once in platform fees and again in engineering hours. Handling data issues ends up taking engineer time from higher-value work like analytics enablement and AI productization.

• Governance fit. If teams can’t enforce quality checks, lineage, and privacy rules at the integration layer, bad data risks propagating into downstream decisions and reporting.

When these constraints dominate, building a custom integration layer is the more rational choice. 

Tailored tools let data engineers optimize pipelines around unit economics, bake compliance and audit requirements into workflows by default, and move faster during M&A or product pivots, while keeping cloud spend predictable.

Bottom line

There’s no universal answer to choosing a data integration platform. Managed platforms minimize operational burden but limit customization. Open-source tools offer flexibility but require more engineering effort, and custom systems provide deep governance but add operational overhead. 

The right choice depends on your team’s capabilities, data volumes, compliance requirements, and tolerance for operational overhead.

Start by identifying where your current approach is failing, whether that’s reliability, cost, flexibility, or governance, and evaluate platforms against those pain points rather than feature lists alone.

The post Data integration tools compared: Fivetran, Airbyte, DLT, dbt, Informatica appeared first on Xenoss - AI and Data Software Development Company.

IBM’s CIO, Matt Lyteson, explains what modern businesses can do to succeed with agentic AI:

Our focus is, how do we scale agents across more and more use cases to bring value to the organization, and how do I really understand the outcomes, the data that I’m going to need to give the agents, and then how to manage and control them? If organizations can do that, we’re going to see a lot more adoption and a lot more success.

Over the next few years, the focus will be on building, adopting, and implementing AI agents that are scalable, controllable, and produce measurable results. This will come from a deep understanding of your company’s processes, data management practices, and long-term strategic goals.

In this guide, we’ll discuss how the enterprise agentic AI market has evolved and how to implement domain-specific agents to maximize business benefits. 

How to differentiate between genuine agentic AI and “agent washing”

Before we dive into the latest developments and implementation best practices for agentic AI, it’s important to understand what agentic AI is and how to avoid “agent washing”.

The concept of “agent washing” was introduced by Gartner and refers to offering standard chatbots, AI assistants, and robotic process automation (RPA) as agentic AI. In one of our articles, we show the clear difference between generative and agentic AI.

Vendors of such solutions provide false promises to enterprises, eventually eroding their trust in AI and even causing reputation and financial damage. The confusion over definitions makes it easier for AI vendors to engage in these underhanded tactics. 

A computer scientist and writer, Santiago Valdarrama, gives the following definition of AI agents:

Agents are systems capable of performing tasks dynamically and autonomously. They offer flexibility and model-driven decision-making at scale.

An AI model is an agent’s “brain”; consequently, the level and accuracy of decision-making depend on the model you choose.

Different types of AI systems commonly mistaken for AI agents

Capability	Chatbot	Copilot	RPA	AI Agent
What it is	Q&A interface	Assistant inside tools	Scripted automation	Goal-driven system that plans and acts
Primary value	Faster answers	Faster work for employees	Faster repetitive operations	End-to-end execution with adaptability
Takes actions in systems	Rare/limited	Sometimes	Yes (fixed steps)	Yes (dynamic tool use)
How it “decides”	Responds to prompts	Suggests next steps	Follows rules	Plans, executes, and adjusts
Handles edge cases	Weak	Human handles	Breaks unless updated	Learns/recovers via retries and policies
Best for	FAQs, internal knowledge	Drafting, analysis, guided work	Data entry, repetitive tasks	Procurement triage, IT resolution

Key takeaway: If a vendor cannot explain what actions the agent performs, which systems it touches, and how it’s controlled and audited, you’re likely dealing with a copilot or automation product wearing an “agent” label.

Agentic AI platforms compared: Copilot Studio, Agentforce, Vertex AI & Bedrock

The BCG report revealed that agentic AI accounted for 17% of AI value in 2025 and is expected to reach 29% by 2028. Plus, the gap between AI leaders and laggards is widening due to the emergence of agentic AI capabilities.

Microsoft Copilot Studio

Microsoft Copilot Studio is the natural choice for organizations deeply invested in the Microsoft ecosystem.

They launched major innovations in Q4 2025, including integration with GPT-5 models and other third-party model providers (offering modern model choices for customers) and integrations with more than 1,400 services via a Model Context Protocol (MCP). The company also introduced Agent 365 for enterprise agent orchestration and control. 

Pricing starts at $30 per user per month for Microsoft 365 Copilot. Copilot Studio is included for customers with qualifying Microsoft 365 licenses, with consumption-based pricing for additional capacity.

Best for: Organizations with extensive Microsoft 365 deployments needing cross-functional automation across productivity, CRM, and collaboration tools.

AWS Bedrock AgentCore

AWS Bedrock AgentCore provides maximum model flexibility within a secure enterprise environment. Unlike platform-specific offerings, Bedrock offers access to Claude, Titan, Llama, and other models through a unified API, allowing teams to select the best model for each use case.

AgentCore, launched in late 2025, added policy creation features for granular control over agent actions, real-time performance evaluation, and simplified deployment through a standalone runtime. The platform also supports MCP server integration for standardized tool connections.

Bedrock pricing is based on model inference tokens, with additional charges for agent runtime and knowledge base queries.

Best for: AWS-native organizations requiring multi-model flexibility, strong security controls, and the ability to switch between foundation models without platform lock-in.

Google Vertex AI Agent Builder

Google Vertex AI Agent Builder was enhanced with new observability and deployment tools. Google now allows developers to deploy AI agents with a single command via the Agent Development Kit (ADK), which now also supports the Go programming language, in addition to Python and Java. Simplified deployment and improved observability help enterprises decrease time-to-production and maximize ROI. 

In January 2026, Google also introduced the new agentic commerce protocol, the Universal Commerce Protocol (UCP), to simplify automated commerce by enabling easy connections between customers, retailers, and payment services. 

Vertex AI pricing is consumption-based, with charges for model inference, agent runtime, and data processing.

Best for: Organizations with extensive Google Cloud data infrastructure needing advanced analytics, multimodal capabilities, and BigQuery integration.

Vendors are focusing on customization capabilities, model flexibility, and governance to give enterprises more confidence in their agentic systems and cultivate trusted relationships. Check out our comprehensive guide comparing Google Vertex, Azure AI, and Amazon Bedrock.

When selecting the right agentic AI development and deployment platform for your enterprise, you can follow these recommendations from a Head of Global AI Enablement at MetLife, James Barney:

Look for a system that optimizes the following:

1.uses or supports open source connectors,
2.exposes APIs for invoking agents or collecting information, and
3.works easily within your existing system.

Platform selection framework

Criteria	Microsoft Copilot	Salesforce Agentforce	Google Vertex AI	AWS Bedrock
CRM and sales automation	★★★☆☆	★★★★★	★★★☆☆	★★★☆☆
Office productivity	★★★★★	★★☆☆☆	★★★☆☆	★★☆☆☆
Data analytics integration	★★★★☆	★★★☆☆	★★★★★	★★★★☆
Model flexibility	★★★☆☆	★★☆☆☆	★★★★☆	★★★★★
Enterprise security controls	★★★★★	★★★★☆	★★★★☆	★★★★★
Time to first agent	★★★★☆	★★★★★	★★★☆☆	★★★☆☆
MCP support	★★★★★	★★★☆☆	★★★★☆	★★★★☆

Observability, orchestration, governance, and security are non-optional

A survey conducted by Harvard Business Review (HBR) found that multi-agentic systems would be most effective in enterprises, as engaging multiple applications, systems, and steps enables agents to substitute for entire enterprise workflows. 

But for these workflows to function consistently, enterprises need:

• observability (traceable actions and audit logs)
• orchestration (routing, retries, and escalation paths)
• governance (ownership, standards, and data lifecycle control)
• security (authorized access, least privilege, and protection against misuse)

The LinkedIn community is increasingly supporting the claim that only enterprises with proper AI guardrails will succeed with agentic AI. As Patrick Hogan, a Product Owner at  Digital Health Institute for Transformation (DHIT), claims in his post: 

Agentic AI value will be determined as much by control systems as by model capability. Enterprises don’t care if your agent can “think autonomously.” They care if it operates predictably, escalates appropriately, and leaves an audit trail.

As an additional safeguard, companies implement a human-in-the-loop workflow, in which human workers step in to validate, approve, or cancel the agent’s decision. This is particularly important in regulated industries. However, Deloitte forecasts a shift from human-in-the-loop to human-on-the-loop, where humans are involved only as supervisors of the entire agentic AI system, rather than interfering during the task execution.

So what’s changed in the enterprise AI market so far? AI vendors and in-house enterprise teams aim to increase the efficiency and trustworthiness of AI agents. As only 6% of enterprises currently trust these systems, we’ll see many production-ready agentic systems in the near future, with an emphasis on preserving business continuity and secure access to sensitive enterprise data.

Model Context Protocol: The integration standard connecting AI agents to enterprise systems

The most sophisticated AI model is useless if it cannot access your business systems. Model Context Protocol (MCP) has emerged as the universal standard for connecting AI agents to enterprise tools, and its adoption trajectory signals a fundamental shift in how agents will integrate with corporate infrastructure.

Why MCP matters for enterprise agents

Before MCP, connecting an AI agent to enterprise systems required custom integration work for every combination of model and tool. If an organization used five AI models and needed connections to twenty business systems, engineering teams faced 100 potential integration paths, each requiring separate development and maintenance.

MCP solves this N×M problem by establishing a common protocol. Tools expose capabilities through MCP servers; AI models connect through MCP clients. Add a new tool once, and every MCP-compatible model can use it. Add a new model, and it immediately accesses every existing tool connection.

Organizations using standardized integration approaches spend 60% less engineering effort on connectivity compared to those building point-to-point integrations.

Adoption has reached critical mass

One year after Anthropic introduced MCP in November 2024, adoption metrics demonstrate industry-wide acceptance. The protocol has achieved 97 million monthly SDK downloads, with over 5,800 MCP servers and 300 clients in production.

The competitive landscape shifted in early 2025. OpenAI adopted MCP in March 2025, followed by Google DeepMind, Microsoft, and AWS. In December 2025, Anthropic donated MCP governance to the Linux Foundation’s new Agentic AI Foundation (AAIF), cementing its status as an open industry standard rather than a proprietary advantage.

For enterprise teams, this means MCP integration is no longer optional. If your AI agents cannot communicate via MCP, they will be increasingly isolated from the broader ecosystem of tools, models, and orchestration frameworks.

What MCP enables in practice

MCP standardizes three core capabilities that enterprise agents require.

Tool access. Agents can invoke business applications (CRM updates, ticket creation, database queries) through a consistent interface. A procurement agent can check inventory in SAP, create purchase orders in Oracle, and update status in Salesforce using the same protocol patterns.

Context retrieval. Agents can pull relevant information from knowledge bases, document stores, and data warehouses without custom RAG implementations for each source. MCP’s resource primitives standardize how agents request and receive contextual data.

Action orchestration. Multi-agent systems can coordinate via MCP, with agents delegating tasks and sharing results via predefined message formats. This enables complex workflows in which a customer service agent escalates to a technical support agent, which then triggers a logistics agent to place a parts order.

Security considerations

MCP adoption introduces new security surfaces that enterprise teams must address. Agent permissions, tool authentication, and prompt injection vulnerabilities all require explicit governance.

The protocol itself does not enforce security policies. Organizations must implement authorization layers that control which agents can access which tools, audit logging for all MCP transactions, and input validation to prevent prompt injection through tool responses.

For implementation guidance on MCP security patterns, IBM provides a comprehensive technical overview covering enterprise deployment considerations.

