Deepa Pahuja commands wide respect for driving large-scale energy transformation through cloud, IoT, and AI. Explore her insights on bringing the industry up to speed.
Senior Solution Architect at AWS
At Uniquely Wired, we’re connecting the best brains in data and telecom. Our mission is to explore the edge of science, technology, and innovation – and in that context, a capacity as Deepa Pahuja (DP) is a source of knowledge and inspiration. In our latest Q&A session, you can catch the AWS Senior Solution Architect’s in-depth answers to 10 central questions about AI, cloud, and how to leverage the power of the future without pausing the present.
Deepa - What excites me most is seeing field technicians change how they work almost overnight. I recently worked with a wind farm where teams moved from reacting to failures to predicting them weeks in advance.
One technician, who has been doing this job for more than 20 years, summed it up perfectly.
Turbine operators are now adjusting energy output dynamically based on AI-powered weather insights. And power plant teams are optimizing how they connect to the grid in real time. So, you could say that they’re not just maintaining infrastructure, but actively shaping how the energy system works. When you combine decades of hands-on experience with machine intelligence, you get a level of insight and resilience that simply was not possible five years ago. That way, cloud and AI are fundamentally changing the role of people who work in the energy transition’s frontline.
Deepa - True. Data centers can be seen as something distant and abstract, far removed from what actually happens on factory floors or in power plants. I usually explain it more simply.
Take a smart meter in a residential neighborhood. If it detects an unexpected voltage fluctuation, that signal doesn’t just stay local. It is sent straight to the cloud, where it is analyzed together with grid models, historical consumption data, and real-time distribution conditions. Within milliseconds, recommendations are sent back. Not just to that substation, but to other relevant assets across the network. That is what enables smarter balancing and load decisions across the grid.
In one solution I worked on, edge devices at wind farms handled the immediate, safety-critical responses on site. At the same time, cloud-based AI focused on optimizing long-term performance by learning from fleets of assets around the world. In that setup, physical distance stops being a limitation. Each site benefits from shared intelligence, learning from situations far beyond its own operating conditions. The outcome is a system where local operations continuously become smarter, more resilient, and more efficient.
Deepa - One of the more surprising examples I have seen came from a utility that deployed AI and IoT sensors to predict transformer failures. That was the only goal at the time. As the models started analyzing electromagnetic data from transmission lines, the system began flagging anomalies that didn’t match the usual signs of equipment degradation. At first, that was confusing, but it turned out the AI was picking up subtle changes caused by vegetation growing closer to the lines. It was detecting those shifts weeks earlier than traditional inspections or visual monitoring ever could. So, what initially looked like electrical noise was actually an early warning of environmental change.
As the model improved, it started identifying signal patterns that aligned with optimal wildlife corridor conditions. In other words, the same data used to improve grid reliability was also showing where vegetation supported safer movement for animals. What started as a maintenance project grew way bigger. It helped prevent outages, while also informing environmental stewardship. No one set out to find wildlife insights in electromagnetic data. But fact is that AI connected patterns that people simply wouldn’t have thought to look for. That experience stuck with me, because it proves that the most valuable insights are the ones you did not know you should ask for.
Deepa - One of the most common things I hear from executives in manufacturing and energy is, ‘We cannot risk our critical infrastructure on someone else’s computers.’ That concern makes sense. Many of these systems were designed decades ago and shaped by a very different threat landscape.
What often surprises people is that the risk profile today has flipped. Cloud providers operate at a level of security and compliance (including NERC-CIP requirements) that most utilities would struggle to match on their own. I have seen this firsthand.
With the kind of sophisticated cyber threats we see today, keeping legacy systems on premises can carry more risk than moving to a cloud environment that is monitored and updated all the time. In that sense, cloud and AI adoption is more about strengthening security and resilience than losing control. To free teams to focus on innovation instead of putting out the never-ending infrastructure fires.
Deepa - Industry 4.0 often feels overwhelming because it is presented as a massive technology shift. I try to turn that perspective around by starting with the business part instead of the tech part. Instead of talking about platforms, architectures, or buzzwords, I simply ask 'what operational issue is costing you the most money right now?'. The answers are usually very practical: unplanned outages, asset downtime, and difficulties integrating renewables into existing systems. From there, we focus on one small, clearly defined use case with real impact. In one project with a utility, we started by digitizing and monitoring the outage management system. That was it. Within six months, that alone helped prevent multiple major outages.
