About the Author
Josh Linkner is a five-time tech entrepreneur, New York Times bestselling author, and globally recognized innovation expert. He has founded or co-founded five tech companies that sold for a combined value of over $200 million, and is co-founder and Managing Partner of Muditā Venture Partners, an early-stage venture capital firm. As Chairman of Platypus Labs, Josh helps organizations across industries build cultures of innovation and creative problem-solving.
In This Article
- Five macro shifts reshaping the industry, from AI-powered grid intelligence to the collision of AI’s energy demand with its own sustainability promises
- Real examples of AI at work: Google’s grid partnership with PJM, DeepMind’s wind forecasting breakthroughs, Argonne’s predictive infrastructure tools, and more
- Three developments to watch over the next 12-36 months
- Why human creativity and judgment still matter more than any algorithm
- A 90-day action plan for energy and sustainability leaders ready to move
The energy sector has survived oil shocks, deregulation waves, and the seismic disruption of renewables. It will survive artificial intelligence too, but it won’t look the same on the other side.
AI has moved squarely into the daily operations of utilities, independent power producers, corporate sustainability teams, and grid operators managing billions of dollars of infrastructure in real time. According to Morgan Stanley’s 2026 Powering AI outlook, investments in the energy industry hit a new high of $1.5 trillion in 2025, which is driven in large part by the insatiable power appetite of AI infrastructure itself. That figure alone should get the attention of every leader in the space.
However, the investment story only tells half the picture. The deeper shift is strategic: AI is simultaneously creating the energy challenge and solving it. It is reshaping how electricity grids are managed, how renewable energy is forecasted and integrated, how aging infrastructure is maintained, and how organizations measure and reduce their carbon footprint. The question for energy and sustainability leaders is no longer “Should we explore AI?”, it’s “Are we moving fast enough to stay relevant, and are we doing it responsibly?”
Here’s what you need to know.
Five Ways AI Is Changing Energy & Sustainability
1. The Paradox at the Heart of the Industry
There is a central tension every energy leader must understand: AI is both the source of the problem and the most powerful tool available to solve it. A single AI query consumes roughly ten times more electricity than a standard Google search, and last year the International Energy Agency projected that data center energy consumption would double by 2026 compared to 2022 levels. Meanwhile, global AI spending is projected to exceed $2 trillion this year, with tech giants like Google committing $75 billion on AI infrastructure in 2025 alone.
The same technology driving this demand surge is also the most potent lever available for grid optimization, renewable integration, and emissions reduction. According to the IEA’s landmark Energy and AI report, widespread AI adoption in power plant operations could yield potential cost savings of up to $110 billion annually by 2035. The organizations that understand both sides of this paradox will define the next era of the industry.
2. Grid Intelligence Is Moving from Reactive to Predictive
For decades, grid management has been fundamentally reactive with operators responding to faults, balancing loads after the fact, and scheduling maintenance based on age rather than actual condition. AI is flipping this model entirely. According to Itron’s 2025 Resourcefulness Report, 41% of North American utilities have now fully integrated AI, data analytics, and grid edge intelligence. Utilities deploying AI-enhanced predictive maintenance are reporting 60% fewer emergency repairs and up to 20% improvement in demand forecasting accuracy.
It’s important to note the scale of what’s at stake. Argonne National Laboratory researchers note that the U.S. grid encompasses more than 240,000 high-voltage transmission lines and 50 million transformers, approximately 70% of which have been in service for 25 years or more. AI systems can predict failure before it happens by drawing on real-time sensor data to forecast the remaining useful life of equipment in years, months, and weeks. This is no longer a luxury, but rather an operational necessity.
3. Renewables Can’t Scale Without AI
The intermittency of wind and solar has always been the Achilles’ heel of the clean energy transition. The sun doesn’t always shine and the wind doesn’t always blow. However, AI is turning intermittency from an engineering liability into a manageable variable. AI algorithms are now helping grid operators optimize storage and distribution from renewable sources in real time, while simultaneously predicting demand spikes with accuracy that was previously impossible.