Complementary protocols

MCP is not the only standard in the agentic ecosystem. Google’s Agent2Agent (A2A) protocol addresses multi-agent orchestration, defining how agents discover, communicate with, and delegate tasks to other agents. While MCP connects agents to tools, A2A connects agents to each other.

For organizations building multi-agent systems, both protocols will likely be necessary. MCP handles the integration layer; A2A handles the orchestration layer.

How Fortune 500 companies achieve ROI with agentic AI

Fortune 500 companies often run complex multi-step workflows and work with many mission-critical systems, which require robust safeguards to avoid data breaches or cyberattacks. 

Such workflows are the best way to show that if AI agents can provide value without disrupting anything for large organizations, they can be valuable on a smaller scale as well.

Capital One enhanced the car-buying process with the multi-agent system

Capital One has developed an internal multi-agentic AI assistant, Chat Concierge. They built this system using Meta’s open-source Llama model and enriched it with proprietary data. In addition to answering customer queries and providing car information, the agent also performs actions on the customer’s behalf. For instance, it can schedule appointments with the sales representatives

Even though the company, at its core, used an open-source model, they prioritized maintaining a high level of control and adherence to company policies. 

Here’s what Sanjiv Yajnik, President of Financial Services at Capital One, said regarding the results of this agentic AI initiative:

By leveraging our own internally-developed AI tools, we are able to provide personalized, efficient, and transparent interactions which ultimately help us to reimagine car buying and set a new standard for customer experience in the automotive industry.

Walmart builds “super agents” to improve employee, partner, and customer experience

Walmart developed four multi-agent systems, responsible for different aspects of e-commerce (customer shopping, supplier management, employee onboarding, and software development) and called them “super agents”. The retail giant consolidated dozens of AI tools into four separate company-wide frameworks to better orchestrate their use and achieve unified results. 

By scaling agentic AI across many business functions and investing in other AI breakthroughs, Walmart plans on increasing online sales by 50% within the next five years. This is an example of long-term strategic AI planning, where AI technologies are expected to augment existing processes.

Suresh Kumar, a Global Chief Technology Officer at Walmart, wrote:

I believe in the power of agentic AI to transform industries. At Walmart, it’s enhancing the way our customers shop and engage, how we run the business, and how our partners work with us. We’ve been building agents—fast- for every aspect of the business.

Disney invested in AI ad agents to speed up media planning for advertisers

Disney is investing heavily in developing its custom Disney Ads Agent to simplify media planning for advertisers. An agent can automatically search for inventory, identify the target audience, and track media campaign performance and success.

The company wants to combine generative and agentic AI, with generative AI responsible for creating customized ads and agentic AI for helping in running those ads. That’s the level of end-to-end advertising services they want to achieve. 

By entering into an agreement with OpenAI and its project Sora, Disney has taken another confident step towards an AI-ready future, and more is yet to come.

Agentic AI implementation best practices: From pilot to production

According to the Google AI ROI report, 74% of executives report measurable AI ROI within the first year. We’ve analyzed what differentiates companies that gain benefits from agentic AI from those that don’t and composed a list of best practices that might help you better plan your next agentic AI initiative. 

• Differentiate between generative and agentic AI projects. To truly benefit from agentic AI, this technology requires a separate roadmap. There is no one-size-fits-all deployment approach for all AI technologies. For instance, multi-agentic systems require a unique software architecture with communication protocols, such as agent-to-agent and model context protocols.

• Prepare the data. AI agents can work with legacy systems or fragmented data across multiple systems, but you still need to make it accessible to them, ensure it’s high-quality and well-cleaned. That’s where comprehensive data engineering consulting can come in handy.

• Start with expected business outcomes and measure them along the way. Define specific use cases for agentic AI; this could include automating overwhelming HR processes or internal software development projects. And then define the outcomes you expect from using agentic AI, like increased operational efficiency and employee productivity.

• Assign leaders responsible for implementing agentic AI. Such a person could be a Product Owner or, as it’s getting common to assign them now, a Chief AI Officer. A leader is necessary to supervise, manage, and organize the process and ensure it’s aligned with the long-term business strategy.

• Prioritize change management and AI literacy. Training, upskilling, and reskilling your teams to use agentic AI are also among the success factors that differentiate AI ROI leaders from AI laggards. This could be specific training programs that AI vendors can develop for you, workshops with AI engineers, or custom courses on your corporate learning management system (LMS).

• Think big and scale faster. Following Walmart’s example, scale delivers higher ROI and builds your trust in AI as you see cross-company improvements faster.

Rather than fearing failure, Ramanujam Theekshidar, Chief Digital Officer at U.S. Electrical Services suggest completely the opposite:

Have the mindset that there are going to be failures. But mitigate the risk so that if you fail, you learn fast and still deliver business outcomes.

Timeline and cost expectations

Deployment type	Timeline	Initial investment	Annual operations
Single workflow, mature data	20-24 weeks	$250K-$500K	20-25% of initial
Single workflow, data remediation needed	28-36 weeks	$500K-$1M	25-30% of initial
Multi-agent system, complex integrations	40-52 weeks	$1M-$2M+	25-30% of initial

These estimates assume dedicated project resources. Organizations attempting agent deployment as a side project for existing teams typically see timelines extend by 50-100%.

Bottom line

The key takeaway is that enterprise AI agents are becoming more popular, and over time, their business value and adoption will only increase. The more enterprises crack the code of their successful adoption, which involves ensuring AI agents fit unique business workflows and establishing rigid guardrails, the more valuable the market will be.

But amid all the hype, it’s important to remain reasonable and adopt agentic AI only when you have a supporting team, a reliable vendor, a solid data foundation, and a clear plan with milestones that help keep a pulse on KPIs. 

Xenoss is one of the few companies that provides all of the above. We support, build, prepare data, and strategize your agentic AI adoption to deliver the fastest ROI possible.

The post Enterprise AI agents: Implementation roadmap appeared first on Xenoss - AI and Data Software Development Company.

With every major broadcaster launching over-the-top (OTT) offerings and independent players multiplying, the CTV advertising market is getting critical traction.

As of mid-2025, streaming accounted for 44.8% of total TV viewership, surpassing the combined share of broadcast (20.1%) and cable (24.1%) for the first time in history.

CTV ad spending is set to grow from $33.35 billion in 2025 to $46.89 billion by 2028, when it will surpass traditional TV ad spending ($45.10 billion) for the first time, according to eMarketer

However, media buyers are right to have mixed feelings about CTV advertising. 

The lack of transparency and proper safeguards in CTV costs advertisers an average of $700,000 in wasted spend per billion impressions. 

Advertisers point out that it’s difficult to tell whether CTV buys are reaching viewers due to the highly fragmented ecosystem. A DoubleVerify report found that only 50% of all CTV impressions offer full transparency, and even so, CTV advertising is still perceived as difficult to measure. 

Fortunately, connected TV ads can provide data points as relevant as those from other digital channels with a proactive approach to partnerships and interoperability. 

In this post, you’ll learn about:

• The fragmented CTV market landscape and its implications for AdTech companies 
• The main challenges of CTV advertising measurement and attribution 
• Best tech practices for gaining CTV measurement data that buyers need 

CTV market overview: Platforms & operating systems (OS)  

The CTV market is an ecosystem. Participants include smart TV device manufacturers, standalone media players, OTT providers, and content distribution platforms. All of them have a heavy hand in the market because they own (but do not always share) consumer data. 

To gain full visibility into CTV ad performance, ad platforms have to integrate data from multiple sources. What makes CTV measurement even harder is that no single player dominates the smart TV OS market or the OTT market.  

Main types of CTV players 

• Smart TVs with native OS (e.g., Samsung TV, LG TV, Sony, Vizio with embedded Chromecast) 
• Stand-alone streaming devices and media players ( e.g., Roku, Amazon Fire, Chromecast, or Apple TV) 
• OTT video-streaming services (e.g., AT&T TV, HBO Max, Hulu, Netflix, Paramount+, Rakuten TV, etc.)
• Content distribution platforms (e.g., Amagi, Castify.ai, BitCentral, Viaccess-Orca, etc.) 

That said, the global CTV market has its “big four” players, holding most of the audience data (and advertising dollars). 

Samsung Connected TV 

Samsung was among the first to release competitively priced smart TV sets. Since its market launch in 2015, the installed base of Samsung Tizen has grown to 270 million TV and smart signage devices worldwide. 

On a global scale, Samsung remains a leader, though the competitive landscape has shifted significantly. Android/Google TV is now the leading Smart TV OS, accounting for over 24% of global shipments, with Tizen at 16.9%, WebOS at 11.8%, and Roku at 9%. 

Hisense’s VIDAA OS has emerged as a major competitor at 7.8% global market share, followed by LG WebOS at 7.4%, with Roku and Amazon Fire TV tied at 6.4%. However, Samsung continues to trail in the North American market, where Roku leads the CTV device market share at 37%, followed by Amazon Fire TV at 17%, while Samsung holds just 12%.

Roku 

The first Roku streaming device was released with Netflix in 2008. Since then, the company has expanded its hardware product range, developed the Roku OS, and launched a programmatic CTV advertising network.

Roku reached more than 90 million streaming households as of the first week of January 2025, making it an attractive platform for OLV advertising. Roku’s Platform revenue surpassed $1 billion for the first time in Q4 2024, growing 25% year-over-year. In the Q4 2024 earnings call, Roku’s CEO noted that at least one Roku-powered device is in half of US broadband homes.

However, Roku’s devices segment faced challenges with a full-year 2024 gross margin of -14% and a Q4 gross margin of -29% due to increased seasonal discounts.

Amazon Fire TV 

Amazon entered the CTV space with affordable Fire sticks, went on to launch Fire TV (an edition of smart television sets), signed Fire OS distribution deals with popular device manufacturers (Insignia, Toshiba, JVC, Grundig, and, more recently, Panasonic). 

To date,  Amazon has sold more than 250 million Fire TV devices globally since the platform’s launch in 2014, with an increase of 50 million since late 2023

Amazon has also been exploring the emerging in-car video streaming market. At CES 2022, Amazon announced a pact with Ford Motor Co. to embed Fire TV in Ford Expedition and Lincoln Navigator models, and separately announced a deal with Stellantis to integrate Fire TV into Wagoneer, Grand Wagoneer, Jeep Grand Cherokee, and Chrysler Pacifica models.

Google TV (Android TV)

Google entered the connected TV space with Chromecast devices (smart TV sticks), but quickly assembled a larger ecosystem of products. The Android TV platform is the original Google OS for smart TV sets.

In 2020, Google released a major upgrade to Android TV and rebranded its offering as Google TV. At its core, Google TV is a new interface running on top of the original Android TV OS. 

It comes pre-installed on the Google TV Streamer (which replaced the Chromecast line in 2024) and is the primary interface for smart TV manufacturers that opted for Android TV OS. 

Google is progressively phasing out the older Android TV interface in favor of Google TV across all devices. Google TV now comes pre-installed on smart TVs from brands like TCL, Sony, Hisense, Sharp, Philips, and others. As of September 2024, Google TV is active on over 270 million devices monthly

What CTV market fragmentation means for the AdTech Industry

Device and data fragmentation is the bane of all new channels, like in-game advertising or DOOH. Sourcing data from multiple smart TV sets, OTT providers, and OS is technically complex. In addition to many conflicting requirements and limitations is a lack of standardization. Combined, these factors complicate CTV ad measurement.

On the other hand, as Tal Chalozin, CTO and Co-Founder at Innovid, an independent CTV measurement platform, rightfully noted: 

Fragmentation means competition, and competition means lower prices. When platforms have to compete against one another to secure ad dollars, then the number one lever available to them is their price. As long as the connected TV space remains heavily fragmented, marketers will benefit from a buyer’s market.

