OCI compute is 57% cheaper than AWS EC2 for equivalent configurations. Block storage is 78% cheaper than AWS EBS. Data egress costs 13 times less, with 10 TB free globally every month. And for organizations already paying Oracle support fees, OCI has a financial lever that no other cloud offers: a rewards program that can reduce your Oracle support bill to zero. 

This article covers the support math, the real AI workload cost differences, and the infrastructure details that compound at scale. We also cover where AWS is genuinely the better fit, because the answer isn’t always OCI.

Key takeaways

• The licensing and support angle is underappreciated: OCI’s 1:1 BYOL ratio combined with Oracle Support Rewards (up to $0.33 per dollar spent on OCI applied against your support bill) means large Oracle shops can recover significantly more value from OCI than the compute price gap suggests.
• Database performance isn’t close: Oracle Autonomous Database on Exadata delivers 25x lower IO latency than AWS RDS for Oracle and scan rates 384x faster. These are hardware-level differences.
• AI costs depend on scale: A production Llama 2 70B deployment on 4x A100s runs $8,838/month on OCI versus $13,570 on AWS. For managed model inference via API, AWS Bedrock is still the faster path.
• OCI’s multicloud model changes the decision: Oracle Database@AWS (GA since July 2025) and Oracle Database@Azure (33 regions) mean you don’t have to choose between platforms. Oracle’s multicloud database revenue grew 817% year-over-year in Q2 of fiscal 2026.

OCI vs AWS

Pricing reflects published list rates as of Q1 2026; actual costs vary by region, contract, and commitment tier.

Database workloads: where OCI excels

Running Oracle Database on AWS and on OCI are not the same product. The infrastructure and performance are different, and for organizations with existing Oracle investments, the economics are substantially different. This is worth understanding in detail because it’s the most consequential part of the platform decision for most enterprises.

Oracle Exadata X9M on OCI delivers sub-19-microsecond IO latency, 25 times faster than AWS RDS for Oracle and 50 times faster than Azure SQL. Scan rate is 384 times faster than Amazon RDS. The Exadata X8M supports 12 million read IOPS and 5.6 million write IOPS; the maximum an AWS RDS instance supports is 80,000. These differences are architectural. Exadata’s smart processing moves SQL execution closer to the data, drastically reducing data movement for both transactional and analytical workloads.

Oracle Autonomous Database adds self-tuning, self-patching, and automatic indexing on top of that hardware. Scaling CPU is an online operation with no downtime; AWS RDS still requires planned maintenance windows for vertical scaling. 

For financial systems, operational databases, and high-volume transactional workloads, this combination of hardware and software produces real application throughput differences that show up in user experience and SLAs.

BCC Group ran Oracle’s ONE Platform on both OCI and AWS across NYSE, LSE, and Frankfurt exchange regions in 2025. Their published benchmark found OCI consistently faster and more reliable for latency-sensitive market data delivery, citing OCI’s non-oversubscribed network design with guaranteed, SLA-backed bandwidth and standardized pricing across regions as key structural advantages.

Why it matters: If you’re running Oracle Database on AWS today, you’re paying AWS infrastructure pricing, absorbing a 2:1 licensing penalty, and running on general-purpose hardware. 

Moving Oracle workloads to OCI cuts costs and upgrades performance simultaneously. Migrating to an AWS-native database avoids the licensing cost but requires application refactoring for Oracle-specific features, a risk that’s often larger than it looks.

The licensing and support math

The two financial levers that make the biggest difference for Oracle-heavy enterprises, BYOL core factor and Oracle Support Rewards, rarely get proper treatment. Together they can represent more value than the compute discount entirely.

BYOL core factor: 1:1 vs 2:1

OCI uses a 1:1 core factor for Bring Your Own License deployments: one Oracle processor license covers one OCPU, where one OCPU equals two vCPUs. AWS enforces a 2:1 ratio, meaning one license covers only half an EC2 vCPU. 

For an enterprise running a 32-vCPU Oracle Database instance: on OCI, that’s 16 OCPUs requiring 16 licenses; on AWS, that same workload requires 32 licenses. The difference compounds with cluster size. 

For organizations already holding Oracle Database Enterprise Edition licenses at roughly $47,000 each, this alone drives six-figure annual differences on medium-scale deployments.

Oracle Support Rewards: a benefit that no other cloud offers

Oracle’s Support Rewards program lets enterprises earn credits against their Oracle on-premises support bill based on OCI consumption. Standard customers earn $0.25 for every dollar spent on OCI. Customers on an Unlimited License Agreement (ULA) earn $0.33 per dollar. Those credits apply directly to Oracle technology support fees, for products including Oracle Database, Oracle WebLogic, and related middleware, down to zero.

Oracle’s own documentation: an enterprise with a $1M annual Oracle support bill that spends $2M on OCI earns $500K in rewards, cutting the support bill in half. Spending $4M on OCI wipes the support bill entirely. For large Oracle shops paying $500K to $2M+ in annual support fees, this program changes the ROI calculation considerably, and it doesn’t exist on AWS, Azure, or Google Cloud.

Combining both levers: an enterprise with a 64-vCPU Oracle Database deployment and $800K in annual Oracle support costs would need twice as many licenses on AWS as on OCI, while also receiving no support credits. On OCI, the same deployment requires half the licenses, and a proportionate OCI spend can eliminate a significant portion of the support bill. The combined effect routinely outweighs the headline compute savings.

Why it matters: The BYOL ratio and Support Rewards are specific to Oracle workloads, so they don’t factor into a generic cloud cost comparison. But for enterprises with Oracle Database at the center of their stack, these two mechanisms alone can justify the platform decision before you’ve compared a single compute instance.

AI infrastructure: real workload costs

The AI infrastructure comparison between OCI and AWS depends heavily on what you’re building. For large-scale custom model training and inference on proprietary models, OCI’s economics are compelling. For managed access to foundation models and integrated ML pipelines, AWS is more capable today. The mistake is conflating the two.

For a concrete production benchmark: running Llama 2 70B on 4x A100 GPUs with 15 TB of monthly egress costs $8,838/month on OCI versus $13,570/month on AWS, a 35% difference driven by a combination of lower A100 instance pricing and OCI’s free egress tier. The OCI A100 VM runs at $2.95/hour; the comparable AWS p4d configuration runs at $4.10/GPU. That gap compounds with cluster size: the larger the GPU deployment, the more OCI’s egress advantage adds up.

At the cluster scale, OCI Supercluster supports up to 131,072 NVIDIA B200 GPUs, 65,536 H200s, and 32,768 A100s within a single cluster connected by RDMA InfiniBand networking. That architecture is built for distributed training at a scale that’s relevant to large language model development and inference infrastructure, not typical enterprise ML workloads. 

For Xenoss clients exploring AI infrastructure for large-scale model fine-tuning, OCI’s cluster performance-to-cost ratio is often the deciding factor. Our analysis of the OpenAI-Oracle Stargate expansion covers the broader infrastructure investment direction Oracle is taking.

Where AWS holds a clear lead: the managed layer. Bedrock provides serverless access to foundation models from Anthropic, Meta, Mistral, and NVIDIA without infrastructure management. SageMaker handles end-to-end ML workflows with tooling that OCI’s generative AI service doesn’t match in maturity or breadth. For teams building applications on top of foundation models rather than training them, AWS is faster to production.

Why it matters: Before you compare GPU specs, be clear about what your team is doing. Running inference on a large proprietary model at scale: OCI wins on cost. Building an application that calls foundation models via API: AWS Bedrock is the more complete platform. Many enterprises assume they need the former when their actual workload is the latter.

Infrastructure economics: what compounds at scale

The per-unit pricing differences between OCI and AWS are meaningful. What’s less obvious is how several of OCI’s structural pricing decisions interact to create larger savings at scale.

OCI flexible compute shapes vs AWS fixed instance types

OCI lets you configure compute instances in 1 OCPU and 1 GB increments, scaling CPU and memory independently. AWS requires selecting from predetermined fixed instance types. In practice, that means AWS customers frequently overprovision: you need 10 GB of memory and 3 vCPUs, so you pick the next-larger instance type and pay for 16 GB and 4 vCPUs. OCI’s flexible shapes eliminate this systematically, right-sizing every instance to the workload. Across a fleet of hundreds of instances, this prevents a consistent layer of waste that doesn’t show up in per-instance comparisons.

Consistent global pricing vs AWS regional premiums

OCI charges the same rate for every service in every region globally, public, sovereign, and dedicated. AWS and Azure charge 10-30% more in non-US regions: London, Frankfurt, Tokyo, and Sao Paulo all carry regional premiums. For the same 10 TB of egress, AWS in Zurich costs 17% more than AWS in Northern Virginia. For multiregional enterprises running workloads in Europe or Asia-Pacific, this differential compounds across every service in every region, and it’s invisible in single-region pricing comparisons.

Egress: the cost that scales with success

OCI includes 10 TB of free outbound data transfer per month globally. AWS offers 100 GB free, then charges from byte one. At 50 TB/month, OCI charges roughly $400-800 (40 TB above the free tier at $0.01-0.02/GB); the same workload on AWS runs $3,900-4,300 at tiered rates. For data-intensive applications, analytics pipelines, model serving, large-scale API responses, egress is often the largest single cloud cost, and it’s the one most teams underestimate at architecture time. Our data engineering trends overview covers how this shapes multi-cloud architecture decisions.

Why it matters: None of these individual factors is dramatic in isolation. Flexible shapes save 10-15% compared to overprovisioned fixed instances. Global pricing consistency saves 10-30% in non-US regions. Free egress saves thousands per month at scale. Together they produce a compounding TCO gap that grows with workload size. That’s why the cost difference between OCI and AWS often looks larger in production than in pre-deployment estimates.

Multicloud: the option that removes the binary choice

Oracle’s fastest-growing business in fiscal 2026 is the multicloud model, with database revenue up 817% year-over-year in Q2. 

Rather than migrating off one platform onto another, enterprises are running Oracle Database inside their existing AWS or Azure environment through private interconnect agreements.

Oracle Database@AWS became generally available in July 2025 in US East and US West, with three additional regions added in December 2025 including Ohio, Frankfurt, and Tokyo, and 17 more in the roadmap. 

Oracle Database@Azure is available in 33 regions. Both follow the same model: Oracle manages the database software and hardware inside the hyperscaler’s data center; the hyperscaler provides the connectivity and data center infrastructure. 

Oracle Multicloud Universal Credits, launched in October 2025, let enterprises purchase a single credit pool usable across OCI, Oracle Database@AWS, @Azure, and @Google Cloud.

For a data engineering team running Redshift, S3, and SageMaker on AWS, Oracle Database@AWS means adding Oracle Autonomous Database with Exadata-class performance inside the same environment, billed through the same AWS account, without managing a separate cloud relationship. The performance gap on the database layer closes; the egress pricing still follows AWS rates.

Why it matters: The practical implication is that ‘which cloud should we choose for Oracle’ is increasingly the wrong frame. The multicloud model lets engineering teams pick the database platform on its merits and run it wherever the rest of the stack lives. Most of the migration risk goes away when you’re not migrating the application tier.

When to choose OCI vs AWS

There’s no universal answer, but the decision follows consistent patterns across the workloads.

Choose OCI when:

• Your applications depend on Oracle-specific features: PL/SQL, Oracle RAC, Data Guard, Exadata performance, or Autonomous Database capabilities.
• You have significant Oracle BYOL licenses and Oracle support contracts. The 1:1 core factor and Support Rewards combined can cut total Oracle costs more than compute savings alone.
• Your workload has high monthly egress volumes. The free 10 TB tier and lower per-GB rates produce compounding savings at scale.
• You need large GPU clusters with RDMA networking for custom model training, particularly at the scale of four or more A100s or H100s where OCI’s economics shift favorably.
• You operate in multiple regions and want consistent pricing globally, without regional premiums.

Choose AWS when:

• Your team is building on AWS-native services: SageMaker, Bedrock, Lambda, Glue, or Redshift form the backbone of your architecture.
• You need access to foundation models via managed API (Bedrock) and don’t plan to train or fine-tune at scale.
• You require coverage in 25+ AWS regions or need geographic redundancy across more points of presence than OCI currently offers.
• Your Oracle Database dependency is low and you’re comfortable running Oracle on RDS or migrating to a cloud-native database engine.

Use the multicloud model when:

• You want Exadata-class database performance without a full platform migration. Oracle Database@AWS and Oracle Database@Azure give you Oracle’s database technology inside your existing cloud.
• Your team has deep AWS expertise and operational continuity matters more than the infrastructure savings from a full OCI migration.

Bottom line

OCI’s case for Oracle-centric enterprises is stronger than most comparisons convey, and the two financial levers that make it strongest, the 1:1 BYOL core factor and the Oracle Support Rewards program, are the pieces that most articles skip. When you combine lower compute and storage rates, free egress, flexible compute shapes, consistent global pricing, and the ability to earn $0.25-$0.33 per OCI dollar back against your Oracle support bill, the TCO gap between OCI and AWS for Oracle-heavy workloads is substantially larger than a compute price comparison suggests.

AWS is still the better platform for teams building on managed AI services, native AWS tooling, or architectures that don’t depend on Oracle Database. And for organizations that want Oracle’s database performance without a platform migration, Oracle Database@AWS and the multicloud model increasingly resolve the trade-off. Oracle’s 817% multicloud growth in Q2 of fiscal 2026 reflects enterprises discovering that the either-or choice is largely gone. The right decision starts with your actual workload, your Oracle license position, and your egress profile, not vendor preference.

If you’re building out or inheriting a cloud architecture with Oracle at the center of it, the cost model repays modeling properly. The numbers are often more favorable than teams expect when they run them completely.

The post OCI vs AWS: Oracle Cloud Infrastructure comparison for enterprise workloads appeared first on Xenoss - AI and Data Software Development Company.

The irony is that supply chain optimization is one of the areas where AI delivers the clearest returns. Shell monitors 10,000+ pieces of equipment using ML models that process 20 billion rows of data weekly and cut maintenance costs by 20%. 

UPS estimates that eliminating a single mile per driver per day saves $50 million a year. Maersk uses AI to calculate fuel-efficient shipping routes in real time. The technology works. The problem is how organizations implement it.

This article covers where AI delivers the biggest supply chain cost reductions, what separates implementations that work from those that don’t, and why off-the-shelf tools consistently fall short for mission-critical logistics operations.

Summary

• AI supply chain optimization delivers measurable cost reductions in demand forecasting (up to 75% accuracy improvement), inventory management (25% reduction), and transportation (30% cost cut), according to industry benchmarks.
• Most supply chain AI initiatives lack strategic direction. Only 23% of organizations that have deployed AI have a formal strategy. The rest build disconnected, project-by-project solutions that add complexity without compounding value.
• Off-the-shelf platforms hit ceilings on domain-specific problems. Proprietary APIs, equipment-specific failure modes, SCADA/IoT integration, and edge deployment requirements consistently exceed what generic tools can handle.
• Custom AI solutions outperform generic tools on mission-critical flows by 30-50% on prediction accuracy when trained on your sensor data, maintenance history, and operating conditions.

Where AI delivers the biggest supply chain cost reductions

Supply chain optimization covers a wide territory, from raw material procurement to last-mile delivery. But AI does not deliver equal value everywhere. The highest-ROI applications cluster around three areas where the gap between human decision-making and machine capability is widest.

Demand forecasting and predictive analytics

Traditional demand forecasting relies on historical sales data, seasonal adjustments, and a healthy dose of manual override. The models are backward-looking and brittle. When conditions shift rapidly (geopolitical disruptions, sudden demand spikes, raw material shortages), these models break.

ML-based forecasting pulls in signals that statistical models can’t process: weather patterns, social media trends, competitor pricing changes, macroeconomic indicators, and real-time point-of-sale data. The accuracy gains are significant. 

AI-driven supply chain forecasting reduces forecast errors by 20-50%, which translates directly into fewer stockouts and lower inventory costs Gartner predicts that 70% of large organizations will adopt AI-based demand forecasting by 2030.

American Tire Distributors, for example, switched from fixed forecast intervals to dynamic AI-driven planning using ToolsGroup’s probabilistic forecasting engine. The shift let their team collaborate on demand-responsive decisions with both suppliers and retailers instead of reacting to outdated weekly projections.

Inventory optimization

Overstocking ties up working capital. Understocking loses sales. The sweet spot between the two is narrow, changes daily, and varies by SKU, location, and season. AI models optimize this tradeoff continuously, adjusting reorder points and safety stock levels based on real-time demand signals rather than static rules.

Gaviota, an automated sun protection manufacturer, deployed AI-powered inventory optimization and achieved a 43% reduction in stock levels, slashing inventory from 61 to 35 days while maintaining service level targets. 

At the energy sector level, bp used AI-driven optimization to substantially reduce working capital locked in inventory, with real-time tracking improving operational cash flow projections.

Route optimization and transportation costs

Transportation is often the single largest line item in supply chain costs. AI-powered route optimization considers variables that human planners cannot process simultaneously: traffic conditions, weather, delivery windows, vehicle capacity, fuel prices, driver schedules, and real-time disruption events.

DHL’s optimization engine analyzes 58 different parameters to determine delivery routes, delivering a 15% reduction in vehicle miles and a 10% decrease in carbon emissions. 

UPS’s ORION system produces route savings at a scale where a single mile per driver per day translates to $50 million in annual savings. 

Maersk uses AI to optimize container loading, route planning, and scheduling, factoring in real-time weather data for fuel-efficient routing.

Why this matters: Shell, UPS, DHL, and Maersk have been running these systems at production scale for years. The technology is proven. The question for most organizations is how to implement it without creating the fragmented, expensive “franken-systems” that Gartner warns about.

Why most supply chain AI projects underdeliver

On one hand, AI-driven supply chain optimization can reduce transportation costs by 30%, decrease inventory by 25%, and improve forecast accuracy by 75%. 

On the other hand, 77% of supply chain professionals still haven’t integrated AI into their operations, and Gartner predicts that 60% of supply chain digital adoption efforts will fail to deliver promised value by 2028.

Three patterns explain why organizations struggle to close the gap between AI’s potential and their own results.

Project-by-project investment without a strategy. Gartner’s survey found that most chief supply chain officers focus on short-term wins rather than building a defined AI investment strategy. Each team picks a tool, solves a narrow problem, and moves on. Over time, the organization accumulates a stack of disconnected point solutions: one for demand planning, another for warehouse optimization, a third for route planning. None of them shares data, models, or governance frameworks. Maintaining the stack costs more than any individual tool saves.

Technology-first, domain-second thinking. Organizations buy a platform because it looks impressive in a demo, then try to fit their supply chain problems into the platform’s capabilities. This is backward. Across Xenoss client engagements, 80% of AI project success comes from proper problem analysis and domain understanding, not from choosing the right vendor. A demand forecasting model trained on generic retail data will not work for a manufacturer with 6-week lead times from China and volatile raw material pricing.

Treating AI as automation, not as a decision system. The most common first move is automating a manual task: generating purchase orders, classifying supplier invoices, producing demand reports. These are valid starting points, but they tap into maybe 10% of AI’s supply chain potential. The real value comes when AI moves from automating tasks to informing decisions: which suppliers to prioritize during a shortage, where to pre-position inventory before a predicted demand spike, and whether to reroute a shipment based on real-time port congestion data.

