Artificial intelligence (AI) is increasingly transforming the global energy sector, offering significant benefits such as predictive maintenance, smarter grid management, and accelerated renewable energy integration. Despite this potential, adoption has been slow — with fewer than 10 % of energy companies deploying AI at large scale, largely due to a lack of trust in these technologies and concerns over their reliability in high‑stakes environments. Experts note that while AI can optimize production and reduce downtime, hesitancy persists because mistakes in energy operations can have massive financial and social consequences.
A major barrier to trust is ethical governance. AI can efficiently process data and make recommendations — for example, suggesting operational changes to cut costs — but it cannot assess the broader ethical or social impact of such decisions. Energy companies are therefore urged to maintain strong human oversight, clearly defined accountability frameworks, and transparent decision‑making processes so that vital economic and community considerations aren’t overshadowed by algorithmic outputs.
Another significant challenge lies in accuracy and technical reliability. Large language models and other AI systems can struggle with ambiguous inputs, hallucinations, or inconsistent predictions, making them risky as standalone decision tools in complex energy contexts. Industry practitioners suggest that the most effective AI applications are those that combine AI interfaces with validated machine learning and physics‑based models, ensuring that insights are both fast and grounded in reliable science.
Finally, data security and intellectual property concerns also hinder widespread AI adoption. Energy companies hold vast amounts of sensitive operational and commercial data, which they are understandably reluctant to share. Robust contractual frameworks, strict data governance, and advanced cybersecurity measures — such as zero‑trust architectures — are essential to protect this information while allowing AI systems to learn and improve. Overall, energy industry leaders emphasize that building trust in AI isn’t just a technical challenge; it requires cultural change, ethical commitment, and strategic leadership to realize AI’s full benefits responsibly.