More advertisers consider CTV advertising. AdTech companies that can develop better CTV ad measurement solutions and provide precise attribution metrics will emerge on top. 

CTV advertising measurement challenges

CTV attribution is hard primarily due to the absence of shared standards for measurability.

Back in the day, Nielsen pioneered measurement for linear TV advertising. Though the company made a tentative move into CTV measurement, both of its frameworks are often criticized for inaccurate audience counts. 

Brands (and their agency partners) are on the hunt for a better measurement solution. Which one will it be? The following could resolve the CTV measurement and attribution issues. 

Lack of common identifiers

The digital advertising space relied on third-party cookies for years to identify, track, and report user behaviors. Now the industry works towards universally acceptable cookieless tracking and shared user ID solutions.

CTV ad space faces a similar dilemma: It needs cross-platform identifiers. IP addresses have been the most common means of identifying households as they are easy to capture. Most programmatic CTV advertising uses IP addresses for targeting and remarketing. 

But is an IP address a reliable ID? No. Many consumers share streaming accounts and use various devices to view the content (i.e., the IP address changes, but the user stays the same or vice versa). Because neither supply-side platforms (SSPs) nor demand-side platforms (DSPs) can precisely ID users, a lot of budgets are wasted. For example, if a brand buys connected TV ads through Roku and via a DSP platform, they risk marketing ad duplication. According to the Innovid x ANA Report: 

The average CTV campaign frequency was 7.09 in 2024, with an average CTV household reach of only 19.64%. As campaign sizes grow, so does the risk of oversaturation: high-investment campaigns with over 200M+ impressions saw frequency rise to 10+.

So what are the good options? CTV-specific user identity graphs may help. Digital ID providers like Ramp ID (former IdentityLink) and Tapad offer connected TV capabilities as part of omnichannel identity graphs. However, both solutions primarily rely on IP addresses for initial user identification. Then they augment the created identity with other data points.

No viable alternatives to IP addresses have been found so far, apart from first-party-based ID solutions built by different players in the ecosystem. That said, IP addresses aren’t definitely going away just yet. So the industry has time to come up with new ID types like device graphs or universal user ID graphs. 

Multitude of different CTV measurement methodologies

When you ask Ad Ops which CTV measurement metrics they use, you’ll get an entire spreadsheet of answers: 

Buyers want both familiar linear TV metrics and programmatic ones. Yet, many DSPs and SSPs struggle to deliver such a large roster of accurate insights. So brands are eager to test multiple CTV attribution options on the table. The Trade Desk and Viant Technology already went with iSpot. Xandr, ABEMA, Smadex, and tvScientific have selected Adjust. 

Why do brands want multiple partners? Because the “big four” CTV platforms (Samsung, Roku, Amazon, and Google) employ proprietary approaches to measurement (which they don’t fully disclose).

While Nielsen has expanded into CTV measurement, its cross-platform coverage is still evolving, leaving gaps in independent verification.

Also, fragmentation exists on the AdTech level, where buyers can purchase CTV ads via different ad platforms directly. This further splinters audience data and complicates measurement.

Complex device identification process 

Since most platforms rely on IP addresses for user identification, it’s hard to determine who saw the ad: the same person on two different devices, multiple people on one device, or multiple people via the same OTT app. 

Also, CTV/OTT ads rely on the server-side ad insertion (SSAI) mechanism. It seamlessly integrates ad videos into the streamed content. SSAI is resistant to ad blockers and allows low-latency ad serving. However, SSAI needs accurate device ID data to deliver accurate impression counts. 

IAB Tech Lab’s original 2019 guidelines for CTV/OTT device and app identification recommended using “app store IDs” where available, but significant challenges persist. A lack of standardization around the syntax of Bundle IDs has led to confusion around targeting and measurement, creating a vulnerability that fraudsters could exploit.

To address these persistent identification challenges, IAB Tech Lab created the Ad Creative ID Framework (ACIF) in 2024 to simplify ad creative management and tracking across platforms. It supports the use of registered creative IDs that persist in cross-platform digital video delivery, particularly in CTV environments. The ACIF Validation API entered public comment in December 2024, and ACIF Version 1.0 was released in March 2025.

Using the WURFL device detection database is one workaround. It streamlines user device identification (device model, browser, OS, screen width, etc.). WURFL can be used to improve CTV attribution when paired with machine learning. Still, the setup process is quite complex. 

Cross-media measurement

Market fragmentation means that consumers have a lot of choices. Naturally, most switch between watching linear TV, using CTV apps, and OTT services on mobile. 

The wrinkle? Few exchange data with one another. Audience data is siloed between:

• Digital multichannel video programming distributors (MVPDs) 
• Direct-to-consumer OTT apps
• Smart TV manufacturers
• CTV OS distributors 
• SSPs, DSPs, and ad networks 

As a result, procuring data points such as device ID, audience demographic, or average viewership is hard, even for original content owners. Distributors typically hold most of the data to attract demand, though some publishers now buy back audience insights. Getting a consolidated view of video content viewership rates is somewhat problematic. 

CTV advertising fraud 

Programmatic ad fraud is a gruesome industry issue. CTV ads are no exception. 

Complex attribution stands behind high IVT rates in CTV advertising. Because verified data is hard to produce, faking ad impressions for CTV is easier than for desktop or mobile devices (although sophisticated ad fraud detection mechanisms might help).

Organizations like IAB Open Measurement, Media Rating Council (MRC), Trustworthy Accountability Group (TAG), and Brand Safety Institute have released comprehensive CTV ad fraud prevention guidelines. The challenge, however, lies in implementing them. 

6 best practices of CTV measurement 

No single metric can indicate the success of a CTV ad campaign. To reassure the buy-side, AdTech players have to provide a roster of cross-channel metrics, proving ad validity and viewability. 

Of course, the best industry minds are working on the CTV measurement problem. In May 2024, IAB Tech Lab expanded its Open Measurement SDK (OM SDK) to include Samsung and LG platforms, now covering 40% of CTV households.

The framework continues to evolve as a common standard for interoperability, with IAB Tech Lab releasing Device Attestation support in late 2025 to combat device spoofing in CTV environments.

OM SDK gives advertisers flexibility and choice in the verification solutions from their preferred providers by making it easier for publishers to integrate one SDK and enable ad verification with all verification vendors.

The IAB Tech Lab announcement

OM SDK is a helpful tool, but not a stand-alone solution. To improve CTV measurement, you need to combine several best practices. 

Employ a hybrid approach to cross-channel attribution 

Because access to audience data is constrained, no best-of-breed user attribution solution is available. Instead, the industry tests various methods for identifying users and tracking their interactions with content.

IAB suggests that the path pass forward would be using hybrid measurement approaches that combine:

• Automatic content recognition (ACR) methods, such as audio fingerprinting or watermarking 
• Passive panel metering technologie,s such as people meters 
• Digital metering using linked mobile devices or home router-level meters
• Third- or first-party census feeds

The combination of these signals can enable industry players to minimize ad duplication and better distinguish between linear TV, CTV app feeds at the household and individual levels, and broadcast video on demand (BVOD). 

Separately, user ID data such as identifiers for advertising (IFAs), CTV IDs, device IDs, and IP addresses could be cross-matched with audience profiles across platforms. In fact, most market players are making strides in this direction. 

Verizon Media ID 

Yahoo DSP (formerly Verizon Media) ConnectID includes CTV household data. In 2021, the company partnered with smart TV manufacturer VIZIO to gain viewership data from some 18 million VIZIO Smart TVs. 

However, the CTV landscape has shifted significantly since then, and Walmart acquired VIZIO in 2024. Now, one of the largest US retailers’ ecosystems is linked with a major source of TV viewership data, creating new opportunities for retail media targeting on CTV.

Roku Advertising Watermark

In early 2022, Roku released Advertising Watermark, a platform-native way to validate video ads’ authenticity on the Roku platform. The technology has since evolved significantly: in 2023, Roku launched Watermark 2.0, which detects fake impressions at both the device and app level and can be passed through the programmatic bidstream. 

Working with partners like DoubleVerify and HUMAN, the watermark has helped combat major fraud schemes, including CycloneBot, which generated up to 250 million fake ad requests daily.

Roku reports a marked reduction in fraudulent ad requests imitating its device traffic since 2023. The watermark is now integrated with Roku Ads Manager, which has replaced OneView as Roku’s primary ad-buying platform.

Determine the optimal approach to audience measurement

Since CTV is a cookieless environment, precise audience measurement is complex but possible. The Media Rating Council (MRC) has an exhaustive list of standards and approaches to cross-media CTV audience measurement. 

In short, there are two main options:

• pixel-based technology to capture an impression, video start, and completion data; and to detect and report on Invalid Traffic.
• embedded SDK or client-side measurement code for cross-channel measurement (such as OM SDK by IAB).

Once again, leaders don’t settle for one option. Most establish extensive audience measurement with Automatic Content Recognition (ACR) technologies. 

ACR matches individual objects in a video with database records to identify and recognize streaming content. The technology includes either or both video pixel detection (video fingerprinting) and audio capture (acoustic fingerprinting).

ACR-supported devices (smart TVs, smartphones, and tablets) allow ad networks to capture these data points: 

• Platform type – linear, CTV, MVPD, or another VOD service 
• Geo-location data 
• IP address 
• Demographics data 
• Viewing behaviors – average watch time, ad competition rates, channel surfing parameters, etc. 

Tech-wise, ACR algorithms generate library-side fingerprints for the publisher’s media. Fingerprints are designed to compare sample video/audio content against references in the publisher’s database to identify the played content. When a viewer browses content via an ACR device, they generate extra fingerprints, which then get matched to stored records. 

Based on matches, AdTech platforms access the above data for targeting, measurement, and attribution. Next, ACR data can be cross-validated with passive or digital metering for even higher accuracy. 

iSpot audience measurement with ACR 

iSpot has developed a robust cross-channel TV measurement tech suite for detecting ACR-sourced ad impressions across 83 million smart TVs and set-top boxes. Following its 2023 acquisition of 605, the platform combines smart TV data from VIZIO and LG with set-top box data from 16.6 million homes.

The platform relies on intelligent algorithms for matching impression counts against set-top box data and a person-level panel for extra precision, with direct integrations with over 400 streaming publishers. Separately, ad impressions are verified manually by a team of editors.

Such a comprehensive TV ad measurement stack, bolstered by four acquisitions since 2021, has made iSpot a leading challenger to Nielsen. Its publishing partners include NBCUniversal (which certified iSpot as a cross-platform currency vendor), Warner Bros. Discovery, Paramount, and Roku, among others. On the AdTech side, iSpot has secured deals with The Trade Desk, Google, and an exclusive data partnership with TVision.

Figure out how to best report on CTV ad performance

Brands can track connected TV ads using standard performance metrics like ad viewability, quartile rates, and completion rates. However, these don’t always provide an accurate picture. 

Ad verification firm DoubleVerify found that one in four CTV platforms continued playing content, including recorded ad impressions, after the TV set was turned off. Ouch, this better get fixed, and it likely will be. 

In June 2022, GroupM launched an initiative to co-create a streamlined measurement framework and best practices for verifying that ads only get served when CTV screens are on. A joint study with iSpot found that 8-10% of streaming impressions play when the TV is shut off. Companies including Disney, LG Ads Solutions, NBCUniversal, Paramount, VIZIO, Warner Bros. Discovery, and Fox/Tubi committed to the effort. 

