Advancing AI in public utilities

3,527 feet. Lake Powell is down 34 feet from last year. It sits at just 23% capacity. Have you been there? If it drops another 37 feet, the turbines at Glen Canyon Dam stop spinning. Clean power for millions vanishes. This isn't just an environmental headline. It is a stark warning about what happens when critical infrastructure is managed using yesterday's data in a tomorrow that no longer looks like the past. For decades, we governed finite systems using static historical baselines and static spreadsheets. That playbook is officially dead. Here is how advanced tech and AI are stepping into the breach to solve modern systemic risk: From Historical Baselines to Predictive AI: Physics-Informed Neural Networks (PINNs) combine real-time satellite imagery, soil moisture telemetry, and deep snowpack data to forecast runoff with pinpoint precision—months before a reservoir hits a critical threshold. From Static Management to Basin Digital Twins: Interactive digital twins allow operators to simulate thousands of climate and allocation scenarios in seconds, giving leaders the exact data needed to make hard policy decisions before crisis hits. From Grid Fragility to Smart Orchestration: When hydro capacity collapses, AI-driven grid orchestration dynamically rebalances regional energy loads in real time, dispatching renewables and battery storage to prevent cascading blackouts. The lesson goes far beyond water and energy: If you manage 21st-century complexity with 20th-century tools, you are managing on borrowed time. The companies and infrastructures that survive the next decade won't be the ones with the most resources. They will be the ones with the best real-time intelligence. #AI #Infrastructure via @blog_by_beth #Technology #PredictiveAnalytics #DataScience #Innovation #Leadership

Across industries, legacy systems - often weighed down by years of heavy customization - are no longer enabling innovation. They’re holding it back. When Scottish Water faced the obsolescence of its on‑premises billing system, it chose to see opportunity instead of constraint. Partnering with Capgemini, they migrated to a cloud‑native Oracle Utilities solution purpose‑built for the realities of water utility operations. But the real value wasn’t simply moving to the cloud. It was what that modernization unlocked. By systematizing their operations and eliminating unnecessary customizations, Scottish Water dramatically reduced its cost of ownership and enhanced its ability to adopt emerging AI capabilities, from intelligent customer interaction profiles to AI-powered risk scoring and consumption insights. This is the pattern we're seeing across utilities and other sectors globally: organizations that modernize their core systems today are positioning themselves to lead with AI tomorrow. You can read the full story here: https://lnkd.in/eQwehMag

TL;DR: Today Google is sharing some promising updates on our progress to help rapidly support AI growth - without the added time and costs to build new generation or transmission - by making our machine learning (ML) demand more flexible. 📉 Read more about our new utility partnerships here: https://lnkd.in/g8vCNG6Q We’ve been working hard to push forward technologies that can generate abundant, reliable power and enable a clean path to growth. But supply is just one side of the equation. Grid operators typically only utilize about 50% of available generating capacity. This is by design: they must plan and build enough power plants to meet the highest demand at any given time, but peak demand only occurs during a small fraction of the hours in a year. Research shows that even a small amount of flexibility for large energy loads, like ML, during peak times can reduce the need to build new power plants while accommodating new energy loads much faster - making more efficient use of existing generation. See this study for example 👉 https://lnkd.in/guB6XCWg While this is still early stages, we see demand flexibility as an important tool that can be deployed quickly to manage AI growth and help utilities reliably and cost-effectively serve their customers. More on how this works below👇 None of this would have been possible without our 24/7 carbon-free energy ambition, which made us think holistically and creatively about how to align our own hourly energy consumption with the needs of electricity grids. As part of that effort, we developed the capability to flexibly shift compute tasks to when and where the grids that serve our data centers are running on the cleanest energy sources. Fast forward to today, we’ve developed new capabilities to shift compute tasks - and most notably ML workloads - to help meet the world’s growing energy needs while minimizing the time and costs required to add new generation to the system. As we implement these capabilities in early partnerships with Indiana Michigan Power, Tennessee Valley Authority, and Omaha Public Power District, we’ll learn more about where and when it can be most effective. Fast forward even further to the future, and we hope this capability can enable the growth of AI to benefit more people while better optimizing the electricity system for everyone. Kudos to the brilliant team behind this work Varun Mehra, Manali Gokhale, Alina Gurova, Savannah Goodman, Binz Roy, Pratyush Kumar, Ellen Zuckerman, Nekabari (Neka) L. Goka, Louise Wood, Rabo G., Colleen R., Sana Ouji, Maud Texier, Will Conkling, Amanda Peterson Corio.

