As artificial intelligence presents the possibility of becoming an integral driver of the energy transition, the industry finds itself at an important moment. AI promises enormous benefits – from real-time grid optimisation and predictive maintenance to customer service enhancement and market forecasting. Yet the urgency to decarbonise, digitise and decentralise the energy system should not come at the expense of ethics, security, or trust.
In its first formal guidance on this issue, Ofgem – the UK energy regulator – outlines an ethical framework for AI in the energy sector, offering a detailed synthesis of best practices, governance standards, risk management principles, and legal obligations. For executives in both the public and private sectors, this document offers a valuable playbook – not only to mitigate harm but also to strategically enable innovation that is safe, fair, secure and environmentally sustainable.
AI in the energy transition
The UK’s net zero target by 2050 hinges on a smarter, more resilient energy system. AI is already accelerating this vision. Applications include:
- Predictive analytics for infrastructure resilience and maintenance.
- Dynamic optimisation of electricity demand and storage.
- Enhanced forecasting for renewables, especially in variable conditions.
- Automated trading and pricing algorithms in wholesale markets.
- Customer interaction tools that personalise services and identify vulnerabilities.
However, these opportunities are inextricably linked with complex risks: explainability of models, model drift, algorithmic bias, cyber vulnerabilities, and impacts on market competition. What distinguishes AI from conventional digital systems is its probabilistic nature and often opaque reasoning pathways – particularly in black-box models like deep learning. As a result, traditional assurance methods fall short. This necessitates a shift towards lifecycle risk management and context-sensitive governance.
Four pillars of ethical AI: safety, security, fairness, and sustainability
Ofgem’s ethical framework for AI in the energy sector is anchored in four core outcomes:
- Safe AI: AI deployment must not compromise operational safety or national infrastructure. This includes anticipating potential misuse and ensuring fail-safes, especially in mission-critical systems such as grid balancing or remote asset control.
- Secure AI: The proliferation of AI introduces attack surfaces that differ from conventional systems. AI models can be vulnerable to adversarial inputs, model poisoning, and prompt injection. Cybersecurity must therefore be embedded throughout the AI lifecycle – not bolted on post-deployment.
- Fair AI: Bias in training data can result in systemic discrimination, particularly against vulnerable or digitally excluded consumers. Transparency, accountability, and redress mechanisms are crucial for maintaining trust and ensuring equitable treatment.
- Sustainable AI: While AI can optimise energy consumption and carbon emissions, the infrastructure supporting AI – especially data centres and model training – can be energy-intensive. Stakeholders must adopt net-positive strategies, ensuring that AI’s use contributes to, rather than undermines, sustainability targets.
Governance and accountability
Executives bear ultimate responsibility for ensuring that AI aligns with corporate values, regulatory obligations, and public expectations. Ofgem recommends the following practices:
- Strategy-first AI adoption: Organisations should define a board-level strategy articulating the purpose, risks, and intended outcomes of AI initiatives.
- Leadership accountability: Oversight can be housed within an existing committee (e.g. technology or audit) or a dedicated AI ethics board. This governance layer must have authority, transparency, and access to technical expertise.
- Defined roles and responsibilities: Clear delineation across procurement, development, deployment, and monitoring phases is vital. This includes the designation of AI officers or similar roles with cross-functional oversight.
- Performance metrics: Regular reporting on AI risk status, operational incidents, performance indicators, and external benchmarking is recommended. Scenario planning should be used to simulate worst-case outcomes and stress-test controls.
- Policy infrastructure: Organisations must ensure that AI usage complies with all relevant legal, ethical and operational policies – data protection, equality, human rights, and health and safety among them.
Managing AI risks in dynamic systems
AI’s integration into complex energy systems – often involving cyber-physical components – demands a proportionate, outcome-based approach to risk. Ofgem discourages prescriptive rules in favour of frameworks that empower innovation while protecting consumers. Key risk practices include:
- Use-case validation: AI should be demonstrably superior to conventional alternatives for the given task. For each deployment, stakeholders must assess the lifecycle impact, from training to decommissioning.
- Comprehensive risk assessment: This involves evaluating legal, reputational, operational, and systemic risks, supported by frameworks such as NIST’s AI Risk Management Framework and ISO 42001.