The initiative has since evolved, with NBCUniversal and GroupM conducting successful tests in 2024 using IAB Tech Lab’s Ad Creative ID Framework (ACIF) for cross-platform ad tracking.

DoubleVerify has continued to expand its MRC-accredited CTV measurement capabilities. Its Fully On-Screen certification, first accredited in 2021, ensures ads are only displayed when TV screens are on. In April 2024, DV earned additional MRC accreditation for Video Viewable Impressions in CTV, which is significant given that DV’s research shows over one-third of CTV impressions serve into environments where ads fire when the TV is off, contributing to an estimated $1 billion in wasted ad spend annually.

IAB also recommends using the cost-per-completed viewable view (CPCVV) metric since it’s the most efficient and value-driven option. 

Provide tools to track brand lift and incremental reach 

Most advertisers choose CTV to improve ToFU metrics like brand awareness and consideration. Also, they want to understand how many unique audiences OTT video campaigns engage on top of linear TV campaigns. 

Respectively, buyers want to see brand lift and incremental reach stats in their dashboards. In CTV/OTT advertising platform development, you have several ways to deliver these stats.

Brand lift tracking options:

• Partner with CTV/OTT providers and/or third-party measurement companies to access intel.
• Employ statistical modeling methods to estimate CTV ad exposure. 
• Augment extrapolated data with passive exposure tracking panels, such as mobile metering and fingerprinting technologies.
• Issue in-device surveys to capture viewers’ sentiment towards promoted brands. 

Incremental reach tracking

• Use ACR technology (audio or acoustic fingerprinting) to identify consumed content and viewing patterns. 
• Add a passive metering device to capture audio watermarks for higher precision. 
• Combine ACR data with device graphs to better distinguish between users who saw linear vs. OTT campaigns (and vice versa). This tech combo can also help retarget exposed users with a sequential campaign across channels, plus re-optimize display frequency. 

Consider ML-based contextual targeting as an add-on 

ACR is a firmware-based solution. ML-based contextual targeting is a conceptually similar solution, but on a software level. This option might be better suited for AdTech companies that don’t want to source ACR data from multiple CTV platforms. 

Apart from monitoring user behaviors similar to ACR, ML-based contextual targeting systems can:

• Forecast advertising inventory volumes across networks 
• Model accurate campaign performance predictions
• Facilitate audience segmentation and data-driven audience modeling 
• Promote better CTV ad fraud detection and prevention 
• Improve user/device identification and ad measurement tracking 

Combined, these qualities make ML-based contextual targeting a competitive add-on for your ad network. 

Integrate a third-party CTV ad measurement SDK

At the end of the day, brands want guarantees. Many CTV platforms have already voiced their support for OM SDK:  

• Apple TV
• Amazon Fire 
• Android TV (Google TV) 

What about the remaining options like Roku, Samsung Tizen, LG Web OS, and others? If you work with those providers, you’ll have to build a custom SDK for integrating third-party measurement partners. You can turn to professional tech consultants like Xenoss to build a custom SDK for integration and resolve other challenges of the CTV/OTT advertising platform development.

Final thoughts 

Connected TV advertising is still a “Wild West” for AdTech providers. Some chose to go “cowboy style” and accelerate their entry into this environment without having CTV ad measurement and attribution tools. This tactic might have worked a couple of years back, but in today’s swiftly maturing CTV landscape, vendors that cannot send a wealth of data down the bid stream will soon turn obsolete. 

As CTV platforms continue to compete with one another for ad dollars, smarter AdTech players can focus on developing better CTV measurement solutions to fit into this nascent ecosystem.  

Want to be at the vanguard of CTV ad measurement? Xenoss can help you get there with our in-depth AdTech market expertise and technical know-how. Contact us to discuss your project.

The post CTV measurement: AdTech stack for the fragmented market 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.

Dimension	Consumer AI	Enterprise AI
Primary goal	Personal productivity, creativity, and convenience	Measurable business outcomes (revenue, cost, risk)
Users	Individuals	Teams and entire orgs
Context	General and user-provided prompts	Deep org context across data and systems
Data	Public/general knowledge and personal files	Sensitive proprietary data and regulated datasets
Tolerance for errors	High (“good enough” is acceptable)	Low (hallucinations create real business/safety risk)
Outputs	Suggestions, drafts, answers	Content, decisions, or actions inside workflows
Integration	Minimal (standalone apps)	Heavy (ERP/CRM, data platforms, IT/OT systems)
Governance	Optional	Mandatory (policies, approvals, audit trails)
Security model	Basic user-level controls	Enterprise IAM, access boundaries, compliance controls
Evaluation	Subjective (“Does it help me?”)	Formal (SLAs, test suites, KPIs, monitoring)
Reliability requirements	Nice-to-have	Non-negotiable (resilience, fallback paths)
Change management	Low	High (training, adoption, process redesign)
Deployment	App updates	Controlled rollout (staging, guardrails, versioning)
Buying decision	Individual purchase	Requires procurement, legal, security, and finance approval
Success metric	Engagement and satisfaction	Measurable business impact, accountability, and auditability

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.

But knowing data matters doesn’t tell you where to start. Leaders keep asking: Do we need to fix data management issues first, or focus on AI-ready data instead to avoid losing competitive momentum? Should we hire an internal data team, or would it be better to outsource our data engineering solutions? For different companies, the answers to those questions vary. 

In this guide, we examine data engineering services from a business standpoint to help you choose the right path. You will:

• Learn what to focus on when you’re just starting your data improvement journey
• Get a clear decision framework for selecting the right delivery model
• Understand how to select a suitable data engineering partner based on their service offering

What to focus on in the general data management strategy

A Deloitte survey shows that, depending on their data management maturity level, Chief Data Officers (CDOs) set different priorities for their businesses. The graph below shows that starting with AI/GenAI initiatives is only worthwhile if you have a high level of data maturity. Whereas companies with less streamlined data management should prioritize data governance, strategy, and quality.

That’s why a “best practice” data strategy is rarely universal. To succeed in the coming years, you need to anchor your approach in two factors:

• Domain requirements (what data matters in your industry and why)
• Maturity level (how reliably your organization can manage, access, and operationalize that data)

The era of blindly following competitors and offering the same services with the same technologies is over. Now, companies plan to use data as fuel for their market differentiation.

Joe Reis, a Data Engineer and Architect, and an author of the Fundamentals of Data Engineering book, mentions in his post:

Many organizations say that AI-ready data is their top priority, yet they still struggle with basic data management and data literacy. That tension is catching up to CDOs and data leaders. Turns out, data matters more than ever.

More than that, a comprehensive study of 228 cases across sectors found that companies that align data initiatives with strategic business goals outperform those that adopt technology without a strategic context.

The problem is that many companies still treat data as “infrastructure work,” separate from commercial priorities. In fact, 42% of business leaders admit their data strategies are not aligned with business goals. The result is predictable: teams invest in platforms, pipelines, and dashboards, but struggle to translate them into revenue growth, improved customer experiences, operational efficiency, or risk reduction.

Once you define the right top-level priorities based on your maturity and domain needs, you can move from strategy to execution and select the data engineering services that will address the most urgent constraints first, one step at a time.

How to choose a fitting data engineering service depending on a business problem

Data engineering services often sound interchangeable on paper, but in practice, the right choice depends on what problem you’re solving and how urgently the business needs results. Some teams need a foundation (architecture, governance, standardization). Others need stabilization (pipelines, reliability, observability). And in many cases, the biggest lever is a targeted service that removes the constraint blocking analytics, AI, or cost control.

Use the table below as a decision map: start with your current business scenario, then match it to the service type that delivers the fastest and most sustainable improvement.

Business need/scenario	Recommended data engineering service	What this service includes	Best for
Fragmented data across systems with no single source of truth	Data architecture & platform design	Target data architecture, data models, platform selection (data lake, warehouse, lakehouse), governance foundations	Companies early in data maturity or post-M&A
Data pipelines are unstable, slow, or frequently break	Data pipeline engineering & modernization	Ingestion, transformation, orchestration, monitoring, failure handling	Teams struggling with unreliable reporting or analytics delays
Growing data volumes are driving cloud costs out of control	Data platform optimization & FinOps	Cost audits, storage tiering, query optimization, and compute scaling strategies	Cloud-native organizations with rising data spend
Analytics exists, but business teams don’t trust the data	Data quality & observability services	Data validation rules, anomaly detection, lineage, and SLA monitoring	Regulated industries or KPI-driven organizations
AI/ML initiatives stall due to poor data readiness	Data engineering for AI & ML enablement	Feature pipelines, training data preparation, and real-time data access	Companies moving from BI to predictive, generative, or agentic AI
Legacy systems block modernization efforts	Legacy data migration & modernization	Data extraction, schema redesign, phased migration, parallel runs	Enterprises with mainframes or on-prem data stacks
Multiple teams build duplicate pipelines and dashboards	Enterprise data platform consolidation	Tool rationalization, shared pipelines, centralized governance	Large organizations with decentralized data teams
Need fast results to validate a business hypothesis	Data engineering PoC/MVP	Narrow-scope pipelines, rapid prototyping, measurable KPIs	Leaders testing ROI before scaling investment
Compliance, security, and audits are becoming risky	Data governance & compliance engineering	Access controls, audit trails, retention policies, and compliance mapping	Finance, healthcare, enterprise SaaS
Internal team lacks capacity or niche expertise	Dedicated data engineering team/augmentation	Embedded engineers, architects, and long-term delivery ownership	Scaling organizations with aggressive timelines

With data issues clear and an understanding of how core data engineering services work, your next step is to define the delivery model you’ll use to start improving your current data infrastructure.

Decision framework: Build vs. buy vs. outsource your data stack

When choosing between these three paths: to buy, build, or outsource your data stack, you have to back up every decision with common sense and your current team’s capacity and skills. Tool- or hype-driven data strategies won’t work. Aim at avoiding situations like a Fractional Head of Data, Benjamin Rogojan describes in his post:

You know your data team is going to have a rough 18 months when a VP returns from a conference and tells you that the company needs to switch all its data workflows to “INSERT HYPE TOOL NAME HERE.” They’ve been swindled, and now your data team is going to pay for it.

To determine which option is best for your business, consider the aspects below.

Decision factor (what leaders should evaluate)	BUILD in-house (own the stack)	BUY platforms/tools (managed stack)	OUTSOURCE/PARTNER delivery (partner-led execution)
Primary business goal	Create a durable competitive moat through proprietary data products and workflows	Accelerate time-to-value with proven, scalable capabilities	Ship outcomes fast when internal bandwidth or expertise is limited
Best fit maturity level	High maturity (clear ownership, strong data standards, platform mindset)	Low-to-mid maturity (need stable foundations quickly) or high maturity (optimize commoditized layers)	Low-to-mid maturity (needs structure) or high maturity (needs specialized execution)
Time-to-value expectation	Slowest initially (platform investment before payback)	Fastest path to usable analytics/AI workloads	Fast, especially when paired with bought platforms
Upfront cost profile	High (engineering time and platform build effort)	Medium (licenses/consumption and enablement)	Medium-to-high (delivery fees, but predictable milestones)
Long-term TCO profile	It can be the lowest if you have scale and strong operations; it can become the highest if maintenance is underestimated	Often predictable, but consumption can spike without FinOps	Predictable during engagement; it depends on the handover model afterward
Operational overhead (on-call, upgrades, reliability)	Highest (you own everything)	Lowest (vendor absorbs much of the ops burden)	Shared (partner builds/operates; you decide who runs it long-term)
Customization/control	Maximum control and custom logic	Moderate (configurable, but bounded by platform constraints)	High in delivery, moderate in tooling (depends on what’s selected)
Risk profile	Execution risk is high; success depends on talent and operating model	Vendor dependency risk; lock-in considerations	Delivery dependency risk; mitigated with knowledge transfer and documentation
Security & compliance needs	Best if you require deep customization and strict controls	Strong if the software provider supports the required certifications and controls	Strong if the partner implements governance and audit-ready data processes correctly
What leaders get wrong most often	Underestimate maintenance, incident load, and long-term ownership cost	Assume tools fix process/ownership issues automatically	Treat it as staff augmentation instead of outcome-based delivery
When NOT to choose it	If you need results in <90 days or lack platform engineering maturity	If you need extreme customization and can’t accept vendor constraints	If you can’t allocate an internal owner or want “set-and-forget” delivery

Real-life case studies with measurable ROI

Let’s see what results teams achieve by following different delivery models.