Once people see real results, the conversation begins to change. Teams gain confidence, leadership sees value, and momentum builds on its own. My approach is to start small, prove value quickly, and expand step by step as people, processes, and technology mature together.
Deepa - People still rely on deep operational experience, but they are increasingly making decisions with data and AI in the loop. Control room operators are no longer just monitoring systems. They are actively balancing renewable supply, demand, and grid constraints using AI-driven insights. And field technicians are shifting away from constant firefighting toward proactive asset optimization, stepping in before problems escalate.
In some utilities, this shift has even led to new roles, such as a Digital Energy Analyst, who is are often an experienced plant operator that has been upskilled to work with analytics across multiple sites. Their intuition has not gone away. It has become more effective because it's combined with cloud-based intelligence. In that way, technology extends what people are capable of. Over the next five years, I think the companies that do well will be the ones that give their people the tools and skills to operate confidently in a more connected, data-driven industrial environment.
Deepa - Advanced materials play a bigger role in data centers and energy infrastructure than most people realize. In many cases, they are what make progress possible. Take grid-scale storage. Better battery materials mean higher energy density, longer lifetimes, and more stable performance. That stability leads to better data, which makes AI models more effective. Those insights then shape how new renewable assets are designed, pushing demand for the next generation of materials.
At the edge, this matters even more. Solar farms in deserts or offshore wind platforms depend on components that can handle heat, corrosion, vibration, and long service intervals, without sacrificing performance. Improvements in semiconductors and thermal materials directly translate into better reliability and safer operations. Over time, advances in materials science mean smarter energy systems and infrastructure that can actually scale.
Deepa - Every power plant, wind farm, and transmission line is a skilled musician. For a long time, they have mostly played on their own. Sometimes it works. Sometimes things drift out of sync. Cloud and AI act like the conductor who can see the entire score. Demand, weather, asset health, grid constraints, all at once. With that context, the system can adjust in real time and keep everything (and everyone) working together more smoothly.
Deepa - When organizations use cloud-based AI to optimize generation, storage, and dispatch, they can reduce emissions while getting more out of the assets they already have. I have also seen manufacturing teams improve overall equipment efficiency by using cloud-connected real-time energy monitoring. Also, moving workloads from on-premises systems to modern cloud data centers often lowers energy use and carbon footprint, simply because those environments are designed to run at much higher efficiency.
The biggest shift comes from insight. Cloud-scale AI makes it easier to understand how energy, materials, and waste interact across an operation. That visibility helps companies find new ways to reduce waste, support circular processes, and operate more responsibly.
Deepa - We are moving toward a model where operators interact with industrial systems through natural language and intent, rather than dashboards and tools. In practical terms, this means that an operator can define a goal such as maximizing renewable integration while maintaining grid stability and controlling costs. AI agents then coordinate across weather forecasts, market signals, asset health, storage systems, and grid constraints in real time. The AI will plan, execute, and continuously adapt operational strategies.
This shift is underway, and early adopters are already implementing these capabilities in controlled environments today. By lowering the technical barrier to advanced decision-making, generative AI has the potential to significantly accelerate Industry 4.0 adoption.
Deepa Pahuja is a Senior Solutions Architect at Amazon Web Services (AWS), where she leads AI- and cloud-powered transformation initiatives for Fortune 100 Energy & Utilities enterprises. She operates in a uniquely demanding niche that requires simultaneous mastery of hyperscale cloud architecture, advanced AI/ML systems, cybersecurity frameworks, and operational energy technologies. Her solutions and architectural innovations have been adopted by major industry leaders globally.
Her technical leadership has established her as a respected industry voice. Ms. Pahuja serves as the Distinguished Technical Advisory Chair of the IEEE Computational Intelligence Society and is a Fellow of the Energy Institute. She is also an active contributor to the IEEE Power & Energy Society. An accomplished speaker, author, and mentor, she publishes widely read technical blogs and whitepapers and presents her innovations at leading global conferences, shaping dialogue at the intersection of artificial intelligence, cloud computing, and the future of energy.
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