From Paradox to Progress A Net Positive AI Energy Framework 2025

2.2 Deploy for impact Using AI to reduce energy use across sectors The second action driver, “deploy for impact”, is where AI drives measurable energy system and sustainability gains, renewable integration and decarbonization towards net-positive energy. AI optimizes grids, storage, industry, buildings and logistics to reduce demand, enhance reliability and lower emissions. The task at present is scaling proven solutions beyond pilots. With electrification accelerating, the challenge is not feasibility but the large-scale deployment of AI as a structural enabler of sustainable energy.Key levers include: –Smart grid optimization: Real-time dispatch, outage prediction, load balancing, unlocked capacity –Industrial process control: Predictive maintenance, energy optimization –Building energy management: AI-enabled HVAC, lighting, occupancy-based control –Renewable forecasting: Improved solar/ wind prediction –Transport and logistics: Route optimization, fleet electrification, modal shift planning Use case insights and takeaways: Around 83% of use cases reflected deploy for impact, the most common driver. They show AI delivering sustainability outcomes, from direct energy savings to system-wide gains, such as better outage prediction, load balancing and cost optimization, that enhance grid resilience.Strategic recommendations: –Scale proven solutions through public-private partnerships. –Integrate AI into national decarbonization strategies. –Set interoperability standards for high-impact sectors.Deploy for impact – key levers FIGURE 6 Smart grid optimizationIndustrial process control Building energy managementRenewable forecastingTransport and logisticsDeploy for impact The second action driver, “deploy for impact”, is where AI drives measurable sustainability gains, advancing efficiency, renewable integration and decarbonization towards net-positive energy. From Paradox to Progress: A Net-Positive AI Energy Framework 16