Partner: Accenture helps the Bank of England upgrade a system supporting $1 trillion settlements in a day

The data management improvement story can also begin with updating a core processing system, as happened at the Bank of England. They partnered with Accenture to improve their Real-Time Gross Settlement (RTGS) service. To do this, the most important task was centralizing financial data in a centralized cloud storage system with APIs connecting the system to external financial entities across the globe.

In just the first two months after launch, the new platform successfully processed 9.4 million transactions valued at $48 trillion, including a peak of 295,000 transactions in a single day, demonstrating immediate performance at national-system scale. 

The necessity of quickly launching a system of national importance without disruption justified the choice of partnership in the case of the Bank of England.

Build: Airbnb created Airflow to scale data workflows internally

Airbnb’s data team chose the “build” path when off-the-shelf workflow tools couldn’t keep up with the growing complexity of their analytics and ML pipelines. At the time, the company relied on a mix of practices, which made workflows expensive to maintain as the number of dependencies increased. To solve this, Airbnb engineers built Airflow, an internal workflow management platform that introduced a clear structure for pipeline orchestration. 

As a result, teams could define workflows as code, reuse components, track execution state in one place, and reduce manual firefighting caused by broken jobs and invisible failures. 

The strategic payoff of the “build” approach was that Airflow didn’t just stabilize Airbnb’s internal data operations; it became an industry-standard orchestration layer that Airbnb later open-sourced, turning a costly internal investment into a widely adopted data tool.

Buy: Snowflake AI Data Cloud in the Forrester study

Forrester conducted the Total Economic Impact study for the Snowflake product by interviewing four companies that use this service. Before purchasing the Snowflake solution, the companies used fragmented on-premises data solutions, which created data silos, operational overhead, and technical complexity.

The study highlights 10%–35% productivity improvements across data engineers, data scientists, and data analysts, translating into nearly $7.7 million in savings from faster time-to-value and streamlined workflows. It also reports more than $5.6 million in savings from infrastructure and database management.

However, the “buy” option required these companies to invest in internal labor costs to migrate data, set up data pipelines, and customize the platform to each company’s needs.

Our research revealed that only a few companies decide on the internal building strategy. They realize that upfront investments are high and the payback period is longer, which is a luxury in a world where AI wins the market so quickly. 

Choosing the “build” approach should be well-justified and have a clear competitive edge, as was the case with Airbnb.

Selecting a data engineering partner based on their service offerings

A reputable data engineering services partner offers a comprehensive suite of end-to-end data capabilities, including building, optimizing, and maintaining your organization’s data lifecycle and infrastructure.

Data pipeline development and orchestration

This service involves designing, developing, and implementing data pipelines that ingest data from various data sources, transform it, and load it into target systems such as data warehouses or data lakes. It’s possible with the help of ETL (extract, transform, load) and ELT (extract, load, transform) processes.

Partners should demonstrate hands-on expertise in widely adopted orchestration and data integration tools and frameworks, such as Apache Airflow, Dagster, Prefect, Argo Workflows, and cloud-native options like AWS Step Functions, Google Cloud Composer, and Azure Data Factory to automate complex workflows end-to-end, from data ingestion and transformation to monitoring and recovery.

Data storage, management, and architecture strategy

Effective data storage and management are crucial for data accessibility and performance. Data engineering partners help design and implement optimal data architectures, whether that involves a traditional cloud data warehouse for structured data analytics (e.g., Amazon Redshift), a data lake for raw, unstructured data, or hybrid data lakehouse architectures.

All-around data storage services include strategies for data partitioning, indexing, and schema design to ensure efficient querying and cost management. Partners will guide you in selecting and configuring scalable data storage solutions or on-premises infrastructure, ensuring scalability and performance that align with your company’s business and data platform strategy (e.g, the choice between Snowflake or Google BigQuery).

Data quality, validation, and observability

A professional data engineering services company also provides robust data quality checks, profiling, cleansing, and standardization. Data specialists establish automated data validation rules and processes to identify and rectify data anomalies early in the pipeline.

Another key aspect is data observability: the ability to understand the health and performance of your data systems through monitoring, logging, and alerting. These procedures help engineers detect data issues and resolve them proactively, building trust in the business and customer data.

Data governance, security, and compliance

Qualified data engineering partners provide expertise in establishing frameworks for data ownership, data catalog, metadata management, data lineage tracking, and access control policies. 

However, true experts also realize that the concept of data ownership has evolved. Malcolm Hawker, a CDO at Profisee, claims that modern data ownership is more flexible than it used to be.

Data doesn’t behave like an asset you can lock in a vault. It behaves more like a shared language, where its meaning, value, and risk profile shift based on context. That means effective governance isn’t about controlling data. It’s about orchestrating accountability across contexts.

Apart from data accountability, experienced partners ensure that data-handling practices comply with relevant regulations (e.g., GDPR, CCPA, HIPAA), safeguard sensitive information, and maintain data privacy. Plus, they implement strong security measures for data at rest and in transit to protect against unauthorized access and breaches.

Advanced analytics and AI/ML enablement

Beyond foundational cloud infrastructure, comprehensive data engineering services are critical for enabling advanced analytics and AI/ML initiatives. Data science and engineering specialists prepare and curate datasets, engineer features, and build the necessary data pipelines to feed machine learning models. 

They ensure that data is accessible, well-structured, and performant for model training and inference. This includes integrating with AI/ML platforms, establishing MLOps pipelines, and ensuring data readiness for complex analytical workloads, thereby bridging the gap between raw data and actionable intelligence for data scientists and business users alike.

Future-proofing your data infrastructure

If you remember one thing from this guide, let it be this: your data infrastructure doesn’t need to be “perfect.” It needs to be reliable enough to run the business and structured enough to scale, without turning every new initiative into a fire drill. The fastest way to get there is to choose one priority bottleneck (trust, speed, cost, or governance), fix it with the right service, and ensure the solution is production-ready: monitored, documented, owned, and measurable.

As part of our end-to-end data engineering consulting services, Xenoss can help you assess your data maturity, design a realistic data improvement roadmap, and build the data foundation that supports large-scale analytics and AI.

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Companies often make the same mistake over and over: placing unrealistic expectations on developers to modernize legacy applications as quickly as possible, without realizing what these projects entail. Instead of investing enough time, effort, and just the right expertise, they waste time and money on modernization that never brings the expected ROI. As a result, they end up in an endless loop of “transformation theatre” where no significant changes occur, but real money is burnt.

In this guide, we will demystify the process of application modernization, translating complex technical concepts into clear business outcomes to help you avoid costly mistakes. We will move beyond the fear of disruption and lay out a strategic framework for achieving a transformation with zero operational downtime, zero business risk, but with tangible business value.

What is application modernization? (and what it isn’t)

At its core, application modernization is the process of updating older software to benefit from modern technologies, architectures, platforms, and engineering practices. But it’s more than simply buying off-the-shelf software. It involves a strategic re-evaluation of your existing applications to align them with current and future business objectives. 

A seasoned programmer in the past and now a full-time journalist, Dave McKay compared modernization to changing an aircraft’s propellers to jet engines while the aircraft is airborne. It’s difficult, risky, and sometimes failure seems more probable than success. But with due preparation and a professional team, it’s possible.

In the business setting, application modernization can involve:

• migrating applications to the cloud or hybrid environments
• decomposing monolithic systems  into smaller, more manageable services
• rewriting parts of applications to improve performance, security, and maintainability

For example, in healthcare, modernization may mean preserving mission-critical clinical systems while updating scheduling, billing, and data access applications to reduce administrative burden and improve patient experience, without disrupting care delivery.

The goal of every modernization project is to retain the valuable business logic embedded in your legacy systems while eliminating the technical debt and limitations that hold them back.

Here’s what Mayank Madhur, Practice Leader at HFS Research, says on the prospects of legacy modernization:

The legacy application modernization (LAM) market is shifting toward more elastic, scalable, cost-efficient, cloud-native, AI-driven, and microservices-based architectures. Future evolution will be on hybrid environments, automation, and sustainability, realizing legacy value through composable, modular systems for ongoing innovation and shifting digital business needs.

Why delaying modernization is riskier than modernizing

Postponing application modernization often feels like a safer choice. In reality, this inaction accumulates a hidden tax on your business, creating risks that far outweigh the perceived challenges of an upgrade. 

Quantified delay costs

Operational cost escalation:  42% of enterprise decision-makers report that maintaining outdated software significantly increases operational costs, and 

Digital transformation barriers: 38% and 36% of respondents struggle with digital transformation and software scalability issues, respectively.

Security issues: Older systems often lack modern security protocols because vendors no longer support them, leaving them more vulnerable to cyber threats. 42% of business leaders cite enhanced security as one of the top priorities for application modernization. 

Compliance bottlenecks: As data privacy regulations such as GDPR and CCPA become more stringent, legacy systems lack the architectural flexibility to ensure compliance, exposing organizations to hefty fines and reputational damage.

The decision to keep legacy systems as-is is riskier because these systems affect other internal software, decrease employee productivity, and require frequent, costly fixes. You may need to invest more upfront in their modernization, but this investment eventually pays off in improved customer experience, employee satisfaction, and enhanced business services.

Plus, modernization makes your business more resilient in response to market changes. You become more competitive and better prepared for integrating new technologies such as AI and ML.

Modernization paths: Choosing the right approach

There is no single “best” way to modernize legacy software. The right approach depends on how critical the system is to your business, how much operational risk you can tolerate, and what outcomes you are trying to achieve.

The foundational step in any modernization journey is a thorough assessment of your entire application portfolio against key business criteria:

1. Business impact analysis

• Revenue criticality: Direct revenue dependence and customer-facing impact assessment
• Operational centrality: Mission-critical process dependence and business continuity requirements 
• Strategic alignment: Future business model support and competitive advantage potential 
• Regulatory requirements: Compliance obligations and audit trail maintenance needs 

2. Technical condition evaluation

• Architecture assessment: Monolithic vs. modular design, integration complexity, scalability limitations
• Security posture: Current vulnerabilities, patch management status, encryption capabilities 
• Code quality: Technical debt volume, documentation completeness, maintainability score
• Performance metrics: Response times, throughput capacity, reliability statistics 

3. Financial analysis

• Total cost of ownership: Licensing, infrastructure, maintenance, support costs
• Modernization investment: Development, migration, training, operational transition costs
• ROI projections: Business value realization timeline and financial return expectations 
• Risk quantification: Potential loss from delays vs. transformation investment

4. Integration and dependency mapping

• System interdependencies: Data flows, API connections, shared database relationships
• Vendor relationships: Third-party integrations, support agreements, licensing constraints
• Operational workflows: User processes, automation dependencies, reporting requirements
• Change impact radius: Systems affected by modernization decisions

This assessment allows you to prioritize your efforts, focusing on high-impact, high-value applications first and choosing the most appropriate modernization strategy for each one. The Red Hat survey revealed that 41% of organizations first modernize their core backend applications, 35% – their data analytics and BI apps, and 14% – customer-facing ones.