Why this matters: A Gartner survey of 509 supply chain leaders found that 86% say agentic AI adoption will require new processes for developing talent pipelines. The technology is changing not just what supply chain teams do, but how they are structured. Organizations that treat AI implementation as a procurement exercise (buy tool, plug it in, wait for results) will keep underdelivering.

Why off-the-shelf tools fall short for supply chain optimization

Platforms like SAP Integrated Business Planning, Blue Yonder, and Kinaxis offer solid baseline capabilities for demand planning, inventory optimization, and supply chain visibility. For organizations with standard supply chains and mature data infrastructure, they are a reasonable starting point.

They start breaking down when the supply chain has any of the following characteristics:

Proprietary equipment and sensor data. Manufacturing supply chains generate data from SCADA systems, IoT sensors, PLCs, and custom instrumentation that no off-the-shelf platform natively supports. Shell’s predictive maintenance system processes data from 3 million data streams, not because a vendor offered that capability, but because Shell built custom ML models trained on their specific equipment and failure patterns. Generic platforms lack equipment-specific failure modes, and the connector limitations of standard tools create bottlenecks that only get worse as you add more data sources.

Edge deployment requirements. Warehouse operations, fleet management, and remote manufacturing facilities often need AI models running at the edge, where connectivity is unreliable and latency is unacceptable. Off-the-shelf supply chain platforms are cloud-centric. They assume stable internet, reasonable latency, and centralized compute. For a port terminal processing thousands of container movements per hour, or an oil platform in the North Sea, that assumption does not hold.

Complex business rules and regulatory compliance. Pharmaceutical supply chains must track chain-of-custody for every shipment. Food and beverage companies must manage cold chain integrity with per-SKU temperature thresholds. Defense contractors must enforce ITAR compliance on every logistics decision. These are not features you configure in a vendor dashboard. They are domain-specific rules that need to be embedded in the optimization logic itself, which requires custom development.

Cross-system integration with legacy infrastructure. Most enterprise supply chains run on a patchwork of ERP systems, warehouse management platforms, transportation management systems, and custom databases accumulated over decades. Integrating these systems through a generic AI platform’s pre-built connectors rarely works for the critical data flows. Custom ETL handles proprietary APIs, complex transformation logic, and the real-time streaming requirements that mission-critical supply chain operations demand.

Why this matters: The build vs. buy analysis for supply chain AI consistently favors custom development for the data flows that matter most. Generic tools handle 80% of use cases adequately. The remaining 20%, the use cases that involve proprietary data, edge deployment, or regulatory compliance, are where competitive advantage lives and where off-the-shelf platforms consistently fall short.

What to build custom and what to buy off the shelf

Not every supply chain AI capability needs to be custom-built. The right approach is a layered strategy that combines platform capabilities with custom models where they create the most value.

The custom components should share a common data platform and governance framework with the off-the-shelf tools. 

This prevents the “franken-system” problem: each piece serves a distinct purpose, but they all read from and write to the same governed data layer. Xenoss engineers typically implement this as a lakehouse architecture where platform tools and custom models co-exist on the same storage and metadata catalog.

Bottom line

Supply chain optimization with AI is not a technology problem anymore. The models work, the compute is available, and the ROI is well-documented. Shell, UPS, DHL, Maersk, and dozens of other organizations have proven that at scale.

The problem is implementation strategy. Only 23% of supply chain organizations have a formal AI strategy. The rest are building disconnected point solutions that add complexity without compounding value. Gartner expects 60% of these digital adoption efforts to fail by 2028, specifically because organizations underinvest in the domain expertise and integration work that makes AI deliver on its promise.

For organizations running complex supply chains with proprietary equipment, regulatory constraints, or legacy infrastructure, the path forward is a layered approach: use platforms for standard capabilities, build custom where your competitive advantage lives, and connect everything through a shared data layer that prevents the “franken-system” accumulation. The 20% of supply chain problems that generic tools cannot solve are worth 80% of the optimization value.

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Florasis, a Chinese beauty products manufacturer, has developed an ML-based “smart brain” system with seven digitalized production lines. The system gathers real-time data from the factory floor and transfers it to operations managers to enhance decision-making, optimize energy management, and improve anomaly detection. Thanks to AI and automation technologies, Florasis has achieved operational excellence on par with global manufacturing giants, increasing their annual production capacity to 50 million units.

By improving the most crucial manufacturing KPI, OEE, effective AI implementation can become your competitive advantage. AI helps businesses stay connected to their customers, ensure real-time visibility into what’s happening on the shop floor, and intervene promptly to prevent losses. And all of that can happen through a single interconnected AI system that orchestrates the factory operations while human workers have time to make balanced decisions, ideate new products, or deepen relationships with customers.

This article breaks down how artificial intelligence and machine learning improve each OEE component: availability, performance, and quality, and what data infrastructure you need to make it work.

The Six Big Losses that impact operational equipment efficiency

The Six Big Losses framework, derived from Total Productive Maintenance (TPM) (a Japanese company management strategy), categorizes all sources of OEE degradation.

Equipment failure and unplanned stops

Unexpected breakdowns stop production entirely. A recent survey revealed that factories incur up to 30 hours of downtime per month, or 360 hours per year, at a cost of more than $250,000 annually.

Cody Bann, VP of Engineering, and John Oskin, Senior VP at SmartSights, share an example of how businesses can use AI to address equipment failures:

…the integration of AI in MES is revolutionizing how manufacturers operate, bringing unprecedented levels of automation, predictive analytics, and decision-making. It can leverage root cause analysis to predict failures and reduce defects; draft easy-to-follow dynamic work instructions; and augment operator stations by offering live, AI-supported troubleshooting and operating guidelines, helping companies be more flexible, efficient, and intuitive in meeting end-users’ needs.

Setup and planned stops

Switching between products or batches takes time, and that time often gets underestimated. Changeover inefficiencies compound quickly across shifts and product variants. Although it’s impossible to avoid setup and adjustments stops, they can be optimized and reduced in time.  

For instance, single-minute exchange of die (SMED) is a Japanese approach to planned stops, requiring changeovers to be completed in less than 10 minutes. When combined with AI, this approach becomes twice as efficient and can reduce changeover times to even fewer than 10 minutes.

A case study on Digital SMED shows that integrating IoT, AI algorithms, and data analytics with traditional SMED procedures substantially streamlines setup processes and improves OEE in machining operations.

Idling and minor stops

Brief interruptions, such as jams, misfeeds, or blocked sensors, rarely trigger formal downtime tracking. But these micro-stops accumulate, sometimes consuming 5-10% of total production time. For instance, in the returnable PET lines industry, minor stops account for up to 50% of all Six Big Losses. An average OEE score for PET lines is also 50%.

Companies can significantly reduce, or even eliminate, these stops through computer vision, predictive analytics, and proactive equipment maintenance. 

Reduced speed and slow cycles

Running equipment below its designed speed due to wear, operator caution, or material issues quietly erodes performance without setting off alarms. As with minor stops, low operating speed is also often underestimated and remains untracked. With AI, you can not only track speed in real time but also perform deep-dive root cause analysis to discover why slow cycles occur.

Process defects and rework

Units that fail quality standards during steady-state production require correction or scrapping. Each defect wastes materials, energy, and machine time. However, only 31% of organizations fully realize the impact of quality on financial performance. AI and ML solutions can help manufacturers efficiently control quality and reduce defects and rework.

Startup rejects and reduced yield

Defects produced during warmup, changeover, or process stabilization are often accepted as inevitable. AI-driven process control can significantly shrink these windows by learning optimal ramp-up curves from historical data, detecting multivariable instability patterns, and dynamically adjusting process parameters in real time.

Traditional OEE tracking vs AI-powered analytics

Manual data collection, spreadsheet tracking, and reactive maintenance aren’t effective for comprehensive OEE tracking or, particularly, for suggesting improvements. 

For instance, a user on Reddit shares how their company tracked OEE four years ago:

Quality: MES is used to log good parts vs bad parts (nonconformance reports)

Performance: largely based on time studies vs quantity (MES / SCADA). For the automation parts, you can see the time on job vs idle.

Availability: is usually just a pre-planned amount. X/hrs a day, etc. If we tracked maintenance better, we could separate planned and unplanned downtime better, but we don’t yet.

What stands out most in this quote is that the company is unable to differentiate between planned and unplanned stops, a distinction that can be most indicative of improving OEE. 

While it’s possible to use traditional solutions to track and improve OEE, you won’t get a comprehensive factory performance report, your teams will lack real-time visibility, and their actions and decisions will be mostly reactive.

By contrast, AI-powered analytics can help your company become more proactive. The dashboard below shows how a manufacturing company can track OEE and identify which areas to prioritize.

World-class OEE benchmarks and AI-driven targets

OEE itself combines three factors into a single percentage: availability, performance, and quality. A machine running 90% of scheduled time, at 95% of ideal speed, producing 99% good parts, delivers an OEE around 85%. That number is often cited as “world-class,” though it varies by industry. Let’s compare typical, world-class, and AI-powered OEE benchmarks.

AI-driven OEE targets are higher because AI systematically identifies and removes hidden, compounding losses across availability, performance, and quality. By predicting failures, stabilizing cycle time, and preventing defects before they occur, AI shifts OEE from reactive measurement to proactive optimization, allowing manufacturers to exceed traditional world-class benchmarks.

Traditional vs AI-powered OEE tracking and improvement

How AI analytics improves OEE availability

Questions to assess equipment availability:

1. What percentage of planned production time is lost to unplanned downtime?
2. What are the top three recurring causes of downtime, and how frequently do they occur?
3. What are the current MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair)?
4. Is maintenance reactive, preventive, or predictive, and what percentage of failures are anticipated before they occur?

To increase equipment availability, companies can use predictive maintenance techniques, anomaly detection algorithms, and virtual sensors. 

Virtual sensors can complement physical IoT sensors by providing additional computation and measurements that physical sensors cannot. Virtual sensors use machine learning models to infer hard-to-measure variables, such as tool wear, remaining useful life, probability of quality deviation, and internal thermal states, by analyzing combinations of vibration, current, pressure, and process data. These inferred measurements extend monitoring capabilities beyond what physical IoT sensors alone can capture. 

Plus, virtual sensors can temporarily replace physical sensors when the latter are malfunctioning or producing anomalous data due to poor signal quality in certain environments. 

Anomaly detection algorithms identify subtle deviations from normal operating patterns that human operators would miss. A bearing running slightly hotter than usual, a motor drawing marginally more current, a cycle time drifting upward by fractions of a second, these signals provide lead time to intervene before failure.

Predictive maintenance technology also relies on machine learning models that analyze sensor data (e.g., vibration signatures, temperature trends, pressure fluctuations, and current draw) to forecast equipment failures before they occur. The most common use case is predicting the remaining life of the equipment to know exactly when to replace it and avoid unexpected failures. For instance, one study confirms that applying an XGBoost ML algorithm in water treatment facilities achieved 92.6% prediction accuracy.

By gathering comprehensive information about equipment from virtual and physical sensors and applying predictive analytics to timely repair manufacturing equipment, businesses can achieve 95% or even 100% equipment availability.

How AI analytics improves OEE performance

Questions to assess equipment performance:

1. How close is the actual cycle time to the theoretical or ideal cycle time?
2. How frequently do minor stops occur per shift?
3. What percentage of time is equipment running below its rated speed, and why?
4. Are speed losses correlated with specific products, operators, materials, or environmental conditions?
5. Do we have real-time visibility into performance degradation, or only detect issues after shift reports?

AI addresses performance losses through timely:

Micro-stop detection. Computer vision systems continuously monitor production lines, detecting real-time obstructions to product flow, misfeeds, or blocked sensors. When patterns emerge, such as jams occurring every 47 minutes on a specific conveyor, AI flags the systematic issue rather than treating each incident as random.

Dynamic scheduling and throughput balancing. Workload imbalances across machines create bottlenecks that limit overall throughput. AI-driven scheduling redistributes work in real time, keeping all equipment running at sustainable capacity rather than alternating between overload and idle states.

How AI analytics improves OEE quality

Questions to assess production quality:

1. What is the first-pass yield rate, and how does it vary by product line or shift?
2. What are the top recurring defect types, and at what production stage do they occur?
3. Are quality deviations detected in real time or only during final inspection?
4. Is there a measurable relationship between process parameters (temperature, speed, pressure, etc.) and defect rates?
5. What percentage of production requires rework, and what is the cost impact?

AI-based predictive quality solutions can help organizations avoid or at least reduce the production of poor-quality parts and products. On average, quality compliance costs manufacturers up to 5% of revenue, and the cost of poor quality (CoPQ) can reach 20% of revenue.

AI can help manufacturers shift from reactive inspection to proactive quality management through:

AI-driven defect detection. High-resolution cameras feed images into deep learning models trained to identify cracks, misalignments, surface defects, or incorrect assembly. Unlike human inspectors who may miss certain defects due to fatigue or distraction, these systems maintain consistent accuracy across every unit.

Root cause analysis. AI analyzes hidden patterns across the six Ms: Manpower, Machine, Material, Method, Measurements, and Mother Nature to identify quality deviations before they become costly defects. The system compares current operations with historical data to detect deviations caused by operator variation, equipment drift, raw material inconsistencies, or changes in process methodology.

First-pass yield (FPY) measures the percentage of units manufactured correctly without rework. Low FPY directly impacts both quality and availability components of OEE, as rework consumes production time and resources.

Raw material quality variations inevitably lead to downstream issues, but AI capabilities transform management by analyzing patterns across supplier performance, delivery timing, and quality outcomes to predict and prevent issues before materials enter production.

Data infrastructure requirements for AI-powered OEE

AI effectiveness depends entirely on data quality and integration. The most sophisticated algorithms deliver nothing without the right inputs.

Real-time data pipelines from sensors and PLCs

AI models require streaming data from equipment sensors and programmable logic controllers (PLCs). Predictive maintenance models might tolerate seconds of delay, but real-time quality inspection requires millisecond response times.

Integration with MES, SCADA, and ERP systems

Equipment data alone lacks context. AI systems connect to manufacturing execution systems (MES), supervisory control and data acquisition (SCADA) systems, and enterprise resource planning (ERP) systems to correlate machine behavior with production schedules, material batches, and quality records.

Feature engineering and data quality governance

Raw sensor data rarely feeds directly into ML models. Engineers transform readings into meaningful features: rolling averages, rate-of-change calculations, frequency domain representations that capture the patterns models learn from. Data quality issues like gaps, outliers, and mislabeling degrade model performance significantly.

Why improved OEE brings you closer to smart manufacturing

OEE analytics represent a foundational capability for broader Industry 4.0 initiatives. Once you have real-time visibility into equipment effectiveness, adjacent capabilities become possible.

Digital twins (virtual replicas of physical equipment) use the same sensor data to simulate scenarios and optimize operations without risking production. Autonomous optimization loops adjust processes without human intervention, responding to changing conditions faster than operators can. Edge computing pushes AI inference closer to equipment, enabling millisecond-level responses for quality inspection and process control.

But even implementing AI-powered OEE requires more than algorithms. It demands robust data engineering, integration with industrial systems, and production-grade reliability. Xenoss brings deep experience in real-time data architectures, predictive modeling, and system integration, connecting sensors, PLCs, MES, and ERP systems into a coherent analytics platform.

The post AI-powered OEE: Improving availability, performance, and quality in manufacturing appeared first on Xenoss - AI and Data Software Development Company.

With generative AI, agentic AI, and other machine learning advancements, not leveraging deep learning and related technologies would make most companies outliers in an increasingly AI-enhanced world.

Key points of the article

• Specifics of the AI engineering job function
• Salary benchmarks for in-house teams and freelancers
• AI team structure
• Different approaches to recruiting an AI developer
• Hiring process for AI developers at Xenoss

Why do teams hire AI developers?

Seeing how artificial intelligence helped offset recession fears, business leaders and investors felt a sense of urgency. Indeed, machine learning can add trillions of dollars in value to most industries, but tapping into the market requires a specialized team.

While experienced software architects can transition into AI engineering to cover your organization’s machine learning needs, having an expert on board with an excellent command of specific AI tools and technologies increases the odds of product success.

Here are the AI engineer responsibilities that drive progress in product teams:

• Guide product design to ensure that AI helps achieve business goals and delivers value to end users.
• Manage research and development efforts to determine which AI tools and technologies would deliver the highest ROI.
• Offer the most accurate and cost-effective solutions to a specific problem.
• Navigate the regulatory landscape, monitor potential challenges in deploying AI models, and design workarounds.
• Explain AI/ML technologies to non-technical teams and help them leverage machine learning.

Is it difficult to hire an artificial intelligence engineer?

In the last two years, tech companies have become increasingly aware of the importance of leveraging AI. As a result, demand for AI talent has grown exponentially, while supply has failed to keep pace. To understand the scale of the talent shortage, we examined data from global sources.

• A WEF report highlights that large segments of the global workforce will need reskilling to meet rising AI demand, a dynamic that continues to make skilled AI engineers and related roles among the hardest to hire for.
• The top ten fastest-growing Information and Communication Technology (ICT) jobs are: 
  • AI Risk & Governance specialist (234% of job demand growth); 
  • NLP Engineer ( 186%)
  • AI/ML Engineer (145%) 
  • AI Business Consultant (134%)
  • AI Infrastructure Engineer (124%) 
  • AI/ML Researcher (98%)
  • Cloud Engineer (89%) 
  • Cyber Threat Intelligence Consultant (84%)
  • Data Scientist (76%) 
  • Automation Engineer (72%)
• Cisco’s Chief People Officer, Kelly Jones, admits that filling operational AI and ML roles is difficult. She says, “The qualified pool is so small, and the demand is so high”. Senior executives across large companies like OpenAI, Meta, and Cisco have to personally get on the call with the best candidates to secure them.
• 50% of executives consider a talent shortage a key barrier to scaling AI initiatives in the engineering, research, and development (ER&D) domain, and 58% say that there isn’t enough engineering talent with the necessary AI skills.

This data shows that hiring AI engineers is a global challenge for businesses, regardless of their size.

In startup hubs, such as Silicon Valley, Boston, NYC in the US, or London, Paris, and Berlin in Europe, finding a skilled and affordable engineer is a struggle due to the many high-profile offers and high AI developer salaries.

Salary benchmarks across countries and regions

Salary benchmarks for AI and ML engineers vary significantly by country, seniority, and specialization. The figures below reflect median base salaries and do not include additional employment costs such as software tooling, hardware, payroll taxes, medical insurance, equity, bonuses, or compliance overhead, all of which increase the fully loaded cost of an in-house AI team.

Findings are from StackOverflow and Glassdoor.

Key takeaway: US-based AI engineers command the highest compensation globally. In practice, compensation frequently exceeds median values when companies require senior-level engineers, deep ML expertise, or experience with production-grade AI systems.

AI engineer compensation by seniority (United States)

Source: 2026 US Market Report by MRJ Recruitment

These ranges reflect base salary only. Once benefits, payroll taxes, tooling, security requirements, and ongoing training are included, the total annual cost of a senior or staff-level AI engineer in the US is often 30–50% higher than base compensation.

Europe: lower salaries, higher regulatory readiness

The European AI engineering market is generally more cost-efficient than the US, with typical salaries ranging from €60,000 to €100,000, depending on the country and seniority.

