Accenture
Debanjan Chakraborty is Managing Director, Accenture Technology Strategy. Miguel Torreira is Managing Director, Accenture Global Utilities Strategy Lead. Jim Mazurek is Managing Director, Accenture North America Utilities Strategy Lead.
Utility leaders are confronting a convergence of pressures their organizations were never designed to handle. Electrification, regulatory mandates, and the rapid integration of distributed energy resources are challenging long-standing operating models.
At the same time, customer expectations have shifted decisively. Consumers now expect real-time insight into usage, costs, service status, personalized engagement, and digital experiences that resemble those of leading service industries.
Together, these shifts are unfolding under mounting economic pressure, sharpening leadership focus on one overriding issue: affordability. As energy prices rise and infrastructure investment requirements grow, utility bills are placing increasing strain on households, particularly low- and middle-income customers.
Legislators and regulators are responding accordingly. For example, the California Public Utilities Commission (CPUC) affordability framework requires utilities to explicitly address rate impacts and socioeconomic customer metrics in regulatory proceedings. Other jurisdictions are moving in the same direction, reframing how utility performance is measured. Reliability and sustainability remain essential, but cost effectiveness has become equally important.
The affordability challenge is intensifying just as electricity demand begins to accelerate again. By 2033, up to 23% of U.S. electricity could be consumed by data centers alone, according to Accenture research. Utilities are being asked to expand grid capacity and reliability while constraining costs, a challenge traditional operating models were not built to address.
Debanjan Chakraborty: For CIOs, the affordability imperative reframes the AI agenda entirely. The question is how AI can be deployed systematically to bend the cost curve, without compromising reliability, safety or trust.
For utilities, affordability is the outcome regulators and customers care about, but cost structures and operating efficiency are the levers leaders can directly influence. Utilities need to change their long-term cost trajectory — so costs grow more slowly, stabilize, or decline — even as demand and investment requirements rise.
This dynamic explains why artificial intelligence — especially emerging agentic systems that analyze situations, produce recommendations, and take action under human direction — is gaining traction in the utility sector. Utilities facing both rising demand and mounting cost pressures increasingly view AI not only as a tool for incremental improvement but also as a fundamental way to transform operations at scale.
Accenture research points to more than $10 trillion in potential global economic value from responsible, at-scale generative AI adoption by 2038. This projection signals a material opportunity for capital-intensive industries, including utilities, to transform cost structures and operational performance.
Additionally, 86% of C-suite executives plan to increase their AI investment in 2026, according to the November-December 2025 Accenture Pulse of Change survey. Predictive maintenance, automated restoration, and AI-enabled customer service already demonstrate meaningful cost reductions at scale. AI applications are also expanding beyond operational use cases into corporate and regulatory functions, from supply chain planning and financial forecasting to permitting, regulatory filings, and capital program optimization.
Miguel Torreira: In regulated utilities, AI does not scale on technical merit alone. It scales by earning institutional trust. Regulators, employees, unions, and customers must be confident that AI systems are transparent, controllable, and aligned with public interest outcomes.
And yet, despite this potential, many utilities struggle to move beyond pilots. Legacy systems, fragmented data landscapes and entrenched information technology-operational technology (IT-OT) silos remain persistent barriers.
For CIOs, the affordability imperative reframes the AI agenda entirely, requiring not only technological leadership but coordinated action across operations, finance, and customer organizations. The question is no longer whether AI matters but how it can be deployed systematically to bend the cost curve, without compromising reliability, safety or trust.
In fact, almost two-thirds (65%) of CTOs and CIOs report AI is helping their organizations shift from activity-based metrics, such as hours or transactions, toward outcome-based measures of performance and impact, according to the Accenture Pulse of Change survey.
Five mandates define how technology leaders can scale AI to deliver sustained cost efficiency across the enterprise:
Jim Mazurek: By anchoring AI to value pools, industrializing platforms, rewiring how work gets done, earning stakeholder trust and operating AI with product rigor, CIOs can turn technology into a sustained force for cost containment.
Anchor AI to Affordability-Driven Value Pools. Affordability does not improve through isolated innovation. It improves when utilities attack the cost drivers embedded in core operations.
