How to transform energy and utilities: A strategic framework for AI integration

Insights

• As global electricity consumption surges — AI presents both a contributor to demand and a solution for managing it efficiently.
• Over 50 AI applications are in use across the energy system, with companies like ExxonMobil and EDF using AI to reduce downtime, optimize grid performance, and cut carbon emissions.
• The Infosys AI Business Value Radar shows that higher investment doesn't guarantee success.
• Use cases like building electrification and energy trading have strong business value potential — but demand significant changes in operations, workforce, and systems.
• Success with AI isn't just about tools — it depends on strategic alignment, governance, and cultural readiness. Organizations need structured frameworks to scale AI from pilot to enterprise-wide value.
• Infosys’ structured approach helps bridge the AI value gap by integrating data, talent, and governance, enabling organizations to move from experimentation to enterprise-wide impact.

Artificial intelligence (AI) could revolutionize how energy and utility companies modernize operations and meet ballooning global energy demand. But how will it work?

Over the next three years, global electricity consumption is projected to grow by 3,500 TWh — equivalent to adding Japan’s annual energy consumption to the world’s total each year. This surge is partly driven by the usage growth of artificial intelligence (AI), a technology that is infamously resource hungry. But AI could also be a part of the solution by helping to enhance efficiency, predict maintenance needs, and boost overall performance.

Indeed, AI is already utilized in over 50 different applications within the energy system, with its market potentially reaching $13 billion. For instance, a multinational energy corporation ExxonMobil uses AI for predictive maintenance, leveraging machine learning algorithms to analyze oil samples from drilling equipment. This approach has significantly reduced unplanned downtime and labor costs, providing insights into equipment conditions and recommended actions, ultimately enhancing operational efficiency and saving millions in potential losses.AI for predictive maintenance, leveraging machine learning algorithms to analyze oil samples from drilling equipment. This approach has significantly reduced unplanned downtime and labor costs, providing insights into equipment conditions and recommended actions, ultimately enhancing operational efficiency and saving millions in potential losses.

Similarly, EDF, a UK-based low carbon electricity generator, has partnered with Hypervolt, a provider of charging points for electric vehicles (EVs), to leverage AI and real-time data analytics for optimizing energy production and scheduling. EDF says this collaboration will help the company automatically adjust energy usage and balance the grid during peak demand, saving on electricity costs and reduce their carbon footprint.

The AI paradox in energy: High investment, low return

Energy and utilities organizations are making bold investments in AI, targeting a range of high-impact areas from energy trading and carbon capture to predictive maintenance and environmental modeling. However, insights from the Infosys AI Business Value Radar reveal that not all investments are translating into business value. Use cases like environmental impact modeling and predictive maintenance, despite significant funding, show low viability scores of 0.65 and 0.9, respectively — indicating limited or less potential to deliver positive outcomes.

In contrast, initiatives such as building electrification, which the Infosys AI Business Value Radar identified as having a viability score of 1.13 and energy trading with a viability score of 1.12, display high potential for business impact. These high viability scores reflect a greater likelihood of AI deployments meeting business objectives. However, they also demand substantial transformation efforts — from reimagining operating models to upskilling teams — underscoring high impact requires high readiness.

The viability score is a weighted measure that reflects how often AI deployments achieve full or partial success in meeting business goals. A score above 1 indicates above average potential for value realization, while a score below 1 highlights underperformance or misalignment. This makes viability a more meaningful indicator than investment alone.

These findings point to a deeper truth: Simply funding AI initiatives is not enough. Without clear strategic direction, strong governance, and a culture prepared to embrace change, even the most advanced AI tools can fall short.

Bridge the AI value gap in energy: From investment to impact

The disconnect between investment and realized value reveals a critical challenge for the sector: The AI value gap. While organizations are eager to harness AI, the real hurdle lies in execution specifically, in aligning technological initiatives with strategic business outcomes. It’s not the lack of technology or funding that stalls progress, but rather the absence of organizational readiness, including effective governance, workforce enablement, and cultural openness to change.