Yesterday, I shared survey findings from AlphaStruxure on how companies are handling grid constraints in the face of rapid AI growth. Today, I’m excited to share our whitepaper on AI applications and solutions. AI presents a dual challenge: it causes grid strain but is also a revolutionary tool for tackling global challenges like climate change. As we navigate the energy transition, I am inspired by AI's transformative power. By placing sustainability at the heart of innovation, we can unlock a future that is intelligent, inclusive, and low-carbon. Our latest report, “AI: The Power Behind a Sustainable Future,” developed with NBCUniversal Catalyst, explores how AI is driving change across data centers, energy grids, cities, and water systems.  Key insights include AI-Driven Efficiency, Smart Grid Evolution, Urban Innovation, Sustainable Water Use, and Responsible AI. The convergence of AI and sustainability represents one of the greatest opportunities and challenges of our time. By centering our efforts on sustainability, we can create a future that is technologically advanced and environmentally responsible. Download the report: https://lnkd.in/gfFzmG-S

Australian utility companies stand at pivotal crossroads with artificial intelligence; while the potential of AI to transform the industry is immense, widespread adoption across the sector is still lagging. Why? Significant barriers such as stringent regulatory requirements, data quality concerns, outdated data management and data governance practices, and lack of workforce readiness hinder AI adoption. At EXL, we specialise in helping utilities overcome inherent obstacles and adopt AI successfully through: 🔸𝗠𝗼𝗱𝗲𝗿𝗻𝗶𝘀𝗶𝗻𝗴 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: We identify data inefficiencies and develop data strategies to fully harness the available data. 🔸𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: Our advanced data governance frameworks and data cleansing techniques ensure AI-driven decisions are based on accurate, reliable data. 🔸𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝗻𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: We help utilities build robust regulatory frameworks, ensuring AI implementation is both secure and compliant. 🔸𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: From targeted training to change management, we prepare teams for AI adoption and reduce cultural resistance. 🔸𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Leveraging EXL’s AI solutions, such as EXELIA.A and MIA, we improve service delivery and enhance customer engagement for large energy providers. To learn more, read the article by senior vice president and growth leader, Vilas Sagar Madan: https://bit.ly/4iA9rCq #AI #DigitalTransformation #UtilitiesInnovation

AI adoption is accelerating faster than the energy systems built to support it. Data centers are already among the most power-intensive assets on the grid and are seeing demand rise at rates that legacy infrastructure, static operating models, and fragmented regional grids were simply not designed to handle. The consequence is predictable: higher costs, growing emissions, and mounting pressure on utilities and operators trying to maintain reliability while integrating renewables. I’ve spent much of my career working at the intersection of technology, energy policy, and industrial systems, and this challenge is proving to be one of the defining infrastructure questions of the decade. It’s increasingly clear that the sector needs new ways to manage load, forecast demand, and coordinate resources across highly variable conditions. This week, I had the opportunity to hear from senior leaders at Hanwha Qcells about a model they are developing that aims to address these pressures. What stood out to me was the architectural shift behind the technology: using AI, interoperable language, and digital twins to unify diverse equipment, link operations to real-time grid signals, and automate many of the repetitive, checklist-style decisions that currently consume operator time. This broader concept of treating data centers as intelligent, grid-aware assets aligns with conversations happening across industry and government. The framework they described integrates clean generation, storage, and control software into a single adaptive system. The goal is straightforward but ambitious: reduce wasted energy, cut emissions, and improve resilience as AI demand grows. Their lofty projections (20–30% cost reductions, up to 35% emissions cuts, faster response times through agentic operations) reflect why approaches like this are gaining momentum. What interests me most is how these ideas fit into the larger trend: the shift toward an “Intelligent Age” where digital growth and energy management are inseparable... remember when VPPs were unheard of? Solutions that improve transparency, interoperability, and operational flexibility will be essential, and not just for data centers, but for manufacturing, transportation, and other power-intensive sectors facing similar constraints. As we look ahead, the real opportunity is in building systems that scale, adapt, and operate with far greater situational awareness. The conversation with Qcells underscored how quickly this space is evolving and why collaboration across utilities, technology developers, operators, and policymakers will be critical in the years ahead. Article link: https://bit.ly/4qggMLd #Hanwha | #HanwhaQcells | #Microsoft | #AI | #DataCenters | #EnergyManagement | #GridModernization | #CleanEnergy | #Innovation

