Many utilities are asking an important question: how can we use AI to improve planning, forecasting, operations, customer service, finance, and reliability?

But there is another question that should come first: is our data ready to support better decisions?

That distinction matters.

AI depends on the data behind it

AI does not create value in a vacuum. It depends on the quality, context, governance, ownership, and usability of the data behind it. If the data is fragmented, inconsistent, duplicated, outdated, or poorly understood, AI will not solve the problem. It may simply make the problem faster.

For utilities, the missing layer before AI is decision-ready data. The goal is not to have more data, but to make better decisions, faster, and with greater confidence.

Where the time goes in a data project

According to a note published by Quanam specialists at the Utility Analytics Institute (UAI), data governance is critical for utilities because the industry is moving from centralized reporting toward more decentralized, self-service analytics. In that environment, technology and analytics teams must help ensure data quality so users can make decisions with confidence. UAI also notes that a significant challenge is that 80% of project time is commonly spent on data acquisition, often by staff without technical training.

That is not only a technology issue. It is a business problem.

When too much time is spent finding, cleaning, reconciling, and questioning data, less time is available for analysis, planning, and decision-making.

This is especially relevant now because the decisions utilities need to make are becoming more complex and more interconnected. Load growth, data centers, electrification, capital investment, storm resilience, reliability, affordability, regulatory reporting, and customer expectations are no longer separate conversations. They increasingly affect each other, and they require a common foundation of trusted information.

Finance needs data from operations. Operations need data from assets. Planning teams need customer, load, infrastructure, and market data. Leadership needs a common view of risks, assumptions, constraints, and trade-offs.

If the data foundation is weak, the decision process becomes slow, fragmented, and difficult to defend.

Data governance comes before AI

APPA has also been highlighting the growing role of AI and analytics in public power. In its article on the risks and possibilities of AI for utilities, APPA notes that AI can support enhanced operational awareness, forecasting, complex modeling, predictive maintenance, and system planning. But it also emphasizes that utility teams need to understand what these applications mean for work quality and system safety.

That is exactly why governance matters.

Before AI becomes a meaningful decision support capability, utilities need clarity on the data that feeds it. They need to know who owns the data, where it comes from, how it is defined, how often it is updated, whether it can be trusted, and whether the assumptions behind it can be traced.

DAMA International’s DAMA-DMBOK provides a useful frame for this. It helps organizations structure, govern, and optimize data assets, aligning data management with business strategy, compliance, and emerging technologies such as AI, machine learning, and cloud computing. DAMA also describes data governance as the function that establishes accountability, policies, and decision rights to ensure data is managed properly.

For utilities, this is not academic.

What decision-ready data is: five characteristics

A “single version of the truth” is useful, but it is not enough if that truth arrives too late, is too narrow, or is disconnected from the decision.

Utilities need data that is not only accurate, but ready to be used in planning, forecasting, investment analysis, regulatory conversations, and operational decisions.

Decision-ready data has several characteristics. It is trusted because people understand its source, lineage, and quality. It is connected because financial, operational, asset, customer, and planning data can be analyzed together. It is timely because it is available when decisions need to be made. It is governed because ownership, definitions, access, and accountability are clear. And it is actionable because it supports a decision, a scenario, a trade-off, or a next step.

AI in finance and planning does not replace governance

This is especially important as AI moves into finance and planning.

AI can accelerate forecasting, support variance analysis, detect patterns, assist with scenario modeling, and improve reporting. But AI cannot replace governance. A utility that lacks trusted data will struggle to build trusted AI. A finance team that cannot trace assumptions will struggle to defend AI-supported scenarios. A leadership team that does not share common definitions will struggle to act on AI-generated insights.

The next stage of utility transformation will not be defined only by better algorithms. It will be defined by better data foundations, clearer ownership, stronger governance, and a closer connection between data and decisions.