Modernization projects fail when organizations default to a one-size-fits-all approach across application types. But successful modernization starts with understanding which strategic modernization options are available and the trade-offs each brings.

Incremental vs. full replacement

One of the first decisions business leaders make is whether to modernize existing systems gradually or replace them outright.

Incremental modernization focuses on improving systems step by step while they remain in use. When businesses decide on this approach, they can spread investment over time, reduce operational risk, and realize value earlier. It is often the preferred path for systems that support daily operations, revenue processing, or regulated activities.

Full replacement, on the other hand, aims to replace a legacy system with a new one. While this approach can promise a cleaner long-term foundation, it carries a higher upfront cost, longer timelines, and a greater risk of delays or disruption.

Parallel run vs. cutover

Another critical decision is how to introduce change into live operations.

A parallel run approach allows new and existing systems to operate side by side for a period of time. Running old and new systems in parallel gives teams the ability to validate results, manage risk, and gradually transition data and users to the new system.

A cutover approach switches from the outdated systems to the new ones at a defined point in time. It can reduce short-term costs and complexity, but it concentrates risk into a single moment.

For business leaders, the choice often comes down to control versus speed. Parallel runs favor resilience and predictability, while cutovers favor faster transitions but require a thorough risk assessment during the pre-cutover phase.

Encapsulation vs. reinvention

Modernization does not always require changing how a system works internally.

Encapsulation focuses on preserving existing business logic while improving how the application interacts with internal and external services by wrapping legacy code with modern APIs. This technique allows companies to protect years of accumulated knowledge and processes while removing bottlenecks in data exchange.

Reinvention involves rethinking processes and capabilities from the ground up. Using this method can help you develop new business models and improve customer experiences, but it also requires deep organizational alignment and significant investment.

From a return-on-investment standpoint, encapsulation often delivers faster, lower-risk gains, while reinvention is a longer-term bet aimed at transformational change.

In practice, most organizations apply different modernization paths, or combinations of them, to different systems. Critical platforms may evolve incrementally with parallel validation, while less critical applications are replaced or reimagined more decisively.

The role of leadership is to set clear priorities: decide where stability must be preserved, where speed matters most, and where transformation will deliver meaningful business value.

Technologies that support non-disruptive business modernization goals

The technologies that underpin application modernization, such as cloud, microservices, DevOps, and AI, directly translate into the business capabilities required to win in the modern economy: speed, scalability, and efficiency. 

Cloud advantage: Scalability, resiliency, and cost optimization

Cloud migration lies at the center of most modernization efforts. The cloud provides on-demand scalability, allowing your applications to handle peak loads without the cost of maintaining idle legacy infrastructure.

Cloud-native architectures are built to keep services running even when individual components fail, reducing the likelihood and impact of outages on customers and operations. 

Plus, cloud deployment helps businesses shift technology spending from a capital expenditure (CapEx) model of buying servers to an operational expenditure (OpEx) model, allowing you to pay only for the resources you use and align costs directly with business activity.

Migrating to the cloud-managed services also involves planning out a thorough data migration process. It consists of selecting, preparing, and migrating data from on-premises to the cloud or a hybrid environment.

Source: kubus IT

Microservices and containers: Driving flexibility and faster innovation

Legacy application modernization often involves decoupling monolithic architectures into a manageable, loosely coupled microservices architecture. For simplified and consistent deployment, each service is containerized using tools such as Kubernetes or Docker.

Where legacy applications are large, monolithic blocks, a modern architecture based on microservices is like a set of interconnected LEGO bricks. Each “brick” is a small, independent service responsible for a single business function. In our detailed architecture guide, we cover the architecture patterns for implementing microservices.

The essence of this application architecture is in its flexibility. Small, autonomous teams can work on different services simultaneously without interfering with each other, accelerating development cycles. 

For instance, if you need to update your payment processing, you only touch the payment service, not the entire application. This reduces the risk of unexpected changes and allows you to roll out new features and respond to market demands faster than you could with a monolithic legacy application.

Source: Uber

DevOps: Accelerating delivery, enhancing quality, and reducing risk

DevOps is a cultural and operational philosophy that bridges the traditional gap between software development (Dev) and IT operations (Ops). It focuses on automation and collaboration to build, test, and release software faster and more reliably. For the business, this means a significant acceleration in time-to-market.

The extensive use of automation tools in testing and deployment catches errors early. It reduces the risk of manual mistakes, leading to higher-quality, more stable releases, which are particularly crucial during the application modernization stage.

Source: government institution

AI in intelligent modernization

According to McKinsey, using AI-driven modernization tools, companies can accelerate legacy transformation timelines by up to 40%–50%.

Artificial intelligence tools can analyze vast legacy codebases to identify dependencies, automatically map business processes, and even suggest the most efficient modernization ways. With this technology, companies can reduce the manual effort and guesswork involved in the initial assessment phase, de-risking the project from the start.

In response to a question about using AI tools for application modernization posted on the Gartner Peer Community site, the VP of Information Security described their use of AI as follows:

We continue to explore and use AI tools for application modernization. At this point in time, we have been exploring or using [AI] for the following:
1. Code analysis and understanding
2. Automated code refactoring and transformation
3. Test case generation and automation
4. API generation and management
5. Security vulnerability detection and remediation
6. Database migration and optimization.

Source: Morgan Stanley

In every case study we covered, technologies solve a particular business problem and are a part of custom modernization roadmaps. The next step for leadership is to track these modernization initiatives against clear success metrics, so that modernization progress translates into tangible returns and long-term business resilience.

Measuring success of application modernization: ROI, TCO reduction, SLA adherence, and compliance 

Effective leaders define success upfront and measure modernization against four non-negotiable dimensions: financial return, cost structure, operational reliability, and risk exposure.

Success criteria	What leaders should measure	What it signals to the business
Return on investment (ROI)	Time-to-market for new features or services Revenue uplift from new digital capabilities Reduction in manual work or process bottlenecks	Modernization is creating business opportunities, not just consuming the budget
Total cost of ownership (TCO)	Ongoing maintenance spend Frequency of emergency fixes Cost predictability across systems	Financial control has replaced reactive spending
Service reliability (SLA)	System availability during and after the change Incident frequency and recovery time Customer-facing disruption	Modernization is increasing resilience without operational risk
Operational efficiency	Time spent on manual workarounds Cross-team dependencies Speed of internal processes	Teams can focus on value creation instead of firefighting
Compliance & risk exposure	Audit readiness Security incidents or near misses Regulatory exceptions	Risk is actively managed rather than tolerated
Organizational agility	Ability to adapt systems to new regulations or market demands Effort required to support change	The business can evolve without major disruption
Customer experience impact	Customer satisfaction or retention trends Service continuity during upgrades	Customers feel progress without feeling the change
Leadership confidence	Predictability of outcomes Clarity of decision-making	Modernization is under control and strategically aligned

Final takeaway 

This business-focused modernization article is the last one in our series of application modernization guides. So far, we’ve covered de-risking strategies for modernization, approaches to selecting modernization vendors, migration strategies for COBOL-based software, and the selection criteria of an appropriate architecture approach for the modernization project.

Our aim with this last piece of the puzzle was to debunk any remaining concerns or myths about modernization. You now realize why postponing modernization can pose more risks than modernization itself and why modern businesses should seek new ways to remain competitive. 

The selection of the modernization path and technologies depends on how mission-critical your application is and how deeply it’s embedded into your IT infrastructure. Xenoss can help you estimate the complexity of your current legacy stack and, based on the findings and with the help of AI-assisted engineering tools, develop the most appropriate software modernization roadmap.

The post Application modernization: How to modernize legacy software without business risks and service disruption  appeared first on Xenoss - AI and Data Software Development Company.

Technically, LLM capabilities improved across the board. We got smarter coding models, improved data processing, longer focus times, better image generation, and excellent video generation.

AI agents, although not new, have found their place in the enterprise, and more companies now have a vision for specific use cases where AI agents can provide support. 

At the same time, halfway through the year, it became clear that “AGI by 2027” predictions are too far-fetched. Despite improvements, models continued to hallucinate and make embarrassing mistakes, making it harder to imagine AI reliably running any complex process end-to-end. 

As the AI community had to accept the reckoning, fear started creeping in on whether the global economy is not putting too much stock in the AI bubble and what the world will look like if that bubble collapses. 

This review covers what mattered most in 2025: releases, wins, and risks of AI adoption in the enterprise, the state of the talent market, and the global impact of the AI explosion. 

1. Anthropic and Google caught up to OpenAI

At the start of the year, OpenAI’s GPT o3 was one of the most powerful chain-of-thought models. 

But by the end of the year, OpenAI no longer holds a decisive technical lead. Google and Anthropic caught up to the AI race with powerful models. 

At the time of writing, Gemini 3, GPT-5.2, and Claude 4.5. seem to be locked in a stalemate when it comes to agentic task completion, coding, multi-modal generation, and document processing.

On the other hand, Amazon, Meta, and Apple have fallen behind and not made meaningful LLM contributions this year. 

The table below recaps the top large language models released by three leading AI labs in 2025 and the impact of each on the development of machine learning. 

It’s fascinating to think about how much the approach AI labs take to building frontier models has changed since the first LLM release of 2025 (o3-mini). 

With Claude 3.7 as the trendsetter, LLMs started giving users more control over how long a model should think on a query. Now, AI labs allow users to enable or disable “Extended thinking” that encourages LLMs to think “deeper” about the prompt. 

Another area where AI labs have leaped astronomically is context windows. Gemini 3 Pro and Claude 4.5 Sonnet have a context window of 1 million tokens, GPT-5.2 supports up to 400,000 prompt tokens. 

Now that there are fewer concerns over LLMs’ capability to digest high data volumes, enterprise teams can train off-the-shelf models on higher volumes of corporate data without necessarily requiring a separate RAG module. 

Another important shift is how significantly the focus on LLM performance has shifted towards engineers. OpenAI’s release of 4.1. was API-only and marketed as an “improved coding model”. 

When launching o3 and o4, Sam Altman’s team also focused on math, science, and coding benchmarks to prove the excellence of these models. 

In the same vein, Anthropic didn’t implement image and video generation – instead, the company positioned Claude 4 as the “world’s best coding model”, capable of not losing focus on long-running tasks and multi-step agentic workflows. 

Google also emphasized improved agentic coding skills in Gemini 3 Pro documentation and increased the context window size to let teams feed entire code repositories to the model. 

This positioning tracks with where enterprises see the fastest, most defensible ROI: software delivery, workflow automation, and operational copilots. But it also creates a perception risk. When labs optimize their narratives around engineering benchmarks, non-technical users can read it as a deprioritization of writing quality, creativity, and broader “everyday” usefulness.

The takeaway: By the end of 2025, frontier LLM development looked less like a single-lab advantage and more like convergence across three major players. 

Differentiation shifted toward product strategy and distribution, including reasoning modes, cost and latency tiers, context scale, and enterprise deployment controls.

2. Open-source LLMs went mainstream

Before this year, there were only a handful of open models capable of rivaling GPT, Claude, and Gemini in evaluations, with Mistral and Llama model families leading the landscape. 

However, after DeepSeek R1 was released on January 20th, 2025, and took over the LLM community, open-source models became so influential that even SOTA AI labs had to admit to being on “the wrong side of history”.  

Following high demand from engineers, AWS, Google Cloud, and Microsoft Azure added the model to their offerings, allowing teams to comfortably add it to their AI products.