A key differentiator is regulatory familiarity. European AI engineers are increasingly required to work within the constraints of the EU AI Act, currently the most comprehensive AI regulation globally. As a result, many European teams have hands-on experience with:

• Risk classification of AI systems
• Data governance and model transparency requirements
• Compliance-by-design approaches to AI development

For organizations operating in or targeting the European market, this regulatory expertise can reduce legal risk, rework, and time to approval, an important factor beyond pure salary comparison.

Hourly rates: Freelance AI engineers

For companies seeking maximum cost flexibility, hiring AI engineers on an hourly basis is often the most affordable entry point.

Source: Upwork.

However, while freelancers can reduce short-term costs, AI initiatives carry higher-than-average delivery risk due to:

• Fragmented ownership of data, models, and infrastructure
• Limited accountability for production reliability and security
• Lack of formal guarantees around quality, continuity, and compliance

Choosing the right engagement model

For organizations building business-critical or regulated AI systems, partnering with an enterprise AI engineering company such as Xenoss offers a middle ground between in-house hiring and freelancing.

You gain:

• Access to senior AI developers for hire at freelance-like rates
• A structured delivery model with formal SLAs
• Clear accountability for quality, security, and long-term maintainability

This approach reduces execution risk while avoiding the fixed overhead and hiring delays associated with building a full internal AI team from scratch.

AI Engineering team structure

A lack of AI engineering expertise leaves 88% of AI projects at the proof-of-concept stage. Building a balanced team is vital to avoid stagnation and push the project ahead.

Xenoss has over 15 years of experience in building high-performing AI teams. A consistent finding that emerged over time was that no two teams were alike in the roles they prioritized. Depending on the scale of the project (internal tool, narrowly specialized user-facing tool, or multi-purpose large-scale platform), the list of people who should steer the project varies, and the emphasis on ethics and regulations can sometimes be more pronounced.

Every step of data collection, processing, and deployment as part of an ML model aligns with a specific role:

Data engineer responsibilities

• Build and test ETL pipelines
• Architect SQL and NoSQL data stores
• Build strategies for data processing, integration, transformation, and storage
• Oversee AWS/Google Cloud/Microsoft Azure maintenance
• Collect, clean, and filter structured and unstructured data

Data scientist responsibilities

• Align with business stakeholders on high-priority problems
• Collaborate with data engineers
• Test machine learning models
• Support other teams (sales, marketing, product) with data needed for strategic decision-making

Data analyst responsibilities

• Apply large data sets to solving business problems through a range of analytical and statistical tools
• Help identify success metrics in product teams, build growth projections, and monitor the progress across selected metrics
• Use data to identify emerging trends and opportunities that help steer the product
• Closely partner with engineering, product, marketing, and other teams to inform their reasoning

AI developer (ML engineer) responsibilities:

• Deploy, maintain, and scale machine learning models
• Engineer the infrastructure surrounding machine learning models
• Platform engineering and MLOps: develop and administer Kubernetes clusters
• Security scanning and investigations
• Release engineering

These are the roles directly involved in building AI models. Other professionals typically support these functions:

• Project manager responsible for overseeing the project lifecycle: defining project scope, goals, timeline, budget, etc.
• Domain expert: a professional who provides domain expertise and context for machine learning models. In some cases, this role can be carried out by AI engineers themselves if they are well-versed in the project’s field.
• Systems Architect helps build a suite of machine learning tools within the organization’s IT framework, ensuring alignment between ML initiatives and broader organizational goals.
• AI data analyst specializes in using artificial intelligence tools and techniques to analyze complex datasets. This role requires a deep understanding of machine learning, data mining, and statistical analysis to extract meaningful insights and inform business strategies.
• AI architect: responsible for building an enterprise-wide AI pipeline for the organization. These professionals also play a role in connecting other members of the engineering team: data scientists, DevOps, MLOps, and business leaders.
• AI product manager: oversees the development and implementation of AI-based products, balancing technical feasibility with market needs and user experience. This role involves strategic planning, cross-functional collaboration, and a deep understanding of AI technologies to guide the product lifecycle from conception to launch.

We’d like to point out that a cookie-cutter approach is typically ineffective when assembling an AI engineering team. Instead, it’s better to look for tech professionals with specialized skill sets that align with AI technologies and the tools the product team has in mind.

Here’s an example of how the critical skills of AI engineers on a team can vary depending on the type of final product.

Hire AI developer: Job description examples from OpenAI and other companies

After defining which AI engineering roles can enable fast, efficient AI software development, team leaders should focus on finding professionals whose skills align with their responsibilities.

Rather than relying on a one-size-fits-all approach, we recommend crafting a custom job opening tailored to your domain, product or service type, budget, and expected responsibilities for each AI role.

However, having a clear understanding of what top companies are listing in AI developer openings can help align expectations with the reality of current AI development tools and technologies.

To help engineering team leaders create job descriptions that attract skilled talent, we analyzed how top AI players craft job descriptions for a range of roles.

Hire AI engineers: Three widely used approaches

The tight AI engineering job market calls for open-mindedness and creativity in hiring decisions. Hiring a full-time in-house engineering team has been the industry standard for a long time, but difficulties in securing talent and a fluctuating economy are challenging that practice.

Alternative approaches to hiring, like relying on contractors or committing to outstaffing, are gradually becoming more widespread among organizations.

Let’s examine their strengths and shortcomings to draw a line between these ML developer hiring strategies.

There are different ways to use outstaffing to hire AI engineers. For example, tech teams can use the model for point-based hiring (e.g., hire AI engineer to strengthen existing teams) or for building entire AI teams from scratch.

Look at the projects where Xenoss recruiters helped source AI engineers and related specialists: data scientists, analysts, and other professionals.

How we work at Xenoss

Xenoss has supported teams in machine learning, data engineering, and AI adoption for over 15 years. When beginning a new project, we focus on building a team with a deep understanding of the client’s domain (including AdTech, MarTech, manufacturing, healthcare, and financial services) and a robust set of machine learning tools and technologies. Through a series of technical interviews and culture fit assessments, we ensure that Xenoss AI engineers are a tight fit for the client’s project. 

Check out our detailed guide on how to work with AI and data engineering partners to find out how to map your business and technical requirements to the right AI and data expertise.

Xenoss has a robust pool of vetted and battle-tested AI engineers. If one of our developers meets the project’s requirements, we introduce them to the core team and schedule a technical interview. This approach allows us to cut hiring time and recruit skilled AI engineers in a matter of days.

If no AI engineers in our talent pool meet the client’s need, Xenoss hiring experts will source skilled candidates by sharing curated job openings in trusted tech communities.

Building a winning AI engineering team with Xenoss typically looks as follows:

Discovery call

Our engineering team assesses your project proposal to determine the type of AI expertise required. A deep assessment of the product plan and roadmap enables Xenoss recruiting experts to hire skilled engineers and deliver the solution with minimal time-to-market.

CV screening and preliminary assessment

Based on the client’s requirements, our specialists create detailed job descriptions that provide developers with a clear understanding of their responsibilities and required skills.

The candidates for each application are screened to match the following criteria:

• Proven track record in the relevant field
• Proficiency in using machine learning tools and frameworks (PyTorch, Scikit, NumPy, TensorFlow, etc.)
• Domain knowledge in the client’s industry
• English fluency
• Additional project-specific criteria

Vetting of shortlisted candidates

All candidates deemed skilled enough to move to the interview stage are thoroughly vetted by our HR department to ensure their experience, education profiles, and other data are legitimate.

Here are the steps of our vetting process:

• Contact the companies candidates worked at previously
• Confirm education and other credentials
• Validate the recommendations provided by the applicant
• Check publicly available social media profiles and other data sources

Interviews: Procedures and questions to ask

To confirm that an AI engineering candidate is a tight fit for the project, Xenoss’s recruiting team has developed a time-tested approach to interviewing applicants. We use a three-step process to gauge a candidate’s knowledge:

Step 1. Culture-fit interview

The HR department conducts a culture-fit interview to align expectations and determine whether the candidate aligns with the company’s culture.

Question examples:

• What type of work environment helps you perform at your best, and what tends to slow you down?

• Tell us about a situation where project priorities changed mid-delivery. How did you adapt?

• How do you handle feedback from non-technical stakeholders or clients?

• What motivates you most when working on long-term, complex projects?

• How do you typically collaborate with distributed or cross-functional teams?

Step 2. Deep technical interview

Our AI Engineering Lead prepares questions that assess the candidate’s prior experience and ability to apply skills from prior projects (e.g., deploying and scaling machine learning models, managing data pipelines, and infrastructure engineering) in the context of a client’s organization.

Question examples:

• Walk us through an AI or ML system you’ve taken from development to production. What challenges did you encounter after deployment?

• How do you approach model monitoring and performance degradation in production?

• Describe your experience building or maintaining data pipelines that support machine learning workloads.

• How do you decide between different model architectures or tools when working under business constraints such as cost, latency, or explainability?

• Tell us about a time when a model performed well in testing but failed in production. How did you diagnose and resolve the issue?

Step 3. Final interview

The HR department closes this cycle by discussing in more detail salary expectations, responsibilities, and collaboration models.

Question examples:

• What level of ownership do you expect to have over technical decisions in a client project?

• How do you prefer to communicate progress, risks, and trade-offs to stakeholders?

• What type of projects or AI use cases are you most interested in working on, and which ones would you prefer to avoid?

• How do you balance individual contribution with team-level accountability in delivery-focused work?

• What are your compensation expectations, and how do you evaluate offers beyond salary alone?

Based on a client’s preferences, our recruiters and the HR department, in collaboration with the client’s in-house engineering/executive team, develop test tasks to assess the candidate’s motivation and engineering skills. We focus on tailoring the assignment to the candidate’s day-to-day tasks and responsibilities.

Onboarding and continuous support

After assembling the AI engineering team that matches the client’s needs, Xenoss experts stay on standby and help the core team manage international talent by offering assistance in:

• Payroll and taxation
• Health insurance
• Legal documentation
• Benefits distribution

The ability to delegate administrative burden to Xenoss experts allows tech teams to refocus efforts from administrative minutiae to team management and collaboration.

Final thoughts

The AI engineering market is booming; over the next 7 years, it’s expected to grow at a 30.6% compound annual rate.

Interest in machine-learning-enabled projects among users and investors is high, encouraging product teams to explore and adopt these technologies.

A growing talent shortage of skilled developers is the side effect of the AI boom. To stay afloat in a highly competitive talent market, tech leaders need to think beyond the standard hiring playbook and embrace alternative hiring practices, such as outstaffing.

At Xenoss, we helped startups leverage the power of outstaffing to successfully integrate AI in software development. Explore our work to see the impressive performance and cost-reduction results our AI engineers helped diverse organizations achieve. To discover how outstaffing can support your AI development project, get in touch with our team.

The post Hire AI developers: Salary benchmarks, team structures, and vetting process appeared first on Xenoss - AI and Data Software Development Company.

From a technology perspective, the market’s direction is clear. Tristan Handy, Founder and CEO at dbt Labs, points to two dominant vectors shaping modern data engineering:

And so now the big axis of innovation, I think, is in two places. One is in open standards, things like Delta and Iceberg, that’s at the file format or the table format level. And then the other one, obviously, is in AI.

But technology momentum is colliding with a less mature data reality inside most organizations:

• 83% of companies cite data integration challenges as a major barrier to legacy modernization.
• 63% are unsure whether their data management practices are sufficient for AI adoption.
• 60% of AI initiatives are expected to fail through 2026 due to a lack of AI-ready data.

Moving toward lakehouses, open formats, or AI-driven analytics without well-organized, governed datasets often amplifies existing problems rather than solving them. In practice, enterprise data architecture patterns must evolve in step with data maturity, organizational readiness, and business priorities.

Dylan Anderson, a Head of Data Strategy at Profusion, gives the following definition and warns his audience against overcomplicating the concept of a data platform:

A data platform is a generic, catch-all term that encompasses the many technologies that underpin the process of making data accessible to business users, leading to better decision-making and insights. 

In his Substack article, Dylan also highlights that the core purpose of a data platform is to help businesses make sense of their data, an important lens when choosing the best data platform for enterprise needs.

Data maturity assessment: The first step before building a data platform

The first step is to assess the correlation between your business performance and the condition of your data infrastructure. Ideally, you would need a detailed list of questions to ask your data engineering team, grouped by sections (from financial to operational).

Question examples: 

• How many distinct data storage systems exist in our organization? (1-5 / 6-15 / 16-30 / 30+)
• How many data sources and data pipelines feed our analytics environment? (< 10 / 10-50 / 50-100 / 100+)

Honest answers to the right questions help determine whether the organization is mature enough for advanced architectures such as a lakehouse, or whether foundational steps, such as legacy data warehouse replacement or consolidation, should come first. Common data maturity assessment frameworks, such as DAMA DMBOK2 and DCAM, define five levels of data maturity, ranging from ad hoc/reactive to optimized/strategic data management. 

On each level, there should be a different data platform development roadmap. For level 1, it might be necessary to create an inventory of data sources and business datasets as a basic data platform. On level 2, it might be efficient to develop a central data warehouse for cross-company data consolidation. Whereas levels 3, 4, and 5 provide a solid foundation for enhancing your data platform with new capabilities, such as increasing storage capacity or tapping into advanced or AI-powered analytics.

Data warehouse vs data lake vs lakehouse: Architecture comparison

At the heart of the enterprise data platform architecture lies centralized data storage, which provides an organization with access to consolidated business data, enables cross-company analytics, and powers decision-making.

We’ve compiled a detailed table outlining the core characteristics of each data storage type, including cloud data warehouse selection criteria, data lake implementation specifics, and data lakehouse features.

Data warehouse: When structured analytics and BI workloads dominate

A modern data warehouse is a well-organized, centralized data storage for storing structured historical data from the entire organization. The main purpose of this storage is data integration from multiple sources to enable online analytical processing (OLAP) for data analytics, business intelligence, and reporting. Data warehouses maintain ACID transactions (atomicity, consistency, isolation, durability) to ensure that data is stored and transferred safely. 

Another common concept is an enterprise data warehouse (EDW), which provides enterprise-wide data storage for comprehensive analytics.

For instance, in the healthcare industry, an EDW (e.g., Amazon Redshift) consolidates data from multiple sources, such as electronic health record (EHR) systems, picture archiving and communication systems (PACS), and laboratory information systems (LISs). The centralized warehouse then applies consistent schemas, business logic, and governance controls, enabling reliable analytics across clinical outcomes, resource utilization, and financial performance, capabilities that are difficult to achieve when data remains fragmented across operational systems.  

A data warehouse is the oldest form of centralized data storage, and some claim that it’ll soon become obsolete. But here’s what Bill Inmon, a famous computer scientist and the “father of the data warehouse”, wrote on the matter:

So when does data warehouse die? Data warehouse dies whenever the corporation does not need to look at enterprise data. Come the day when marketing, sales, finance and accounting do not need to look across the enterprise and understand what is going on in the corporation, that is the day when data warehouses are not needed.

A data warehouse remains a core component of many enterprise data architecture patterns, especially where governance, consistency, and BI performance are critical.

When to choose: Consistent data workflows are a priority, and BI is the core data analytics solution.

Data lake: Flexibility for unstructured data and advanced analytics

The data lake emerged to address limitations of the data warehouse, such as the inability to store growing volumes of unstructured and semi-structured data from social media, IoT devices, third-party services, and server logs. A data lake (e.g., Amazon S3) allows storing vast amounts of data of different types in a single source of truth without the need to transform the data first, as was necessary in a data warehouse. 

With the advent of the data lake, it became common to store data in the cloud as volumes grew and storage costs rose. At this point, object data storage emerged, allowing companies to “dump” their enterprise data and figure out later what to do with it.

Unlike ACID compliance of the data warehouse, a data lake follows the BASE (basically available, soft state, and eventually consistent) principle, which prioritizes data availability over consistency. This principle largely led many data lakes to become “data swamps” filled with raw, poorly queryable data. That’s why companies couldn’t fully abandon their well-structured data warehouses and switch entirely to easily scalable, yet disorganized, data lakes.

When to choose: If data volume is constantly increasing and cost-efficient object storage is the priority.

Data lakehouse: Unified architecture for AI-ready enterprises

When Databricks coined the term “lakehouse”, they promised to deliver the data warehouse’s performance and ACID compliance alongside the data lake’s flexibility. An engineering community is certain that they delivered upon the promise. The introduction of open table formats for metadata management, such as Apache Iceberg, Apache Hudi, and Delta Lake, created an opportunity for data warehouse-like data querying while providing vast storage for raw data, as in data lakes.

Even though many companies can use data warehouses and data lakes together, lakehouses are more cost-efficient because they eliminate duplicate data, optimize storage, and reduce data ingestion latency across systems. Due to these benefits, 67% of business leaders plan to run all their analytics on data lakehouses within the next three years.

When to choose: This architecture decreases time-to-insight and is considered a better option for AI/ML workloads. In fact, 85% of organizations use data lakehouses to support their AI development initiatives. But you can cooperate with a data lakehouse implementation partner if you need an all-in-one platform and have a data engineering capacity to set it up. 

You don’t have to limit yourself to one solution; you can even combine all three data platform architecture patterns if business goals justify it and the data infrastructure allows.

In general, each data storage platform serves the same purpose: to ensure your data is easily accessible for analytics. The differences appear once we ask how quickly this data becomes available and how to prepare it.

Technology stack selection: Databricks, Snowflake, and BigQuery

We’ve written a detailed guide on data platform vendor evaluation. In this section, we’ll provide a more general overview, focusing on the most recent feature developments (to gauge each company’s innovation pace), core use cases, and real-life ROI examples.

BigQuery vs Databricks vs Snowflake comparison

Databricks: When AI/ML workloads drive architecture decisions

The Databricks Data Intelligence Platform is a data lakehouse solution that not only consolidates enterprise data but also offers a wide range of AI/ML processing and analytics capabilities. One of the Gartner reviews sums up what the platform offers and what its limitations are:

DB delivers an outstanding unified lakehouse that lets engineering, BI, and ML teams work from the same governed data, cutting pipeline sprawl and hence speeding up projects. Performance is excellent on Apache Spark, clusters spin up fast, and support has been consistent in response and knowledge. Caveat: steep learning curve for newcomers and tight control on costs.

Unification has its costs, as it makes the platform difficult to manage and can lead to accumulated expenses as data processing capacity increases.

Recent features

Databricks continues to expand beyond traditional analytics and data warehousing solutions toward a unified AI and data platform. The company has recently introduced Agent Bricks (a no-code AI agent builder), Lakebase (a serverless transactional database for processing more than 10,000 queries per second), and enhanced integrations with OpenAI and Anthropic models to support AI-centric workloads directly within the platform.

Use cases

• Large-scale data engineering and transformations with Delta Lake and Apache Spark integration.
• Integrated AI/ML pipelines (feature engineering, model training/serving) leveraging unified compute and storage.
• For business cases, where advanced analytics and AI workflows should co-exist with traditional reporting.

ROI example

After surveying multiple Databricks clients, Nucleus Research’s findings confirm that Databricks delivers a 482% ROI over three years, with a four-month payback period. Surveyed companies also admit a 52% reduction in time-to-production of their data and AI projects.

