Why We’re Still Solving the Wrong Problem in AI

02.12.26

Head of AI, Data & Applied Intelligence

AI pilots succeed, but production deployments fail, and it’s not because of dirty data. This research reveals the architectural blind spot that’s actually preventing AI from scaling in enterprise environments. Read the full whitepaper here.

The conversation around AI failure has matured, but it has not yet reached the root of the issue.

Where we once attributed stalled initiatives to immature models or unrealistic expectations, the prevailing explanation is more straightforward: AI isn’t failing, data is. The solution seems equally clear. Clean the data, strengthen governance, modernize platforms, and AI will finally deliver on its promise.

It is a compelling narrative. It is also incomplete.

After years of working inside large enterprises and observing AI initiatives as they move from pilots into production, a more consistent pattern emerges.

AI does not fail because there is no data to learn from. It fails because data remains fragmented across systems, delayed by batch processes, and disconnected from the operational moments where intelligence is expected to act. Understanding this distinction changes how leaders should think about data readiness in the age of generative and agentic AI.

“The constraint on AI is not data quality alone. It is how data is integrated, synchronized, and made available in real time across the enterprise. Organizations rarely lack data. What they lack is structural coordination.”

The Real Constraint: Coordination, Not Cleanliness

Most enterprise data environments were built for hindsight. They were designed to capture transactions, consolidate them after the fact, and support reporting and analysis. That architecture tolerates delay and inconsistency because humans sit at the center, interpreting outputs and reconciling discrepancies.

AI operates differently.

Generative and agentic systems embed intelligence directly into workflows. They act across systems and respond to events as they occur. They assume shared definitions, aligned business logic, and timely context.

When those conditions are absent, AI performs exactly as designed but not as intended. Outputs diverge across domains. Recommendations conflict with operational reality. Trust declines. What appears to be a model problem is often a coordination problem.

Data quality is necessary. It is not sufficient.

What Happens When AI Moves to Production

The structural gaps become visible when AI transitions from experimentation to production.

In controlled pilots, systems are simplified and dependencies are limited. Once deployed across real enterprise environments, AI encounters architectural complexity that has accumulated over years. Operational systems and analytical platforms have evolved in parallel. The same business concepts are defined differently in separate domains. Data moves at different speeds depending on the system. Governance frameworks operate in layers rather than as a unified structure.

For years, this fragmentation remained manageable because human judgment bridged the gaps. Analysts reconciled reports. Operators adjusted for inconsistencies. Leaders interpreted conflicting views of performance.

AI removes that buffer.

As intelligence becomes embedded in operational decision cycles, tolerance for misalignment disappears. Systems that were merely inefficient become unreliable. The issue is not that the data is wrong; it’s disconnected from itself.

“As intelligence becomes embedded in operational decision cycles, tolerance for misalignment disappears. Systems that were merely inefficient become unreliable. The issue is not that the data is wrong; it’s disconnected from itself.”

The Missing Layer in Data Readiness

Improving data quality within individual systems does not create synchronization across systems. Governance applied retrospectively does not ensure consistency in real time. Even modern data platforms, if still organized around batch processing and domain isolation, cannot provide the continuous shared context that AI requires.

What is missing is a coordination layer. It is an operating model that enables enterprise systems to share meaningful changes as they occur, apply consistent business definitions across domains, and enforce governance automatically at the point of interaction.

We describe this as a Unified Information Fabric. It is not a product or an additional platform. It is a structural approach to integration and information flow.

This layer does not replace existing data foundations. It connects them. It aligns operational and analytical contexts so that intelligence can move across the enterprise with consistency and confidence

Why This Matters Now

As generative and agentic AI move closer to core business operations, enterprises can no longer rely on delayed reconciliation or manual oversight to correct systemic inconsistencies. Autonomous systems act in real time. They amplify both the strengths and weaknesses of the underlying architecture.

Organizations that establish coordinated, real time information flows will scale AI more effectively and with less risk. Those that focus exclusively on cleaning data, without addressing how information flows and aligns across the enterprise, will continue to see AI underperform.

The accompanying whitepaper details the research and enterprise experience behind these conclusions. It explains why common narratives around data quality fall short and outlines a practical, incremental path toward building the coordination layer required for AI at scale.

The challenge is not fixing AI. It is evolving the enterprise data ecosystem so that intelligence has a coherent, synchronized foundation on which to operate.

AI reflects the structure it depends on.

If that structure is fragmented, outcomes will be fragmented. If it is integrated and coordinated in real time, intelligence can perform reliably at scale.

Arun Sahu is Head of AI, Data and Applied Intelligence at alliant, where he leads advanced technology evangelization with a business-first approach. He brings extensive experience as a former Global Chief Technology Officer within a large global IT services organization. In his previous roles, Arun founded and scaled enterprise Data and AI practices, delivering solutions across agentic systems, digital humans, geospatial and industrial AI, public sector platforms, and synthetic data.

Arun Sahu

Head of AI, Data and Applied Intelligence