03
We are not ready for the rapid and profound impacts that AI will have on the energy system

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

Introduction

Artiﬁcial Intelligence (AI) is rapidly transforming the energy sector, inﬂuencing every stage of the value chain—from generation and trading to distribution, retail, and customer engagement. As digital systems become more deeply embedded, AI technologies are emerging not only as signiﬁcant energy consumers but also as powerful enablers of intelligent, adaptive infrastructure. While some forecasts anticipate a surge in energy demand driven by AI workloads, others highlight its potential to unlock eﬃciencies that could mitigate or even reverse that trend. These developments are not conﬁned to electricity alone; in gas and water sectors, AI is already being used to enhance forecasting accuracy, detect anomalies in real time, and optimize resource distribution—underscoring its broad relevance across utilities.

AI is both a pressure point and a problem-solver. It can intensify load on aging grids, yet also enable real-time optimization, predictive maintenance, and better integration of clean energy sources. The challenge lies in navigating this complexity: ensuring that the tools we build to drive progress don’t outpace our ability to support them.

Rather than asking whether AI will disrupt the energy system, the more urgent question is how we can guide that disruption toward resilience, sustainability, and shared benefit.

Bragadesh Damodaran

Vice President: Global Energy & Utilities Industry Platform Leader, Capgemini

Bragadesh is a seasoned E&U Industry and Strategy Consultant with a career
spanning over 24 years. He has worked globally in more than 12+ countries, delivering value for major multinationals through to regional organizations, driving E&U value chain strategies and CXO Advisory.

Mercy Paul

Program Manager: Energy & Utilities Industry Platform, Capgemini

Mercy Paul is an experienced professional in the Energy and Utilities sector with two decades of work spanning smart grids, asset management, and energy transition initiatives. At Capgemini, Mercy contributes to the Energy & Utilities industry platform, supporting capability development, energy transition and operational excellence-focused oﬀerings, and thought leadership.

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

The Promise of AI in Powering the Future

AI is delivering transformative value across the energy value chain. In generation, it enhances asset performance through predictive maintenance, improves forecasting for renewables, and supports real-time dispatch optimization.

In distribution, AI helps utilities detect faults, manage voltage fluctuations, and optimize network planning, improving reliability and reducing technical losses.

For grid operations, AI enables smarter load balancing, power flow optimization, and outage prediction, helping reduce downtime and operational costs. According to IEA, the integration of today’s AI applications in power plant operations and maintenance can yield potential cost savings of up to USD 110 billion annually worldwide to 2035.

FIGURE 1
Promise of AI in Powering the Future

Widespread Adoption Case refers to a scenario where existing, real-world AI applications are scaled across the energy sector by 2035, assuming key barriers like data availability and interoperability are overcome. It reﬂects realistic adoption levels without assuming full theoretical potential ¹.

Source: IEA

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

In trading, AI supports real-time analytics for price forecasting, market bidding strategies, and portfolio risk management, improving agility and competitiveness.

On the customer side, AI powers smart devices and dynamic pricing models that enable demand-side flexibility and personalized energy services. These capabilities not only improve user experience but also reduce peak load stress on the grid.

Across all these domains, AI drives measurable business outcomes: reducing OPEX through automation, optimizing CAPEX by extending asset life and deferring upgrades, and improving TOTEX eﬃciency through smarter lifecycle planning. It also accelerates time-to-market for new services and unlocks revenue growth through models like energy-as-a-service and advanced analytics. AI is not just a tool for operational eﬃciency—it’s a strategic enabler for innovation, resilience, and long-term value creation in the energy sector.

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

Real-World Momentum: How AI Is Already Transforming Energy

As the energy sector explores the transformative potential of AI, early deployments are beginning to yield results. Yet, it’s essential to balance ambition with a clear understanding of what has been achieved to date.

Many large and mid-sized utilities have laid the groundwork—establishing data infrastructure, identifying high-impact AI initiatives, and deploying several use cases at scale. However, the outcomes have been mixed, with some falling short of initial expectations. A recent IEA survey of grid operators across 13 countries—including the U.S., Germany, Japan, and Australia—shows that only 23% currently use AI for real-time operations. In contrast, 54% have adopted AI for grid development planning, and nearly 70% for asset maintenance and operational planning. These figures suggest that while AI is gaining traction, its integration into mission-critical operations is still evolving.

