What challenges are involved in integrating AI with operational data ?

Modern AI applications need operational data, not the stale snapshots in data warehouses, but live views of what's happening right now across your business. A fraud detection system needs to see account balances, transaction history, and risk scores synchronized to the same moment. A personalized recommendation engine needs current inventory, customer behavior, and pricing data. An AI agent needs a coherent view of your business to take meaningful actions.

The problem is that operational data lives in siloed systems built for transaction processing, not for the complex queries and cross-system integrations that AI applications require. This creates a fundamental tension: AI needs operational data in a form that operational systems weren't designed to provide.

Organizations typically try to bridge this gap through some combination of data warehouses (which introduce too much latency), direct database queries (which can't handle the complexity), or custom streaming pipelines (which require specialist engineers and months of development). Each approach involves tradeoffs that ultimately constrain what AI applications can do.

The difficulties of integrating AI applications with operational data cluster around five fundamental problems: latency, cost, operational complexity, and development velocity. Understanding these helps clarify what any solution needs to address.

Traditional data warehouses process data in batches. An event occurs, gets extracted from an operational database, transformed through a pipeline, and loaded into a warehouse. By the time this process completes, the data may be minutes or hours old. For AI applications responding to changing conditions (dynamic pricing, fraud detection, personalization), this latency makes the data unusable.

Operational databases provide fresh data but struggle with the queries AI applications generate. Joining data from multiple tables, aggregating across large datasets, and computing features for machine learning models puts substantial load on systems designed to handle individual transactions quickly. Read replicas help distribute this load but don't solve the fundamental mismatch: complex queries are expensive to run on systems optimized for transactional workloads.

The queries get even more expensive at scale. A single AI inference might trigger multi-way joins across five or more tables, aggregations over time windows, filtering on nested JSON structures, and subqueries with complex predicates. When these queries run at hundreds or thousands of requests per second, databases start to struggle. Some organizations denormalize data to improve query performance, but maintaining denormalized views as source data changes introduces its own complexity and latency.

AI workloads are expensive. They consume significant compute and memory resources, and their resource consumption is often unpredictable. A poorly optimized query or an unexpected spike in inference requests can overwhelm shared database resources, impacting other applications that depend on the same systems.

This creates several cost challenges:

Organizations provision dedicated read replicas for AI workloads, implement query throttling and rate limiting, or create separate database instances for different applications. These help contain the blast radius but don't solve the fundamental issue: it's hard to predict resource needs before running a query, and scaling resources for unpredictable workloads is expensive.

The cost problem worsens with cloud-managed databases that charge for compute and storage together. To support peak AI workload requirements, organizations often overprovision resources that sit idle during off-peak hours. The economics become particularly challenging when supporting multiple AI applications with different usage patterns. Each application's peak might occur at different times, but you need to provision for the combined peak across all applications.

Stream processing frameworks can handle transformations on data in motion but require running a complex stack: CDC tools to capture database changes, message brokers to transmit events, stream processors to transform data, multiple caching layers, and custom services to coordinate everything. This architecture has high baseline costs even before handling any AI workload. The infrastructure runs continuously whether or not AI applications are actively querying it.

Organizations supporting AI applications with operational data often end up with architectures that require specialized expertise to operate:

Change Data Capture (CDC) tools to extract database changes Message brokers like Kafka to transmit events Stream processors to transform data in motion Multiple caching layers to improve query performance Custom coordination services to tie everything together

Operating this architecture demands expertise in distributed systems debugging, stream processing frameworks, cache invalidation strategies, and schema evolution management. The operational burden increases costs directly (through specialized headcount) and indirectly (through slower development as engineers spend time managing infrastructure instead of improving AI models).

When things go wrong, debugging is challenging. A problem might originate in the CDC tool, the message broker, the stream processor, the cache, or the coordination layer, and diagnosing which component is at fault requires deep expertise across multiple systems. During incidents, this complexity translates to longer mean time to resolution and greater business impact.

The complexity of traditional approaches to operational data integration creates a development velocity problem. Building a new data pipeline or modifying an existing one often requires:

Stream processing expertise (Kafka, Flink, or similar frameworks) Understanding of distributed systems concepts Knowledge of domain-specific languages for stream processing Experience with failure handling in stateful streaming systems

These skills are specialized and in high demand. Organizations either need to hire scarce streaming engineers or train their existing teams, both of which are time-consuming and expensive. Even with the right expertise, development cycles are slow. Engineers must write code in specialized frameworks, manage state across distributed systems, handle failure scenarios manually, and test complex integration paths.

Raw operational data rarely has the structure AI applications need. A fraud detection model might need features computed from transaction counts by merchant category over the last 30 minutes, standard deviation of transaction amounts by day of week, time since last transaction for this card, and comparisons to typical spending patterns for this customer segment. These transformations need to run continuously as new data arrives, and the complexity multiplies when multiple AI applications need different transformations on the same source data.

