Energy sector’s AI rollout stalls amid technical, social and policy gaps

Although AI has shown promising applications in energy forecasting, maintenance scheduling, and decentralized control systems, its implementation is far from seamless. The study systematically reviews the literature and finds that the energy sector faces deeply interwoven challenges that have not been adequately addressed in current deployment strategies. Among these, technical barriers such as data silos, lack of interoperability, opaque algorithms, and inconsistent reliability in operational settings rank high.

A new study identifies a web of systemic challenges hindering the widespread adoption of artificial intelligence (AI) in the energy sector and also proposes a comprehensive governance solution designed to accelerate sustainable, ethical, and inclusive deployment. Published in the journal Sustainability with the title “Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector,” the research offers a rare integrative roadmap to confront both technical and socio-political obstacles impeding AI transformation across critical infrastructure.

Despite the anticipated benefits, the pace of AI adoption remains slow and fragmented across the global energy landscape. The study attributes this lag not to technological immaturity, but to unresolved challenges that span ethics, policy, institutional readiness, and stakeholder alignment. Crucially, the study goes beyond identifying problems in isolation and instead develops a lifecycle-based governance framework to manage these complexities holistically.

Why AI adoption in the energy sector remains fragmented and incomplete

Although AI has shown promising applications in energy forecasting, maintenance scheduling, and decentralized control systems, its implementation is far from seamless. The study systematically reviews the literature and finds that the energy sector faces deeply interwoven challenges that have not been adequately addressed in current deployment strategies. Among these, technical barriers such as data silos, lack of interoperability, opaque algorithms, and inconsistent reliability in operational settings rank high. However, the author stresses that these issues are often symptoms of larger structural limitations within the governance ecosystem.

The organizational culture in many energy institutions remains resistant to digital transformation. Legacy infrastructure constrains integration capacity, while decentralized ownership of energy assets complicates collective action. Regulatory systems often lack the precision and agility to accommodate rapidly evolving AI technologies, leading to mismatched compliance frameworks and uneven risk assessments. Beyond these operational issues, the study also addresses ethical and social concerns, including the risk of bias in decision-making algorithms, threats to employment due to automation, and fears around surveillance and privacy.

The study found that the majority of existing approaches fail to recognize the interdependence of these barriers. Technical challenges are frequently treated in isolation from institutional or societal ones, resulting in fragmented solutions that stall progress. Park’s review illustrates that successful AI adoption in energy must account for systemic interactions, between hardware, software, human factors, and policy environments, rather than treating deployment as a strictly engineering issue.

How a lifecycle-based governance framework could reshape AI deployment in energy

To counter these layered challenges, the study proposes a governance framework grounded in four foundational principles: trustworthiness, sustainability, equity, and collaborative adaptation. These principles reflect a normative vision for how AI should be developed and applied in the energy sector, ensuring not only operational efficiency but also ethical legitimacy and inclusive participation. Park’s model breaks from conventional regulatory thinking by embedding these principles into a structured, lifecycle-based approach that spans planning, implementation, evaluation, and scaling.

The framework begins with a diagnostic phase in which institutions assess readiness, define roles, and align objectives across stakeholders. It then proceeds to a design and development phase, where systems are engineered with transparency, auditability, and cross-sector input. In the deployment stage, real-time monitoring tools and explainable AI techniques are integrated to ensure safe and accountable operation. The final stage involves systematic evaluation and feedback, enabling continuous adaptation and evidence-based policy refinement. Each of these stages is supported by a matrix of actions tailored to specific stakeholder groups, from government agencies and utilities to technology providers and civil society.

The governance model is further reinforced by a set of measurable key performance indicators. These indicators assess dimensions such as algorithmic transparency, environmental outcomes, workforce impact, and public trust. The inclusion of quantitative and qualitative metrics enables energy organizations to track progress over time and respond to emerging risks or gaps. By emphasizing feedback loops and multi-stakeholder engagement, the framework transforms AI adoption from a one-time intervention into an iterative process aligned with long-term sustainability goals.

Reframing AI as a public infrastructure challenge, not just a technical one

Park’s study reframes AI deployment in energy not as a narrow engineering challenge, but as a public infrastructure issue that demands systemic governance innovation. The research argues that if AI is to play a meaningful role in addressing climate change, stabilizing energy markets, and democratizing access to power, then its integration must be managed with the same care and foresight applied to physical infrastructure. This requires moving beyond quick-fix solutions and embracing a governance model that accommodates complexity, uncertainty, and value pluralism.

Rather than proposing a universal blueprint, the framework is designed to be adaptable across contexts. It can be customized for use in highly centralized utility markets as well as more fragmented, decentralized energy systems. It is equally relevant to high-income countries with mature digital ecosystems and low-income regions embarking on foundational energy transitions.

The study also encourages international cooperation to develop shared standards for AI ethics, cybersecurity, and data sovereignty in energy systems - a pressing need in an era of cross-border energy flows and integrated digital grids.