FIGURE 2
Utilities using AI applications by category, 2024

Source: IEA

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

The following table presents real-world examples that illustrate how AI is being successfully applied across the energy landscape. As the sector moves forward, maintaining a realistic view of progress will be key to unlocking AI’s full potential.

Source: IEA

Organization	Use Case Description	Impact
British integrated energy company	Uses AI-powered predictive models to forecast energy demand accurately, improving grid eﬃciency. Reduces waste, improves energy supply planning, and balances supply and demand ﬂuctuations.	Improves grid eﬃciency, reduces waste, improves energy supply planning, and balances supply and demand ﬂuctuations.
UK based water and sewerage services provider	Uses generative AI for asset management and operational optimization, including predictive maintenance. AI generates synthetic sensor data to predict equipment failures and schedule maintenance, improving operational eﬃciency and reducing downtime.	Improves operational eﬃciency and reduces downtime.
British multinational electricity and gas utility company	Uses AI to optimize grid management and enhance energy eﬃciency. AI-driven grid monitoring systems analyze data in real-time to identify and address potential issues before they occur, preventing unexpected breakdowns and reducing disruptions to the power supply.	Prevents unexpected breakdowns and reduces disruptions to the power supply.
Oilfield services company and European energy supplier	Drilled the most autonomous well section to date using digital technologies for surface automation, autonomous on-bottom drilling, and directional drilling. Achieved 99% of a 2.6-kilometer section in autonomous control mode. Added AI-driven geosteering for more eﬃcient and productive wells.	Achieves more eﬃcient and productive wells.
Microsoft, Remix: The Soul of Innovation, Maverick IQ, Ayika Solutions, VCC	Developed an AI-powered microgrid in West Atlanta. Improves energy eﬃciency, resilience, and equity by integrating solar power, smart home upgrades, and AI-based energy management.	Improves energy eﬃciency, resilience, and equity.
US based energy corporation	Using AI extensively in its Permian Basin operations to boost oil extraction eﬃciency, reduce cycle times, and identify optimal drilling opportunities. AI-driven sensors and remote operations enhance safety and productivity, while AI also helps reduce methane emissions by 60% in upstream operations.	Boosts oil extraction eﬃciency, reduces cycle times, identiﬁes optimal drilling opportunities, enhances safety and productivity, and reduces methane emissions by 60%.
British multinational oil and gas company	Employed a deep-learning-based quality control agent to automate seismic processing workﬂows, replacing human eﬀort in workﬂow parameter optimization and achieving comparable end processing results. Substantially reduced the cycle time for exploration decision-making.	Substantially reduces the cycle time for exploration decision-making.
Australia's leading energy retailer	Implemented Evercamâ€™s AI-powered Gate Monitoring system to track peak construction traﬃc for its BESS project. Enabled real-time vehicle classiﬁcation, optimized traﬃc schedules, reduced security staﬃng, and ensured compliance reporting, resulting in signiﬁcant cost savings.	Optimizes traﬃc schedules, reduces security staﬃng, ensures compliance reporting, and results in signiﬁcant cost savings.
US based investor-owned utility	Deployed Atomic Canyonâ€™s Neutron Enterprise, a generative AI-powered search engine, to manage 2 billion pages of records at Diablo Canyon nuclear plant. Achieves 98% top-10 and 93% top-5 search accuracy, streamlining document retrieval and compliance for nearly 9% of Californiaâ€™s electricity and 17% of its zero-carbon energy.	Streamlines document retrieval and compliance for nearly 9% of Californiaâ€™s electricity and 17% of its zero-carbon energy.

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

Generative AI and Agentic AI are also entering the picture, though at diﬀerent stages of maturity. Strategic planning for GenAI is underway in many organizations, while Agentic AI remains largely conceptual. However, momentum is building. According to Capgemini’s 2024 CRI report, 24% of organizations in the energy and utilities sector have implemented generative AI across some or most functions or locations—up sharply from just 3% in 2023. This signals a rapid shift from experimentation to broader adoption. Despite the challenges, AI is already delivering value in areas such as predictive maintenance, demand forecasting, and stakeholder engagement. Real-world deployments are reinforcing this momentum. Organizations across the globe are applying Generative and Agentic AI to solve complex operational challenges and enhance decision-making. These examples illustrate how AI is being embedded into the core of energy infrastructure—supporting not just automation, but adaptive, context-aware systems that can respond to dynamic conditions.