Throughout the year, the open-source boom continued, mostly led by Chinese AI labs. Out of US-based models, GPT-oss was the most powerful open-source model released in 2025, though the AI community argued it tied Kimi K2 on most benchmarks.  

Besides adding variety to the roster of AI models, the open-source explosion shook the standard foundations of generative AI. 

Discovery #1: Frontier-level training no longer requires frontier budgets

DeepSeek directly challenged the belief that state-of-the-art performance demands massive teams, proprietary pipelines, and multi-billion-dollar compute clusters. The team reported training costs of approximately $294,000, a negligible figure compared to the estimated $250 billion collectively invested by US-based labs in AI infrastructure in 2025.

Discovery #2: Keeping the codebase private doesn’t help protect AI safety

Before 2025, many AI leaders cautioned against open-sourcing large-language models, arguing that doing so would increase the risk of misuse. 

Open models largely undermined that position. Once high-performing weights, fine-tunes, and tooling are widely available, the marginal safety benefit of a single lab keeping its models closed diminishes sharply. Capable systems can be reproduced, adapted, and deployed well outside any one organization’s control.

Closed models can still reduce risk through stronger platform controls and faster patching compared to open-source models, but “closed by default” is no longer a credible standalone safety argument in a world where open alternatives like DeepSeek and Kimi K2 already meet many real-world use cases.

The takeaway: In 2025, open-source LLMs crossed the point of no return: once models like DeepSeek proved that frontier-level performance, low training costs, and cloud-native deployment could coexist, “open” stopped being an alternative and became a default option for builders. 

The growth of the open ecosystem put structural pressure on closed labs, and we may be entering the era where capability diffusion, not code secrecy, defines the generative AI landscape.

3. MCP became the  number-one agentic connector

In 2024, Anthropic released Model Context Protocol, an open standard that helps connect AI agents to external tools like GitHub, Figma, and others. This year, it went from a niche technology to a universally accepted industry standard. 

In March, instead of building a proprietary alternative, OpenAI used MCP to connect its model to external data sources. In April, Google followed suit, and MCP became the universal framework that top models use to connect their agents to other tools. 

By the end of the year, MCP adoption surpassed that of tools with a similar purpose (e.g, LangChain). 

At the time of writing, Anthropic lists over 10,000 active MCP servers. The protocol is actively adopted by engineers, where the Python SDK now has over 97 million downloads. 

On the other hand, as MCP adoption grew, teams became more aware of its limitations. Enterprise companies called out Anthropic for weak authorization capabilities, poor integrations with SSO providers, and high risk of prompt injection.

The takeaway: MCP’s rapid adoption demonstrates how open standards can become infrastructure when ecosystem incentives align. However, its spread exposed critical gaps in enterprise readiness: security, identity, and governance weaknesses that must be addressed before production-scale deployment.

4. GPT-5 fueled a wave of speculation on whether LLMs have “peaked”

On August 7, 2025, OpenAI unveiled GPT-5 with a livestream and a ton of fanfare. 

Expectations were unusually high. Among researchers, executives, and the broader public, there was a belief that GPT-5 might represent the next meaningful step toward AGI.

It was not. 

During the demo livestream, the plots capturing GPT-5’s superior benchmark performance were mislabeled, and the early rollout was riddled with bugs, ranging from simple math to GPT failing to switch to agent mode. 

Despite technically sweeping key benchmarks, the real-world impact of GPT-5 felt a lot less significant than that of other releases we got this year, namely Claude 4. 

The reason GPT-5 release still deserves a separate spot on our AI recap is that it changes the way we set expectations for AI models – instead of hoping to reach AGI, teams will be hoping to get well-rounded models that don’t feel “dumb” and drive quantifiable productivity gains. 

More releases are going to look like Anthropic’s Claude 4, where the benchmark gains are minor, and the real-world gains are a big step. There are plenty of implications for policy, evaluation, and transparency that come with this. It is going to take much more nuance to understand if the pace of progress is continuing, especially as critics of AI are going to seize the opportunity of evaluations flatlining to say that AI is no longer working.

Nathan Lambert, “GPT-5 and the arc of progress”

The fumbled release of GPT also fueled a different debate: are scaling laws hitting a ceiling? 

In 2020, when OpenAI published ‘Scaling Laws for Neural Language Models, ’ the idea that throwing exponentially larger datasets at models would make them exceptionally powerful was quite bold. 

However, when OpenAI applied it in practice with GPT-3, and then, even more convincingly, with GPT-4, scaling laws became the guiding principle of LLM training. 

Despite throwing more data and compute at newer generations of models with GPT-5, as well as other LLMs, they fail to deliver significant intelligence leaps. 

The doubt about the limitations of scaling laws, initially raised by a small group of skeptics (led by Gary Marcus, an AI researcher and author), is becoming mainstream. 

Engineering teams are exploring alternative methods for model improvements. 

Post-training techniques, reinforcement learning refinements, and fine-tuning strategies that help models better interpret existing data became standard practice. These methods improved reliability and task performance, but none yet matched the transformative impact scaling had earlier in the decade.

The takeaway: Despite significant improvements in coding and math LLMs reached in the beginning of the year, the AI community is looking into 2026 with uncertainty about the future of this technology. It will take a new substantial breakthrough to convince an increasingly skeptical crowd that large-language models are really a bridge to AGI. 

5. AI agents became the hottest corporate AI application of 2025

This year, AI agents went from the technology accessible primarily to frontier labs (the technology itself went mainstream in January when OpenAI released Operator) to a practical tool that enterprises adopted to streamline workflows. 

The first major agentic releases coming outside of leading AI companies were Agentforce 2dx by Salesforce and Joule Studio by SAP. 

Unlike OpenAI’s general-purpose agents, these niche releases cover a smaller list of applications. Salesforce’s agent helps sales, marketing, and customer success manage client tickets and sales pipelines, while SAP Joule Studio offers tools for automating workflows in HR, finance, and supply chain. 

By mid-year, it became clear that niche, workflow-specific agents delivered more value to enterprises than general-purpose agents. Constraining scope reduced hallucinations, simplified governance, and made ROI easier to measure.

By December 2025, major Fortune 500 companies will have successfully dabbled in building both internal and user-facing AI agents. 

To support growing interest in agentic systems, cloud vendors and data platforms are building an infrastructure to support AI agents.

Databricks empowers enterprise teams with a dedicated toolset for agent development that includes Mosaic AI Agent Framework, Unity Catalog, and built-in evaluation and monitoring tools. 

With these services, teams can build agents that safely reason over proprietary data, invoke tools, and operate inside governed production environments. 

AWS Bedrock helps enterprises bring agents to production with Amazon Bedrock AgentCore. The platform is a one-stop shop for building, deploying, operating, securing, and monitoring agents at scale. With AgentCore, engineers who host their infrastructure on AWS can connect multi-agent workflows to AWS-native identity, permissions, and data stack.

The takeaway: Agentic systems are still in their early stages, but a powerful infrastructure to help deploy and scale autonomous workflows is developing rapidly. 

Companies began seeing first wins from AI agent adoption in increased employee productivity, reduced error rate on manual tasks, and improved cross-department workflow integration. 

The next phase will be less about agent novelty and more about disciplined execution, governance, and scaling agents into core business processes.

6. “Vibe coding” took over no-code and prototyping

When Andrej Karpathy coined the term “vibe coding” in a tweet, he probably anticipated AI-assisted coding to become a trend. Still, it’s unlikely he predicted the speed with which his new term became a buzzword in the AI community. 

In early 2025, tools like Cursor and Microsoft Copilot were already empowering hands-off programming, but the inflection point came in late February, when Anthropic released Claude 3.7 and previewed Claude Code.  

Claude Code was no longer just auto-complete. It wrote and read code, edited files, wrote tests, pushed code to GitHub, and used the CLI with minimal human involvement. 

Claude Code gave engineers a massive productivity boost, allowing them to build up to four projects at a time, but, at the end of the day, it is still an engineer-facing tool. 

Vibe-coding went mainstream when tools like Lovable and Replit gave team managers and entrepreneurs with a layman’s understanding of engineering the power to transform plain-language ideas into ready-to-deploy pilots. 

In the year since its release, Lovable has hit 8 million users and has been used by over half of Fortune 500 companies. 

Among enterprise companies, tools like Lovable or Replit are rarely deployed for user-facing products or internal tools for organization-wide adoption, but are helpful for prototyping. 

I used to bring an idea to a meeting. Now I bring a Lovable prototype.

Sebastian Siemiatkowski, CEO of Klarna

Vibe-coding drives real productivity gains.

As with any trend threatening the status quo of traditional engineering departments, vibe-coding is controversial. Users have reported bugs in their Lovable MVPs and, on one occasion, Replit accidentally deleted a user’s entire database. 

Nevertheless, vibe coding is likely to stay because it is already delivering tangible value. 

A Forrester Research report found that using agentic coding tools saves enterprise companies up to ~$44.5M in risk-adjusted employee time savings over three years. A different survey showed a 206% ROI uplift following vibe coding adoption and a 50% time-to-market reduction.

According to Lovable’s internal data, a prototype built on the platform saves teams between $50,000 and $90,000 in engineering costs.

The takeaway: Vibe coding was one of the clearest productivity inflection points of 2025, shifting software creation from an engineer-only activity to a rapid, language-driven prototyping capability accessible to managers and founders. 

While not production-ready by default, its impact is already measurable in faster time to market, six-figure cost savings per prototype, and enterprise-scale ROI that makes experimentation cheaper, broader, and strategically unavoidable.

7. The MIT study discovered that 95% of enterprise AI applications still bring no impact

In August, MIT-backed NANDA initiative published the “The GenAI Divide: State of AI in Business 2025” report, with one finding particularly standing out. 

According to the study, only 5% of enterprise AI pilots bring revenue, while most deliver little to no measurable impact. 

You may have seen the MIT study that 95% of generative AI projects fail. I believe this. The challenge isn’t AI itself — it’s the ability to rethink workflows, redesign processes, and operate differently.

Mohamad Ali, SVP and Head at IBM Consulting

It’s a bold number, but the real story is subtler – and in some ways, more damning. The divide isn’t about model quality. It’s about how organisations wrap those models.

On one side sits a shadow economy of employees using ChatGPT, Claude, or Copilot on personal accounts – flexible, cheap, and immediately useful. On the other side sit enterprise AI projects – often custom-built or pricey vendor tools – that collapse under the weight of workflow fit, governance, and brittle, hard-coded logic.

Tony Seale, former Knowledge Graph Architect at UBS, founder of The Knowledge Graph Guys

But not everyone was on board. Several enterprise leaders called the study out on methodology blunders. 

Dave Kellogg, Executive in Residence at Balderton Capital, pointed out an overlap of what NANDA presented as the solution to the problem (an “agentic web” for distributed AI with its own focus on building networking agents. 

Kevin Werbach, a Wharton professor, highlighted that the 95% claim making headlines was never explicitly mentioned in the study. The closest possible claim is that 5% of respondents successfully implemented custom AI enterprise tools, but that conclusion is not anywhere near as far-reaching as “95% of AI pilots generate zero returns”. 

One of the reasons the MIT study exploded so effectively was that its release overlapped with an underperforming release of GPT-5. As teams were disappointed with the lack of meaningful improvements in the model that marketed itself as a “pocket PhD”, the MIT study further strengthened these concerns. 

The takeaway: Regardless of methodological debates, the MIT study succeeded in shifting enterprise AI conversations toward pragmatic deployment strategies. The heightened focus on clear use cases, reliable data infrastructure, and measurable business outcomes represents a healthy correction from earlier hype-driven adoption approaches.

