AI Readiness Is Defined by Data Readiness

Enterprises have never looked more prepared for AI. Large language models are embedded into products, copilots are rolling out across functions, and agents are beginning to appear inside core operational workflows. Yet beneath that momentum, confidence drops sharply.

Only a small minority of organizations believe their data infrastructure can support AI at scale. Research from CData finds just 6% consider their data environments ready for production AI. That gap is often structural for SAP-centric enterprises.

Leading enterprise AI use cases increasingly depend on real‑time data from multiple systems – such as SAP ECC or S/4HANA plus adjacent cloud applications – as finance, supply chain, HR, and customer processes move to the center of AI programs.

What once worked for reporting and batch analytics begins to strain when AI systems are expected to reason across live operational data. The disconnect reflects how quickly AI expectations have outpaced the way most enterprises manage data in SAP environments.

Moving from promising pilots to scalable AI requires a clearer view of how transactional data flows, connects, and behaves across systems—and a willingness to rethink long-standing integration assumptions in the process.

Data Connectivity Is the Real AI Battleground

With AI pilots proliferating across industries, many enterprises now assume experimentation is worth pursuing; the constraint emerges later when AI moves from pilot to production, and existing data infrastructure struggles to scale with it.

Research from CData shows a clear link between AI maturity and data maturity, with high-maturity organizations almost always the ones that have already built centralized, consistent data access layers rather than brittle, ad hoc integrations.

The State of AI Data Connectivity: 2026 Outlook found that 60% of organizations at the highest levels of AI maturity also report highly mature data infrastructure, while 53% of companies with low AI maturity continue to rely on immature data systems.

The finding reframes the battleground for scalable AI: reliable, contextual, real-time data infrastructure matters more than the choice of model.

Amit Sharma, CEO and co-founder of CData, puts it this way: “The organizations winning with AI aren’t the ones with the best algorithms; they’re the ones with connected, contextual, and semantically consistent data infrastructure.”

The practical takeaway for technology leaders is stark: AI isn’t constrained by models anymore—it’s constrained by data.

Where Data Limits AI

Early AI pilots often look promising. Models perform well, demos impress stakeholders, and initial use cases deliver value. The strain appears later, when those same systems are asked to operate continuously, draw from multiple live sources, and serve users in real time. At that point, data architectures built for reporting and batch analytics begin to fail.

In a recent CData e-book, enterprise software advisor Mark Palmer outlines a set of data architecture shifts required to move AI from experimentation to scalable, production use.

The challenge is not isolated failures, but a set of architectural assumptions that no longer hold once AI is expected to reason and operate continuously on live data.

When AI Runs on Yesterday’s Data

Early signs of strain often appear around data freshness. Many AI systems are still fed through batch pipelines designed for reporting, leaving copilots and agents to operate with delayed or incomplete context and miss what has changed since the last refresh.

Teams expect systems to reflect live inventory, current customer status, or in-flight transactions. Yet the underlying data arrives late. What worked for dashboards and periodic analysis begins to undermine trust once AI is asked to interact, recommend, or act.

Palmer argues the issue is data access, not models, leaving AI without sufficient context. Addressing this gap requires rethinking how AI accesses data, prioritizing live, governed views of operational systems over static snapshots.

When Real-Time Becomes an Operational Burden

To compensate, teams often double down on integration. Pipelines are refreshed more frequently, new data flows are added, and systems run continuously to approximate real-time behavior. Over time, these always-on pipelines become costly and fragile, introducing latency and inconsistency just as AI use cases demand faster, more reliable access.

According to Palmer, this treats real-time access as an operational upgrade, locking teams into integration patterns that fail to scale with AI workloads. Avoiding this trap depends on treating integration as an on-demand capability rather than a permanently running layer.

When Centralization Becomes a Bottleneck

As teams push for fresher data and faster access, centralized data platforms begin to show their limits. Data lakes and warehouses were designed to aggregate and analyze data in batches, not to serve AI systems that need to explore, correlate, and respond in real-time.

Centralized platforms become bottlenecks rather than enablers as AI use cases multiply. Scalable AI depends on live, governed access to operational data at the point of use.

When Integration Speed Limits AI

Once AI gains live access to operational data, the number of required connections rises quickly. Early integrations typically center on a small set of core systems. AI changes that model. Each new use case draws in additional applications and data sources as models correlate signals across finance, supply chain, customer, and external systems in real-time.