The IEA’s analysis finds that AI could unlock up to 175 GW of additional transmission capacity in existing lines. That’s a staggering figure. It means the capacity bottleneck constraining the energy transition is, to a significant degree, a data and optimization problem that AI is uniquely positioned to solve.
4. Carbon Management Is Being Automated at Scale
For most organizations, measuring and reporting greenhouse gas emissions has historically been a manual, error-prone, and expensive exercise. AI is dismantling that bottleneck. According to Capgemini research, 48% of organizations now use AI to measure and reduce emissions, with AI-powered tools turning what once required months of consultant work into near-real time insights.
BCG and CO2’s 2025 AI Climate Survey found that over 80% of companies that have deployed AI for decarbonization report measurable economic benefits, with some capturing a return on investment exceeding 10% of revenue. Leading platforms like CO2 AI are generating detailed emissions reports in minutes rather than weeks by processing millions of rows of supply chain data to match emission factors automatically. This shift from periodic compliance exercise to continuous optimization is changing what’s possible for sustainability teams operating under increasing regulatory scrutiny.
5. The Talent and Governance Gap is Widening
The energy sector’s AI transformation is accelerating faster than its workforce is adapting. The Global Energy Transition Outlook notes that while AI adoption in energy is moving from experimental to operational, persistent challenges remain: cybersecurity vulnerabilities in operational technology environments, data quality and governance gaps, and a shortage of professionals who combine deep energy domain expertise with AI fluency.
This isn’t simply a technology problem. Organizations that will lead the transition are those that develop integrated strategies across energy, data, and talent. The firms investing in change management, workforce upskilling, and robust data governance alongside their AI deployments are the ones that will convert pilots into sustained competitive advantage.
How AI Is Actually Being Used Today
Google’s AI-Grid Partnership with PJM
In April 2025, Google announced a partnership with PJM Interconnection, the largest grid operator in the United States, and its Alphabet subsidiary Tapestry to modernize the U.S. electric grid using artificial intelligence. The initiative’s specific goal is to cut the interconnection approval process from years to months by using AI to automate and optimize the study of new power projects. For context, the backlog of clean energy projects waiting for grid interconnection approval has been one of the most stubborn bottlenecks in the energy transition. Beyond being operationally userful, using AI to compress that timeline could meaningfully accelerate the deployment of gigawatts of renewable capacity.
DeepMind’s Wind Energy Optimization
DeepMind and Google’s collaboration on wind energy forecasting achieved something that had long seemed out of reach. It was able to accomplish AI-generated wind power predictions 36 hours in advance, with enough accuracy to reduce energy costs at wind farms by approximately 20%. By enabling operators to schedule power delivery more precisely, the AI system effectively increased the economic value of wind output. This is the kind of breakthrough that changes investment models and project economics across the entire renewable energy sector.
Argonne’s Predictive Grid Maintenance
Scientists at Argonne National Laboratory have developed AI-enabled software that predicts when grid components will fail before any visible problems occur. The system draws on vast amounts of sensor data already collected by energy companies and builds predictive models that forecast equipment wear over time, recommending optimal repair or replacement windows. For a grid where 70% of large transformers are operating past their expected lifespan and load growth is accelerating, this tool offers the ability to turn maintenance from a cost center into a strategic advantage.
AI-Powered Carbon Footprinting in the Supply Chain
General Mills offers a concrete example of how AI is changing corporate sustainability at scale. The company is working with CO2 AI to integrate carbon data directly into its core operational systems, automating emissions reporting, improving accuracy across Scope 1, 2, and 3 emissions, and tracking how individual suppliers affect the company’s overall climate footprint. This kind of continuous, automated carbon intelligence is what separates organizations with real decarbonization progress from those making headline pledges without operational follow-through.
What’s Coming Next: Three Moves to Watch (12-36 Months)
1. Virtual Power Plants Will Move from Pilot to Infrastructure
The concept of a Virtual Power Plant has been in development for years. The idea involves thousands of distributed energy resources, from home batteries to commercial EV chargers, coordinated in real time by cloud-based AI to act as a single flexible grid resource. This idea is now approaching scale. According to clean energy analysts at Yale’s Clean Energy Forum, AI-driven VPPs can pre-position stored energy, shift loads, and pre-cool data centers when renewable output dips. This effectively turns millions of distributed assets into a coordinated grid stabilizer. As AI forecasting accuracy improves, the economic case for VPPs strengthens dramatically. Expect the next two to three years to see major utility announcements and regulatory frameworks that bring this model from the edges to the center of grid operations.