A leading energy company’s experience with adopting New Relic as an AI platform for real-time visibility, supported by Infosys, illustrates this vividly. What began as a technical rollout became a broader transformation challenge where one required reshaping mindsets, redefining success metrics, and creating clear pathways between AI capabilities and business goals.

Recognizing these barriers, Infosys has developed a structured approach to closing the AI value gap. By aligning technology with data, talent, and governance, this framework equips energy organizations to go beyond experimentation and move toward scalable, sustainable AI adoption. It provides a practical roadmap for embedding AI across the enterprise, ensuring that every investment drives measurable impact.

Navigate AI: Aligning strategy with value

Bridging the AI value gap begins with one critical step: Strategic alignment. Technology alone cannot deliver value unless it is purposefully directed toward clear business outcomes. Organizations must ensure their AI initiatives are not only innovative but also deeply connected to operational realities and long-term goals.

Infosys’ structured approach starts by evaluating readiness across three essential vectors — data, technology, and talent. It considers factors like data complexity, infrastructure maturity, and workforce capability, creating a realistic picture of where an enterprise stands. From here, Infosys maps proven use cases to specific organizational challenges to measurable business outcomes while using a responsible AI framework to ensure deployments are ethical, compliant, and sustainable.

In the energy company’s case, the strategic alignment turned New Relic from a monitoring tool into a business enabler. Infosys’ workplace experience team focused on aligning technical implementation with user needs by mapping observability metrics to end-user journeys and establishing meaningful key performance indicators. With continuous feedback loops and refinement mechanisms in place, adoption became an evolving process anchored in business relevance and measurable outcomes.

Enable AI: Building the right foundations

Once strategic alignment is achieved, the next step is to enable AI by building the right organizational foundations — technological, structural, and cultural. Without a strong, scalable backbone, even the best-aligned AI strategies can stall.

At the core of enablement lies a robust data architecture — with standardized governance, secure access protocols, and scalable cloud-native platforms that ensure data is clean, connected, and available in real time. But foundational technology is only part of the equation. Workforce readiness, from AI literacy to cultural buy-in, is equally critical.

In the energy company’s transformation journey, Infosys helped build this foundation through tailored onboarding experiences, custom upskilling paths for different user personas, and a scalable community-of-practice model. Proactive communication strategies ensured transparency and fostered collaboration across teams. This holistic approach not only introduced employees to new tools, but also empowered them to use those confidently and effectively.

Do AI: Turning strategy into scalable action

With alignment in place and enablers activated, organizations can begin to operationalize AI. This means embedding it into daily workflows, decision-making, and innovation cycles. This is where strategy becomes action and AI begins to generate tangible, repeatable impact.

The Infosys approach supports this transition through a structured innovation pipeline that includes components like the enterprise data pipeline, which automates the flow of governed, high-quality data; the integrated data refinery, which enriches and contextualizes data for real-time decisions; and an AI testbed, which allows for iterative experimentation with minimal risk.

For the energy company, these principles enabled the deployment of New Relic in a way that encouraged safe, continuous innovation. Infosys delivered modular toolkits, real-time user support, and ongoing improvement cycles to create a dynamic, feedback-driven ecosystem. As a result, observability evolved from a back-end function to a strategic capability fueling smarter operations, faster responses, and deeper insights across the organization.

Conclusion: From potential to performance

The journey to realizing AI value in the energy and utilities sector is not linear — it demands more than investment in technology. It requires a structured, end-to-end approach that aligns AI initiatives with business objectives, builds the right organizational foundation, and embeds innovation into daily operations.

The experience of the energy company illustrates this clearly: Real impact comes when AI is treated not just as a tool, but as a catalyst for transformation. With the right strategy, support, and cultural readiness, organizations can move beyond pilots and isolated use cases to scale AI meaningfully across the enterprise.

By leveraging frameworks like the Infosys structured approach, energy companies can systematically close the AI value gap, ensuring that every step, from ideation to implementation, drives measurable outcomes. In doing so, they not only future-proof their operations but position themselves as leaders in a rapidly evolving energy landscape.