Use Case	Project / Organization	Application	Benefits
Energy Demand Prediction	Major DSO in the UK	Anticipates energy demand using weather, time-of-use, and user behavior	Improves real-time supply-demand balance
Renewable Energy Optimization	Renewable Energy Company in Australia	Optimizes renewable energy production using climate data	Maximizes integration of renewables into the grid
Predictive Maintenance	Multinational technology conglomerate	Analyses sensor data to generate predictive diagnostics	Reduces outages and improves maintenance eﬃciency
Crisis Scenario Simulation	Multinational technology conglomerate	Uses AI-powered digital twins to simulate grid disturbances	Tests grid resilience and supports operator training
Operator Decision Support	Electricity Transmission Network company in France	Provides natural language recommendations to dispatchers	Enables faster, more informed decision-making in critical situations

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

These examples reﬂect a broader trend: AI is not only enhancing operational eﬃciency but also enabling more intelligent, resilient infrastructure across electricity, gas, and water systems. As adoption scales, the focus is now shifting toward managing the associated risks—ranging from data governance and model transparency to ethical concerns and system interoperability.

As organizations move beyond early pilots, they are beginning to explore a broader range of applications where AI, GenAI and Agentic AI can deliver strategic and operational value. The following use cases highlight areas with strong potential for impact—spanning customer engagement, grid operations, asset management, and beyond.

Category	AI Use Cases	Generative AI Use Cases	Agentic AI Use Cases
Smart Metering	Fault location Network technical analysis (voltage, etc.)	Synthetic Data Generation Anomaly Detection Narratives Forecasting Services	Autonomous Consumption Optimization Agents Self-healing Meter Networks
Grid Operations	Weather conditions forecasting Load curve and demand forecasting Market forecasting Control room automation (drones, LIDAR, image analysis) Anomaly detection Non-technical losses detection Prioritizing maintenance Fieldworker assistance Predictive maintenance Vegetation management Power quality management Operating cost optimization Predictive failure/outage prediction	Incident Response Plans Control Room Simulators Load Flow Scenario Generation	Autonomous Grid Balancing Agents Real-time Contingency Management
Asset Management	Asset investment planning Real-time asset health management Network resilience assessment Automated monitoring of grid assets Asset performance optimization (connected DER) Environmental analysis (wildlife, cables, BESS)	Maintenance Report Generation Failure Mode Simulation Investment Planning	Autonomous Inspection Agents Lifecycle Optimization Agents
Digital Twin	Help building the replica Asset investment planning platform Network simulation and optimization	Scenario Simulation Grid Optimization Algorithms Training Scenarios Generation	Autonomous Scenario Testing Agents Self-learning Grid Models
Client Services	Customer sentiment analysis Customer interaction intelligence modules	Conversational Agents Proactive Recommendations Multilingual Support	Proactive Energy Advisors Autonomous Issue Resolution Bots

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

The Hidden Risks of AI in Energy

As AI becomes more integrated into energy systems, it brings new risks that must be actively managed. Cybersecurity is a top concern, with AI systems vulnerable to threats like data poisoning and adversarial attacks—such as tampered smart meter inputs or manipulated sensor data. The Global Cybersecurity Outlook reports that 66% of organizations expect AI to significantly impact cybersecurity in the coming year.

AI adoption also raises workforce and equity challenges. Automation is reshaping roles—reducing manual tasks while increasing demand for digital and analytical skills.

FIGURE 3
Cybersecurity vulnerabilities predicted by professionals

Source: WEF_Global_Cybersecurity_Outlook_2025.pdf

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

Despite growing adoption, AI talent remains scarce in parts of the energy sector. According to the IEA, utilities lag other industries in AI-specific capabilities, which could hinder safe deployment. Moreover, AI decisions—like outage prioritization or billing recommendations—can unintentionally reinforce bias if not carefully designed.

FIGURE 4
Key roles of business transformation

Source: WEF_Future_of_Jobs_Report_2025.pdf

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

Finally, AI systems can behave unpredictably under extreme conditions, such as climate anomalies. Ensuring transparency, explainability, and rigorous validation is essential to building trust and resilience in AI-powered energy systems.

