Understanding The Real Secret To Utility Tech Adoption

We talk all the time about the grid of the future in terms of hardware and AI-driven transformations that define utility tech adoption. The thing is, tools and tech are almost always secondary to the strategic mindset required to enable them. What does it actually mean for a new piece of software to enable change? And how does that change create immediate value while paving the way for long-term resilience?

Andy Quick, former Chief AI Officer at Entergy, is someone who asked and answered those questions as part of a nearly three-decade career at the organization that serves approximately 3 million utility customers. He’s seen firsthand how easy it is to get caught up in questions about new technology when what’s most important are answers about frameworks for measurable value creation that are based on changes to existing processes and systems.

Now, as Senior Industry Advisor for Noteworthy AI, Quick is helping utilities better define those answers by doing things integrate AI into inspections to fundamentally improve day-to-day operations. We caught up with him to explore what the approach to doing so looks on a practical level, the importance of identifying a “champion” to drive change, the evolving role of data in adoption, and much more.

Three Critical Adoption Questions

While managing AI remains a top industry priority, Quick advocates for a broader strategic shift that could be utilized as part of any adoption process. It means developing a workforce capable of moving technology from the pilot phase into full-scale production with a focus on the creation of value as part of a three-step process.

“What we tried to do was answer three critical questions,” Quick said in reference to how he approached the formation of a new AI department. “First, how do we create material value with AI in a significant way? Second, how do we enable the organization to improve productivity? And third, how do we mitigate the risks associated with it?”

Those answers can define how a tool is adopted by any team or entire organization, but Quick noted that the model around doing so with AI, whether it’s centralized or decentralized, isn’t a first-level priority. He’s seen countless models work, which is why what’s most important is identifying where an organization wants to cluster AI capabilities to have the greatest ability influence change. However, the success of such efforts is about more than models or the adoption/development of a given tool.

The Build vs. Buy Dilemma

At the heart of any adoption strategy is a “buy versus build” evaluation process that can underestimate the opportunity cost of developing tools or systems in-house. Market solutions that have already benefited from millions in R&D are readily available, but focusing on one or the other without first answering more fundamental questions can lead to significant challenges. 

“Before you even enter that debate, you have to ask if you’ve found a problem worth solving,” Quick said. “Do you understand the level of disruption and change that’s going to go into either? Because if you don’t, it doesn’t matter whether you buy or build.”

Unless a problem is entirely novel, Quick argues the “buy” path is almost always faster. As an example, building a custom customer-facing solution rarely makes sense when so many tools already exist to support those interactions at scale. That said, the ultimate success of such decisions is more about the people making them.

Finding the Champion for Change

We’ve talked about how perfect data is the enemy of utility data, and it’s a concept that Quick strongly agrees with. He’ll often hear that teams don’t have enough quality data or they need more data to move forward, but to him, that’s the tail wagging the dog. He advocates for a focus on viability based on the information at hand rather than what’s possible, all of which needs to be driven by an internal champion who can push past fossilized processes that otherwise stall innovation.

“Finding those champions is more art than science,” Quick told Factor This. “There isn’t a formula for it, but you need to find or become a leader who’s focused on solving a particular problem. It’s about finding leaders with the passion to solve specific, high-stakes problems so that you can avoid pilot purgatory and running small experiments that fail to scale because they require too little actual change to the status quo.”

Ultimately, the structural placement of something an AI department is secondary to the leadership driving that change. Whether a utility chooses to build or buy, or centralize or decentralize, the core evaluation must be about whether the project’s benefits justify its inherent costs and risks. That can require a fundamental shift in utility planning, which could have industry-wide ramifications.

AI in the Field and Beyond

Looking ahead, Quick is most excited about getting AI into the hands of the field-based workforce. From performing visual inspections via truck-mounted cameras to reducing the administrative tasks that slow down distribution employees, his work with the Noteworthy AI platform is designed to better leverage existing fleets and routine operations, but as ever, it’s all a matter of execution and expectation that isn’t about the platform, but people.

“I’ve spent my whole career integrating technology with business processes,” Quick said. “Whether it’s AI, RPA, or legacy IT, technology is rarely the wall. AI is just another tool in the belt. We even have an opportunity to embrace it to make the regulatory ratemaking process significantly more efficient.”

That embrace of change is why he sees a significant opportunity for AI to disrupt the regulatory space, making the ratemaking process more efficient for both utilities and public service commissions. Ultimately, success in this new era isn’t about algorithms but an organization’s willingness to embrace and evolve existing human-defined processes and systems.

“It’s all about change,” Quick said. “The more you’re willing to change, the more value you’re going to get.”

Watch the full interview here or listen to the podcast episode.