In 2026, utilities are facing a structural shift: the need for reliability and affordability are overtaking decarbonization as the primary decision driver. This shift is reshaping capital allocation, technology priorities and operating models. Over the past year, utilities have been forced to rebalance priorities across sustainability, reliability and cost efficiency under increasing regulatory and market pressure.
Rising demand driven by electrification and AI data centers, combined with increasing capital and operational costs, is forcing utilities to prioritize grid reliability and affordability.
Utility executives should be prepared for the following realities:
AI investments will underdeliver without operating model and data integration changes
Capital programs will be constrained by regulatory approval, not just funding availability
Workforce limitations will slow transformation more than technology readiness
Grid modernization will depend on integrated platforms and data strategies
Decarbonization, Energy Sources and Storage
Decarbonization strategies are shifting toward a more pragmatic energy mix, including renewed investment in nuclear and emerging small modular reactor (SMR) technologies. Energy storage and distributed energy resources (DERs) are becoming critical to stabilizing renewable generation and supporting grid stability. Rapid load growth from AI and data centers is accelerating the need for reliable, low-carbon baseload generation.
Regulatory approval for capital investments will increasingly depend on demonstrated improvements in reliability and clear cost impacts to customers. Utilities will need to justify capital investments through data-driven assessments of reliability outcomes and customer cost impact.
What are the implications? Utilities should:
Prioritize investments that deliver measurable reliability improvements.
Adopt generation strategies that balance decarbonization goals with baseload stability
Build data-driven justification of capital spend for regulatory approval.
Driving Operational Efficiency Through Generative and Agentic AI
Operational efficiency remains a top priority, but utilities are now under pressure to deliver measurable outcomes from AI investments. As generative AI matures and agentic AI capabilities emerge, utilities are aligning AI adoption with cost reduction and regulatory expectations. Utilities are deploying AI across outage prediction, grid-load forecasting, vegetation management and regulatory documentation to improve grid operations and service delivery. These applications enable faster outage response, improved load forecasting accuracy and reduced manual effort in regulatory and operational processes.
Despite AI’s strong potential to create business value, utilities face barriers, including fragmented data environments, limited integration between operational and IT systems and regulatory concerns around automated decision-making. Scaling AI will require integrating capabilities into core operational workflows supported by strong data governance and cross-functional alignment.
Utilities leaders should prioritize AI use cases tied directly to operational KPIs, integrate AI into core systems rather than isolated pilots and establish governance models for regulatory and data risk.
Capital Projects: Investing in Resilience and Reliability
Utilities in 2026 must prioritize capital allocation toward grid resilience while operating under increased regulatory scrutiny on affordability. Significant resources will go toward modernizing grid infrastructure by implementing core platforms such as ADMS, DERMS, OMS and GIS. Utilities are increasingly investing in initiatives that deliver near-term reliability improvements and deferring longer-horizon programs. These platforms enable real-time grid visibility, faster outage response and improved coordination of distributed energy resources. Utilities that treat these investments as isolated system upgrades rather than integrated platforms will struggle to realize full value.
Moreover, utility companies relying on legacy enterprise systems, such as SAP or Oracle, must accelerate long-delayed ERP transformations as legacy systems constrain scalability and regulatory responsiveness. The transition to cloud-based solutions and adoption of platforms like S/4HANA will enable greater scalability, improved data integration and more responsive regulatory reporting.
What does this mean for utilities leaders? Three things they should do:
Focus capital on reliability-driven outcomes.
Address integration and data architecture early.
Accelerate ERP modernization to support transformation.
Enhancing Customer Engagement
Customer expectations are evolving, but utilities must balance customer experience with regulatory constraints and rate sensitivity. The demand for new products and services related to electrification, EV integration and smart home technologies are increasing. Electrification and distributed generation are transforming customers into active participants in the energy ecosystem. Customers are increasingly expecting transparency in pricing, proactive communication during outages and programs that directly impact affordability.
Generative AI capabilities have matured, and agentic AI is being introduced, enabling more efficient, automated and personalized customer interactions. Utilities must leverage consumption and grid data to enable demand response programs, EV optimization and distributed energy participation.
What does this mean? Utilities need to:
Align innovation for customers with regulatory frameworks.
Focus on programs tied to load growth and grid optimization.
Use data to drive targeted engagement, not generic personalization.
Workforce Dynamics: Bridging the Gap
Workforce constraints are emerging as a primary limiter of transformation across the utilities sector. Organizations must adjust hybrid work models and retention strategies to balance workforce expectations with operational requirements. Field-based employees cannot operate remotely, leading to potential friction between business units. Training employees on new technologies, including generative and agentic AI, and providing the appropriate change management, will be critical to retention and success. An aging workforce is accelerating the loss of institutional knowledge, particularly in field operations. AI will play a key role in augmenting workforce capabilities and supporting knowledge transfer. This will require role redesign, large-scale training programs and structured organizational change management initiatives.
Utility CIOs will need to take proactive measures to foster a strong culture of collaboration, ensuring that the IT department effectively supports the organization’s operational needs. Stronger integration between IT and operational teams will be required to support transformation at scale.
What does this mean for next steps? Utilities CIOs should:
Invest in workforce upskilling alongside technology adoption
Use AI to augment, not replace, critical roles
Address structural gaps between field and corporate teams
Conclusion: Charting a Sustainable Future
Over the next 12-24 months, utilities will face critical trade-offs between reliability, affordability and transformation speed. Utilities must align business and technology functions and prioritize investments that deliver measurable outcomes. ISG helps utilities align capital investment, technology strategy and workforce transformation around a reliability-first mandate. Clients that work with us advance innovation that delivers measurable improvements in the areas that matter most to them. Contact us to find out how we can help you.
About the authors
Korey Barnard
Jon Brock
