emerging
AI-Driven Optimization and Innovation
AI adoption will accelerate in 2025, with energy and commodities companies focusing on use cases that promote optimization and innovation.
Themes
Timeframe
near-term
Subcategories
Impact areas
Detailed Analysis
The increasing confidence in AI's capabilities is driving a shift from proofs of concept to full-scale implementations. "AI and generative AI offer new opportunities to optimize and streamline how digital ecosystems integrate with external as well as internal systems," says Alberto Bruno, highlighting the potential for cost reduction and operational improvements. This integration is crucial for optimizing operations and reducing costs across the industry. Furthermore, AI is proving its worth in diverse applications, from predictive maintenance in oil and gas to optimizing planting schedules in agriculture, showcasing its transformative potential across various sectors.
Context Signals
Increased confidence in AI use cases
Transition from POCs to full-scale implementations
Growing market value of AI in renewables
Edge
AI-powered predictive analytics could enable dynamic pricing models for energy, optimizing revenue generation and consumer demand.
Generative AI could be used to design more efficient renewable energy infrastructure, leading to cost reductions and improved performance.
AI-driven risk management systems could help companies navigate volatile market conditions and geopolitical uncertainties more effectively.
