The AI Reinvent Challenge
Across the utility industry, the conversation around AI has grown louder — and sometimes shallower.
Executives are bombarded with promises of automation, insight, and efficiency, yet many find themselves stuck in pilot purgatory: isolated experiments that may prove concepts but fail to deliver even a fraction of the value promised. Worse, it misses the opportunity to truly reinvent the way utilities do business.
The truth is that AI won’t transform utilities by itself — people will. How those people choose, implement, and evolve AI will determine whether this technology becomes a short-lived fascination or the foundation of a truly modern utility.
Start with People, Not Platforms
The heartbeat of any utility is its people — from field crews and call-center reps to engineers and analysts in control rooms. Too often, AI strategies start with technology first, overlooking the workforce that will ultimately decide whether these systems succeed.
AI will absolutely change jobs. Being honest about that is critical. But with the right approach, it can elevate employees rather than replace them — freeing them from repetitive tasks and enabling them to focus on judgment, safety, and customer connection.
The most forward-thinking utilities are educating, engaging, and empowering their teams early. They invite line workers, dispatchers, and service reps into the AI design process, creating advocates instead of skeptics. That shift turns AI from a perceived threat into a catalyst for professional growth.
Reinvent the Process Before You Automate It
Many AI pilots fail because they attempt to automate legacy processes that were never designed for the digital era. A bot that speeds up a broken workflow simply produces bad results faster.
The real impact of AI comes when utilities pause to ask a harder question: Should this process even exist in its current form?
For example, rather than trying to reduce call volumes, some utilities are redesigning the customer interaction entirely — blending predictive insights, proactive outreach, and AI-assisted resolution so the “conversation” happens before the problem does.
Rapid pilots can still play a role, but reinvention beats replication every time.
Get Your Data House in Order
Every successful AI story begins with strong data foundations. Yet many utilities still operate in silos — with AMI, CIS, OMS, and DER systems that don’t communicate effectively. No algorithm, however advanced, can overcome that fragmentation.
Data readiness goes beyond accuracy. It’s about accessibility, governance, and interoperability. Leading utilities treat data as shared infrastructure rather than departmental property. They’re building enterprise-wide frameworks that allow information to move securely and seamlessly — the essential prerequisite for every future-ready capability.
Co-Innovation Over Isolation
Utilities that move the fastest with AI aren’t doing it alone. They’re co-innovating — partnering with vendors, technology providers, and peers to test ideas safely and learn quickly.
This collaborative mindset shortens the learning curve and spreads the risk. Instead of waiting for the perfect internal solution, these utilities prototype, measure, and iterate together, often uncovering insights that no single organization could have achieved in isolation.
AI thrives in ecosystems, not silos.
Measure Early, Learn Fast, Scale Wisely
AI’s potential is immense — but without metrics, it’s meaningless.
Successful utilities define success before they start: shorter handle times, faster outage restoration, lower billing exceptions, higher customer satisfaction.
Pilots without measurement are science projects. Leaders should treat every proof-of-concept as a data-driven experiment — measure early, learn fast, and scale only when value is proven. That discipline earns credibility internally and builds trust with regulators and boards alike.
Fit-for-Function AI: One Size Does Not Fit All
Not every function needs the same kind of AI. The solution that transforms customer service may not translate to field operations or grid optimization.
“Fit-for-function” means selecting tools that align with the unique context of each utility function — from basic "knowledge-assist copilots" that support internal service teams to more quantitative and predictive AI use cases forecast asset health or DER variability.
Utilities that resist the one-platform myth and instead curate a portfolio of purpose-built solutions will capture value faster and avoid costly misalignment.
From Experimentation to Reinvention
Foundational work doesn’t have to slow progress. In fact, it can — and should — happen in parallel with AI pilots. The key is intentionality: thinking deeply about people, process, and data from the outset so experiments don’t live in isolation.
AI isn’t a silver bullet — it’s a force multiplier when it’s built on the right foundation.
· Start with people.
· Reinvent the process.
· Ready your data.
· Partner wisely.
· Measure relentlessly.
· Choose tools that fit.
Utilities that follow this path will move beyond the AI hype cycle into a state of true reinvention — where AI amplifies human intelligence, strengthens operational resilience, and builds enduring customer trust.
The future-ready utility won’t just be automated. It will be fundamentally transformed.