No Foundation, No Future: Why Fragmented Data Stalls Grid Intelligence
When a storm takes out power, customers want to know one thing: when is it coming back?
Improving ETR (Estimated Time of Restoration) looks like a natural first problem for AI to help utilities solve. The stakes are high, the outcome is measurable and every utility manages the problem daily. The problem lies in where the evidence actually lives. A crew member calls from the field to report a tree on the line. A drone captures imagery of damaged conductors. The as-built drawings for the affected circuit sit in scanned PDFs, some signed by hand.
The problem isn’t that this information doesn’t exist — it’s that a utility often cannot use it as one operational record. A field report may never be captured. An image may lack the asset or location tags a system needs. Two platforms may assign different identifiers to the same transformer. An AI model fed that partial picture will produce a partial answer.
This is the data-readiness problem behind grid intelligence. Utilities aren’t short on data. They lack usable connections between operational knowledge, enterprise systems and the decisions AI is supposed to improve.
However, readiness doesn’t mean businesses should focus exclusively on a multiyear cleanup before work can begin. It starts with a specific decision and a disciplined sequence.
Four Moves That Make Grid Data AI-Ready
1. Digitize & Structure What Operations Already Know
You cannot AI what you have not captured. Scanning a document is not the same as making its contents usable.
Take our storm scenario: voice-to-text can turn crew calls into searchable records. Image analysis can interpret inspection and drone footage. AI-assisted extraction can pull equipment, location and condition data from engineering drawings and handwritten paperwork. Materials management systems can replace warehouse tickets with electronic tags that show what’s in stock where. Digitization matters when each item retains the asset, location and condition details that let a person or system use it later.
2. Name the Outcome Before the First Data Table
Data for data's sake is not the point. Before choosing what to migrate, define the decision or process you want to improve.
Do you want to reduce restoration time on storm-affected feeders? Improve load-forecast accuracy in a fast-growing service territory? Shorten interconnection studies?
Then identify the inputs, owners and quality thresholds that outcome requires. This protects the budget and gives the program a practical definition of ready. A dataset is ready when it’s reliable enough for a specific decision, not when every record across the enterprise is perfect.
3. Choose a Platform for the Workload & the Economics
Utilities face a crowded field of data platforms. Deciding which one to use is crucial. Decide wrong, and you pay for it — whether in diminished ROI or making the expensive decision to reverse it.
Businesses need to evaluate their choice based on several factors, including integration, governance, security, scalability and cost management and models total cost of ownership against the organization's actual needs, among others. A platform choice should follow the use case and operating model, not stand in for the strategy.
4. Build in Stages & Govern from the Start
Start by picking one or two high-impact use cases, map the data they need and move only that data first. This creates value while the broader foundation matures and gives teams a controlled way to learn the platform.
For one European utility, EPAM built an automated pipeline that processed and quality-checked data from 250,000 smart meters within a two-hour daily window. The same solution generated a seven-day forecast for 80,000 meters in under 10 minutes.
That’s the type of work a staged program can prove: a bounded problem, explicit performance targets and infrastructure the next use case can reuse.
Governance keeps that model from sprawling. Clear ownership, access rules and cost guardrails should arrive before scale.
You’re Probably Not as Behind as You Think
Nearly every executive we speak with feels behind on AI. In practice, most organizations are earlier in this transition than the headlines suggest. A utility does not need a perfect enterprise architecture to make a meaningful start, but it does need to start.
A utility does not become data-ready once and for all. Readiness depends on the decision at hand. The necessary data is usable, the platform fits the long-term economics and the controls are strong enough to expand safely. Each completed use case leaves behind reusable data, controls and operating knowledge, making the next one faster and less expensive.