The Human Side of Energy’s Digital Transformation

Over the past 15 years, I’ve worked at the intersection of engineering, software, and traditional industries going through digital transformations—helping build tools and teams that bridge old-school mechanical know-how with new-school technology. I’ve also seen firsthand what happens when organizations buy the latest tech but don’t invest in the people who have to use it.

Spoiler alert: it’s not pretty. I definitely don’t have the answers, but I’ve been around enough to spot patterns in what works and what flops.

#Setting the Stage

Imagine walking into a modern utility’s control room—rows of monitors lighting up with real-time data: power flows, weather forecasts, market prices, you name it. One engineer, fresh on the job, is scratching her head at a fancy analytics tool straight out of sci-fi. A veteran operator across the table eyes a new dashboard and wonders, “Why should I trust these numbers if I don’t know how they’re crunched?”

These moments aren’t unique to energy. They’re a refrain across industries whenever digital tools make their grand entrance. Whether it’s VR going beyond gaming or entire sectors learning to speak “cloud,” the lesson is the same: cool tech alone doesn’t solve problems unless people know how—and why—to use it.

In energy, that means we can’t just ship software to a utility and expect immediate success. Someone on the ground needs to champion the tool, get trained, and understand how it fits into the bigger picture. Otherwise, the tech’s a no-go from the start.

#Tech + Human Capacity = Real Change

You’ve likely heard claims about AI catching equipment failures early or next-gen forecasting that leaves old-school models in the dust. But if you chat with folks who run the grid, we often hear a common refrain:

We have the hardware and software, but not enough people who feel empowered (or incentivized) to use it.

Sometimes, the issue is comfort: a longtime engineer who’s never typed a line of Python might be wary of slick data visualizations. Or a hotshot coder might know everything about machine learning but nothing about switchgear in a substation.

That’s not a sign of failure — it’s just reality. It’s also why we see the same pattern in other sectors: if you don’t equip your workforce with the right skills, even the best tech can fall flat.

#Lessons From Other Industries

The energy sector isn’t alone in figuring out how to blend traditional expertise with digital smarts. Take a look at these playbooks:

• Automotive: From Mechanics to Data-Centric Rides
  Cars were once all about pistons and gears. Then onboard computers turned them into rolling servers. Automakers realized they needed to train mechanical engineers in software and vice versa.
  Relevance: The grid is like one giant machine—but now it’s also a data hub. Automotive’s journey shows us that bridging mechanical and digital worlds can work when you invest in cross-training.

• Finance: From Handshakes to Algorithmic Trading
  Traders once yelled deals across a trading pit. Over time, software-driven trading took over. Banks responded by creating “quant bootcamps” and hiring talent who could speak both finance and algorithms.
  Relevance: Energy markets are getting more complex and digital. We can learn from finance’s approach to building teams that understand both the domain and the data.

• Healthcare: From Paper Charts to EHRs
  Hospitals switched from handwritten records to electronic systems—sometimes overnight. Many stumbled because the software wasn’t intuitive and staff hadn’t been trained. Hospitals that prioritized user-friendly design and “clinical champions” made smoother transitions.
  Relevance: If a grid operator finds the analytics tool too complicated, they’ll go back to spreadsheets. Good design and thoughtful rollout can save countless hours of frustration.

• Manufacturing: Smart Factories and IoT
  Sensors, robotics, and real-time analytics turned “old-school” plants into “smart factories.” Success hinged on training programs that bridged mechanical know-how with data skills.
  Relevance: As the grid gets more connected devices, we’ll need teams who can handle both the physical infrastructure and the digital intelligence layered on top.

In each case, the lesson is obvious but easy to overlook: technology by itself isn’t enough—people need to feel confident, supported, and encouraged to use it.

#Making Training and Teamwork a Priority

So how do we help create the talents within? We can borrow a few ideas from the examples above:

• Bootcamps and Workshops
  Targeted programs can quickly arm mechanical or electrical engineers with core data skills. And for software folks, short courses on power flows or data basics can do wonders.

• Mentoring From Both Sides
  Like finance pairing traders with quants, energy can pair grid operators with data scientists. When they swap knowledge in real time, the combined expertise is greater than the sum of its parts.

• User-Centered Platforms
  If an interface is clunky, it won’t last. Getting real operators involved in the design process will make the tools more intuitive—and more likely to stick.

• A Culture of Continuous Learning
  Big transformations don’t happen overnight. It’s about building a steady rhythm of training, cross-functional projects, and even simple “lunch and learns” where teams can share knowledge.

• Incentives That Matter
  If someone devotes time to learning a new software or bridging the gap between IT and operations, acknowledge it. Little rewards, public shout-outs, or formal certifications can go a long way.

#Building the Future Together

So how do we get there, collectively?

• Big-Tent Collaboration
  In manufacturing, IoT consortia helped different companies share data and insights. In energy, we can form similar groups where utilities, software vendors, and regulators hash out challenges together.

• Public/Private Partnerships
  Government-backed incentives can encourage utilities to invest in the next generation of digital skills, just like policy nudged healthcare toward EHR adoption.

• Prove It in Real Life
  The finance world won over skeptics with real-world success stories. For energy, showing that data and AI can handle real-time load management or predictive maintenance under stress will build trust.

• Iterate and Evolve
  Pilot programs for new software, frequent feedback loops, and ongoing refinements can help everyone learn what works best.

#Why All This Matters

Renewables, microgrids, EVs, AI forecasting—there’s a wealth of new technology already lining the horizon. But if there aren’t people ready to harness these tools, we’ll end up with great ideas that never fully take off.

This isn’t a critique of the energy sector. It’s more of an invitation: other industries have navigated similar transformations, and many have emerged stronger on the other side. There’s every reason to believe energy can do the same—maybe even better.

#About Texture

Texture helps energy innovators, grid operators, and utilities create software solutions that are as practical as they are powerful. We believe technology should serve real-world needs, and that great products are built by teams with the right mix of domain expertise and digital savvy. Our approach focuses on providing data tools that are real-time, secure, and available where you need them to bridge the gap between engineers, operators, and developers. For more information, visit www.texturehq.com or contact me directly at sanjiv@texturehq.com.