When database schemas evolve (tables get new columns, data types change, relationships shift), integrations often break. Teams face difficult choices: maintain multiple versions of transformation logic, accept downtime while updating integration code, or build complex abstraction layers. The tight coupling between database schemas and AI applications slows down both database teams (who must coordinate changes carefully) and AI teams (who must update their integrations).

The result is that building new AI features takes weeks or months instead of days, and iteration cycles are slow enough to be a competitive disadvantage.

Some organizations have adopted a different approach that treats operational data integration as a first-class architectural concern rather than an afterthought. This approach centers on a live data layer, a system that maintains continuously updated views of operational data from multiple sources and makes those views available through a standard interface.

The live data layer approach does the computational work when data arrives (the write phase) rather than when queries execute (the read phase). This shifts the performance problem from query time to update time, where it can be handled more efficiently through incremental computation.

The core mechanism:

Connect operational data sources using change data capture for databases, direct integration with event streams like Kafka, and webhooks or polling for external APIs Define transformations using standard SQL to join, filter, and aggregate data across sources Incrementally maintain results as source data changes, updating only what's affected rather than recomputing everything Serve results through standard interfaces that applications can query using familiar protocols

The live data layer approach directly addresses each of the five challenges identified earlier:

Latency: By processing data when it arrives rather than when queried, live data layers eliminate the tradeoff between freshness and query performance. Transformations run incrementally as source data changes, so results are always up-to-date. Applications query pre-computed results that are both fresh (milliseconds behind source systems) and fast (no expensive joins at query time). The approach shifts computational cost from the critical path of serving queries to the background process of maintaining materialized views.

Cost: The separation of storage and compute allows independent scaling based on actual needs. Storage scales with data volume while compute scales with update and query rates, eliminating the overprovisioning required by coupled architectures. Incremental computation is more efficient than reprocessing entire datasets—when a single row changes, only affected results update rather than recomputing everything. This efficiency reduces baseline costs and makes resource consumption more predictable, as the system processes a steady stream of updates rather than unpredictable query spikes.

Operational complexity: Live data layers replace complex distributed architectures with a single integrated system. Rather than operating CDC tools, message brokers, stream processors, caches, and coordination services separately, organizations manage one system that handles ingestion, transformation, and serving. This consolidation reduces the expertise required for operations and simplifies debugging—when issues arise, there's one system to investigate rather than diagnosing problems across five different components.

Development velocity: SQL-based transformations eliminate the need for specialized streaming expertise. Engineers define what they want rather than how to compute it, using a familiar language rather than learning framework-specific APIs. When schemas evolve, the live data layer can handle updates automatically, propagating changes through dependent data products. This allows teams to iterate quickly, building new data products in hours or days rather than weeks or months.

A key insight of the live data layer approach is treating transformed views not just as query results but as data products, governed, reusable datasets that other teams can depend on. A data product might represent "customer transaction history," "current inventory levels," or "risk scores," meaningful business concepts derived from underlying operational data.

Data products can depend on other data products, forming chains where downstream products automatically stay synchronized as upstream data changes. This composability enables an operational data mesh, a pattern where teams create and share live data products that others can discover, reuse, and build upon.

For example:

A data engineering team creates a "Customer" data product that combines CRM data, transaction history, and support tickets from three different source systems A fraud team builds a "Risk Assessment" data product on top of the Customer data product, adding transaction pattern analysis.

This approach provides several advantages:

• Build once, use many times: The Customer data product encapsulates complex cross-system joins that don't need to be duplicated across applications
• Faster iteration: New AI applications can build on existing data products using SQL rather than writing streaming code from scratch
• Clear ownership: Each data product has a defined schema, documentation, and team responsible for maintaining it

At scale, an operational data mesh becomes a digital twin of your business, a live, queryable representation that mirrors the state of your operational systems. AI agents can interact with this digital twin using standard SQL or APIs, getting coherent answers to questions like "What is this customer's current subscription status?" or "What inventory do we have available for next-day shipping?"

This digital twin provides the semantic layer that AI applications need. Instead of each AI application figuring out how to join customer data from the CRM with order data from the e-commerce platform and support data from the ticketing system, they query the Customer data product that already represents this integrated view.

The live data layer approach is most valuable when:

• You have data across multiple operational systems that need to be joined or correlated for AI applications to function effectively
• Latency requirements are measured in seconds or less, making batch processing inadequate
• Multiple teams or applications need to work with similar derived datasets, making reusable data products valuable
• You want your existing engineering team to build with operational data rather than hiring specialized streaming engineers
• Requirements evolve frequently, making the flexibility of SQL-based transformations more valuable than the control of custom code

The approach may be less suitable when:

• All your operational data lives in a single database that can handle your query load
• Stale data (minutes to hours) is acceptable for your AI applications
• You have a large team of streaming engineers and want maximum control over every component
• Your use cases are stable enough that the upfront investment in custom streaming pipelines pays off over time

The key is matching the integration strategy to your requirements. For example, some applications can tolerate stale data, while others need sub-second freshness. Understanding these tradeoffs helps you choose an approach that solves the right problems without introducing unnecessary complexity.