Most enterprise data was designed to be consumed by people. But automation and AI agents will soon make up a much larger share of the workforce. KPMG’s Global Tech Report predicts that digital assistants will account for 36% of core technology teams’ capacity by 2027, up from 28% in 2025.
This is why data readiness has risen to the top of technology leaders’ priority lists. In a separate KPMG survey, 58% of leaders cited data readiness and access as one of their biggest challenges to deploying AI agents.
Data that works for reports and dashboards can create problems for AI. Definitions may differ across the business, with data stored separately from the rules governing its use.
Preparing for an AI workforce starts with making enterprise data searchable, contextual, and trusted.
Three steps to AI-ready data
1. Make enterprise data searchable.
AI agents need to identify which data to use and where to find it across multiple data products and technology environments.
An ontology maps business concepts to the data associated with them. When an agent receives a question or action, it breaks the request into relevant domains and concepts. The ontology then identifies which data products and assets contain the information it needs.
Consider a wealth advisor asking an AI assistant about a customer’s recent branch interactions to identify potential cross-sell opportunities. Answering might require pulling product data from one data platform and branch experience data from another entirely.
2. Give AI the context it needs.
AI also needs to understand what enterprise data means. Let’s say you ask AI: “What’s our best-selling product in the mid-market segment?” The answer hinges on how the business defines “best-selling” – by units, revenue, or margin – and which accounts qualify as “mid-market.”
Knowledge engineering captures and makes this organizational context available to AI. It does this through:
- technical metadata: identifies what data exists and how it flows through systems
- semantics: define business terms, definitions, and synonyms
- ontologies: establish relationships among business concepts
- knowledge graphs: connect those relationships to enterprise information
These elements help AI interpret data using the same business meaning employees apply to it. Knowledge engineering can also incorporate institutional knowledge that agents can draw on when making autonomous decisions.
3. Build trust into AI-ready data.
AI agents need to make decisions from authoritative, governed data. Existing data controls provide a good starting point.
Enterprises also need to determine whether an agent inherits the access privileges of the person using it or receives its own permissions. Those guardrails govern what information an agent can use when making a decision.
Finally, governance should extend to the contextual layer, with clear ownership of the definitions, relationships, and business rules that determine how agents interpret information. These rules create a traceable path from source to context, making decisions easier to explain and validate.
AI readiness starts today
Making enterprise data AI-ready is a no-regret investment, as long as the work is tied to business value. AI can help accelerate that work. What delivers value today can also prepare the business for more advanced AI capabilities in the future.
Learn more here.