Abhay Gupta is cofounder and CEO of Bidgely, evolving energy analytics for utilities with the power of data and artificial intelligence.
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For the past decade, the utility industry has been in a massive data collection phase. Billions have been invested in advanced metering infrastructure (AMI), followed by a heavy lift to migrate that information into platforms such as Snowflake, Databricks and AWS.
This effort has successfully moved the industry from a mindset of data scarcity to one of data abundance. It is a high-value problem to have, and the next logical step is to activate these vast data lakes so that the sheer scale of information enhances visibility rather than obscures it.
The timing couldn’t be more critical. As unprecedented load growth, infrastructure limitations and affordability mandates intersect, the priority has shifted. It has become more important for utilities to move beyond simply warehousing data to operationalizing it as a tool for real-time problem-solving.
Utility leaders are asking:
• How do we convert these assets into tangible value?
• How do we deliver meaningful outcomes to the customers we serve today?
• How do we scale that value across the entire enterprise?
I've seen more utilities begin turning to artificial intelligence (AI) to help answer these questions, ensuring that data isn't just sitting in a silo but actively working to make the entire organization more agile and informed. Because AI can help map precisely when and how energy is consumed, utilities can pivot from being data collectors to data orchestrators.
Take load shaping versus load shifting, for example. Load shifting is a reactive, immediate measure. If the grid is peaking because too many people are charging their EVs at high-demand times, the utility can trigger a demand response event to force a shift in usage and protect grid stability. Load shaping is more proactive. Rather than a constant string of one-off shifts, a utility can focus on establishing tiered, time-of-use (TOU) rate plans that gradually influence consumer habits over time for a more sustainable load curve.
This change is made possible by unifying previously siloed strategies into an integrated workflow that compounds outcome value. The same intelligence that identifies EV-driven feeder stress allows planners to align rate incentives with infrastructure needs that simultaneously defer capital upgrades and lower customer bills.
Having a unified data approach also shifts the perspective on demand drivers. When a massive data center requests a grid connection, a data-orchestrated utility no longer relies on blunt, aggregate forecasts. Instead, they can view the grid at a granular level to pinpoint specific feeders with latent capacity versus those nearing a breaking point. With visibility this precise, the origin of the load—be it suburban EV charging or a sprawling data center—matters less than the utility’s ability to manage it as a single, cohesive ecosystem.
While this level of coordination is built on a foundation of high-quality data, success—and scalability—ultimately depends on how effectively that data serves the people who use the grid and the teams who manage it. By moving away from fragmented point solutions, utilities can create a feedback loop where data-driven insights power every interaction.
To effectively enable the utility workforce to lead this transition, leadership must focus on empowering the veteran operations and engineering teams who already understand the grid. Create cross-functional working groups that pair front-line engineers directly with IT analysts. By working side by side, teams can run practical data queries that deliver relevant insights and solve real operational bottlenecks. It’s also helpful to build data sandboxes, where teams can comfortably experiment with predictive insights and use these tools to augment their expertise, not automate it away.
Too often, organizations assume they must complete a multiyear data management overhaul before AI can deliver value. This expectation often leads to a paralysis-by-analysis loop. The trick is to identify specific, high-value use cases, such as pinpointing EV-driven feeder stress, where you can establish success and prove ROI before scaling to broader use cases.
It’s important to note that these tools work best when integrated directly into daily workflows. Organizations tend to assume they have to completely rip and replace their existing systems. However, they can actually layer AI analytics directly into legacy systems. By embedding these actionable insights into everyday tools, leadership can work to eliminate platform fatigue and empower teams with a seamless, user-friendly experience.
As the industry realizes the potential AI can bring to smart meter investments, active data orchestration can help utilities confidently manage surging demand while still upholding core customer satisfaction and energy efficiency mandates. In addition to helping balance the grid, data orchestration can create a shared language of insight that scales across the entire enterprise, empowering analysts forecasting long-term capacity and engineers managing the grid edge, while simultaneously providing call center agents with the clarity needed to offer proactive, personalized support.
By adopting an AI-first approach to data, utilities can do more than solve immediate inefficiencies; they can establish the agile foundation required to seamlessly embrace the next generation of autonomous systems and agentic innovation.
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