Utilities in the AI era: Powering ahead to a smarter future

From optimizing energy storage and enhancing grid resilience to forecasting demand and integrating renewable energy sources, artificial intelligence is helping utilities manage the complexity of a rapidly changing business environment with greater speed and precision.

94% of utility executives expect AI to contribute significantly to revenue growth within the next three years.

 
A new IBM Institute for Business Value (IBM IBV) survey reveals the momentum of change: 94% of utility executives expect AI to contribute significantly to revenue growth within the next three years, and 88% say it will deliver measurable competitive advantage. The message is clear—AI is seen not just as a tool for automation, but as a driver of industry reinvention.
 

Where is AI helping utilities reshape utility operations and management?

More than two in five utility companies use AI in field workforce optimization, predictive maintenance, outage management, and energy demand management. Adoption levels are expected to surge—even for complex applications—in some cases approaching near-total deployment by 2028. These tools enhance efficiency and responsiveness and enable better grid integration of renewable energy sources.
 

How is AI driving measurable improvements in grid performance and customer engagement?

Utilities executives report significant gains in key areas, including a 10% improvement in service reliability, an 11% boost in grid uptime, a 10% increase in energy efficiency, and a 10% improvement in customer satisfaction. These gains reflect a growing shift toward data-driven decision-making and smarter infrastructure and grid management.
 

What is the role of AI in powering new energy business models?

Over half of utility company executives expect AI adoption to unlock new technology capabilities that fundamentally transform their business models. From optimizing renewable energy generation to managing distributed energy resources (DER) and market integration, AI enables the agility and intelligence needed to lead in new business models during the energy transition.

70% of utility executives agree that AI will enable their organizations to expand into entirely new service areas, especially in the way utilities interact with customers.

 
What should leaders do now to realize AI’s potential in the utility industry?

• Build AI-ready leadership and workforces across utility domains. Equip utility leadership with AI fluency to guide governance decisions. Map AI and digital competencies and address skill gaps in high-value use cases such as predictive grid maintenance and DER optimization. Prioritize hiring in specialized roles such as machine learning engineers, digital twin developers, energy systems modelers, and algorithmic pricing analysts. Launch internal AI bootcamps to train field engineers, maintenance teams, call center staff, and other key roles.
• Govern AI investments through a regulatory and reliability lens.
  Create an AI governance council with leaders from operations such as grid, pipeline, and water network teams, finance for value modeling and ROI tracking, and IT/OT to manage secure and integrated deployment. Prioritize AI powered initiatives aligned with core utility outcomes: improving the system average interruption duration index (SAIDI) and the system average interruption frequency index (SAIFI), reducing water loss, increasing grid DER hosting capacity, and increasing customer satisfaction.
• Establish a continuous AI pilot pipeline tied to operational performance.
  Choose high-impact, measurable AI pilot projects. Develop digital twins for critical infrastructure, such as substations and gas compressor stations. Feed real-time sensor and environmental data into AI models for simulation and optimization. Integrate line worker and plant operator insights to improve AI-driven usability and model accuracy and track pilot success with operational KPIs.
• Modernize IT/OT stacks to scale AI.
  Modernize with AI-centric architecture and focus on AI-grade data quality. Enable real-time analytics and model training for operational and customer-facing AI. Prioritize systems with high business impact such as transformer loading data and pipeline flow sensors. Evaluate and improve key data pipelines from AMI, SCADA, CRM, GIS, and outage management systems. Define robust data governance policies and set ownership, lineage, and access rules across business units.
• Create and scale AI-enabled utility business models.
  Design AI-enabled services, such as predictive maintenance as a service, and real-time tools to help customers reduce peak usage or detect inefficiencies. Explore data monetization, such as selling anonymized DER performance data, to third-party energy service providers. Leverage AI for regulatory compliance and to automate reporting on emissions, service reliability, demand forecasting, and environmental compliance.

 
Download the report to learn how AI is becoming a dynamo for innovation and business transformation in the global utility sector.