The energy sector is undergoing one of its biggest operational changes in decades. According to Deloitte, electricity demand in the US is projected to grow by about 26% by 2035—driven by AI data centres, electrification, and industrial reshoring. Data centres alone could require up to 176 GW of capacity. Meanwhile, another Deloitte report highlights that power demand from AI data centres could grow more than thirtyfold, reaching almost 123 GW by 2035, a huge rise from 4 GW in 2024.
At the same time, electricity prices for consumers have been rising. For utilities and energy service providers, improving operational efficiency is no longer optional. It is the financial foundation on which grid modernisation, affordability, and energy transition depend.
Here are the sector's most impactful cost optimisation levers:
Managing cost in a volatile power procurement market
Power procurement covering both internal generation and external supplies from independent power producers (IPPs) represents one of the largest cost categories for any energy company. Long-term power purchase agreements (PPAs), especially for renewables, help stabilise costs and reduce exposure to fossil fuel price volatility. Increasingly, renewable PPAs now deliver electricity at a fraction of conventional generation costs. Demand response programs offer another lever. By shifting industrial consumption away from peak periods, utilities can reduce procurement needs by up to 5–10%.
AI and predictive analytics are transforming procurement decisions. They help align purchases with low-price windows and seasonal demand curves—particularly during peak cooling periods. EY notes that AI-driven forecasting improves visibility across planning horizons and supports better operational decisions.
Maintenance optimisation: From reactive to predictive
Maintenance remains a major cost lever. Reactive maintenance can cost 2–5 times more than preventive approaches. Unplanned downtime in utilities can cost thousands of dollars per hour, depending on asset type.
Predictive maintenance, enabled by IoT sensors monitoring parameters across transformers, turbines, and substations, is transforming this area. According to McKinsey & Company, predictive maintenance can:
- Reduce maintenance costs by 18-25%
- Cut unplanned downtime by 30-50%
- Extend asset life by 20-40%
Drone-based inspection of transmission lines, just-in-time spare parts procurement, and AI-driven work order management collectively shift maintenance from a cost centre into a strategic reliability function.
Workforce productivity and energy outsourcing services
A retiring skilled workforce, rising overtime costs, and growing technical complexities exert huge pressures on the operations and maintenance (O&M) budgets of the energy sector. Unplanned overtime alone can inflate labour costs by up to 15%.
To address this, companies are focusing on:
- AI-driven workforce scheduling that dynamically optimises shifts using demand forecasts, weather patterns, and grid performance data
- Targeted reskilling programs that equip workers for renewable energy operations and smart grid management
- Energy outsourcing services that transfer non-core functions to specialised third-party providers
Energy outsourcing services covering functions such as Advanced Metering Infrastructure (AMI) operations, meter data analytics, customer service, finance processing, and field support convert fixed costs into variable, scalable expenditures. This shifting of costs is particularly valuable during seasonal demand peaks or major weather events, where surge capacity is needed without permanent headcount. Key drivers pushing utilities toward outsourcing are high general and administrative (G&A) costs, workforce retirements, and the capital intensity of grid modernisation.
Advanced cost management
Three financial approaches strengthen cost management in energy services, yet they remain frequently underdeployed across the sector.
Driver-based costing identifies the specific operational variables—demand patterns, equipment usage intensity, geographic service complexity—that actually drive cost. It transforms cost management from a budget exercise into a precision tool for resource allocation across procurement, maintenance, and workforce functions.
Cost-to-serve analysis calculates the true total cost of delivering energy to specific customer segments or regions, including direct costs (generation, transmission) and indirect costs (customer service, maintenance). It typically costs significantly less to serve urban customers than rural or remote customers due to infrastructure density differences. These are insights that directly influence pricing strategy and investment prioritisation.
Working capital optimisation involves payment cycles of 60–90 days. This can create liquidity pressure that results in slower capital deployment. Deloitte highlights tax-credit monetisation—with nearly $30 billion in clean energy credits traded in early 2025 alone—and securitisation as emerging liquidity tools, alongside digital billing automation, predictive spare parts inventory management, and extended supplier payment terms. Together, these levers improve liquidity without new capital expenses.
Renewable portfolio and grid modernisation
Renewables now dominate new capacity additions. According to Deloitte, they accounted for 93% of new capacity through mid-2025 in the US. However, integrating renewables introduces new system costs such as storage, balancing, and transmission upgrades, all of which demand active portfolio management. McKinsey confirms that power demand from data centres could grow at nearly 25% annually until 2030, reshaping how utilities must plan and manage their capital programs.
At the same time, PwC highlights that AI-driven data centre expansion is placing significant pressure on grid infrastructure and accelerating the need for modernisation. Investments in grid automation and resilience help reduce long-term costs by avoiding outages and emergency repairs. In the first half of 2025 alone, the US experienced 15 weather-related disasters each exceeding $1 billion in damages.
For energy services providers, structuring long-term renewable PPAs with fixed tariffs, deploying energy storage systems to reduce peak procurement costs, and positioning themselves as advisory partners all represent rapidly growing service line opportunities within energy outsourcing services.
Conclusion
The energy sector today faces a dual challenge: investing heavily in infrastructure while keeping energy affordable. This makes cost optimisation a strategic priority, not a supporting function. The levers, as discussed above, are quite well-established. BCG research confirms that companies that meet their cost targets outperform peers and reinvest those savings into the growth investments the energy transition demands. The real challenge is not just identifying these levers, but applying them consistently at scale.
How can Infosys BPM help?
Infosys BPM’s Solutions for the Energy & Utilities Industry span the entire value chain – from oil and gas exploration and production to transmission, sales, and customer engagement. We leverage advanced technologies such as generative AI to improve grid management, support renewable energy initiatives, and enhance customer experience. With our deep domain expertise, we deliver tailored solutions that address operational challenges and sustainability goals. Our global presence, combined with strong knowledge of local regulations, enables seamless, scalable execution across diverse markets.