We’ve spent years making the electric grid 𝗱𝗮𝘁𝗮 𝗿𝗶𝗰𝗵. More sensors. More telemetry. More connected assets. More data lakes. But as AI moves closer to real grid operations, I believe we’re discovering something important: 𝗠𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗰𝗿𝗲𝗮𝘁𝗲 𝗺𝗼𝗿𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. A voltage reading tells AI 𝘸𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘦𝘥. Context tells AI 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗺𝗲𝗮𝗻𝘀. Topology. Asset history. Weather. Maintenance records. Engineering models. Real-time telemetry. Human operational experience. And increasingly important confidence that the underlying data can actually be trusted. This is where the next chapter of the Digital Grid gets interesting. The utilities best prepared for AI may not be those with the most data. They may be those that can turn decades of operational knowledge into 𝗺𝗮𝗰𝗵𝗶𝗻𝗲-𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. In this week’s 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗶𝗱, I explore why the journey from a data-rich grid to a 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗿𝗶𝗰𝗵 𝗴𝗿𝗶𝗱 may ultimately determine how intelligent—and trustworthy—utility AI becomes. 𝗧𝗵𝗲 𝗔𝗜-𝗥𝗲𝗮𝗱𝘆 𝗚𝗿𝗶𝗱 𝗪𝗶𝗹𝗹 𝗕𝗲 𝗕𝘂𝗶𝗹𝘁 𝗼𝗻 𝗖𝗼𝗻𝘁𝗲𝘅𝘁, 𝗡𝗼𝘁 𝗗𝗮𝘁𝗮 I’d welcome your perspective: 𝗜𝘀 𝘆𝗼𝘂𝗿 𝘂𝘁𝗶𝗹𝗶𝘁𝘆 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜-𝗿𝗲𝗮𝗱𝘆 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲—𝗼𝗿 𝗮𝗻 𝗔𝗜-𝗿𝗲𝗮𝗱𝘆 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲? #DigitalGrid #ArtificialIntelligence #Utilities #GridModernization #DigitalTwin #EnergyTransition 

We are entering a new phase for energy. More distributed systems. More dynamic demand. More pressure for reliability, compliance, and performance. This system cannot be managed with disconnected tools and delayed insights. It requires a different foundation. AI must operate at the core. Not as an add-on. As infrastructure. Gigawatt is building an AI-centric operating system for utilities. A system that connects to existing environments, unifies data, and enables intelligence across operations, service, revenue, and grid performance. This is not about replacing everything at once. It is about building a new operating foundation. Starting small. Expanding as value is proven. This is how modernization becomes real. https://hubs.ly/Q04c-jcX0 — Brad Keywell #Gigawatt #AIforUtilities #UtilityModernization #GridModernization #EnergyTransition #EnergyInfrastructure #UtilityOperations #DigitalUtilities #FutureofEnergy

Realizing the potential benefits of AI requires a resilient energy system and skilled workforce to build and maintain it. Today in a new paper, Ruth Porat, Kent Walker, James Manyika and I are sharing policy opportunities to reach these benefits that focus on three areas -  Accelerating innovation and investment in advanced energy generation: We must promote investment in many types of energy sources, like geothermal and advanced nuclear, and flexible energy resources, such as demand response. Optimizing use of the existing grid and unlocking construction of new transmission infrastructure: Today's grid is a significant bottleneck to the delivery of abundant energy. Policy solutions can help use the existing grid more efficiently, unlock new construction, and improve planning. Developing the necessary labor force: To build our commitment to electrical professional training programs, policymakers can focus on further advancing electrical workforce development programs. To help address this, Google.org is launching a program to provide training to 100,000 current electrical workers and all electrical training ALLIANCE (etA) apprentices nationwide. The program will help electrical professionals learn foundational AI skills, AI best practices, and enhance curriculums with advanced tools like AI-powered learning and AR/VR aids. Read more about our recommendations and programs: https://lnkd.in/gwcSUEDR

Artificial intelligence is no longer just a vision for the future—it’s revolutionizing how government agencies operate today. A recent Google-commissioned IDC study found that 50% of federal agencies report high AI maturity, embracing innovation and piloting generative AI to enhance mission impact. Here are the top AI trends we’re seeing shape the public sector in 2025, with real-world success stories: 🔹 Multimodal AI – Hawaii DOT is using Google Earth Engine and Cloud AI to tackle climate challenges and prioritize infrastructure investments. 🔹 AI Agents – Sullivan County NY government implemented virtual agents using Google AI to serve more citizens 24/7, even with limited staff. 🔹 Assistive Search – The Air Force Research Lab (AFRL) is leveraging AI and cloud computing to accelerate research in materials science, bioinformatics, and human performance optimization. 🔹 AI-Powered Citizen Services – Wisconsin Department of Workforce Development (DWD) scaled unemployment insurance claims processing with AI, reducing fraud and improving response times. 🔹 AI-Driven Security – New York City faces 90 billion cyber events per week—AI-powered automation helps filter down to the most critical risks. AI is already transforming government agencies across the country. To continue this momentum, agencies must invest in AI leadership, governance, and security to build trust and resilience. Download the AI Trends 2025 in Public Sector report to explore how AI can support your mission: #GooglePublicSector #PublicSectorInnovation