Scaling AI Shouldn’t Mean Rebuilding Data Foundations Every Time

The 2027 planning season is right around the corner for enterprises, and AI investments are once again at the top of C-suite conversations. Akeneo’s recent survey of IT decision-makers showed 93% reported productivity gains from AI, yet 43% said AI has also added new responsibilities to their roles.

These findings highlight the significant amount of groundwork required to bring AI projects to life. Data preparation, in particular, represents a substantial part of that work. Among IT leaders Akeneo surveyed, 36% said their organizations spend more than half of the total effort on an AI project preparing data. Another 53% reported spending between 26% and 50% of their AI project effort on data preparation. While this preparation is necessary, the opportunity lies in ensuring that the work completed for one AI deployment can be reused to support the next.

AI may eliminate work in one part of the business, while creating a different set of tasks for IT teams as they prepare data and rebuild context for each new deployment. The opportunity now is to make more of that work reusable, so each deployment builds on the last instead of starting from scratch.

Human Context at the Center of AI

Humans remain central to all AI pilots and deployments. IT teams are embedded throughout the lifecycle of new technologies, from understanding business needs and evaluating solutions to implementation, training, process development, and ongoing security. Over time, teams may fail to document the exceptions and unwritten rules they encounter, including instances where the same term is defined differently across systems. That context often becomes institutional knowledge held by individual team members rather than information the broader organization can reliably access.

This work can continue until the person with knowledge of the answer is unavailable. When employees change roles, leave the organization, or simply aren’t available when a problem arises, the context is gone. Capturing that knowledge in a structured, digitally accessible format makes it available beyond any one person. It also allows individual teams to maintain ownership of their expertise while ensuring that knowledge can be accessed and reused across the organization, rather than rediscovered with every new deployment.

Good Data Isn’t Always AI-Ready Data

It is tempting to describe all of this as another data-quality problem, but Akeneo’s research suggests something more complex. 92% of IT leaders rate the product and operational data used by their AI systems as good or excellent. Even though the vast majority spend more than a quarter of an AI project’s total effort preparing data.

Good data does not guarantee it is AI-ready, particularly because what qualifies as “good” can depend on who or what is consuming it. Models and agents need more than accurate information. They need to understand where data comes from, how it relates to other information, which source takes priority, and under what conditions it can be used. These rules can be easy to overlook because they are often implicit in how experienced teams already work.

McKinsey’s recent research on AI data readiness makes a similar point about context. Not all information will be valuable to models or agents. But when information is valuable, its context must be transmissible. Once information moves beyond its original source, its origins and relationships to surrounding data must remain clear so its meaning is not lost.

This is where the architecture behind AI starts to matter just as much as the model itself. Unlike traditional software, LLMs can often work across fragmented data and disconnected systems, even when information is structured or labeled differently across sources. They can identify relationships that may not have been explicitly defined before. The bigger challenge is the absence of data altogether. As models and agents work across systems, the information and context they need must exist and be accessible in the first place.

PwC’s 2026 research on the AI execution gap found that integration remains the leading reason technology investments fall short of expectations. Connecting a wide web of unstructured data takes time, often slowing projects before they reach full deployment.

The work required to solve those problems for one implementation can make the next implementation easier.

Making AI Infrastructure Reusable

For one AI project, much of that preparation can happen behind the scenes. IT teams resolve inconsistencies, prepare the information an application needs, and integrate the application with other systems to move the project into production.

The problem is when that work only solves the immediate use case. Data preparation will always be part of AI deployment, but much of that effort should carry forward. If a team discovers that the same field or term has different meanings across two systems, resolving the ambiguity should create a definition future applications can reuse. If they determine which source should be trusted when systems disagree, that decision should continue to future deployments.

The same principle applies to integrations. Instead of creating a new path to product and information for every model or agent, organizations should establish consistent ways for AI systems to find and use trusted information.

The first implementation may still require significant work because teams are uncovering definitions, relationships, and exceptions that previously lived in people’s experience. That effort can carry forward as institutional knowledge increasingly becomes part of the organization’s infrastructure. Successive deployments can benefit from that work, with the extent of that benefit depending on how much common ground exists across their data and use cases.

That is increasingly important as AI moves beyond generating answers and begins taking actions across enterprise systems. NIST’s AI Agent Standards Initiative highlights the importance of agents being able to interact securely with internal data and external systems. As agents take on more responsibility and interact with more systems and models, organizations need to ensure the right context and controls are set for each use case, without needing to fully restart.

Governance has to Become Part of the Environment

Reusable infrastructure also requires reusable governance. EY’s September 2026 AI Risk and Governance Survey found that 91% of senior AI executives said their organizations were piloting or deploying agentic AI, yet 49% of respondents whose organizations use agents said their existing governance frameworks had not been updated specifically for agentic AI requirements and risks.

As AI takes on more responsibility within a business process, the rules governing that process must become part of the environment in which the AI operates. An agent may be able to access certain information independently, while other points in the process may require human review or approval before it can proceed. Those decisions cannot remain only in a policy document or in someone’s understanding of how the process is supposed to work.

Governance decisions need to extend beyond internal reviews and into the systems AI operates within. The policies, permissions, and approval points that guide a system should be defined as part of the deployment itself. Without those controls embedded into the system, teams will be forced to reinterpret policies and permissions every time a new AI use case is deployed.

Preparing for the Next Wave of AI

As enterprises put their growing AI budgets to work in 2027, IT teams will be responsible for an expanding set of models, integrations, and systems. The bigger test will be whether organizations build infrastructure capable of supporting what comes next.

If teams are still resolving the same definitions, rebuilding the same connections, and translating the same rules, AI may be scaling faster than the infrastructure needed to support it.

That offers a different way to think about AI readiness. Progress should not be measured only by how many models or agents make it into production, but by whether each implementation leaves the enterprise better prepared for the next.

AI is already making individual tasks faster. The next productivity gain will come from making the work behind those systems easier to reuse. AI continues to be embedded more in business processes, requiring more systems to depend on the same foundations of data. Building that foundation now will determine how easily organizations can put the next generation of AI to work.