Part 3 of the “From Electrons to EBIT” series: the strategic path to commercializing AI in energy.
Building on the monetization of flexible assets (Part 1) and the securing of a fragmented grid edge (Part 2), this final part explores how to turn AI into a revenue and profit engine for utilities as well as their technology providers.
While AI will certainly be a core enabler of the digital energy transition, its path to profitability is largely unknown today. In the next few years, AI will create the most immediate value when embedded into monetizable systems — such as flexibility platforms and trading engines — rather than sold as a standalone software product.
While structural hurdles like long procurement cycles and security concerns[20] can delay direct adoption, the fastest ROI is found by bundling AI with revenue-generating assets like Virtual Power Plants (VPPs) or Commercial & Industrial (C&I) energy management. Looking toward 2030, as trust architectures mature, the industry will transition from AI as a decision-support tool to autonomous agents capable of negotiating flexibility and trading energy independently.
Where is AI — and why does the money take longer?
AI is unquestionably a core enabler of the digital energy transition.[21] It underpins nearly every value pool in energy digitalization:[22] flexibility optimization, trading, forecasting, cybersecurity, asset performance, and grid operations. However, AI itself is not (yet) the fastest path to standalone value, particularly when directly dealing with utilities, power producers, and their hardware and software ecosystems.
AI as an enabler, not the immediate product
In the near to mid-term, AI can generate value especially for the following use cases:[23]
- AI amplifies arbitrage and flexibility revenues by improving forecasting, dispatch, and optimization.
- AI reduces costs and risk through predictive maintenance, fault detection, and cyber anomaly detection.
- AI enables scale, allowing digital operators to manage large numbers of assets and transactions economically.[24]
In most use cases, AI is monetized indirectly — through higher margins, better asset utilization, or increased capture of existing value pools.
Why utilities are slow buyers of “pure AI”
There are structural reasons why selling AI algorithms to utilities takes longer:
- Long procurement and approval cycles. Utilities operate in highly regulated environments with conservative risk cultures. New AI solutions must pass technical validation, regulatory scrutiny, cybersecurity reviews, and operational acceptance — often taking years.[25][26]
- Unclear ownership of AI-generated value. Unlike flexibility or energy, the economic value of an AI model is harder to isolate and contractually define. Utilities often struggle to justify paying explicitly for “better decisions” without clear, auditable ROI.
- Data governance and liability concerns. AI systems learn from operational data. Utilities are cautious about exposing sensitive grid or customer data and about allowing third-party models to retain learned information — and liability is unclear if AI-driven decisions cause outages or market errors.[27]
- Integration complexity. AI rarely works out of the box. It must be deeply integrated into legacy OT, SCADA, EMS, DERMS, and market systems — making AI adoption a system-integration project rather than a software purchase.[28]
Where AI monetization does work
AI monetization accelerates when it is bundled into revenue-generating or risk-critical use cases, such as:
- Flexibility platforms and VPPs. AI-driven forecasting, dispatch, and bidding directly increase revenues — so AI is paid for through performance uplift.[29]
- Trading and portfolio optimization. Even small percentage improvements in forecasting or execution can yield material P&L impact.[30][31]
- Cybersecurity and grid resilience. AI-powered detection and response systems are easier to justify commercially because they mitigate existential risks rather than optimize efficiency.[32]
- C&I energy management. In the C&I context, AI-driven optimization (e.g. of production scheduling and energy use) has a clearer and faster ROI than in regulated utility environments.[33]
The medium-term shift: from AI tools to AI agents
Looking beyond 2030, AI will increasingly move from decision support to autonomous, agent-based systems: AI agents negotiating flexibility;[34] machines trading energy and capacity;[35] automated grid-edge control and response.
This shift will unlock new revenue models — but only once trust, security, and governance frameworks are in place. Without secure identity, provenance, and control over what AI learns and exports, utilities will remain cautious adopters.[36]
Bottom line on AI monetization
While AI is becoming an industry essential, it isn’t a silver bullet on its own. To maximize returns, focus on three key shifts in value:
- Immediate gains. The fastest money is made where AI is embedded into flexibility, trading, and security platforms.
- Adoption barriers. Expect a slower burn for standalone AI algorithms. Regulatory hurdles, risk management, and governance constraints mean utilities won’t buy these as plug-and-play products overnight.
- The long game. As trust architectures — the frameworks that ensure AI safety and reliability — mature, AI’s role will shift from a tool for cutting costs and boosting performance to a primary engine of revenue generation.[37]
Key takeaway: Success in the utility sector depends on moving AI from a siloed experiment to an integrated core component of market-facing platforms.
Additional digital use cases
Beyond these primary value pools, digitalization offers several additional revenue streams:[38]
- Grid and infrastructure digitalization: platforms for planning, load optimization, and AI-driven predictive maintenance.
- Smart consumption: recurring digital services based on smart meters or home energy management systems (HEMS).
- Energy data and market services: AI-driven monetization of energy data, including new use cases for regulatory planning and wholesale trading algorithms.[39][40]
- Financing and investment: opportunities to fund energy tech startups or create specialized smart-grid infrastructure vehicles.