- System-level testing: Since AI often forms part of a larger system, assurance should focus on both the AI and the surrounding technical or human context. Techniques include digital twins, diversity in AI models, and real-time anomaly detection.
- Failure mode analysis: Redundancy planning and scenario testing are essential, especially where AI systems could cause cascading effects in critical infrastructure.
- Human-AI interaction: Over reliance or distrust in AI can both undermine performance. User training and oversight procedures should be carefully calibrated to the AI’s role and risk level.
Skills and organisational readiness
The rapid evolution of AI demands parallel development in institutional capability. Ofgem advises that organisations:
- Define baseline AI literacy across the workforce, including for senior decision-makers.
- Implement tailored training plans for technical, operational and governance roles.
- Designate experts responsible for ethical, responsible and legal AI use – whether internal or external.
- Regularly update knowledge management systems to reflect emerging risks, threats and technologies.
- Establish communities of practice and collaborative forums to share lessons and refine standards.
For many organisations, developing in-house capabilities will require investment in learning, talent acquisition and partnerships. Horizon scanning and proof-of-concept trials are useful in navigating future technological shifts and regulatory landscapes.
Use cases across the energy value chain
Ofgem illustrates its guidance with concrete examples that highlight the practical challenges of ethical AI:
- Customer interactions: AI chatbots or decision aids can reduce handling times and improve personalisation. However, bias in customer data or errors in summarisation can lead to unfair treatment. Oversight, transparency, and clear escalation procedures are essential.
- Identifying vulnerable consumers: AI can help pinpoint digital exclusion or vulnerability during service disruptions. But data sparsity can itself be a source of bias. Fairness audits and scenario-based testing are recommended.
- Forecasting and prediction: From load forecasting to equipment failure prediction, AI enhances operational planning. Yet these tools must be validated against traditional models and used with appropriate confidence intervals.
- Cyber-physical systems: Drones for inspection, autonomous grid-balancing tools, or AI for cybersecurity monitoring all introduce high-stakes risks. Failure to anticipate edge cases can result in serious consequences – physical damage, cascading system failures, or cyber breaches.
- Market trading and pricing: AI’s use in price setting and automated trading could breach competition law if not carefully governed. Firms must ensure AI systems do not tacitly collude, manipulate markets or exchange sensitive information. Robust audits and documentation are legally required.
- Black box systems: LLMs and other opaque AI models pose particular challenges for explainability. Organisations must adopt testing, output validation, and clear explanation strategies – or be prepared to reject use where risks are too high or irreducible.
Regulatory context
While Ofgem’s guidance on responsible AI in the energy sector is not prescriptive regulation, it operates within a broader and binding legal framework. Key legislative touchpoints include:
- The Gas Act 1986 and Electricity Act 1989
- The Competition Act 1998, particularly concerning anti-competitive behaviour through AI
- The Data Protection Act and GDPR
- Health and safety, equality and human rights legislation
Ofgem retains enforcement powers under sectoral regulation and competition law, including the authority to levy fines up to 10% of turnover, impose redress orders, and disqualify directors for breaches. The regulator has made it clear: reliance on AI will not excuse non-compliance with regulatory obligations.
Industry implications
Ethical AI is more than a compliance issue – it’s a reputational, operational, and strategic priority. Key takeaways include:
Make ethics a board-level issue: Governance structures must be designed to anticipate both the risks and the opportunities of AI.
Build capabilities for resilience: The pace of AI development necessitates constant learning, agile risk management and investment in institutional competence.
Design for trust: Consumers, investors and regulators will demand increasing transparency and accountability in how AI is deployed and governed.
Align AI with net zero: Sustainability is both a constraint and an opportunity – AI must serve the transition, not add to its environmental burden.
Prepare for future regulation: This guidance is likely a precursor to stricter, more targeted rules. Early adoption of best practices will confer strategic advantage.
AI in the energy sector is a present force shaping its future. Ofgem’s guidance provides a timely blueprint for aligning innovation with ethics, resilience with responsibility. For leaders across the energy ecosystem, the challenge now is to embed these principles not just in policy, but in culture, technology, and practice.