From Paradox to Progress A Net Positive AI Energy Framework 2025

AI-driven predictive maintenance for reliable low-carbon power BOX 4 Challenge Industrial energy plants face equipment degradation in turbines and boilers, causing unplanned downtime and disrupting low-carbon power generation. Traditional maintenance remains reactive, increasing inefficiency and outage risk. Solution –AI-driven predictive maintenance deployed with a UK-based energy company –Internet of things (IoT) sensor data and anomaly detection models identify “good behaviour” patterns –Real-time deviation alerts enable early intervention and optimized scheduling Impact Near-term impacts realized or anticipated (less than one year) –Earlier detection of equipment degradation –More efficient use of maintenance resourcesFurther impacts realized or anticipated (more than one year) –£1.3 million in operational cost savings –£450,000 in parts and labour savings Reviewing the levers in action Industrial process control: Predictive maintenance and energy optimization across industrial plants Smart grid optimization: Indirect benefits through reliability improvements* Building energy management: Applied in facility management modules* Transport and logistics: N/A Renewable forecasting: N/A *See Table 3 for relevant “deploy for impact” use case examples. Sources: AI Energy Impact public database submission, Envision; AVEVA. Business case AI is lowering energy intensity across operations. Predictive analytics cut downtime, extend asset life and optimize renewables, storage and demand, advancing economic competitiveness, energy security and sustainability across energy-intensive sectors. Avoiding unnecessary or unconscious AI use The third action driver, “shape demand wisely”, focuses on how AI use is governed, timed and incentivized to align energy demand with sustainability goals. Building on the design for efficiency driver, it emphasizes proportionate, purpose-driven deployment, using smaller models and adaptive scheduling where possible and reserving large-scale systems for high-value needs. By incentivizing and enabling more flexible and condition-responsive AI consumption, demand can be shaped intelligently rather than simply constrained.Key levers include: –Use-based pricing models: Price signals and tiered models incentivizing efficiency –Digital sobriety campaigns: Education on query and model energy impacts –Model selection guidance: Promote smaller, fit-for-purpose models –Consumer dashboards: Real-time visibility into energy impact –Regulatory nudges: Efficiency defaults and disclosure standards2.3 Shape demand wisely “Shape demand wisely” focuses on how AI use is governed, timed and incentivized to align energy demand with sustainability goals. From Paradox to Progress: A Net-Positive AI Energy Framework 18