The outlook 2026–2030
By 2030, the blueprint for profitability will expand far beyond traditional business models. While physical asset ownership remains a cornerstone of the industry, the next era of wealth will be driven by the orchestration of digital assets through secure, intelligent systems.
The market leaders will be defined by their mastery of three core pillars:
- Flexibility: the ability to pivot and adapt to fluctuating market demands.
- Cybersecurity: ensuring the integrity and safety of interconnected digital networks.
- Scalability: expanding operations rapidly without compromising performance.
Ultimately, the competitive advantage belongs to those who can transform both electrons and data into resilient, high-speed revenue streams.[41]
Start from the beginningPart 1 — Flexibility →References
[20] Electric Power Research Institute (EPRI), Artificial Intelligence and Generative AI in the Energy Sector: Cyber Security Use-Cases, Considerations, and Implications, 2024. https://www.epri.com/research/products/000000003002029821
[21] Electric Power Research Institute (EPRI), Artificial Intelligence Applications in the Electric Power Industry, 2023. https://www.epri.com/research/products/000000003002026930
[22] Erdiwansyah, R. et al., Emerging role of generative AI in renewable energy forecasting and system optimization, Sustainable Chemistry for Climate Action, Vol. 7, 2025. https://doi.org/10.1016/j.scca.2025.100099
[23] International Energy Agency (IEA), Energy and AI: Analysis and forecast to 2030, 2024. https://www.iea.org/reports/energy-and-ai
[24] Deloitte, AI in Energy Systems: Interrelated Benefits Point to Vast Transformative Potential, 2026. https://www.forbes.com/sites/deloitte/2026/03/23/ai-in-energy-systems-interrelated-benefits-point-to-vast-transformative-potential/
[25] Electric Power Research Institute (EPRI), Five AI Grand Challenges for the Electric Power Industry, 2021. https://www.epri.com/research/products/000000003002021113
[26] L. Schwartz et al., Utility regulation and the future of innovation in the power sector, Lawrence Berkeley National Laboratory, 2021. https://emp.lbl.gov/publications/utility-regulation-and-future
[27] Mordor Intelligence, Energy Cloud Market Size, Share & 2030 Growth Trends Report, 2025. https://www.mordorintelligence.com/industry-reports/energy-cloud-market
[28] Wirtek, Why energy digitalization projects fail even with the right technology, 2026. https://www.wirtek.com/blog/why-energy-digitalisation-projects-fail-even-with-the-right-technology
[29] Huang, J.; Li, H.; Zhang, Z. Review of Virtual Power Plant Response Capability Assessment and Optimization Dispatch. Technologies 2025, 13, 216. https://doi.org/10.3390/technologies13060216
[30] Gholamreza M., A new machine learning based optimal bidding strategy for virtual power plants with solar power generators, Energy Reports, Vol. 14, 2025. https://doi.org/10.1016/j.egyr.2025.11.093
[31] M. Z. Hossain et al., Machine-learning-based optimal bidding strategy for virtual power plants in day-ahead and balancing electricity markets, Applied Energy, Vol. 363, 2025. https://doi.org/10.1016/j.apenergy.2025.122922
[32] Madabhushi, S., A survey of anomaly detection methods for power grids. Int. J. Inf. Secur. 22, 1799–1832, 2023. https://doi.org/10.1007/s10207-023-00720-z
[33] Bashyal, A., et al., Multi-agent deep reinforcement learning based demand response and energy management for heavy industries with discrete manufacturing systems. Applied Energy, 392, 125990, 2025. https://www.sciencedirect.com/science/article/pii/S0306261925007202
[34] IEEE Xplore, Agentic AI in Wind Energy Systems: Multi-Agent Architectures for Optimization and Resilience, 2026. https://ieeexplore.ieee.org/document/11353439
[35] Agrawal, A. et al., The Coasean Singularity? Demand, Supply, and Market Design with AI Agents (Working Paper No. 32367), National Bureau of Economic Research, 2025. https://www.nber.org/papers/w32367
[36] US Department of Energy, Artificial Intelligence Strategy, 2025. https://www.energy.gov/sites/default/files/2025-09/EXEC-2025-010630%20-%20250923_%20DOE%20AI%20Strategy%20VFinal.pdf
[37] Boston Consulting Group, The AI-First Power and Utility Company: Defining the Industry’s Future, 2026. https://www.bcg.com/publications/2026/the-ai-first-utility-defining-the-industrys-future
[38] IEA, Unlocking Smart Grid Opportunities in Emerging Markets and Developing Economies, 2025. https://www.iea.org/reports/unlocking-smart-grid-opportunities-in-emerging-markets-and-developing-economies
[39] European Commission, Strategic Roadmap for Digitalisation and AI in the Energy Sector, 2025. https://abreuadvogados.com/en/blogs/strategic-roadmap-for-digitalisation-and-ai-in-the-energy-sector/
[40] Deloitte, 2026 Power and Utilities Industry Outlook, 2025. https://www.deloitte.com/us/en/insights/industry/power-and-utilities/power-and-utilities-industry-outlook.html
[41] PwC, Global M&A trends in energy, utilities and resources: 2026 outlook, 2026. https://www.pwc.com/gx/en/services/deals/trends/energy-utilities-resources.html