Snowflake: SQL engine powered with AI capabilities

Snowflake is a unified data platform that integrates with Apache Iceberg and Delta Lake for flexible data management and to help enterprises avoid vendor lock-in. Similar to Databricks, Snowflake supports multiple cloud providers, including GCP, AWS, and Azure.

Recent features

Snowflake’s AI Data Cloud continues to evolve with innovations showcased at Snowflake Summit 2025. These include advances in AI-ready capabilities, enhanced ingestion options, and governed data sharing across organizations.

The partnership between Snowflake’s Cortex AISQL and Anthropic supports agentic AI workflows directly inside Snowflake’s secure data cloud, enabling natural-language analytics and autonomous insights.

Use cases

• Enterprise BI and reporting, which require high concurrency and predictable performance.
• Secure data sharing across organizational boundaries through Snowflake Marketplace and private data exchanges.
• SQL-centric analytics teams seeking a managed platform with minimal operational overhead.
• Organizations that prioritize data governance and compliance with built-in access controls and audit capabilities.

ROI example

Pfizer switched from multiple fragmented data storage systems, which included several data lakes, legacy databases, and scattered files across workspaces and systems, to Snowflake. As a result, they achieved 57% in TCO savings, cut compute costs by 28%, and increased the pace of analytics by four times.

BigQuery: GCP-native AI data platform

Google positions BigQuery as an autonomous data and AI platform that automates the data lifecycle from ingestion to AI. Features include built-in AI integrations (e.g., Gemini in BigQuery) and BigQuery ML for in-warehouse machine learning.

Recent features

BigQuery now supports managed AI functions that allow users to embed AI capabilities directly within SQL workflows for richer analytics and inference.

Plus, Earth Engine in BigQuery became generally available, enabling satellite and geospatial data integration for advanced analytics directly in BigQuery.

Use cases

• Organizations already invested in Google Cloud Platform seeking seamless integration with other GCP services such as Vertex AI, Looker, and Cloud Storage.
• Analytics teams that require serverless, pay-per-query pricing without managing compute resources.
• Companies processing large-scale geospatial data, leveraging BigQuery’s native GIS functions.
• Marketing and advertising analytics, particularly for organizations using Google Ads and Google Analytics data.

ROI example

Stanford University migrated its research data infrastructure to BigQuery and Google Cloud, consolidating previously siloed datasets across departments. The migration reduced query times from hours to seconds for complex genomics research workloads, enabling researchers to iterate on hypotheses faster. Stanford reported a 60% reduction in infrastructure management overhead.

Selecting the right platform is only part of the equation. Many organizations face the more immediate challenge of transitioning from legacy infrastructure to these modern platforms. The migration path (e.g., data lakehouse or data warehouse migration services) you choose can determine whether you realize platform benefits within months or years.

Migration strategies for legacy data platforms

Data platform migration is a challenging but ultimately rewarding step an organization should take if their data management issues are stalling growth. For instance, 41% of organizations have migrated from data warehouses to data lakehouses, and 23% from legacy data lakes.

Typically, migrations cover:

• data warehouse → cloud warehouse
• data lake → data lakehouse
• Snowflake ↔ BigQuery ↔ Databricks
• legacy → modern platform

General migration strategies that would fit any of them are:

1. Lift-and-shift. Move data and schemas with minimal transformation. 
2. Phased migration. Migrate workloads, domains, or use cases one by one while old and new platforms run in parallel. 
3. In-place modernization. Modernize storage or table formats without copying all data (e.g., registering existing data into new table formats).
4. Workload-based migration. Migrate by workload type (e.g., BI first, then ML; historical data first, then streaming; read-heavy workloads before write-heavy ones)
5. Schema-first vs data-first migration. Schema-first: migrate models, then data. Data-first: migrate raw data, remodel later.
6. Domain-driven migration. Migrate data by business domain (sales, finance, operations, product).
7. Cold data vs hot data split. Migrate historical (“cold”) data differently from actively used (“hot”) data.
8. Re-platform and optimize. Redesign models, pipelines, and governance during migration.

The optimal strategy depends on your starting point, risk tolerance, and resource constraints. Organizations with mature data governance and documented pipelines often succeed with phased migration, maintaining business continuity as they progressively shift workloads. Companies facing urgent cost pressures or end-of-life deadlines may need to lift and shift to exit legacy platforms quickly, accepting technical debt that must be addressed post-migration.

Governance and compliance requirements: Building compliant data architectures

Data breaches increased by 22% year over year in 2025, with GDPR fines reaching a staggering €1.2 billion. These figures highlight a growing gap between how fast organizations deploy AI and how well their data architectures control access, usage, and accountability. AI systems amplify risk by replicating data across training pipelines, inference layers, and automated decision workflows, often faster than governance controls can keep pace.

Governance and compliance are not the same thing. Governance defines who can access data, for what purpose, and under which conditions. Compliance is the ability to prove that those rules meet regulatory requirements (GDPR, HIPAA, PCI DSS). When embedded into the data architecture by design, through classification, fine-grained access control, lineage, and auditability, even large, previously ungoverned data lakes can be transformed into secure, compliant platforms.

Secure data architectures enforce these controls at runtime. They include centralized logging, monitoring, and audit trails to detect anomalies and support investigations, along with consistent encryption, masking, and data minimization to limit exposure of sensitive information.

Bottom line

Your data platform decisions should be driven by your business model. If your data is siloed, fragmented, and of poor quality, adopting the most advanced lakehouse architecture will not solve the underlying problems. You will simply have a more expensive platform containing the same unreliable data.

Whether you are modernizing a legacy warehouse, implementing your first lakehouse, or optimizing an existing platform, the principles remain consistent. Align architecture to business needs. Invest in governance and quality. Build for the AI-enabled future. And never lose sight of the ultimate purpose: turning data into decisions that drive your business forward.

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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

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

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

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.

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Modern enterprises manage 250-500+ applications that create data silos, hindering effective automation. 95% of IT leaders see integration challenges as a primary barrier to AI value, and only 28% of enterprise applications are effectively connected. 

This fragmentation makes traditional single-agent AI approaches insufficient because no single agent can effectively navigate hundreds of disconnected systems and data formats. 

Multi-agent systems where specialized agents coordinate autonomously are emerging as a more scalable and flexible alternative to support complex enterprise-grade operations. 

The orchestration market is growing rapidly and is expected to surge from $8.5 billion in 2026 to $35 billion by 2030. MCP, released in late 2024, has become a viral sensation among machine learning engineers. 

In April 2025, Google joined the orchestration market by releasing Agent2Agent (A2A), an open-source protocol designed to foster seamless collaboration between agents. 

This post explores how A2A is transforming enterprise AI by enabling coordinated, scalable automation across complex ecosystems.

What is Agent2Agent?

Beyond this discovery mechanism, A2A supports sharing images and structured data for richer collaboration. For enterprise teams automating complex workflows, A2A offers a way to keep their entire AI estate connected.

How A2A works

To connect agents, A2A uses a server-client model where models exchange structured JSON messages over standard web protocols like HTTP.

A typical A2A interaction works as follows:

1. Discovery: Agents find each other via Agent Cards and select a communication method.
2. Secure connection: TLS encryption protects data transport; authentication credentials attach to each request.
3. Task sharing: The caller sends a task message containing text instructions, file references, and structured data.
4. Execution: Tasks run asynchronously, allowing agents to start long-running workflows while handling other work. Callers can poll for status or receive push notifications via webhooks.
5. Input requests: If the server agent needs more data, it sets the task status to “input required” and waits for additional context.
6. Completion: The server returns output as an artifact (document, structured data, or other format), marks the task “complete,” and logs the interaction for audits.

This approach enables agent-to-agent communication without compromising enterprise security standards or exposing proprietary algorithms.

How A2A is different from MCP

Google positions A2A as complementary to Anthropic’s Model Context Protocol (MCP), not a competitor, because they solve different problems.

MCP connects a single agent to third-party tools (Slack, Figma, coding IDEs) for real-time data and context.

A2A provides a shared language for multiple agents to communicate with each other.

It’s quite common for enterprise teams to combine both protocols to build orchestrated multi-agent workflows that ingest real-time data from external tools.

PayPal deployed this architecture in a merchant-facing agentic workflow connecting a sales agent to a checkout agent that creates and sends PayPal invoice links from natural-language conversations.

How A2A is used: The merchant’s agent contacts an A2A broker to discover a PayPal-provided agent and retrieve its Agent Card. The merchant agent then verifies authenticity, completes authorization, and delegates payment operations. Once delegation is complete, A2A steps out of the execution path.

How MCP is used: After the A2A handshake, the receiver agent uses an MCP client to call PayPal tools for invoice creation and delivery through a PayPal-hosted MCP server.

Together, A2A and MCP created a standardized, secure way for PayPal to orchestrate agents and provide them the context needed to complete tasks.

How Agent2Agent is transforming enterprise AI

By the time Google released A2A, the enterprise market was already sold on multi-agent systems. 79% of organizations were experimenting with AI agents, and over 60% of new pilots involved multiple agents working together.

But reliably connecting agents was still a problem. Without a standard protocol, agents deployed across teams and branches couldn’t easily communicate, leading to format inconsistencies and duplicated effort.

A2A’s standardized approach to agent orchestration is helping organizations reduce that friction and improve how agents work together in practice.

Shift from monolithic to modular architectures

How it worked before A2A: Connecting AI agents required custom point-to-point integration that risked falling apart when either system changed. Adding a new agent or swapping vendors meant rebuilding connections from scratch, slowing deployment, and increasing downtime risk.

What’s different with A2A: A2A enables a shift from monolithic AI builds to modular architectures where capabilities are assembled as interchangeable components rather than tightly coupled code.

In a multi-agent ecosystem, teams can introduce new capabilities, swap vendors, or upgrade parts of the system without costly rewrites or downstream disruptions. 

This modularity shortens time-to-value, reduces delivery risk, and extends the life of AI investments by making systems easier to govern, scale, and maintain.

Improved governance and traceability

Before A2A: Agent monitoring and governance relied on ad-hoc solutions, leaving organizations exposed to unwanted actions or data loss. The gap was palpable to enterprise leaders, 47% of whom reported negative consequences from genAI deployments.

What’s different with A2A: The protocol’s built-in capabilities weave governance and traceability into the foundation. 

• Declared identity and capabilities. Each agent exposes an Agent Card stating who it is, what it can do, and its trust and policy constraints.

• Auditable delegation. Structured A2A messages make agent-to-agent requests, context sharing, and responsibility transfers fully loggable.

• Centralized enforcement. Orchestration layers can enforce routing, approvals, and policy checks at delegation time, creating a verifiable chain of custody across workflows.

For enterprise organizations, the ability to monitor the actions of AI agents with detailed logs and centralized control helps prevent unwanted actions or irreversible data loss. 

A robust ecosystem fueling the growth of A2A

Before A2A: Organizations adopting multi-agent architectures faced a fragmented landscape with no standard way to package or deploy agents across the enterprise. 

What’s different with A2A: A2A isn’t emerging in isolation. Google is building a partner ecosystem that standardizes how agents are packaged, discovered, and deployed.

The foundation is built on two powerful moats.  

• Strong enterprise backers. At launch, A2A was supported by 50+ enterprise technology partners, and the list of backers is expanding as the protocol is gaining traction. 

• Distribution infrastructure. Google has connected A2A to the AI Agent Marketplace and Agentspace, allowing enterprise organizations to deploy third-party agents through cloud buying with little to no procurement friction.

Google is also integrating A2A with purpose-built protocols.

With Agent Payments Protocol (AP2), A2A now has a common language for secure, compliant agent-to-merchant transactions, designed to prevent fragmented payment integrations.

Universal Commerce Protocol (UCP) standardizes the end-to-end commerce journey, helping agents move from “recommend” to “transact” across retailer systems.

Together, these integrations help enterprises move from pilots to monetizable, governed deployments.

Limitations of A2A in enterprise AI

A2A’s positioning as the “HTTP for agents” addresses the problem of AI silos that enterprises are already grappling with, and that will only intensify as teams manage thousands of specialized agents handling high-stakes decisions.

That said, despite strong technology and ecosystem support, the protocol still has functional and operational gaps that should give enterprise leaders pause before treating it as a default.

Slow traction in the enterprise

A2A is still early in enterprise rollout. The A2A Python SDK shows ~1.37M downloads per month, compared to 57.3M for Anthropic’s MCP SDK, and large-scale deployments remain sparse. This may have to do with the fact that Google engineers themselves position the current version as a draft on the path to production readiness.

Still, committing to an evolving framework puts enterprise teams at risk for vendor lock-in, which is why engineering teams still default to simpler, framework-native agent wiring instead.

I think binding to a certain protocol could lead to being tied to a specific vendor. Especially since A2A isn’t as common as MCP. If you use an open-source framework, just sticking with that framework is enough for agents to work together, no need to add more complexity.

Reddit comment highlights the common view on A2A adoption

How to solve this challenge: Position A2A as an optional interoperability layer for specific needs, like cross-language services, independent scaling, and multi-model governance, rather than a default framework. This way, engineers will be encouraged to deploy the protocol only when operational gains justify the setup complexity.

Improving agent connectivity does not necessarily increase reliability

While A2A optimizes communication between agents, it doesn’t solve the harder problem of behavioral control under uncertainty. 

Neither A2A nor MCP prevents agents from making inconsistent decisions, losing track of context, or acting outside their intended scope as workflows grow. 

The black-box nature of LLMs means orchestrated AI systems remain fragile, hard to audit, and prone to failure when many agents are chained together.

How to solve this challenge: To scale multi-agent workflows safely, build hierarchical orchestration where AI models primarily route and coordinate work, while clear rules, enforced limits, and verifiable execution logs constrain decision-making.

No standardized discovery layer

A2A standardizes how agents introduce themselves through Agent Cards, but doesn’t define how agents find each other in real-world systems. 

Christian Posta, CTO at Solo.io, points out that A2A is missing three foundational components for autonomous discovery:

• Agent registry: A curated catalogue of agents
• Agent naming service: Skill-based lookup across environments
• Agent gateway: Secure, policy-aware routing at runtime

Without a discovery plane, teams rely on fixed endpoints, manual configuration, or custom registries that only work within one organization or vendor stack. As agents are added, updated, or removed, these setups become hard to maintain, and A2A-based systems struggle to scale beyond tightly controlled deployments.

How to solve this challenge: Introduce a dedicated agent registry listing approved agents and verified capabilities instead of hardcoding connections.

Add a simple naming and routing layer that resolves requests like “an agent that can do X” to the right agent at runtime. 

Until A2A defines this layer itself, treating discovery as a shared platform service is the most practical path to scalable adoption.

Best practices for enterprise A2A adoption

While A2A standardizes how agents talk to each other, it deliberately leaves core responsibilities, such as security controls, trust boundaries, failure handling, and operational resilience, in the hands of adopters. 

Until the protocol matures further, enterprise teams should consider A2A as a foundation instead of a safety net, and define clear best practices to avoid building poorly secured or unreliable agent ecosystems.

Best practice #1: Design agents as domain-specific services

Limiting an agent’s scope means defining it around a single decision or transformation step in a workflow, such as validating inputs, enriching data, or producing a specific recommendation, rather than an end-to-end task. 

This discipline brings order by eliminating overlapping responsibilities between agents and making A2A message flows deterministic and easier to reason about. 

Managing narrow-scope agents allows engineers to predict exactly which agent should be invoked for a given request. 

As a result, teams spend less time tracing emergent behavior across agents and more time improving discrete components with measurable impact.

Implementation tips

• Define a single business capability per agent and enforce strict input/output contracts with versioning.
• Keep domain logic inside the agent and push shared concerns (auth, logging, rate limits, policy) into platform middleware.
• Treat agents as replaceable components: test them in isolation, deploy independently, and monitor with SLA-style metrics per agent.

Best practice #2: Prioritize structured inputs and outputs 

Although A2A supports plain text, relying on it in production can create interpretation problems at task handoff. 

Getting caller agent tasks in plain text requires server agents to infer intent, extract fields, and filter what’s required from what is optional. 

Structured payloads formatted in JSON solve this problem because they make each exchange explicit, with clearly named fields, constrained types, and instantly detectable missing data. 

When faced with a clearer task message, server agents start behaving deterministically instead of making best-effort attempts.

JSON prompting provides clarity, ensures consistency, reduces ambiguity, and scales across complex workflows. When “normal prompts rely on interpretation, JSON prompts give explicit, predictable outputs.

Prem Natarajan, AI Transformation & GTM leader at Capco

For engineering teams, structured formatting enables faster debugging and safer change management thanks to schema versioning and reduced room for interpretation. 

Implementation tips

• Treat every A2A message as an API call, with a defined schema, versioning, and documentation for required and optional fields.
• Implement strict validation at ingress/egress and return typed error objects so failures are actionable, not conversational.
• Keep text outputs as an auxiliary “explanation” field while all routing, tool calls, and state updates use structured fields only.

Best practice #3: Plan for robustness and scalability

To ensure robustness in A2A systems, teams need to ensure multi-agent workflows maintain business continuity and cost control, without risking customer-facing incidents or runaway compute spend.

Although AI agents are intrinsically unpredictable, engineering teams can limit risks by setting guardrails. 

• Timeouts: Kill requests that take too long before they cause a bottleneck.
• Retry limits: Cap how many times a failed request can retry to prevent infinite loops.
• Circuit breakers: Temporarily stop calling a failing service so it has time to recover.
• Concurrency caps: Limit how many requests run in parallel to avoid overloading resources.

With these checkpoints in place, teams can add new agents without a single slow or failing component cascading into system-wide downtime or retries draining the compute budget.

Agent guardrails need to be specific to the underlying use-case, and implemented in their respective platform components and layers .

Debmalya Biswas, AI CoE Lead at UBS

Implementation tips

• Set explicit limits per workflow (max latency, retries, cost per request) and enforce them at runtime.
• Use circuit breakers and fallbacks so a failing dependency returns a controlled partial response, not a system-wide stall.
• Use historical data to plan capacity for typical traffic patterns and throttle concurrency so growth doesn’t convert directly into higher incident rates and cloud bills.

Best practice #4: Train agents to give clear guidance when context is missing

A generic “can’t proceed” error message from a server agent forces callers to guess what went wrong and start trial-and-error debugging that compounds across multi-agent chains and often requires human intervention to unblock.

On the other hand, detailed error messages can help the caller agent to provide the server with the necessary data with minimal human intervention. 

AI agents may need more rules. But they definitely need more verbose error messages. Your AI agents will get smarter. They’ll learn. They’ll do it right next time; they might not even break flow, just readjust and act accordingly. This is behavioral engineering at scale. 

Kendall Miller, founder and CEO at Maybe Don’t AI

Implementation tips

• Return a structured InputRequired payload that lists missing fields, accepted formats, and an example request object, rather than free-text instructions.
• Include “how to fix” guidance with every validation error (cause + corrective action), and ensure it is consistent across channels and clients.
• Log missing-context patterns and promote them into pre-validation (or upstream UI checks) to prevent recurring failures and reduce rework. 

Bottom line

A2A offers enterprise teams a standardized way to connect AI agents across vendors, tools, and workflows, enabling modular and scalable architectures. 

