How data leaders bridge the gap between AI pilots and enterprise ROI

Company executives are investing heavily in AI while expecting immediate business outcomes—better forecasting, faster risk detection, and smarter operations.

Data leaders, however, face fragmented repositories, inconsistent definitions, and unstructured information that hinder trusted, scalable AI outputs. This creates tension: leadership expects enterprise AI performance while data foundations are still being modernized.

The gap isn’t about models; outcomes depend on data that’s trusted, governed, accessible, timely, and contextual. Then AI moves beyond pilots to deliver measurable ROI.

Data leaders can close this gap. The six actions that follow show how to turn foundational data work into a multiplier that converts AI ambition into enterprise-scale results.

6 Core strategies to turn data foundations into an AI multiplier

1 | Don’t just rely on retrieval AI. Adopt reason AI

Most data leaders rely on Retrieval-Augmented Generation (RAG) AI with internal chatbots and self-service tools that allow employees to ask questions about internal documentation. RAG finds and repackages existing knowledge, making it indispensable.

Reason AI, however, can solve truly novel problems, empowering users to seek specific solutions and receive answers to questions not even asked. Reason AI uses its general knowledge and understanding of concepts to think through a problem and arrive at a new conclusion. Reason AI goes about its analysis in a clear, structured way, concluding with a recommendation.

2 | Adopt a data products mindset

Data products are in a data leader’s toolbox and for good reasons. Each data product has a clear owner, defined access controls, and baked-in quality standards. Data leaders can track usage metrics and solicit feedback from data products users. They also free developers from cleaning and curating data, which advances their starting point for AI applications.

A data products mindset gives data leaders a framework for moving from being a custodian of a cost center to managing a value-generating product portfolio for the entire enterprise. That’s being an AI multiplier in the purest sense.

3 | Operationalize AI-ready data to scale AI beyond pilots

Scaling AI beyond pilots is now the central expectation from executive leadership, and you’re in position to grant wishes. With AI-ready data processed with knowledge engineering and context engineering, companies can unlock the full potential of large language models (LLMs) and move from experimenting with AI in pilots to deploying agentic systems that can autonomously act, adapt, and learn.

AI systems become exponentially more valuable as they learn the nuances of an organization's operations, preferences, and decision-making patterns. Over time, accumulated context becomes a form of operational memory that makes your data even more invaluable.

4 | Shift from traditional data management to knowledge engineering

With leadership wanting AI-ready data for enterprise AI and for ROI, you need AI-ready data without increasing risk. This requires evolving from traditional data management toward knowledge engineering—where business logic, relationships, and rules are encoded so AI can reason over them reliably.

Knowledge engineering transforms expert knowledge into a format that a computer system can use to solve complex problems. It's a key discipline in building AI systems, especially those that need to mimic human-like reasoning and decision-making. Context engineering curates and structures data that an AI model needs to perform a task accurately and reliably.

5 | Frame knowledge engineering as a board-level performance strategy

As a data leader, you’re expected to fuel AI-driven innovation while fighting for budget to conduct the foundational data work that makes it possible. The shift from data management to knowledge engineering gives you a new frame—and a new language to articulate to the board.

A data leader’s AI strategy built around knowledge and context engineering isn’t a data maintenance plan. It’s a performance plan that reduces fraud loss, improves forecast accuracy, speeds up underwriting cycles, and requires fewer compliance exceptions—among other specific processes that can be accelerated with higher accuracy and are of high interest to the board.

6 | Tie AI-ready data to measurable business outcomes and ROI

AI is advancing at a dizzying pace, and leadership views AI’s development with both excitement and trepidation. There is a sense that if the company doesn’t innovate, the competition will. There is a willingness by company leaders to invest in AI initiatives, as well as added pressure on you as a data leader to deliver AI-ready data.

When you’ve built knowledge engineering into your data foundation, you can deliver AI-ready data that makes it possible to scale AI enterprise-wide with ROI. Agents can autonomously break down tasks, automate complex processes, and deliver breakthrough efficiency.

Is Your Data Foundation Ready for Enterprise AI?

Before scaling AI across the enterprise, many data leaders pressure-test their data foundation against five readiness signals:

Semantic alignment:

Core business definitions such as customer, risk, and product are consistent across systems.

Machine-readable governance:

Permissions, lineage, and usage constraints are embedded directly in data pipelines rather than documented separately.

Context-ready data:

Metadata and classification provide AI with the context needed to interpret information accurately.

Normalized unstructured data:

Policies, contracts, operational logs, and other documents are structured so AI agents can reason over them rather than retrieve fragments.

Clear ownership and operating model:

Data domains, governance responsibilities, and AI accountability are explicitly defined.

Organizations that can verify these signals typically move from AI pilots to scalable AI capabilities much faster.

AI-Ready data is the multiplier behind scalable enterprise AI

Knowledge and context engineering helps enable data leaders to turn data relegated to chatbots into free-thinking agents. Modernizing data has a multiplying effect, empowering autonomous agents equipped with your company's nomenclature. The result is highly qualified agents that empower decision-making, drive value, and contribute to the bottom line.

How KPMG can help

To unlock agentic AI, your business must evolve from data management to knowledge engineering, creating a trusted data foundation that allows AI-ready data to understand your business context and operate with accuracy. KPMG LLP brings the essential skills, technology, and domain knowledge needed to unify your data into a source of truth, implement advanced tools, and develop a strategy that helps ensure your AI initiatives deliver real business value. Collaborate with us to navigate agentic AI complexities and turn your data into a strategic advantage.