2. AI Will Become the Primary Interface for Grid Carbon Optimization
Within 12 to 36 months, the largest energy consumers like data centers, industrial manufacturers, and large commercial real estate operators will be running AI systems that optimize their operations for cost, reliability, and carbon intensity in real time. Google is already doing this with their systems by analyzing grid carbon intensity across regions and dynamically shifting workloads to the cleanest available power. As carbon pricing and reporting regulations tighten globally, this capability will become increasingly important. The CSIS analysis on AI for the grid also notes that the White House’s America’s AI Action Plan explicitly calls for AI-driven grid optimization as a nation priority which means regulatory tailwinds are building.
3. Materials Discovery Will Accelerate the Next Generation of Clean Technology
Some of the most consequential AI applications in energy are happening in research labs. The IEA highlights that a battery gigafactory generates up to 10 billion data points per day, and AI systems capable of analyzing this data can detect faults, predict performance, and dramatically accelerate the development of next-generation battery chemistries. AI is also being used to screen perovskite materials for solar applications. Less than 0.01% of possible configurations have ever been experimentally tested, and AI could unlock breakthroughs in solar efficiency that conventional lab methods would take decades to find. The clean energy technologies of 2030 and beyond are being designed by AI systems today.
The Human Factor: Why Creativity Still Wins
With all of this technological change, it’s tempting to conclude that the future of the energy sector belongs to whoever has the best algorithm, but it doesn’t. It belongs to whoever combines the best tools with the most creative, adaptable human thinking.
In my work with leaders across industries, I’ve seen a consistent pattern that the organizations that thrive in periods of disruption aren’t necessarily the ones with the biggest technology budgets. They’re the ones that cultivate what I call a Find A Way™ mindset: an organization-wide commitment to creative problem-solving that prioritizes agility over brute force and improvisation over perfect planning.
In energy and sustainability, this matters enormously. AI can optimize a wind farm’s output 36 hours in advance, but it takes a human to recognize the policy window that makes a particular clean energy project fundable. AI can predict transformer failure with 85% accuracy, but it takes a human leader to build the organizational culture that actually acts on those predictions before the crisis hits rather than after. AI can generate a detailed decarbonization roadmap, but it takes a human to navigate the stakeholder dynamics, regulatory relationships, and community concerns that determine whether that roadmap gets executed.
AI is both the energy problem and its solution. Leaders who can hold that paradox, make strategic tradeoffs, and communicate difficult choices to investors, regulators, and communities will define the future of the industry. That kind of judgment is irreducibly human.
Start small and start now. Maybe it’s your grid operations team running a 90-day pilot on AI-assisted predictive maintenance for a single substation. Maybe it’s your sustainability team deploying an automated emissions tracking tool for one product line before rolling it out enterprise-wide. Maybe it’s a cross-functional workshop where engineers and sustainability analysts work together to identify the three AI use cases most likely to move the needle in the next 12 months. The breakthroughs accumulate, but only if you start accumulating them.
A 90-Day AI Action Plan for Energy & Sustainability Leaders
1. Pick One High-Friction Problem and Solve It Well
Don’t attempt to “do AI” across your entire operation simultaneously. The StartUs Insights AI in Energy Market Report notes that investment in AI companies drove over 71% of all U.S.-based venture capital activity in Q1 2025, but the organizations converting that investment into operational results are the ones going deep on specific use cases rather than spreading themselves thin. The best practice is to choose one, define your success metrics before you start, prove value, then scale.