At the time of writing, the protocol is still maturing, with limited discovery infrastructure and some behavioral unpredictability. Even in this landscape, enterprise organizations can extract a lot of value from A2A’s vendor-agnostic approach to vendor communication, but its adoption will require careful guardrails. 

Organizations that treat A2A as an optional interoperability layer rather than a default framework, invest in structured payloads and robust error handling, and plan for graceful degradation will be best positioned to scale multi-agent systems safely.

The post Google’s Agent2Agent (A2A) protocol: How it works and transforms enterprise AI 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.

The post Data engineering services: Complete buyer’s guide appeared first on Xenoss - AI and Data Software Development Company.

Rather than letting teams crowdsource their own playbooks, executives are stepping up to identify high-yield transformation areas.

Understanding which AI use cases have successfully improved productivity, expanded revenue opportunities, and strengthened the bottom line helps the C-suite make data-backed decisions about their next pilot. 

In this post, we examine 10 use cases where AI adoption empowers employees, improves customer experience, and delivers measurable returns on investment.

1. AI-assisted coding

ROI in numbers: 

• 68% of engineers reported saving 10+ hours per week using AI
• Enterprises record a 376% ROI lift over three years, with payback in under 6 months after the adoption of coding assistants
• In three years of active coding copilot use, leaders have saved up to $48.3M in developer productivity gains and gained $18.4M in revenue impact from accelerated time-to-market

Enterprises are adopting AI copilots embedded in developer environments, IDEs, terminals, code review, and CI/CD, to support code generation, refactoring, reviewing, and testing. 

What began as individual productivity tools used without formal company policy has evolved into a workplace-wide practice after organizations recognized that developers using coding agents are 55% faster than those who don’t. 

Proactive adopters are now introducing tools like Claude Code across both technical and non-technical teams, giving employees greater autonomy to build personalized automation.

Real-world example: Accenture pushes for AI coding assistants 

Approach: Accenture is pursuing a “developer productivity at scale” strategy focused on upskilling its workforce and productizing delivery capabilities around coding assistants. The company initially supported GitHub Copilot adoption without centralized oversight, then partnered with Anthropic in December 2025 to create a dedicated Business Group. 

This task force will train over 30,000 employees on Claude models in what will become the largest enterprise-scale deployment of Claude Code.

Results: Early productivity gains were significant: internal telemetry showed an 8.69% increase in pull requests per developer and a 15% increase in merge rates, demonstrating faster review cycles without compromising acceptance standards. 

Engineers also reported higher job satisfaction. 90% of developers who used GitHub Copilot for more than three days felt more fulfilled, and 95% said they enjoyed coding more with AI assistance.

2. Document processing and data extraction

ROI in numbers

• Teams report a 2–3× reduction in end-to-end processing time and faster downstream decision-making in claims, finance, and compliance workflows.

• Operations teams automate up to 68% of document handling, recovering a significant share of manual capacity and redeploying staff from data entry to exception management.

• Companies report that automating data processing helps manual review and correction drop by ~50%, reducing operating costs and increasing throughput in finance, claims, and compliance teams.

Enterprise document processing teams use AI to automate the intake, classification, extraction, and validation of high-volume documents that previously required manual review. 

These pipelines ingest documents, extract text using OCR and layout-aware models, classify content, identify key fields, and validate data against business rules or master data, such as PO matching, policy checks, or customer records.

AI systems flag low-confidence fields and route only true exceptions to human reviewers, while clean documents flow straight through to downstream systems. 

This enables end-to-end workflow automation with fewer manual touchpoints, shorter cycle times, and predictable processing costs as volumes scale.

Real-world example: Allianz taps into AI-assisted claims processing

Approach: Allianz used to process millions of insurance claims annually, many arriving as unstructured documents (scanned forms, invoices, medical reports, photos, and handwritten attachments) that required manual review and data entry, creating bottlenecks during peak periods. 

To address this, the company deployed an AI-driven document processing copilot combining OCR, classification, and data extraction to automatically identify document types, extract key fields, and validate them against policy and customer records.

Outcome: Following the rollout, Allianz reported an 80% reduction in processing and settlement time for eligible, low-complexity claims in its Project Nemo deployment, cutting turnaround from days to hours and enabling the team to handle sudden spikes during natural catastrophes. Executives now plan to expand AI automation to other back-office claims processes.

3. AI-powered content generation

ROI in numbers

• Using generative AI saves content teams an average of 11.4 hours per week and allows marketers to reallocate time from first drafts to review and campaign execution.

• Deploying generative AI tools helps marketing teams cut creative production time by 50% in an enterprise customer deployment without adding extra headcount.

Generative AI helps creative teams streamline content creation from research and outlining to final polish, while maintaining consistent tone across formats. 

Embedding generative AI into content workflows cuts time-to-draft from weeks to days and enables expansion to new channels and audiences. 

AI writing assistants also support global teams in localizing content, creating nuanced translations, and tailoring blog posts, social media, and ad copy for target demographics.

Real-world example: generative AI helped streamline creative production for Curry’s

Approach: Currys, a UK-based electronics retailer, embedded Adobe Firefly into its internal creative workflow to accelerate ideation, iterate on campaign visuals, and rapidly produce on-brand asset variants for seasonal retail moments.

Outcome: The creative team reduced production time by 50%, translating to significant budget savings, as the marketing team cut third-party agency costs by more than half.

4. Copilots for knowledge management

ROI in numbers

• Enterprise teams save up to 30 minutes per employee per day on drafting internal knowledge bases by adopting Microsoft Copilot

• After adopting a knowledge copilot, 70%+ of enterprise employees report spending less time searching for information

• One enterprise company reported that adopting an internal chatbot resolved over 3,500 employee inquiries in the first three months after adoption.

Combining conversational AI with autonomous agents helps teams support new employees with personalized, on-demand assistance, allowing new hires to direct basic questions to an AI copilot rather than managers, freeing team leaders for higher-priority work.

AI copilots also help experienced employees by automating everyday tasks like reminders, email scheduling, and meeting notes. 

Middle managers can use custom copilots to organize internal task management and build multi-agent workflows connecting critical systems like internal data, task managers, and messaging platforms.

Real-world example: Morgan Stanley’s AI copilot supports new hires 

Approach: Morgan Stanley implemented an internal knowledge copilot, Morgan Stanley Assistant, to help financial advisors query and synthesize institutional knowledge. 

The assistant enables advisors to search, retrieve, and digest content from a large internal database directly within their workflow, eliminating manual searches across research and policy materials.

Outcome: The bank successfully scaled the assistant to support over 16,000 financial advisors, analyzing more than 100,000 internal documents.

5. Invoice matching and reconciliation assistants

ROI in numbers

• AP teams removed 85% of manual effort and reconciliation costs and cut the reconciliation process by 2 days after deploying automation for invoice reconciliation.

• Invoice processing teams reduced manual effort by 80% by using AI that reads invoices and automates invoice entry and coding workflows (AppZen Autonomous AP for SAP Ariba).

In the last decade, RPA was the go-to tool for automating invoice matching and reconciliation. 

While these rule-based systems cut time-per-invoice, they lacked contextual understanding and robust exception handling, so human reviewers remained essential for preventing errors and matching ambiguous items or vendors.

AI agents address these limitations by understanding natural language, analyzing context around each reconciliation item, and continuously improving exception handling by learning from human reviewers.

Real-world example: Dole Ireland increased A/P productivity with AI-based invoice processing

Approach: Dole Ireland automated its accounts payable reconciliation to address growing invoice volumes previously handled manually. The company used AI to streamline invoice-to-statement matching, exception detection, and reconciliation tracking, with human reviewers handling only mismatches or disputes.

Outcome: Dole reported an 85% reduction in manual reconciliation effort and costs, cutting cycle time by two days and improving month-end operations. The automated workflow also identified missed credits and duplicate payments earlier, reducing downstream adjustments and strengthening financial control.

6. Report generation 

ROI in numbers

• 75% of enterprise workers surveyed reported AI improved the speed or quality of their output.

• ChatGPT Enterprise users are saving 40–60 minutes per active day on average on automating reporting tasks. 

• Consulting teams that used generative AI for report generation are achieving ​​25.1% faster completion and 40% higher quality on day-to-day knowledge work tasks. 

For regulated industries like finance and healthcare, reporting consumes significant time and resources. Regulators demand increasingly detailed descriptions of workflows and processes: PwC estimates the average banking institution submits over 67 reports annually, spanning 340,000 data points. Even in less regulated industries, teams lose an average of 180 hours per year to reporting.

AI eases this burden by analyzing requirements, autonomously mining the necessary data, generating reports, and routing them to human reviewers for approval. This shifts reporting from a resource-intensive bottleneck to a streamlined workflow where human oversight focuses on validation rather than manual compilation.

Real-world example: Walki implemented AI-assisted reports

Approach: Walki, a global packaging materials producer, integrated generative AI into its enterprise reporting and analytics workflows by embedding AI capabilities into its existing Anaplan planning platform. 

The company automated traditionally manual aspects of reporting (pulling data, synthesizing trends, and generating narrative commentary), allowing planners and analysts could focus more on interpretation and decision support rather than data assembly and formatting. 

Outcome: After deployment, Walki reported that the use of generative AI in reporting and analytics led to faster report generation, provided deeper analytical insight, and helped teams work more efficiently across planning cycles. 

7. Predictive analytics and quality control

ROI in numbers: 

• Manufacturing teams reached 300–500% ROI lifts by using AI-driven predictive maintenance systems to minimize downtime, improve asset availability, and reduce reactive service costs. 

• The ability to predict and prevent equipment deterioration brought about a 5–10% reduction in maintenance costs and a 10–20% increase in equipment uptime. 

Historically, predictive maintenance relied on vendor recommendations or prior experience with similar equipment, which helped anticipate potential issues but lacked the precision to accurately predict malfunctions.

AI-powered predictive analytics systems provide far more accurate estimates of equipment lifetime and time to failure. 

Using machine learning, these tools create a comprehensive view of each machine based on sensor data, ERP logs, production records, and field reports. Production managers can then catch early failure signals and address issues before they cascade into costly downtime.

Real-world example: Rolls-Royce combines predictive maintenance and digital twins

Approach: Rolls-Royce uses AI-powered predictive analytics alongside digital twin technology to monitor the health and performance of its aircraft engines and critical systems. Embedded sensors feed real-time telemetry into machine-learning models that detect subtle patterns preceding failures, enabling maintenance to be scheduled before faults occur.

Outcome: The integration of AI-driven models with predictive technologies has extended maintenance intervals by up to 48%, allowing engines to run longer between scheduled interventions and reducing unplanned service events.

8. Fraud detection

ROI in numbers

• Intelligent fraud detection helped HSBC reach 60% fewer false positives in financial crime alerts and reduced manual review workload for monitoring over ~900 million transactions per month. 

• Another financial institution reached a 2–4× increase in true positive risk detection in anti-money-laundering operations after AI models replaced traditional rule-based systems.

Although fraud detection is a cross-industry challenge, few domains are more impacted than finance and banking. KYC and anti-money-laundering activities are top-of-mind concerns, yet ROI on these investments remains questionable. Interpol reports that banks detect only 2% of financial crimes despite increasing fraud detection spending by 10% annually.

AI is becoming a powerful support system for these organizations. 

Generative AI platforms detect fraud patterns in call logs and user activity, improving detection accuracy. 

AI agents augment human reviewers by managing fraud detection workflows end-to-end. 

Advanced analytics expand the behavioral and demographic signals processed by traditional systems, further strengthening detection capabilities.

Real-world example: HSBC uses AI to detect financial crime

Approach: HSBC has embedded AI and machine-learning systems into its financial crime and fraud detection operations to augment traditional rules-based monitoring. 

These systems ingest transactional data at scale to detect anomalous patterns across retail, commercial, and payment channels, enabling real-time risk scoring and alert prioritization rather than relying on static thresholds. 

Outcome: HSBC’s AI platforms process over 1 billion transactions monthly, detecting 2–4× more suspicious activity than traditional methods while reducing false positives by approximately 60%. This allows compliance teams to focus on genuine threats rather than false alerts, cutting investigation timelines from weeks to days and significantly reducing operational costs.

9. M&A decision-making and due diligence

ROI in numbers

• Corporate workers report saving the equivalent of 122+ hours per year through AI tools handling routine data synthesis, summarization, and repetitive analytical tasks.

• AI adoption allows relocating 18–24% of research hours to high-impact strategic tasks such as deep analysis and decision support. 

• In research-intensive industries like asset management, AI brings up to 40% productivity gains by streamlining manual research and administrative tasks.

AI hallucinations make M&A teams reluctant to heavily rely on machine learning for mission-critical acquisition decisions. Yet the productivity gains are too significant to ignore, so teams are finding ways to integrate AI into due diligence without undue risk.

Advanced analytics can predict cash flows, growth rates, and discount rates for acquisition targets. 

AI systems also excel at identifying promising candidates by analyzing alignment with the company’s strategic goals, by processing large data volumes that would overwhelm human teams. 

Implementing generative AI across these areas helps teams consider more data points while significantly reducing the time needed to vet M&A candidates.

Real-world example: Kraken used AI to validate the $1.5 billion acquisition of NinjaTrader

Approach: When Kraken pursued its $1.5 billion acquisition of NinjaTrader, the strategy team faced the typical due diligence challenge: manually reviewing vast volumes of financial records, operational metrics, and risk factors would take weeks. 

To accelerate the workflow, Kraken integrated an AI-powered analysis platform to rapidly process large datasets and generate detailed insights, allowing analysts to focus on validating findings rather than sifting through raw data.

Outcome: Research and synthesis that would typically take weeks were completed in hours, significantly compressing the due diligence timeline and reducing the resource burden on the internal team.

10. Personalization and improving customer experience

ROI in numbers

• Companies that adopt AI-enabled customer experiences see a 10–15% increase in sales, thanks to higher levels of personalization maturity.

• Using AI-enabled recommendation engines reduces cost to serve by up to 30%. 

• AI-powered “next best experience” personalization can reduce customer attrition by up to 20% per year.

As AI becomes more embedded in daily life, customer expectations are shifting to where personalization is no longer a “nice-to-have” but an expected default. These expectations are justified, as building highly tailored recommendation engines is now more accessible than ever. 

Engineering teams can leverage out-of-the-box large language models and ready-to-deploy AI agents to capture, contextualize, and respond to customer signals in real time. 

The ROI of AI-enabled personalization solutions has been proven by multiple successful adoption case studies. 

A McKinsey survey of Fortune 500 companies revealed that teams focused on building AI-assisted personalized customer experiences increase revenue by 5 to 8 percent.

Real-world example: Starbucks uses AI-assisted personalization with Deep Brew 

Approach: Starbucks integrated AI into its customer experience through Deep Brew, a machine-learning platform that analyzes transaction and loyalty data to deliver personalized recommendations and offers. The system ingests data from the mobile app, purchase history, and contextual signals like time of day and store inventory to tailor promotions and digital interactions across millions of daily customer touchpoints.

Outcome: Deep Brew analyzes data from 100 million weekly transactions worldwide, delivering a 30% ROI tied to AI-powered offers and personalization through the Starbucks Rewards ecosystem.

The bottom line

Transforming internal processes with generative and agentic AI can feel like reinventing the wheel. Successful transformations often require putting AI at the center of workflows rather than using it for incremental improvements. 

Choosing the wrong area can reduce productivity and increase chaos. To minimize this risk, explore use cases that worked for AI trailblazers and apply their practices and lessons learned to your organization.

The post 10 generative and agentic AI use cases that drive ROI (with real-world examples) appeared first on Xenoss - AI and Data Software Development Company.

For systems processing millions of transactions or managing real-time logistics, even a few minutes of interruption can result in millions of dollars in lost revenue, regulatory fines, and irreparable damage to customer trust. For instance, the UK fashion retailer ASOS lost more than $25 million in 2019 due to a glitch in their warehouse management system (WMS).

Legacy systems sit at the center of this risk. As they age, tightly coupled architectures, old codebases on languages like COBOL, undocumented dependencies, and brittle integrations make failures harder to isolate and recovery slower. 

This is where zero-downtime app modernization services enter the picture, as an architectural and operational discipline focused on resilience, reversibility, and controlled change. Modernization without shutdown means isolating risk, deploying changes incrementally, and ensuring that failures remain contained rather than cascading across the organization.

In this guide, we outline how enterprises can rebuild and modernize critical systems while maintaining business continuity. We cover the architectural patterns, governance controls, and execution strategies that help reduce modernization risk without slowing delivery.

How to plan enterprise application modernization without downtime

Application modernization isn’t so much about never experiencing downtime as about becoming more resilient and flexible. Even if you experience downtime, you should be able to quickly resume work with minimal impact on your customers. 

And that’s the core aim of modernization: how to increase your business’s vitality and resilience to withstand hardships and become even stronger with the help of modern architectures and technologies. 

Here are three concepts that make this possible:

• Complete visibility into the current legacy systems, which means mapping every legacy component, including data flows, dependencies, SLAs, and risk thresholds.
• Certainty about how changes will affect operations. It comes from designing architecture that can coexist with legacy systems to enable safe and incremental modernization. 
• Control over how new components are introduced. You can achieve this by implementing gateways, observability layers, and structured migration paths that ensure transitions are incremental and reversible.

These principles shift modernization from a risky “big move” to a sequence of measurable, low-risk steps. They also clarify why zero-downtime modernization is as much an operational discipline as it is an engineering one. Architecture, governance, and execution models must work together to ensure that progress never compromises availability.

Legacy app modernization patterns that reduce downtime

Zero-downtime application modernization strategy depends less on specific technologies and more on the architectural patterns used to introduce change. The wrong pattern can amplify risk, while the right one allows teams to modernize incrementally without disrupting core operations.

AWS modernization experience-based (ModAX) framework offers seven core cloud modernization services, which allow an organization to ensure a complete application overhaul and achieve the most lasting business benefits. 

But companies don’t have to undergo all of them at once. It’s an incremental process that allows each business to choose the most suitable order of innovation. These pathways include moving to:

• A cloud-native environment, which means designing a new application architecture, such as microservices, event-based architectures, or serverless computing. 
• Containers that require adopting container orchestration services, such as Kubernetes, to package applications for rapid deployment.
• Managed databases that presume offloading data management to the cloud provider, such as Amazon Redshift.
• Managed analytics, which means integrating advanced analytics solutions to extract relevant insights and use them in data-driven decision-making.
• Modern DevOps that requires automated CI/CD pipelines, test-driven development, and AI/ML-enhanced tools.
• Open source, which means reducing licensing costs by transitioning from commercial to open-source technologies, such as moving from Oracle to PostgreSQL. 
• AI that means adding AI capabilities to future-proof applications. 

Taken together, these approaches make legacy application modernization strategies safer and help businesses increase operational efficiency.

Companies modernizing their applications with ModAX report 42% faster IT resource provisioning, 86% more weekly feature deployment, and 25% faster time to resolve downtime incidents.

But to enable all of the above modernization strategies, you would need to make clear architectural decisions, and one of the most important ones is migration from a monolithic legacy architecture to agile microservices.

Decoupling monoliths to microservices 

The core principle of zero-downtime modernization is architecture decoupling. The monolithic nature of legacy applications is their primary weakness, creating tight dependencies that make change risky and slow. The new design must break down the system into loosely coupled, independently deployable microservices. 