FIGURE 5
AI talent concentration by country and sector

Source: Energy and AI

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

Much to Achieve in Realizing AI’s Energy Potential

1. Legacy infrastructure lacks real-time data pipelines and cloud readiness
Many utilities still operate with legacy grid control systems and field devices that were never designed to support modern digital operations. These older systems—common in substations, control rooms, and field assets—lack the ability to send continuous, high-frequency data to centralized platforms.

2. Data sovereignty and platform fragmentation limit scalability
Grid operators and transmission system operators (TSOs) face increasing pressure to comply with national data residency laws and sector-specific cybersecurity mandates. These constraints often preclude centralized cloud architectures, resulting in fragmented data lakes across control centers, asset classes, and geographies.

3. AI talent and trust gaps persist
According to the IEA and Capgemini CRI, 68% of energy executives cite a shortage of AI-literate talent as a major barrier. The sector trails others in upskilling field engineers and control room operators in AI fundamentals.

4. Regulatory complexity adds compliance burdens
Frameworks like the EU AI Act and GDPR impose stringent requirements on explainability, bias mitigation, and human-in-the-loop oversight—especially for critical infrastructure like power grids. These mandates, while essential for safety and ethics, introduce additional layers of compliance for utilities deploying AI in outage prediction.

5. Data quality and governance remain foundational challenges
AI’s eﬀectiveness in grid operations hinges on high-quality, harmonized data. Yet many utilities still operate with siloed asset management systems, inconsistent naming conventions, and outdated GIS records.

6. High upfront costs and unclear ROI slow adoption
Despite AI’s potential to reduce SAIDI/SAIFI, optimize dispatch, and accelerate decarbonization, many utilities remain hesitant to invest. Engineers often prefer deterministic, physics-based models over opaque "black box" algorithms.

FIGURE 6
Potential barriers to the adoption of AI applications in energy

Source: IEA
Energy and AI

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

Powering the Next Phase of AI in the Energy Sector

To fully realize the transformative potential of AI in energy systems, the sector must move beyond experimentation and address foundational readiness gaps. This means investing not only in technology, but also in people, policy, and trust.

1. Operationalize AI Across the Energy Value Chain
AI should be embedded into core operational systems across the energy value chain to drive optimization, enhance asset performance, and streamline market participation.

2. Institutionalize AI Governance
Utilities need robust, cross-functional governance to manage AI across its lifecycle. This includes model validation, monitoring, and risk thresholds—especially for GenAI and Agentic AI, where traceability and human oversight are critical.

3. Mobilize Workforce Capabilities
A digitally ﬂuent workforce is essential. Utilities must upskill operators, engineers, and planners to work eﬀectively with AI tools.

4. Harmonize Data Ecosystems
To enhance forecasting and resilience, utilities must integrate external data—weather, satellite, market signals—into operational platforms.
This requires interoperable architectures and real-time data pipelines across OT and IT systems.

5. Sovereignize data and cloud architecture
As cloud adoption grows, utilities must ensure compliance with data residency laws and maintain control over telemetry and AI environments. Sovereignty should be embedded into digital strategy through secure, ﬂexible, and resilient architectures.

FIGURE 7
Powering the Next Phase of AI in Energy Sector

03 We are not ready for the rapid and profound impacts that AI will have on the energy system

Our Convictions

1

AI is no longer experimental in energy—it is already delivering measurable impact across Generation, grid operations, asset management, trading and customer engagement.

2

Leading utilities across the globe are proving that AI can enhance forecasting, reduce downtime, and optimize energy flows at scale.

3

To replicate this success, the sector must move beyond pilots and embed AI into core systems supported by real-time data and interoperable platforms.

4

Workforce readiness is critical: utilities must invest in upskilling engineers, operators, and planners to collaborate eﬀectively with AI systems.

5

With strong governance, cloud sovereignty, and a commitment to responsible innovation, AI can power a more resilient, eﬃcient, and equitable energy future.

6

AI initiatives must evolve from isolated use cases to scaled deployments that meet business, technical, and operational success factors. While GenAI and Agentic AI are still emerging, their potential is transformative—and every utility should begin laying the foundation now.