Effective CIOs anchor AI investments to clearly defined value pools — such as outage duration, field productivity, asset utilization, customer cost-to-serve, and IT run costs — and align with business leaders on accountability for outcomes. These value pools must be tied to clear expectations for how improvements will be captured — ensuring AI-driven productivity gains translate into sustained lower costs, reinvestment capacity or more affordable rates.
This approach requires treating AI as a business reinvention agenda, not solely an IT initiative. Business leaders must co-own both the benefits and the operational changes required to realize them.
Equally important, successfully leveraging AI to drive cost efficiency — and ultimately improve affordability — depends on enterprise-wide AI fluency. When operations, engineering, finance, and customer teams understand AI well enough to trust its outputs and act on them, cost efficiency becomes operational rather than theoretical. Without this shared ownership and understanding, advanced AI capabilities will remain confined to pilots.
Build Once, Reuse Everywhere. Affordability erodes when utilities repeatedly build the same digital capabilities across functions and operating companies. Scalable AI depends on a shared enterprise data and AI platform that standardizes how data, models, gen AI capabilities, and agents are built, governed and operated. Utilities often struggle to establish this shared foundation due to fragmented data ownership, security segmentation, and the cost of modernizing legacy platforms.
By investing in common capabilities and reusable patterns, utilities reduce duplication, lower long-term run costs, and accelerate deployment. More importantly, this foundation enables AI to be embedded consistently into end-to-end processes — such as outage management, work management, procurement, and customer operations — where the greatest cost savings can be achieved.
In regulated environments, embedding security, explainability, and compliance by design also minimizes rework and regulatory friction, further reducing the total cost of change. Over time, this foundation becomes both a cost-containment mechanism and a funding engine for continued AI adoption.
Modernize Core Systems to Power AI-Enabled Work. The greatest affordability gains come not from insight alone but from changing how work gets done at scale. While business leaders define outcomes, the CIO owns the digital execution layer that enables AI to move work seamlessly across systems and domains, with minimal manual handoffs.
In most utilities, critical processes still span enterprise resource planning, enterprise asset management, outage management systems, geographic information systems, and customer platforms that were never designed to operate together in real time. Enabling agentic AI requires modernizing these systems, often within complex legacy environments, exposing application programming interfaces, enabling event-driven interactions, and simplifying application landscapes, so AI can trigger actions, coordinate tasks, and respond dynamically to changing conditions.
As automated coordination replaces manual handoffs, utilities reduce cycle times, avoid unnecessary truck rolls and other field dispatches, and improve asset utilization. These operational efficiencies reshape the cost structure, translating into lower operating costs over time and, ultimately, more affordable rates.
Scale AI Through Trust and Ecosystem Discipline. In regulated utilities, AI does not scale on technical merit alone. It scales by earning institutional trust. Regulators, employees, unions, and customers must be confident that AI systems are transparent, controllable, and aligned with public interest outcomes.
CIOs play a central role by embedding governance, auditability, and human direction into AI-enabled processes and by building a disciplined partner ecosystem. Hyperscalers, software providers, and integrators should strengthen utility capabilities, not add complexity or hidden costs. When trust is engineered into platforms and partnerships, utilities avoid deployment stalls and regulatory setbacks, allowing AI-driven efficiencies to compound over time.
Operate AI as a Product, Not a Project. Affordability is a sustained outcome, not a one-time achievement. AI delivers durable cost efficiency only when it is managed as a product — with clear ownership, a roadmap, and outcome-based funding — rather than as a sequence of projects.
Product-oriented teams continuously refine models, workflows, and data as conditions evolve, keeping adoption high and benefits durable.
This operating model also enforces financial discipline: low-value initiatives are stopped, savings from productivity gains and IT automation are reinvested, and AI increasingly funds itself. For CIOs, this shift aligns technology, talent, and capital around a single objective of making affordability a structural feature of the utility, not a periodic regulatory exercise.
The CIO’s Affordability Mandate
As utilities confront rising demand, mounting investment requirements, and heightened regulatory scrutiny, affordability has emerged as the defining challenge of the decade. AI offers a powerful lever, but only when deployed with focus, discipline, and scale.
By anchoring AI to value pools, industrializing platforms, rewiring how work gets done, earning stakeholder trust, and operating AI with product rigor, CIOs can turn technology into a sustained force for cost containment. In doing so, they position their organizations to deliver reliable, sustainable, and affordable energy in an increasingly complex world.