Each domain service should own its data and expose its functionality through a well-defined API gateway.

To ensure safe decoupling from a monolith to microservices, engineers typically:

• Apply domain-driven design (DDD) to align business domains with technical boundaries, ensuring services reflect real business capabilities rather than technical convenience. DDD helps engineers avoid overly granular microservices that increase operational overhead and maintenance complexity.
• Adopt asynchronous communication where appropriate, using message-driven patterns for task-oriented workflows and event-driven architectures for real-time data propagation. This reduces tight coupling and prevents synchronous dependencies from becoming single points of failure.
• Introduce a service mesh to manage service-to-service communication, providing built-in observability, traffic shaping, retries, and fault isolation without embedding this logic into application code.
• Add an API gateway to handle client-to-server communication, routing, authentication, and rate limiting. Importantly, the gateway must remain infrastructure-focused and stateless, without embedding business logic, so it does not become a new dependency or bottleneck in a loosely coupled architecture.
• Enforce secure service-to-service communication, using mutual TLS (mTLS), strong authorization policies, centralized secrets management, and comprehensive audit logging to maintain security and compliance as the number of services grows.

Decoupling a monolithic architecture into microservices is a foundational step in legacy application modernization services. Once this separation is established, organizations can control the pace and scope of modernization, choosing which application services to modernize first, which to leave untouched temporarily, and how long old and new systems must coexist. Or, in other words, they select appropriate patterns.

Strangler fig pattern

The strangler fig pattern gets its name from a natural phenomenon in which the roots of a strangler fig gradually surround and replace a host tree. This approach allows you to modernize incrementally without the risks of complete system replacement.

Here’s how to implement it effectively:

Start with a facade layer. Create an interface layer that intercepts all requests to your legacy system. It passes requests through and gives you control over routing decisions as you build new microservice components.

Identify logical boundaries. Look for areas in a monolith where functionality can be extracted without breaking dependencies. Payment processing, user authentication, and reporting often make good candidates for early extraction.

Replace components gradually. Build new microservices to handle specific functions, then redirect traffic from the facade to these services once they’ve been proven to work correctly.

Run systems in parallel. Test new components alongside legacy code to ensure they produce identical results before cutting over completely.

Retire replaced components. Only after new functionality has been validated and is stable should you decommission the corresponding legacy code.

An incremental microservices implementation approach reduces risk while delivering immediate value. Each modernized component improves system performance and reduces technical debt.

Leave-and-layer pattern

Leave-and-layer focuses on stabilizing the legacy core while introducing a modern layer on top. Instead of replacing existing systems, organizations expose legacy functionality via APIs and build new experiences, workflows, or analytics around it.

To enable this pattern, you need to build an event-driven architecture with an event bus service (e.g., Amazon EventBridge). A legacy application serves as an event producer, internal, external, or customer systems serve as event consumers, and EventBridge serves as an intermediary layer. 

The first step in setting up a leave-and-layer pattern is to transform a legacy application into an event producer by integrating an abstraction layer that switches between event buses for asynchronous and synchronous event publishing. The possibility to switch allows enterprises to balance correctness, resilience, and availability without increasing the risk of downtime. 

Asynchronous event publishing prevents downstream failures from cascading back into the legacy system. If a consumer fails or slows down, the core application continues to run. By contrast, synchronous publishing ensures the core application waits for an immediate response when a decision must be confirmed before proceeding, such as validating a payment or creating an order.

This pattern is particularly beneficial when you need to extend your application’s capabilities to get immediate, measurable business value without replacing the system entirely. You ensure zero-downtime application improvements and can release new features quickly. This approach could be particularly useful for extending your legacy application with AI/ML capabilities.

Anti-corruption layer (ACL) pattern

The ACL approach helps businesses enable gradual application modernization by maintaining communication between new microservices and the core system via an anti-corruption layer, so that the potential quality issues in legacy systems (e.g., convoluted data schemas)  can’t affect or “corrupt” the newly created services. When the application is fully migrated to the new environment, an ACL can be retired. 

The ACL works by isolating the core legacy system from the microservices subsystems, allowing microservices to call the monolith safely. For instance, in the architecture below, you can see the extraction of the User service from the monolithic application as a standalone microservice. If other monolithic services, such as a Cart service, call the User service directly, this can cause changes in the Cart service. To avoid this and enable safe data exchange between new and monolithic services, the ACL is introduced.     

This pattern is useful when you need to keep your monolithic application functioning for as long as possible. But ACL is rarely a standalone solution. Instead, it is an additional layer of safety when modernizing legacy solutions.

How to choose the right modernization pattern

The table below helps you to quickly grasp the essence of the core application modernization patterns that prevent business downtime.

Dimension	Strangler fig pattern	Leave-and-layer pattern	Anti-corruption layer (ACL) pattern
Primary goal	Incrementally replace legacy functionality with modern services	Add new capabilities without touching the legacy core	Protect new systems from legacy complexity and data models
Core idea	Gradually “strangle” the monolith by routing functionality to new services	Keep the legacy system intact and layer new services around it via events	Insert a translation layer between legacy and modern systems
Impact on the legacy system	Legacy components are progressively removed	Legacy system remains largely unchanged	Legacy system remains unchanged
Risk profile	Medium: requires careful dependency management	Low: minimal changes to legacy core	Low to medium: depends on integration complexity
Downtime reduction	High: parallel run and gradual traffic redirection	High: new features don’t interfere with legacy flows	High: isolates failures and behavior mismatches
Typical architecture changes	API gateway/facade, new microservices, gradual traffic routing	Event bus (e.g., EventBridge, Kafka), new consumers and services	Adapter or translation layer between systems
Data strategy	Often requires gradual data ownership migration	Legacy remains the system of record; data replicated via events	Data transformed and normalized at the boundary
Best starting domains	Payments, tracking, reporting, and authentication	Analytics, notifications, AI/ML features	Integration-heavy application services with complex legacy models
Time to first value	Medium: requires extraction and validation	Fast: new features can be added quickly	Fast to medium: depends on integration effort
Long-term outcome	Monolith shrinks and can eventually be retired	Monolith persists, but business evolves around it	Clean modern architecture insulated from legacy debt
Operational complexity	Moderate: parallel systems and routing logic	Lower: fewer changes to core operations	Moderate: translation logic must be maintained
When it works best	When the legacy system can be safely decomposed	When the legacy system is too risky or costly to modify	When legacy models are incompatible with modern design
When it struggles	Highly entangled monoliths with shared DB logic	When the legacy must eventually be retired	If ACL becomes overly complex or poorly governed

The list of modernization patterns for efficiently decomposing the monolithic architecture is much longer, but we’ve covered the most common ones. Expert solution architects know which ones to choose depending on the business’s goals and the complexity of the legacy system.

Your modernization decisions should consider both immediate operational needs and long-term strategic goals. The patterns that work best maintain business continuity while building toward a more flexible, scalable architecture. 

An engineering team can combine different modernization patterns to achieve an even more efficient application modernization. For instance, the strangler fig pattern works well in conjunction with the leave-and-layer or the ACL one, and this combination can help you accelerate monolith decomposition.

Phased modernization roadmap with the focus on business continuity

Zero-downtime modernization is rarely achieved through a single architectural decision. It is the result of a phased execution model that balances progress with control. A structured roadmap allows organizations to modernize critical systems while preserving availability, data integrity, and user experience.

Let’s take an illustrative example of an imaginative company and walk through the modernization process with it to see how to avoid downtime in practice. Here’s some background information about the company.

Disclaimer: The modernization path in this section is provided for illustrative purposes only and includes general points without a detailed explanation of every technical and architectural choice.

Phase 1: Comprehensive assessment

Our initial goal is to create a complete application map that highlights data flows, integration points, and hidden complexities that have accumulated over years of patches and modifications in the legacy system. 

This profound deconstruction is essential for planning a safe, incremental legacy application migration. The company uses recovery time objective (RTO) as the metric, which defines the maximum acceptable time a function can be unavailable before causing significant organizational damage. The aim is, of course, to avoid downtime by all means, but if anything occurs, we need to know how much time a team has to restore the system.

Example outcomes:

• 26 cron jobs (scheduled automated commands) are silently orchestrating pricing, warehouse updates, and invoice preparation.
• The Oracle database holds business logic in the form of triggers, stored procedures, and views that the monolith relied on.
• A “simple” tracking API turned out to involve five external carriers, each interacting differently via XML files exchanged via SFTP (a manual, inflexible approach).

Phase 2: Architectural design for resilience and incremental transition

The company decides to decouple a monolith into microservices using the strangler fig pattern. A microservices architecture will allow the company to rebuild the legacy system piece by piece, deploying new services independently without affecting the rest of the application.

Example decisions:

• Introducing an API Gateway as the single point of entry to route traffic between old and new components without disrupting users.
• Selecting Tracking as the first application service to extract from the monolithic architecture because it had fewer dependencies and a clean boundary.
• Postponing modernization of Invoicing because it’s deeply intertwined with the old data schema and poses a high business risk.

Phase 3: Incremental rebuild and parallel operation strategies

Instead of shutting down the monolith or migrating everything at once, the organization rebuilds individual domains in a parallel operating model. New services are constructed beside the existing system, initially hidden behind facades until fully validated. 

Each component processes real production inputs in “shadow mode,” allowing engineers to compare outputs from old and new logic without impacting users. This approach exposes behavioral inconsistencies early, whether due to undocumented legacy rules or complex integration flows, and allows teams to correct them before any user traffic is redirected. 

Parallel operation becomes the backbone of the modernization strategy: a way to introduce new functionality safely, test thoroughly, and transition gradually.

Example outcomes:

• The new Tracking service processed mirrored production traffic, exposing edge cases like timezone offsets buried in legacy helper classes.
• Engineers rebuilt the pricing logic in a standalone service and validated it by comparing 100,000 pricing computations against those from the monolith.
• Legacy warehouse update scripts were replaced with event-driven components that ran in parallel until their output matched exactly.

Over time, more and more functionality is migrated until the legacy system is fully encapsulated and can be safely decommissioned.

Phase 4: Data migration

A zero-downtime rebuild hinges on the ability to migrate data from the legacy database to the new data stores without interrupting data access or compromising integrity. The new microservices architecture typically calls for a decentralized data model, with each service managing its own database. The challenge is to move data from the old, centralized model to the new, distributed one while the application is live and actively writing new data.

Example transitions:

• Tables that only served reporting workloads were moved first, freeing the monolith from heavy analytical queries.
• A change data capture (CDC) pipeline began streaming updates from Oracle to Postgres, enabling real-time synchronization during migrations.
• Batch jobs that imported warehouse files were replaced with a Kafka-based data pipeline that immediately processed updates.
• The new cloud-based data stores were configured with robust access controls, encryption at rest, and comprehensive auditing capabilities.

The Oracle DB didn’t disappear, but it became smaller, simpler, and less risky, turning into a legacy shell rather than the single source of truth.

Phase 5: Rigorous testing

The cost of application modernization failure is high, as 72% of senior IT decision-makers report significant downtime due to resilience issues with the IT infrastructure. That’s why building a comprehensive testing environment is crucial to validate the new system’s functionality, performance, and resilience without disrupting the live production environment.

For instance, contract testing ensures that the APIs of the new microservices are compatible with their consumers. End-to-end tests must be carefully designed to run against the live, hybrid environment, validating user journeys that may span both the legacy monolith and new services.

Example test insights:

• Side-by-side testing revealed undocumented pricing exceptions applied only on weekends.
• Performance tests identified a bottleneck in the new API caused by a legacy XML parsing dependency.
• Integration tests exposed that one warehouse partner returned malformed XML files incompatible with the new parser.

Phase 6: Orchestrated cutover and seamless transition

The cutover is the moment of truth, where production traffic is fully and finally directed to the new system. The company decided on the blue-green deployment, which involves running two identical production environments: “blue” (the existing system) and “green” (the new system). Once the green environment is thoroughly tested and validated, the router is switched to direct all traffic to it. If any issues are detected, the switch can be flipped back to blue instantly.

Every step of the cutover must be reversible. A rollback strategy is as important as the deployment plan itself. It means having automated procedures to instantly switch traffic back to the legacy system if a critical issue is discovered post-cutover.

Example cutover actions:

• 5% of tracking traffic was routed to the new service, then 20%, then 100%.
• Partners were gradually moved from SFTP-based XML feeds to a modern REST API.
• Legacy modules were kept running for weeks after cutover to validate output and ensure no silent failures were introduced.

Phase 7: Post-rebuild optimization

The journey of application modernization is never truly over. But agile microservices architecture allows different teams to work on different parts of the system independently, accelerating innovation and helping the company maintain system responsiveness.

Example retirements:

• Cron scripts replaced by event-driven flows were disabled one by one, each after a 30-day observation window.
• Tables no longer used by any service were archived and removed from Oracle, reducing maintenance complexity.
• The monolith lost responsibility for tracking, pricing, notifications, and reporting, shrinking by roughly 70%.

Engineers now ship weekly releases because the new services deploy independently.
Observability dashboards made issues visible in minutes instead of days. New features, such as real-time shipment visibility, were delivered on infrastructure that won’t break under load.

The logistics company went from “don’t touch anything, it might break” to “we can ship new features safely any time.”

Checklist for choosing an external application modernization partner

Even a gradual application modernization framework can still be complex, and you might need an external partner to help you prepare and manage the process. A partner can make or break your modernization initiative. To make sure you select the right one, use our detailed table below.

Evaluation area	What to assess	Why it matters	Questions to ask	Red flags	What “good” looks like
Legacy system understanding	Ability to analyze complex, undocumented legacy environments	Most failures come from hidden dependencies and assumptions	How do you map business logic, data flows, and integrations before starting?	“We’ll figure it out as we go.”	Structured discovery, dependency mapping, and system forensics
Zero-downtime experience	Proven track record of modernizing live, mission-critical systems	Downtime equals revenue loss, SLA breaches, and reputational damage	Can you show examples of live systems modernized without outages?	Case studies focus only on greenfield builds	Concrete examples with parallel runs and controlled cutovers
Incremental migration approach	Use of patterns like strangler fig, leave-and-layer, phased rollouts	Big-bang rewrites dramatically increase risk	How do you migrate functionality while the system stays live?	One-time cutover plans	Clear, step-by-step migration roadmap with rollback paths
Data migration & integrity	Strategy for real-time data sync, CDC, and consistency guarantees	Data issues are the hardest and most expensive failures	How do you prevent data loss or divergence during migration?	Data migration is treated as an afterthought	Dual writes, CDC pipelines, validation, and reconciliation plans
Operational maturity	CI/CD, observability, SRE, and incident response practices	Modern systems fail without strong operations	How do you monitor, test, and roll back changes in production?	Manual deployments, weak monitoring	Automated pipelines, SLOs, and real-time observability
Business & governance alignment	Ability to align tech decisions with business priorities and constraints	Modernization is a business-oriented program	How do you work with CIOs, architects, and procurement?	Purely technical delivery mindset	Clear governance, transparent planning, business-aligned milestones

A strong modernization partner can explain each technological decision in business terms and develop modern architectures, but in a way that aligns with your domain needs rather than simply applying the same cookie-cutter modernization logic to every client. 

They should treat your legacy software with respect, as one of the most critical components of your IT infrastructure, and aim for the least disruptive way to modernize it. Avoid vendors who overly criticize your current architecture or systems and suggest a complete overhaul within 6 months. This is disrespectful and unrealistic, the worst combination in an engineering partner.

Key takeaways

Once you find a balance between visibility into your legacy stack and its integration with your business operations, certainty in technological and architectural decisions, and control over gradual system updates, legacy modernization becomes manageable, measurable, and far less likely to stall halfway through. 

The key takeaways are as follows:

• Plan meticulously. Success begins with a clear, business-aligned modernization roadmap.
• Architect for transition. Design the system that can coexist with the legacy one, enabling an incremental migration.
• Embrace automation. DevOps, CI/CD, and automated testing are essential for managing risk and complexity.
• Data is the backbone. A real-time data synchronization strategy is the linchpin of a seamless, no-data-loss transition.
• Cutover with control. Use modern deployment strategies, such as blue-green, to make the final transition a low-risk, reversible event.

Our solution architects, business analysts, and data engineers work with CIOs and engineering leaders to provide software modernization services as a managed program, breaking down large-scale legacy system transformation into well-planned, measurable phases that deliver value without disrupting business operations.

The post Zero-downtime application modernization: Architecture guide for rebuilding critical systems appeared first on Xenoss - AI and Data Software Development Company.

Here’s how NVIDIA’s CEO, Jensen Huang, described the era we live in:

We have now achieved what is called the virtuous cycle. The AIs get better, more people use it, it makes more profit, creates more factories, which allows us to create even better AIs, which allows more people to use it. The virtuous cycle of AI has arrived.

There is a fundamental gap between the hype and the mechanisms behind real-world AI systems. Executives hear terms such as “machine learning,” “deep learning,” “large language models,” “computer vision,” and “recommendation engines,” but the definitions often blur together. Without clarity, companies struggle to select the right model for their problems, evaluate vendor promises, or estimate the investment required to operationalize AI.

This guide explains the most common types of AI models in a business context. It focuses on how these models behave, what problems they solve, what limitations they carry, and how to choose the right approach based on business goals. Throughout the guide, examples illustrate how organizations use these models to solve real operational challenges.

The goal is not to turn business leaders into machine learning engineers. Instead, the purpose is to give teams a grounded understanding of the model landscape so they can make informed, confident decisions when designing AI initiatives.

Behind the buzzword: What drives artificial intelligence

Most discussions about artificial intelligence focus on a single umbrella term. Everything now is AI-driven, -powered, and -enabled. But it’s AI’s subfields, like machine learning (ML) and deep learning (DL), that do all the work and receive the least recognition. For instance, the most hyped generative AI technologies are part of DL and work on large language models (LLMs).

Even the most advanced AI models depend entirely on data. It’s their fuel, which is why every data scientist stresses the need for high-quality training datasets. AI, ML, and DL use statistical methods to extract patterns and insights from data, helping businesses and researchers solve real problems. Unlike traditional statistics that aim for a single definitive outcome, AI works in a far more probabilistic way, producing multiple possible outputs depending on the context and input.

When you understand that data is the foundation of every AI system, resource allocation becomes clearer. Performance does not come from the model alone. It comes from the rigor of data preparation: collecting the right information, cleaning it, structuring it, and ensuring it reflects the real conditions where the AI will operate. Neglecting data quality amplifies uncertainty, randomness, and errors.

With this foundational view of AI in place, we can focus on how differentiating between models can benefit your business.

Why modern businesses need to distinguish between AI/ML models

For business leaders, grasping the fundamentals of AI models is critical for several reasons:

• Informed decision-making. Knowing the difference between a classification model and a regression model helps communicate precise requirements to data teams. This prevents underengineering or overcomplicating a solution.
• Strategic alignment. Understanding a model’s capabilities and limitations helps you identify the business problems where artificial Intelligence and machine learning can have the greatest impact. 
• Responsible innovation. Awareness of concepts such as data bias and model explainability is essential for building ethical, trustworthy AI systems that drive sustainable growth.

AI delivers value when you have a clear business issue to solve. When leaders start with the problem, they avoid wasted investments and accelerate time-to-value. Every effective AI project begins with asking why, not what.