2. Audit Your Data Infrastructure Before Buying More Tools
AI is only as good as the data it runs on. In energy, this is particularly acute since operational technology (OT) environments often run on legacy systems with poor data integration, and sustainability teams frequently operate with siloed, inconsistent emissions data. Before purchasing another AI platform, invest in getting your data foundation right. The BCG Climate Survey found that leading companies distinguish themselves not by the sophistication of their AI tools, but by how comprehensively they measure and manage their underlying climate data. Only 7% of large companies comprehensively report their greenhouse gas emissions, which means the vast majority are trying to optimize a process they don’t yet fully understand.
3. Train for Judgment, Not Just Tools
The biggest mistake energy and sustainability leaders make with AI adoption is treating it as a technology initiative rather than a people initiative. Your team needs training not just on which systems to use, but on when to trust AI outputs, when to apply human override, and how to communicate AI-driven recommendations to regulators, communities, and boards who may be skeptical. Build a change management framework for your AI rollout. It will pay dividends long after the current generation of tools is obsolete.
Metrics to Watch
As you execute, track operational efficiency improvements in the specific workflow you’ve targeted, and track AI adoption confidence among your team. If your people are using the tools but not trusting them, you have a governance and training problem. If they’re trusting them blindly, you have a different but equally serious problem. The goal is informed, confident, human-led AI integration.
The Bottom Line
The energy and sustainability sector isn’t being replaced by AI, it’s being reorganized around it. The leaders, firms, and professionals who strategically and creatively engage with this shift will define the next era of the industry. The energy sector sits at a unique inflection point since it is simultaneously the industry most challenged by AI’s growth and the one with the most to gain from AI’s capabilities. That’s not a contradiction to be resolved. It’s a tension to be led through.
The best time to start was yesterday. The second-best time is today. Find a way.
Frequently Asked Questions
How is AI currently being used in the energy sector?
AI is being applied across the full energy value chain through predictive maintenance for grid infrastructure, real-time optimization of renewable energy output, AI-assisted interconnection approval processes, automated carbon emission tracking, and demand forecasting. According to Itron’s 2025 Resourcefulness Report, 41% of North American utilities have now fully integrated AI.
How much energy does AI consume, and why does it matter for sustainability leaders?
A single AI query consumes roughly ten times more electricity than a standard Google search, and the IEA projects data center energy consumption will double by 2026 compared to 2022 levels. This creates a direct tension for sustainability leaders since the AI tools they’re deploying to reduce emissions are themselves significant emissions sources. Managing this paradox is one of the defining challenges of the moment.
Can AI actually accelerate the clean energy transition?
Yes, substantially. The IEA finds that AI could unlock up to 175 GW of additional transmission capacity in existing lines, and widespread AI adoption in power plant operations could yield up to $110 billion in annual cost savings by 2035. DeepMind’s wind forecasting work has already demonstrated 20% cost reductions at wind farms. MIT researchers are actively investigating AI’s role in materials discovery for next-generation batteries and solar cells.
What is a Virtual Power Plant, and why does it matter?
A Virtual Power Plant (VPP) links thousands of distributed energy resources through cloud-based AI, allowing them to act as a single coordinated grid resource. AI forecasting enables VPPs to pre-position stored energy and shift loads in anticipation of renewable output fluctuations. As AI accuracy improves, VPPs are moving from pilot programs toward mainstream grid infrastructure.
How is AI changing corporate carbon management?
AI is transforming carbon management from a periodic compliance exercise into a continuous operational capability. Platforms using generative AI can now generate detailed emissions reports in minutes rather than weeks, automatically match emission factors across complex supply chains, and provide real-time alerts when emissions spike. According to BCG, over 80% of companies using AI for decarbonization report measurable economic benefits.
Where should energy and sustainability leaders start with AI?
Pick one high-friction, measurable workflow and deploy an AI solution with clear success metrics. Simultaneously, audit your data infrastructure to ensure you have clean, accessible, and well-governed operational and sustainability data. Then invest in training your team not just on tools, but on the judgment required to use them responsibly and effectively.
Learn more about Josh Linkner’s keynote speaking in Energy & Sustainability.
Josh Linkner speaks to energy organizations and sustainability leaders around the world about innovation, navigating disruption, and building cultures that thrive in an era of rapid change. To explore how Josh can energize your next event, schedule a call today.