Architectures built for a handful of static integrations struggle to keep up. Adding new connections becomes slow and manual, turning integration into a bottleneck.

When integration speed becomes a limiting factor, it constrains how quickly AI capabilities can evolve. Overcoming that constraint requires treating connectivity as a core, reusable platform capability rather than bespoke plumbing.

When Data Is Built for Humans

As connectivity scales, another limitation surfaces: data platforms were built for human developers, not machine-generated code.

Schemas are inconsistently documented, access rules remain implicit, and integration logic lives in custom scripts. That works when engineers manage connections by hand. It breaks once AI is expected to generate queries, integrations, and workflows automatically.

Palmer frames this as a mismatch between modern development and legacy data design. Data that is opaque to machines slows development rather than accelerating it, limiting how far AI-driven engineering can progress. Without machine-readable structure and governed access, AI-driven automation slows rather than scales.

When AI Becomes the Interface

The final break comes when AI shifts from support tool to primary interface. Traditional data architectures assume humans mediate between systems and decisions. AI assistants remove that buffer, querying data directly, synthesizing responses in real time, and increasingly triggering actions on a user’s behalf.

Palmer emphasizes that this shift raises the bar for data clarity and control. When AI is the interface, permissions, definitions, and context must be explicit and enforced automatically. At this stage, success depends less on presentation layers and more on whether data access is semantic, permission-aware, and consistent across systems.

Platforms that adapt to AI-first interaction models turn assistants into reliable operators.

What Research Reveals About Readiness

Moving AI from pilots to production is less about ambition than preparedness. The research suggests that many organizations stall not because their use cases lack value, but because the underlying data work required to support them grows faster than expected.

According to CData’s research, more than 70% of teams spend over a quarter of their AI implementation time on data integration alone. As AI initiatives mature, that burden increases. Nearly half of surveyed organizations say a typical AI use case requires real-time access to six or more data sources, dramatically increasing integration complexity.

Seen this way, the limiting factor is rarely the model. It is whether existing data architectures are prepared to support AI at real-world scale.

A few diagnostic questions can help surface those limits early:

• Where does AI get its data today, and how fresh is that data?
• How quickly can new data sources be connected as AI use cases expand?
• Which systems become bottlenecks when AI needs live, operational access?
• Where are governance and access controls enforced?
• Can AI tools reliably discover schemas, permissions, and usage constraints?
• What changes when AI assistants become the primary interface to data?

These questions help distinguish scalable AI initiatives from those stuck in pilot mode, revealing whether data strategy supports AI ambitions—or works against them.

Turning AI Pilots Into SAP-Scale Products

The gap between AI pilots and production is becoming visible inside core SAP operational systems. Finance, supply chain, HR, and customer data now spans SAP ECC, SAP S/4HANA, and adjacent cloud platforms, increasing both opportunity and complexity.

Early AI use cases rely on reports and replicas. In production, assistants are expected to reason across live data from multiple systems, in real-time.

Traditional SAP integrations—built around batch extracts, point-to-point APIs, or custom services—were designed to move data, not to serve AI. They struggle to provide the real-time access, consistent semantics, and governed context that AI systems require at scale.

This is where emerging standards like the Model Context Protocol (MCP) come into play.

MCP provides a structured way for AI systems to connect to enterprise data while preserving schemas, relationships, and access controls. CData’s Connect AI platform supports MCP, using it as part of a managed connectivity layer that exposes SAP and non-SAP data sources to AI tools and reduces the amount of custom integration work required.

CIOs and enterprise architects driving SAP transformation face a clear dividing line between promising pilots and scalable AI: not model choice, but whether the data layer can deliver live, governed, semantically consistent access across the enterprise.

What This Means for SAPinsiders

• AI readiness is a data problem. CData’s research shows AI maturity closely tracks data maturity, with scalable AI emerging only where integration, access, and governance are aligned ahead of deployment. This alignment determines whether AI initiatives move beyond pilots or stall under integration and operational complexity.
• AI changes how data must be designed and delivered. AI exposes the limits of batch pipelines and static integrations common in SAP landscapes as systems move toward real-time reasoning and action. Organizations that shift toward live access, shared semantics, and on-demand connectivity are better positioned to scale AI across SAP workflows.
• A dedicated data layer makes production AI more achievable. CData Connect AI applies MCP to expose SAP and non-SAP data with governed, semantic context for AI systems across enterprise environments. This approach allows assistants and agents to operate reliably in production while avoiding custom integration debt and brittle point solutions.