How machines learn: The foundational pillars of AI

The choice of machine learning method indicates which data is necessary for training and which problems a model can solve.

Supervised learning: Learning from labeled data

Supervised learning is the most widely used form of machine learning. Models learn from training data containing input variables and output variables (labels). The latter helps the model differentiate between data points and produce accurate outputs. Depending on the type of expected outcome, AI engineering teams apply: 

• Classification. The output is a category. Examples include spam detection, image recognition (is this picture a cat or a dog?), and medical diagnosis (is this tumor malignant or benign?).
• Regression. The output is a continuous numerical value. This is used for forecasting and prediction, such as predicting house prices, forecasting quarterly sales, or estimating customer lifetime value.

Supervised learning works best when high-quality labeled data is available, and the business objective is clearly defined.

Unsupervised learning: Uncovering hidden patterns

When AI developers train ML models on unlabeled data, they use unsupervised learning techniques. The goal is for the algorithm to analyze the data and discover behavioral patterns, structures, or groupings on its own. This is particularly useful when you don’t know what you’re looking for or when labeling data is impractical. Unsupervised learning models perform the following tasks:

• Clustering. Grouping similar data points. A business might use this to segment customers based on their purchasing behavior.
• Association. Detecting rules that describe large portions of your data, such as the “people who bought X also bought Y” analysis in retail basket analysis.
• Dimensionality reduction. Simplifying complex datasets by reducing the number of variables while retaining important information.

Reinforcement learning: Improving through trial and error

Reinforcement learning (RL) is modeled on how humans learn from experience. An AI model learns to make decisions by receiving continuous feedback in the form of rewards or penalties. This approach is particularly efficient for complex, goal-oriented tasks.

RL is essential for robotics, behavioral fine-tuning in LLMs such as ChatGPT, Claude, and Gemini, and for multi-agent systems that need to continuously improve their performance.

Deep learning model: Neural networks that solve complex problems

As the complexity of the issues in modern businesses grew, a more powerful subset of machine learning emerged: deep learning. Traditional machine learning models often require manual feature engineering, in which a data scientist specifies which features to use.

Deep learning automates this process using artificial neural networks (ANNs), an architecture inspired by the interconnected structure of neurons in the human brain. Data is passed through the network, with each layer learning to identify progressively more complex features. A simple network might have one or two hidden layers, but a “deep” network can have hundreds, allowing it to learn highly abstract representations of the data and resemble human intelligence.

Neural networks can be of different types and trained using different techniques.

Different types of AI models	Training type	Description
Multilayer Perceptron (MLP)	Supervised	A fully connected neural network with multiple hidden layers is used to model complex, non-linear relationships and improve predictive accuracy.
Convolutional Neural Network (CNN)	Supervised	Uses convolution operations to detect local patterns (edges, textures, shapes) in spatial data such as images, making it ideal for vision tasks.
Recurrent Neural Network (RNN)	Supervised	Processes sequential data by retaining memory of previous inputs, enabling the modeling of time-dependent patterns (e.g., language and signals).
Graph Neural Network (GNN)	Supervised and unsupervised	Operates on graph structures, learning relationships and dependencies directly between nodes, widely used for recommendations, fraud detection, and molecular modeling.
Autoencoder	Unsupervised	An encoder–decoder network that learns compact representations of data, often used for anomaly detection, noise reduction, and dimensionality reduction.
Generative Adversarial Network (GAN)	Unsupervised	Consists of a generator and a discriminator trained in competition to produce highly realistic synthetic data, such as images, video, or audio.

Source: arxiv

Neural networks were a real breakthrough as they spurred the rapid emergence of natural language processing (NLP), generative AI, foundation models, computer vision, and multi-modal models. Each of them exists as a standalone solution and can be combined with others to form intricate AI systems that analyze large amounts of data, make predictions, provide recommendations, and generate unique content.

In the following sections, we’ll see how different models apply to the most typical business problems.

Problem #1. Predicting demand, risk, and outcomes

Predictions are the essence of most machine learning models. Companies use them to predict customer churn, end-of-quarter sales, product demand, patient health metrics, or financial trends. Regardless of the industry, the goal remains the same: anticipate what will happen before it happens.

To produce accurate predictions, engineers can use:

• Regression models for numeric forecasts (volumes, prices, demand), e.g., linear regression, time-series, logistic regression, ridge and lasso regression, and support vector regression (SVR).
• Classification models for categorical outcomes (e.g., churn/no churn, default/no default), e.g., gradient boosting, decision trees, k-nearest neighbors (KNN), random forests, or deep learning–based sequence models such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).

Most real-world prediction systems use a combination of regression and classification models. For instance, a financial institution may build a hybrid system that uses a regression model to predict a customer’s likelihood of churn in the next quarter. In contrast, a classification model flags high-risk credit behaviors. This composite score helps teams focus retention efforts, adjust pricing, and make smarter credit limit decisions.

Cross-industry examples

Xenoss example: 

The Xenoss team developed an ML-powered virtual flow metering solution for a US-based oil and gas company. The new system had to replace physical flow meters and help the company reduce costs by providing predictions of oil, gas, and water flow rates.

To ensure stable sensor readings and accurate low-latency predictions under transient conditions, we combined physics-based models with a long short-term memory (LSTM) time-series model and neural networks (RNN/CNN). On top of that, we established machine learning operations (MLOps) to automate model retraining when detecting new data inflow or potential model drift.

As a result, the solution helped the company achieve more than 95% prediction accuracy in flow metering. They also reduced opex by 40% and downtime by 30% through timely anomaly detection.

Problem #2. Customer segmentation and personalization

To enable efficient customer clustering and personalization, AI developers use:

• Clustering models help to identify groups of similar customers without predefined labels. These models are especially useful in marketing, product analytics, and customer lifecycle management.

For example, k-means clustering creates distinct, well-separated customer groups. It works well when the goal is to create a clear set of segments for targeted campaigns. Fuzzy k-means clustering assigns probability-based membership to multiple clusters. This model is useful when customers fit into overlapping categories, and you need to design broader, interest-based marketing strategies.

• Recommendation models help media, retail, entertainment, and digital banking businesses offer personalized services and products to customers. These models rely on ML algorithms to evaluate many possible choices and highlight the most relevant ones for each user. Targeted recommendations can account for up to 30% of revenue.

Common approaches and models for building recommendation systems include:

Context filtering analyzes the circumstances under which a customer interacts with your product, such as device type, location, time of day, or browsing session characteristics, and uses this situational data to generate more relevant recommendations.

Collaborative filtering looks at patterns across many users. If people with similar tastes tend to like or purchase the same items, the system assumes those preferences apply to others in that group. For example, if several users watched Movies A and B, and some of them also watched Movie C, the system will recommend Movie C to others with the same viewing pattern.

To enable these recommendation methods, ML engineers apply matrix factorization (MX) and numerous neural networks (multiplayer perceptrons (MLPs), RNNs, CNNs, neural collaborative filtering (NCF)). MX is an embedding model that mathematically calculates users’ interactions with diverse items.

Deep neural networks (DNNs) can be particularly useful in recommender systems to analyze large volumes of data and provide better, more personalized recommendations (even in cold-start cases with scarce data) than traditional ML algorithms. 

A well-known example is Spotify, which uses a hybrid approach to music recommendations, combining collaborative filtering and context filtering with DNNs to define more complex relationships between users and the music they listen to.

Cross-industry examples

Xenoss example:

Our team developed an AI-powered personalization engine for the digital advertising agency, AdWake. We embedded the engine into an offerwall monetization platform. As a separate module, it continuously learns from users’ behavior and predicts how likely customers are to complete an offer. The result of this implementation is increased conversion rates and an opportunity for the company to build a unique value proposition.

Problem #3. Fraud detection, quality control, and anomaly detection

Fraud detection, quality control, and anomaly detection rely on machine learning models that can identify unusual behavior or deviations from normal patterns. 

These systems help organizations flag risks early, protect revenue, and maintain consistent operational performance. 

The choice of model depends on the industry and the type of input data, whether text, numerical logs, sensor streams, images, or video.

• Classification models (e.g., support vector machine (SVM), Naive Bayes (NB)) and ensembles (e.g., gradient boosting, random forests) trained on large, labeled datasets of fraudulent and legitimate transactions.

These models learn the statistical differences between genuine and suspicious activity, enabling systems to detect anomalies in financial transactions, login attempts, insurance claims, or network access patterns.

• Computer vision models (e.g., CNNs, You Only Look Once (YOLO) for real-time object detection, Vision Transformers (ViTs) for complex visual patterns, and contrastive language-image pretraining (CLIP)) for visual inspection of parts and products.

These models analyze images or video to detect defects, classify components, verify product quality, or identify abnormal conditions on factory floors or production lines.

Depending on the industry and the type of data (text, images, or videos), you can select a suitable classification or computer vision model to detect fraud or anomalies, or to ensure quality control. 

For instance, CNNs are particularly useful in medical imaging, as they can handle high-dimensional, noisy datasets and extract meaningful patterns. When combined with IoT sensors for remote patient monitoring, these models can enable real-time diagnostics in emergencies.

Cross-industry examples

Problem #4. Document understanding and knowledge automation

Extracting information manually from spreadsheets, PDFs, emails, and paper-based documents is slow, costly, and error-prone. With AI-powered document understanding tools, businesses can process vast amounts of data (structured, unstructured, and semi-structured) in a fraction of the time. 

Here are models that can be useful for document and knowledge automation:

• Natural language understanding (NLU) and NLP for contextual understanding: LLMs (Mistral, Llama), transformer models (BERT, RoBERTa, XLM-RoBERTa), encoder–decoder models for summarization and entity extraction, and embedding models (multilingual e5 text embedding model).
• Agentic/multi-agent solutions (based on LLMs) for retrieving, validating, and acting on business-specific data.

Document-heavy industries (legal, insurance, finance, healthcare, marketing) rely on highly specialized terminology and unique document structures. To improve accuracy and reduce hallucinations, engineering teams fine-tune NLP models using supervised learning on:

• labeled enterprise documents
• domain-specific vocabulary
• historical case files
• annotated contracts, invoices, or policies

Fine-tuned models capture organization-specific nuances, significantly improving extraction quality and contextual understanding.

Cross-industry examples

Xenoss example:

Our team developed an LLM-based chatbot for a multinational marketing and advertising holding company. They needed to automate corporate knowledge management and ensure timely access to distributed knowledge bases across different teams.

To achieve a perfect balance between accuracy, relevance, and real-time accessibility, we developed a multi-agent architecture based on Llama 3.1 8B and e5-large embedding model. Our team packaged the system as a chatbot so users could access it through a simple conversation flow. 

This custom solution included agents for retrieval, generation, quality control, and adaptation. To provide the most accurate and contextually rich outputs, we also built a retrieval-augmented generation (RAG) system. 

As a result, the company achieved a 95% accuracy rate in providing employees with business-critical information. This saved time on manual searches and freed employees to focus on more high-value work.

Problem #5. Content creation for enhancing employee productivity

Generative AI represents a paradigm shift, enabling models to generate entirely new content. These models learn the underlying patterns and structure of a dataset and then use that knowledge to create new examples. The most popular AI model types include:

• LLMs act as general-purpose language engines that can be adapted via prompt engineering, fine-tuning, or RAG.
• Generative Pre-trained Transformer (GPT) (e.g., GPT-4, GPT-5.1).
• Diffusion models, Generative Adversarial Networks (GANs) for image/video generation 
• GANs, diffusion, or LLM-based tabular generators for synthetic data generation 

Depending on the type of content you need to create, you can select from various generative AI models. But if you don’t want to limit your workflows to any particular content type, develop customized multimodal models. 

For instance, Coca-Cola provided artists with a custom platform for generating creative assets using GPT-4 (text generation) and DALL-E (image generation) models.

Cross-industry examples

Problem #6. Decision optimization and autonomous control systems

To continuously adapt to changing market conditions, machine states, customer behavior, or financial risk, companies are shifting toward automated decision-support systems. These systems often appear as agentic AI or multi-agent setups, where algorithms learn not from static labeled datasets but from interactions with their environments. Reinforcement learning (RL) sits at the core of this approach. The model performs actions, receives feedback, and gradually learns the most effective strategy.

To build reliable autonomous decision-making engines, AI engineering teams typically combine several model families:

• Reinforcement learning models (Q-learning, deep Q-networks, policy gradients, actor–critic methods) to learn adaptive policies in dynamic, uncertain environments.
• Optimization models (linear programming, mixed-integer programming, constraint solvers) to compute mathematically optimal decisions under strict business constraints such as cost, capacity, risk, or time windows.
• Control models (PID controllers, model-predictive control (MPC)) for continuous, real-time adjustments in physical or industrial systems.

Combined, these approaches enable systems to decide what to do next, even under changing conditions.

Cross-industry examples

Xenoss example:

To help a global retail network automate complex reconciliation processes, we developed a multi-agent solution capable of processing and acting on high-volume reconciliation workload. The system includes three agents (scheduler, reconciler, router) that interact with each other. They also learn from past experiences and adapt to unexpected situations. 

From creating reconciliation schedules to filling approved statements to the ERP, the multi-agent system enables autonomous workflows within the client’s existing enterprise software. Eventually, this solution helped the company automate over 80% of tasks and build a unified reconciliation process across all 60 units, reducing manual effort.

The above list of business problems that AI can tackle isn’t exhaustive, but it shows the intricacy and wide range of available models. When you need to predict, classify, segment, detect, or create anything on a large scale, there is definitely a specific model (or a combination of several ones) for each use case.

Cheat sheet: Mapping your business problem to the suitable model

Business question	Typical output	Most suitable model family	Data required	Explainability needs	Key constraints to consider
“What outcome can we expect from this situation?”	A number (demand, probability, price, time-to-failure)	Forecasting/regression models (Linear models, Gradient Boosting, Time-Series Models, Sequence Models)	Historical tables, logs, metrics, time-series data	Medium–High (forecast accuracy must be explainable for planning)	Quality and quantity of historical data; seasonality; mode performance drift; need for continuous retraining
“Where does this item/dataset/product/service belong to?”	A category (churn/no churn, fraud/not fraud, defect/OK, spam/not spam)	Classification models (Logistic regression, Random Forest, XGBoost, Neural classifiers)	Transaction data, tabular labeled datasets, event logs	High for regulated areas (finance, healthcare)	Label quality, imbalance in classes, threshold tuning, false positives vs. false negatives
“Can we discover natural segments or patterns among selected datasets?”	A grouping (clusters, behaviors, patterns)	Clustering/unsupervised learning (k-means, hierarchical clustering)	Unlabeled data, customer behavior logs, and user events	Low (exploration-focused, not regulatory)	Data preprocessing, feature scaling, interpretability of clusters, and business validation
“What should we recommend next to our users/customers?”	A ranked list (next product, next action, next best offer)	Recommendation/ranking models (Collaborative filtering, deep recommenders)	Behavioral data, user history, transactions, interactions	Low–Medium (depends on personalization policies)	Cold-start problem, data sparsity, real-time serving, privacy constraints
“Can we create new content or variations?”	A generated artifact (text, code, images, synthetic data)	Generative models/LLMs (Transformers, diffusion models, GANs)	Text, documents, code repositories, product data, design assets	Medium–High (hallucination risk, grounding needed)	Guardrails, prompt design, fine-tuning vs. RAG, data privacy, IP risks
“How do we automate actions over time?”	A sequence of decisions (pricing adjustments, robotics control, routing strategies)	Reinforcement learning/optimization models	Simulations, environment data, operational logs, sensor data	Medium (policies may be opaque)	Simulation fidelity, safety constraints, long training cycles, and computational cost

Next steps in the AI journey

Many models intersect and can be used to solve several business challenges. You’ll need to join forces with data scientists, data engineers, and AI engineers to create unique AI systems that work for the benefit of your business.

To maximize AI potential for your company, choose a strategic, data-first approach, which involves:

1. Identifying high-value use cases. Pinpoint a specific task or business problem where AI can deliver a clear ROI, whether it’s optimizing operations, enhancing customer engagement, or creating new revenue streams.
2. Investing in data readiness. Your AI strategy is only as good as your data infrastructure. Ensure you have clean, accessible, and relevant data to train and sustain your models.
3. Starting with a pilot project. Begin with a manageable project to build internal expertise, demonstrate value, and learn the potential of the AI applications.

The beauty of AI lies in its ability to help businesses manage chaotic, changing environments. As companies process more data, engage with more customers, and employ more people, the processes get fuzzy and complicated. AI is meant to make sense from the chaos and help businesses prepare for an even more complex future. And Xenoss will stand by your side, providing the best-fit AI tools, models, and hands-on AI engineering experts.

The post Explaining AI: A сomprehensive guide to the most common types of AI models in the context of real business problems appeared first on Xenoss - AI and Data Software Development Company.

Meanwhile, many companies are already shifting from conversational interfaces to AI capabilities that live within business operations: assistants embedded in tools, agents that automate multi-step tasks, and early multi-agent systems that coordinate entire workflows. The competitive advantage comes from this deeper operational integration. 

After three years of experimenting with 900 AI pilots, Johnson & Johnson now prioritizes 10–15% of initiatives with meaningful operational value. The company CIO said, “We had the right plan three years ago, but we matured our plan based on three years of understanding.” 

They believed in AI, invested in custom experiments, and analyzed results. This helped them shape a unique AI strategy. Shortly after this strategic shift became public in Q2 2025, J&J reported a 5.8% year-over-year increase in Q2 sales. That growth can’t be attributed solely to AI. However, it aligns with J&J’s broader goal of using AI to accelerate innovation and strengthen overall performance.

Despite impressive results of big tech companies, stepping out of the chatbot comfort zone for some organizations can be risky. You may think that “we don’t have time and budget for 900 pilots”. However, this may not hold for every company, because each business operates under different constraints. As AI technologies mature, they open up more ways to introduce advanced AI safely, so teams can test new capabilities, validate, and roll back without disrupting core infrastructure. This creates a practical path beyond chatbots and toward AI that understands workflows, acts autonomously within guardrails, and improves as operations evolve.

This guide will help you determine whether advanced AI is viable for your business by examining:

• approaches for integrating AI into business workflows beyond chatbots;
• use cases where each approach is the most beneficial;
• AI system development strategies; 
• methods for optimizing AI deployment and use to ensure quick and stable ROI;
• the value from integrating advanced AI solutions.

When chatbots stop delivering value

Chatbots like ChatGPT, Claude, and Gemini provide context-rich responses to human queries. They excel at retrieving information, summarizing content, and assisting with text generation. These capabilities make them useful for knowledge workers and can save up to 4 hours of routine work each week. A recent survey shows that employees find AI chatbots helpful for speeding up their work, but less impactful on improving the quality of work.

Despite their convenience, chatbots operate within a single interaction loop: they wait for a prompt, generate an answer, and return control to the user. This model is helpful for supporting work, but it cannot run a workflow. Business processes depend on multi-step execution, validations, approvals, and coordination across systems. A chatbot can guide an employee through the process of filing an insurance claim, but it cannot process it end-to-end. This reveals a fundamental divide between “explaining work” and “doing work.”