8. Competition for top-tier AI talent got fierce

This year, AI engineers got celebrity-level treatment, with employment agents, lucrative salary packages, and intense competition from leading AI labs. 

Meta’s all-in talent war

Both by the pace of hiring and the pay package generosity, Meta took the lead. In June, Zuckerberg’s team offered up to $100 million in sign-on bonuses to poach OpenAI employees. That same month, Meta acquired a 49% stake in Scale AI at a total price of $19.3 billion and had its founder, Alexander Wang, lead the company’s Superintelligence Labs. 

Meta also attempted to acquire The Thinking Machines Lab for $1 billion – Mira Murati, the founder of the startup now valued at over $2 billion, shot down the offer. 

Reportedly, Zuckerberg’s key goal was poaching Andrew Tulloch, a former Meta engineer who continued his career first at OpenAI and, eventually, at Murati’s startup. Despite initially turning down Zuckerberg’s offer, in October, Tulloch changed his mind and will be coming back to work on Meta Superintelligence on a $1.5 billion pay package. 

If you can’t hire them, acquihire them

Meta was not the only big tech company making waves on the job market, but its competitors took a different strategy.

 Instead of poaching top researchers from other AI labs, they strike deals with promising AI startups to add their leading engineers to their teams. 

The Google-Windsurf $2.4 billion deal, confirmed in July, was the biggest licensing move of the year. The team behind Windsurf, a vibe-coding agent, was at the time in $3-billion acquisition talks with OpenAI, but the deal fell through. 

Google’s counteroffer was not an acquisition but a licensing agreement and a move to poach Varun Mohan and Douglas Chen, the co-founders of Windsurf. 

In September 2025, Windsurf was acquired by Cognition and, according to early reports, helped nearly double the company’s ARR. 

For big tech, acqui-hiring AI researchers at up-and-coming startups is an intelligent way to keep growing as the AI talent pool is drying up.

But, for enterprise teams looking for reliable AI vendors, the “acquihire boom” unlocked a new fear: “What if the vendor we chose gets acquired?” 

Historically, startups struggled to survive after their founders jumped ship. Adept, a robotics company that signed a licensing agreement with Amazon, doesn’t have a product yet and only has four people indicating it as their workplace on LinkedIn. 

When shortlisting AI vendors, enterprise companies may need to consider pending acquisition talks. Some startups, like CVector, an industrial AI company, baked “We are not going anywhere” into their positioning and are using stability as a bargaining chip in customer talks. 

The takeaway: The 2025 AI talent war turned top engineers into strategic assets, driving unprecedented compensation, aggressive poaching, and a surge in acquihires as big tech competed for a shrinking talent pool. 

For enterprises, this shifted vendor risk calculus: technical excellence alone was no longer enough, and organizational stability became a decisive factor in AI partner selection.

9. AI became a national security asset

Now that AI is getting more powerful, world leaders are exploring its impact on defense and global economics. 

Steven Adler, a former AI Safety researcher at OpenAI, highlights that AI is on track to become a massive force in the military by helping develop: 

• New weapon systems: both the US and China are actively exploring autonomous and semi-autonomous military units, often described as intelligent “robot legions”.
• Advanced cyber operations: AI-driven attacks capable of targeting high-stakes systems such as power grids, financial infrastructure, or even nuclear command-and-control.
• Enhanced intelligence analysis: models that can synthesize fragmented signals intelligence, satellite imagery, and open-source data at speeds beyond human capacity.
• Upgrades to existing defense technology: including AI-based image recognition for UAVs, sensor fusion, and stealth optimization for aircraft and naval systems.

In 2025, global world powers took different approaches to integrating AI into global trade and military. 

US: continued growth and focus on competition containment

With the release of DeepSeek, Qwen, Kimi-K2, and other Chinese models that now rival SOTA LLMs by performance and reportedly beat them in cost-effectiveness, the American superiority in the AI race started appearing less certain. 

To counter the rapid pace of AI research in China, the US government responded with containment strategies and regulations. 

In January, a few Chinese AI companies were added to the Entity List to enforce stricter controls over chip export and supply chain intermediation between the countries. 

In April, the US tightened controls on the export of NVIDIA H20 chips to China to prevent its number-one geopolitical rival from building state-of-the-art LLMs on American hardware. In December, the US allowed chip licensing but with an added 25% export fee. 

Simultaneously, US-based AI labs are closely working with the government to expand AI involvement in security and state management.  

In January, the White House issued the Executive Order on Advancing United States Leadership in Artificial Intelligence Infrastructure. 

It encourages federal agencies to assist in the development of data centers and energy sources necessary to sustain them, to make sure the US has the resources necessary to build large-scale AI systems. 

In June, OpenAI won a $200 million contract for the US Defense Department for building custom models that help solve security challenges in warfighting and supply chain. 

Anthropic followed the lead by making Claude available for purchase by federal agencies, launching agreements with national laboratories, and building custom Claude Gov models for national security applications. 

China: focus on self-reliance and AI deployment for pragmatic goals

China’s 2025 approach to the AI race is built around ensuring autonomy in core technologies: chips, models, and computing power. The government responded to NVIDIA licensing restrictions with regulations that prioritized domestic AI chipmakers like Cambricon and Huawei over foreign suppliers. 

To boost domestic chip manufacturing, China backed several incumbents in the sector (MetaX Integrated Circuits and Moore Threads)  with valuation growth and got financial backing from the government and VC firms. 

Similar to the US, China also zeroed in on maximizing data center capacity and exploring cheaper compute sources. In December, the government announced the “East Data, West Computing” strategy that plans a state-led build-out of data center clusters and computing hubs in the country’s western regions. 

These data centers, coupled with an expanded power grid that enables cheaper electricity, will help process millions of generative AI workflows generated by Eastern China.

Europe: regulation and responsible AI use

Unlike other powers, European leaders decided not to adopt the “move fast” AI development strategy.

Instead, EU nations focused on enforcing hard regulatory milestones under the EU AI Act.

In February 2025, the European Commission issued formal guidelines clarifying prohibited AI uses and followed them up with detailed governance rules and obligations for general-purpose AI (GPAI) models. 

Although this cautious stance might help make AI development more sustainable long-term, in the short run, it is hurting European AI innovation.  

The State of European Tech survey found that 70% of EU-based founders find the current regulatory environment too restrictive. Others are leaving the region altogether – as was the case for a Dutch messenger company, Bird, that moved most of its business out of Europe due to strict AI regulation. 

The takeaway: In 2025, global superpowers realized the need for state participation in AI development, but they are taking different paths to this goal. 

In the US and China, governments are actively incentivizing AI development and signing massive agreements to build data centers. In Europe, regulation takes the lead, which helps protect the general population from deep fakes and privacy risks of AI misuse, but it is hindering AI innovation. 

10. Concerns about the AI bubble grew stronger

One of the most pressing AI questions that came up in 2025 was: “Are we in a bubble?” Answering this question negatively became harder and harder when Sam Altman himself said he thinks so. 

There are indeed multiple signs of the expectations of AI being blown out of proportion, and reasons to worry about what happens when our current technologies do not hit these benchmarks. 

Concern #1: Circular financing 

Looking into recent investments and partnerships in the AI landscape, it’s clear that billions in financing flows between a small group of companies. 

Infrastructure vendors like NVIDIA or Oracle are investing in cloud intermediaries and AI labs like OpenAI, which then reinvest that capital back into chips, compute, and data center capacity. This creates a feedback loop that amplifies market momentum but also concentrates risk.

NVIDIA is wrapping up 2025 as Wall Street’s hottest company, but a closer look at its earnings reveals that 61% of Q3 revenue came from four customers. If these partnerships fall out, NVIDIA is at risk of losing a large fraction of its cash flow and taking millions of shareholders down with it. 

Economists have also raised concerns about how this growth is being financed. Morgan Stanley estimates that about 50% of the total $2.9 trillion in AI investment is funded via debt financing. If the bubble bursts, global companies that sign billion-dollar debt contracts can dissolve, as did victims of the 2008 financial crisis.

Concern #2: Adoption lags behind the hype wave

There is a growing expectation-reality gap between the “inevitable AI adoption” agenda AI lab leaders are pushing in media and internal communications and the reality of fairly slow and incremental adoption. 

The positive gains of enterprise AI adoption have been widely reported, but they are hardly comparable to the trillions of dollars that tech companies spend on AI infrastructure. 

For enterprise customers, scaling AI organization-wide is still a challenge – only 30% of global teams surveyed by McKinsey say they are actively doing so. UBS, one of the leading investment firms in America, has publicly acknowledged this discrepancy, stating that “enterprise AI spend is moving slowly” and “ROI is less clear.” 

Right now, market leaders are operating on the hope that the enterprise segment will eagerly adopt the latest technologies, but real-world data is not backing that assumption. Should enterprise demand for AI solutions stay tepid, key AI infrastructure spenders will find themselves between a rock and a hard place when justifying their billion-dollar capex. 

Concern #3: Data center ambitions are triggering public concerns

AI labs’ scramble for new energy sources and computing power to keep training the next generation of SOTA models is sending ripples way beyond the AI or data market. 

It’s estimated that increased data center build-outs will drive the total US electricity use from roughly 4% to about 12%. Such a steep rise in electricity demand will negatively impact American households, who will shoulder the burden of higher utility bills. 

In response to the backlash from local communities, state courts may be forced to pause data center construction projects. In November, the court of Virginia ordered a halt to construction of the Digital Gateway data center. Similar interventions are likely as environmental, zoning, and energy concerns intensify.

Until these tensions are ironed out, the infrastructure spend AI companies are allocating into data centers will be threatened by the uncertainty of political and community-driven friction, further destabilizing the landscape. 

The presence of these risks does not mean AI is a dead-end technology. Historically, periods of intense hype often precede durable transformation. 

An MIT Technology Review article argues that it’s more accurate to compare the AI bubble to the dot-com era than to the subprime mortgage crisis of 2008. After the dot-com bubble burst, it still left us the Internet and a handful of promising incumbents (Google and Amazon) that defined the modern technological era. 

The same may be true for the AI bubble. It’s possible that most AI startups on the market today are not equipped to live through the burst. However, a handful of better-positioned market leaders may become the driving force behind the next age of technological growth. 

The takeaway: AI bubble concerns are justified. A: a meaningful share of today’s momentum is being driven by aggressive capital deployment, optimistic timelines, and concentrated bets that can unwind quickly if demand lags. 

At the same time, the presence of froth does not negate the underlying trajectory. AI capabilities are already reshaping how software is built and discovered, and the post-correction landscape is still likely to leave durable infrastructure and a new set of “default” interfaces for the future web.

The bottom line

Although the second half of 2025 forced the AI industry to recalibrate its expectations, the year ia still a net positive. The end of GPT dominance in the LLM arena helps level the playing field. It keeps all AI labs focused on improving both technical capabilities and the experience of interacting with models. 

The growing penetration of AI agents and vibe coding is the first step towards AI democratization. Though it’s not here yet, we may be looking at a future where building an AI platform will require minimal engineering talent. 

There’s uncertainty as to where machine learning as a field should go next if LLMs really hit the ceiling. Researchers already have ideas – world models, neuro-symbolic systems, and cognitive architectures. It’s unclear which of those will power AGI, but ChatGPT itself was the product of a decade of research. 

Our takeaway is: while we wait for AI research labs to figure out the path that takes us to AGI, team leaders and employees should focus on making the most out of the tools they have. 

Most organizations have barely begun to scratch the surface of custom-made AI agents, intelligent copilots, and predictive analytics. Applying these tools will be transformative for nearly every team, and by the time AI agents in the workplace become commonplace, the next frontier may arrive. 

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