Passive outputs

The request–response mechanism limits a chatbot’s usefulness in operations. Once it completes an answer, it stops. It cannot follow through on tasks, trigger downstream actions, or track progress across systems. Processes that require sequencing,  such as onboarding, invoice handling, supply chain updates, or compliance reporting, remain manual because the chatbot cannot execute them.

Memory limitations

This gap becomes even more visible when you consider memory. Chatbots don’t remember past interactions or decisions unless you provide that info each time. But enterprise operations depend on persistence. This includes business rules, past actions, exceptions, service-level agreements, and audit trails. Without the ability to remember and reason over this context, a chatbot can’t own or reliably execute any workflow.

Lack of integrations

Another barrier is integration depth. Chatbots usually interact at the surface: answering questions, summarizing documents, or fetching bits of information. They aren’t embedded into the internal enterprise systems, such as ERP, CRM, financial tools, or internal databases. 

Rule-based chatbots stop delivering value when the business expects them to behave like operational AI. From here, the next layer of intelligence, embedded AI, task assistants, workflow engines, and agents, is beneficial to take companies from “using AI” to running business functions with it.

Chatbots vs. advanced artificial intelligence solutions

Advanced AI becomes useful the moment operational complexity increases. As companies grow, processes become interconnected: procurement affects finance → finance affects the supply chain → the supply chain affects customer experience. A chatbot can’t manage these dependencies.

Its job is to answer questions. Advanced AI systems, on the other hand, perform tasks across systems. They make recommendations, apply business rules, and improve over time. This happens through structured feedback and retraining. As a result, AI becomes an integral part of business workflows.

Dimension	Traditional AI chatbots	Advanced AI solutions
Decision scope	Single query → single response	Multi-step reasoning, branching logic, and workflow orchestration
Context retention	Session-limited; loses context between interactions	Persistent memory across workflows, tasks, and system states
System integration	Simple API calls triggered by user requests	Deep, autonomous AI coordination across multiple systems, tools, and data sources
Learning mechanism	Static training; improves only with new model versions	Continuous adaptation from operational data, feedback loops, and performance outcomes
Actionability	Can provide an answer or suggestion	Can take actions across systems, trigger and streamline processes, update records, and enforce rules
Error handling	Fails silently or asks the user for clarification	Detects anomalies, retries, escalates, logs decisions, and applies guardrails
Workflow awareness	No understanding of business processes	Understands process stages, dependencies, approvals, and constraints
Operational impact	Improves user productivity in conversations	Automates business operations and decision cycles

Incremental progress from conversation AI to advanced AI is one of the best strategies to naturally enhance business operations with AI. It’s a representation of the crawl-walk-run framework, where chatbots represent the “crawl” stage, AI assistants – “walk”, and agentic systems and networks – “run”.

What differentiates each level is the ability not just to respond but to act, and eventually to learn from actions.

As organizations mature, the question shifts from “How do we use chatbots?” to “How do we build AI that operates our workflows?” That is the moment when advanced AI starts creating meaningful business value.

Integrating AI systems: The roadmap from chatbots to agentic networks

The path from basic chat interfaces to autonomous, workflow-driven AI is a four-level maturity curve. Each level builds on the previous one, expanding the extent to which AI contributes to daily operations.

This roadmap helps organizations understand where they stand today and what is required to progress toward systems that act, coordinate, and learn from real business workflows.

Level 1: Assisted intelligence

This type of intelligence includes chatbots as information partners. It’s best suited for workflows in which humans need AI assistance but still do all the work themselves. AI simply optimizes processes, answers queries, and generates content.

Core technologies under the hood: Generative AI, conversational AI, and natural language processing (NLP).

Out-of-the-box solutions: ChatGPT, Claude, Perplexity, DeepSeek, Gemini chat interfaces.

How to customize: Integrate with internal knowledge bases and communication platforms like Slack and Microsoft Teams.

Workflow example: A customer service chatbot that unburdens the support team and provides customers with quick answers.

Business impact: Faster responses and lower support costs, but no meaningful transformation of business processes.

Level 2: Augmented intelligence

At this stage, AI becomes a lightweight decision-support companion. AI copilots and assistants suggest next steps, handle basic tasks, and point out opportunities. However, humans are still responsible for approval and execution. With this level of intelligence, AI augments workflows without owning them.

Core technologies under the hood: Machine learning, generative AI, retrieval augmented generation (RAG), predictive modeling, data analytics, and recommendation engines.

Out-of-the-box solutions: GitHub Copilot for code, HubSpot AI Assistants.

How to customize: Define custom workflows, tune prompts, configure guardrails, and embed copilots directly into internal tools.

Workflow example: A sales copilot recommending outreach sequences or drafting highly personalized emails.

Business impact: Employee productivity improvement with limited impact on business processes.

Level 3: Autonomous workflows

This is the first level where AI moves from supporting work to performing it. Agents can run complete workflows from start to finish within clear limits. They pull data from systems, make decisions, trigger actions, and hand edge cases or high-risk situations over to humans when needed.

Core technologies under the hood: AI agents, workflow engines, structured tool-calling, API orchestration, event-driven automation, vector databases (for context and memory).

Out-of-the-box solutions: Azure AI Agents, Amazon Bedrock Agents, LangChain Agents.

How to customize: Connect via custom APIs, set guardrails, and add evaluation pipelines for quality control.

Workflow example: Automated invoice processing, where an agent extracts data, validates it, checks rules, updates systems, and triggers payments.

Business impact: Significant operational efficiency gains with higher consistency and fewer delays.

Level 4: Agentic networks

At the highest maturity level, multiple specialized agents collaborate to manage interconnected business processes. They communicate through standardized protocols, share context, request actions from one another, and collectively optimize outcomes. Humans shift from task-level supervision to strategic oversight.

Core technologies under the hood: Model context protocol (MCP) server, agent-to-agent (A2A) protocol, shared memory layers, policy-driven orchestration.

Out-of-the-box solutions: Multi-agent frameworks like CrewAI or AutoGen.

How to customize: Design agents around business functions, implement shared context stores, standardize communication, add governance, and human-in-the-loop layers.

Workflow example: Cross-department supply chain optimization, where agents across business departments synchronize decisions in real time: 

• procurement agent tracks supplier delays
• logistics agent adjusts delivery routes
• planning agent recalculates inventory needs
• finance agent updates cash-flow forecasts

Business impact: Deep operational transformation, new service capabilities, and entirely new business models enabled by AI-driven orchestration.

To tap into advanced AI (augmented or agentic), you need to know typical AI development approaches. This includes understanding their business impacts, requirements, technologies, costs, and ROI.

Developing AI systems with learning capabilities: Fine-tuning, no-code, low-code, and code

To develop AI solutions that learn from your workflows and get better over time, you can either fine-tune existing models’ capabilities or develop custom AI solutions from scratch with the help of no-code, low-code, or code-based tools. Each method differs in flexibility, long-term scalability, and adaptability to operational data.

The right choice depends on your workflow complexity, compliance requirements, data maturity, and timeline for achieving meaningful ROI.

AI development with no-code/low-code tools

No-code and low-code platforms such as Microsoft Power Platform, Retool, Glide, Mendix, and Airtable AI enable businesses to build AI-powered apps quickly, without a whole engineering team. These tools support workflow logic, API integrations, vector-based search, document ingestion, and lightweight model prompting. AI can improve as users refine prompts, enhance datasets, or adjust workflow rules.

You interact with these systems via user interfaces that let you compose a custom AI solution from a wide range of drag-and-drop functionality. This way, you focus on the system’s features rather than how it operates under the hood.

No-code/low-code tools improve results over time via:

• iterative prompt optimization (users can refine prompts over time based on what works or doesn’t)
• adding examples (“few-shot” learning) for in-context understanding
• refining workflow rules based on real-time performance analytics
• connecting extra data sources as operations expand

Pros:

• Fastest time-to-value (in hours or days)
• Low learning curve for business users
• Cost-efficient, with no infrastructure or MLOps required
• Supports incremental improvement, letting teams experiment safely before scaling
• Easy integration with SaaS systems (CRM, ERP, analytics)

Cons:

• Limited flexibility for complex or high-stakes workflows
• Restricted access to the underlying model logic
• Scalability may hit a ceiling as workflows become more advanced
• Less control over security, data lineage, and custom reasoning logic

Real-life example: For 59% of business leaders (among 1,000 surveyed), custom-built AI solutions with no-code, low-code tools proved the most transformative in their business operations. This makes sense from an ROI perspective: these tools let teams quickly automate high-volume, repetitive tasks at low cost, without waiting for engineering resources or long development cycles. 

Since the investment is small and the time-to-value is quick, even slight efficiency gains lead to rapid, compounding returns. This makes no-code and low-code options very cost-effective for entering advanced AI.

When to choose: No-code/low-code platforms let teams develop custom AI systems and quickly step beyond simple AI workflows like chatbots. With these platforms, businesses can combine flexibility with speed-to-market, experiment, and spin up workflows quickly without incurring heavy development overhead.

Fine-tuning existing foundation models

Foundational models are key to generative AI. They include large language models (LLMs), computer vision, and generative adversarial networks (GANs). While these models are powerful out of the box, they’re still generalized systems trained on broad internet-scale data. To be truly effective for your business, they often need fine-tuning. This approach involves adjusting the model’s parameters to optimize performance and improve output accuracy for a specific domain. To achieve this, you can train AI models with custom high-quality datasets.

That’s why model fine-tuning requires: 

• establishing a reliable data infrastructure with a single source of truth;
• role-based access controls;
• real-time data pipelines.

The most common model fine-tuning methods are: 

• supervised fine-tuning (SFT) using labeled instruction data; 
• reinforcement learning from human feedback (RLHF). 

With these methods, models produce context-aware outputs, follow instructions, and perform domain-specific tasks.

Pros: Fine-tuning existing models is more cost-efficient than training and inference from scratch. Plus, with the model trained on your proprietary data, you receive more accurate results than with generalized open-source AI solutions and retain full ownership of a custom solution.

Cons: Fine-tuned models may not have access to up-to-date enterprise data or “forget” the parameters they were trained on. To address this, hybrid approaches such as retrieval-augmented fine-tuning (RAFT) have emerged, providing models with continuous access to relevant data while maintaining a high level of explainability through chain-of-thought reasoning.

Real-life example: The Hugging Face team helped the investment firm Capital Fund Management (CFM) implement fine-tuned small language models (SLMs) to solve a financial named-entity recognition (NER) problem. 

Although LLMs provide high performance, smaller models are more cost-efficient. Hugging Face used LLMs’ power only to assist with data labeling, but ran training and inference with SLMs. This helped the investment company reduce model maintenance costs (inference per hour for SLMs costs $0.10, while for LLMs, it costs $4.00–$8.00 per hour) and achieve high output accuracy.

When to choose: Ideal for specialized workflows such as claims processing, contract review, fraud detection, financial analysis, and medical summarization. Businesses benefit from precision and consistency that generic models cannot deliver.

Custom AI system development with code

Building AI systems with languages such as Python, R, Java, C++, or Rust provides complete control over model behavior, data pipelines, guardrails, evaluation logic, and agent orchestration. Developers can implement reinforcement loops, custom model evaluation, automatic retraining triggers, and multi-agent architectures, developing AI systems that continuously learn from real operational data.

Just like with model fine-tuning, the process of custom model development involves establishing a data foundation and building custom data pipelines. The difference is in custom model training, hyperparameter tuning, model evaluation, and versioning. Plus, you’ll also be responsible for integration, deployment, and model governance.

To enable all of this, you’ll need a dedicated team (machine learning engineers, data engineers, and subject matter experts) and compute infrastructure for model retraining, fine-tuning, and inference (GPUs/TPUs, storage).

Pros: 

• Maximum customization and control
• Deep integration into enterprise systems
• Supports advanced guardrails and compliance logic
• Enables faster system evolution into multi-agent or autonomous workflows

Cons:

• The highest initial cost and longer development cycles
• Requires engineering and MLOps expertise
• Ongoing maintenance is needed for scaling, monitoring, and retraining

Real-life example: With direct access to the model’s Python code, a developer built a continuously self-learning AI system. Using Python libraries, memory buffer, and algorithms, such as model-agnostic meta-learning (MAML), he developed an incremental learning loop that enables a model to improve in real time without the need for regular data updates or retraining.

He integrated the system with a real-world fraud-detection mechanism at a financial services company. As a result, the model continuously adapted to emerging fraud patterns and detected new fraud behaviors in real time.

When to choose: Suitable for mission-critical AI systems that run complex, multi-system workflows or autonomous agents. This AI development method offers the best long-term ROI. It’s designed to fit your processes, compliance rules, and proprietary data. Ideal for companies needing strong system integration, custom guardrails, scalable automation, or multi-agent orchestration.

AI development approaches: Comparison table, costs, and ROI timelines

Approach	Key business implications	Requirements	Core technologies	Cost range (realistic)	ROI timeline
No-code AI (Glide, Airtable AI, PowerApps AI App Builder)	Fastest experimentation; low risk; ideal for prototypes and lightweight workflows	Developers with minimal IT oversight	Prebuilt AI blocks, connectors, and form logic	$30–$150 per user/month or $5k–$30k/year for team plans	2–8 weeks
Low-code AI (Mendix, OutSystems, Retool, Power Platform)	Good for operational tools; scalable with moderate customization; faster than full-code	Business analysts and engineers	Workflow engines, API connectors, ML integrations	$20k–$150k/year, depending on seats and environments	2–4 months
Custom code development (Python, Java, Node, Rust backends, ML pipelines)	Full control; highest flexibility; needed for complex, mission-critical systems	Engineering team; DevOps; MLOps infrastructure	Model serving, vector DBs, orchestration frameworks	$150k–$500k+ on the initial development; $10k–$80k/month on maintenance	6–18 months
Fine-tuning foundation models (OpenAI FT, HuggingFace, Google Vertex AI, Azure AI)	Domain-specific accuracy, competitive moat, and improved complex reasoning	High-quality data, ML engineers, and GPU availability	LLMs, adapters, SEFT, RLHF, RAFT, and evaluation pipelines	$50k–$300k per fine-tuned model depending on data & GPUs	3–9 months

You can integrate each AI development approach into your workflows in different ways. No-code and low-code might offer less flexibility, but you can still adapt them to fit your processes, data structures, and brand guidelines. Advanced methods, like custom code and fine-tuning, enable deep system integration and autonomous behavior, making it suitable for end-to-end workflow orchestration and multi-agent architectures. 

AI model deployment 

For successful AI deployment, you can choose either an API integration with cloud providers or AI vendors, an aggregator, an LLM routing service to switch between models, or run models on-premises. What approach to choose depends on your budget, workload type, latency requirements, privacy constraints, and the degree of autonomy your AI systems need.

AI model deployment strategy	How you pay	What you’re paying for	Best for
Model hosting via APIs (OpenAI, Anthropic, Google Vertex, Azure OpenAI)	Usage-based token billing (input and output tokens)	Volume, not users; scales with automation demand	Backend services, high-throughput workloads, agentic AI systems
Aggregators (e.g., Mammoth.ai, OpenRouter, LangSmith routing)	Flat subscription that abstracts multiple model providers	Routing, unified API, lower combined price than using each model provider separately	Teams requiring reliability, model switching, or cost optimization without overhead
Local models (running on laptop/workstation/edge)	Hardware and electricity (compute time)	Open-source models are free; the cost is setup, maintenance, and GPU/CPU time	Privacy-sensitive workloads, offline environments, rapid experimentation, and cost savings

For instance, the go-to approach can be fine-tuning LLMs to fit your niche and then deploying them in the cloud via APIs to avoid the overhead. A mid-sized company could fine-tune a base model like Llama or Mistral on its own data (customer transcripts, contracts, historical cases, or product specifications) to improve accuracy on domain-specific tasks such as classification, forecasting, or compliance checks.

Once the model is fine-tuned, it can be hosted on a managed platform (Azure AI, AWS Bedrock, GCP Vertex, Hugging Face Inference Endpoints), where the vendor handles scaling, uptime, security patches, and GPU provisioning. This reduces your infrastructure responsibilities to almost zero and turns AI into a predictable, pay-as-you-go operational expense.

Benefits of integrating AI systems beyond chatbots

Let’s see how a workflow of processing a customer query could look using only an AI chatbot compared to using an agent.

Step in the workflow	Before (chatbot)	After (AI agent)
Customer inquiry arrives	An employee reads and interprets the email	Automatically reads and parses the email
Order lookup	Chatbot tells the employee how to check the order	Queries the order management system directly
Identify the cause of the delay	Chatbot provides general policies or guesses based on text	Pulls real shipment data and determines the exact cause
Customer context	Employee manually checks CRM history	Fetches CRM history automatically
Decision-making	Employee reviews policies and decides what action to take	Applies business rules (refund? escalate? reship?)
Response drafting	Chatbot drafts text; employee edits	Drafts or sends the final personalized email
System updates	Employee updates CRM/ticket by hand	Updates CRM records automatically
Follow-up actions	Employee sets reminders or tasks	Schedules follow-ups or triggers next steps
Human involvement	A complete workflow requires manual effort	Human only reviews exceptions or approvals
Overall outcome	Faster information access, but no process automation	A complete end-to-end workflow execution with minimal human effort

 From this table, we can form the following benefits:

• Less human intervention. With a chatbot, there is constant back-and-forth communication and validation. Whereas an agent is autonomous and performs most tasks by itself, requiring human validation at the end.
• Faster end-to-end resolution. AI-powered chatbots speed up answers, but they don’t speed up the process. Agents shorten the entire workflow, from identifying the issue to updating systems, resulting in faster resolution times.
• Higher accuracy with fewer errors. Agents rely on real system data rather than surface-level text, reducing manual mistakes such as incorrect updates, missed steps, or misinterpreted policies.
• Consistent decision-making. Where a human might change their judgment, agents apply business rules consistently, improving fairness, compliance, and operational predictability.
• Scalable process automation. Agents scale entire workflows, allowing the business to handle more operational load without adding headcount.

Moving beyond chatbots doesn’t mean completely replacing human employees with AI. It’s about helping people across many departments perform, reason, and act more freely in complex workflows. 

Bryce Hall, the Associate Partner at McKinsey, explains the collaboration of AI and humans this way: 

AI is rarely a stand-alone solution. Instead, companies capture value when they effectively enable employees with real-world domain experience to interact with AI solutions at the right points. The combination of AI solutions alongside human judgment and expertise is what creates real “hybrid intelligence” superpowers and real value capture. AI leaders adopt a set of other practices that point in this same direction, including fully embedding AI solutions into business workflows and having senior leaders actively engaged in driving adoption at scale.

Final takeaway

The aim of this article wasn’t to convince you to abandon chatbots altogether. They remain extremely valuable for frontline communication, internal Q&A, and fast information access. Chatbots are often the first safe, low-risk step in exploring AI. 

But the real, tangible business benefits come from gradually transitioning from conversational AI to workflow-embedded AI and doing so in a structured, measured way to align with your business priorities, risk tolerance, and technical maturity.

The future of AI belongs to multi-agentic, multi-modal enterprise AI systems that can reason, act, collaborate, and learn across your business workflows. Those who wait will eventually adopt AI out of necessity, while those who start now will adopt it out of opportunity. 

With deep expertise in enterprise data engineering and agentic AI, Xenoss helps businesses make this transition safely and strategically, building systems that mature with your operations and position you ahead of the curve.

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