AI maturity in the energy industry

Artificial intelligence (AI) and unstructured data are beginning to shape the energy sector, with many organisations already applying this data format in their AI use cases. However, few have fully realised the broader value unstructured data can offer and how it can enhance AI outcomes. To unlock the full potential of AI, the sector must establish strong processes for managing and leveraging unstructured data, enabling deeper insights, accelerating innovation, and building greater confidence in AI applications.

Findings are based on research conducted by Iron Mountain alongside independent market research specialist Vanson Bourne. Data in this report is based on 203 IT and data decision-makers at organisations working in the energy sector, who have knowledge or involvement in their AI strategy.

Three key takeaways:

• Energy decision-makers report AI for automation is currently the most important tool for organisational success, with use cases around developing new products and services and reducing costs
• The sector is more likely than average to use AI in manufacturing and production (energy: 56% and a global average: 41%), and supply chain operations (energy: 51% vs a global average: 39%)
• Fewer energy organisations than the global average routinely leverage AI for unstructured data (19% vs 23%)

Iron Mountain commissioned independent market research specialist Vanson Bourne to conduct this piece of research. The study included surveying 1,400 IT and data decision-makers who have knowledge of or responsibility for AI strategy at their organisation. Respondents’ organisations had to have 250 employees or more across the following countries: US, UK, France, Germany, India and Australia.

Organisations are from several public and private sectors but there was a strong focus in banking and financial services, insurance, healthcare and life sciences, media and entertainment, the public sector (excluding healthcare) and energy. This summary is based on 203 decision-makers in the energy sector.

Current AI adoption

The energy sector has an average AI adoption rate likely due to factors like regulatory constraints, the complexity of legacy infrastructure, and the need for specialised AI expertise potentially restricting the speed of adoption. However, as the sector transitions towards cleaner energy sources, AI can play a critical role in enabling data-driven decisions and accelerating innovation.

To improve AI adoption and value, energy organisations need to make changes to their current capabilities or re-shape current processes. For instance, half (51%) of organisations in the sector want to scale IT capabilities to handle large volumes of data. 49% want to allocate more resources for AI expertise and project development, and more than the global average want to automate data governance and compliance (48% and 43% respectively). Through these improvements energy organisations can increase value from AI initiatives and accelerate adoption.

The increased focus on automating data governance and compliance likely stems from the growing complexity of managing vast amounts of data and ensuring adherence to strict regulatory requirements such as Renewable Energy Standards (RES) for the EU, or the Federal Energy Regulatory Commission (FERC) in the US. Adhering to these guidelines is critical for the energy industry as failure to do so can lead to considerable financial and reputational risks.

Following the trend of automating tasks, AI-driven automation is ranked as the most important tool for those in the energy sector’s organisational success – slightly more than the global average trend (energy: 18% and global average: 16%). Perhaps the push for clean energy from the government, such as Net Zero by 2050 in the US, is causing many to prioritise automation to streamline operations, optimise resource usage, and accelerate the transition to renewable energy solutions while maintaining cost-efficiency and compliance with sustainability goals.

Other AI use cases for the sector revolve around using AI for developing new products and services (63%), improving customer experience (59%) and reducing costs (58%). Using AI for innovation is seen as an important use case for more in the energy sector than the global trend (57%), indicating that the sector is leveraging AI to push boundaries in technological development, perhaps to enhance sustainability efforts, and meet the growing demand for cleaner, more efficient energy solutions.

Turning to operational areas where AI is used within industry, the energy sector is most likely to use AI for IT and security areas (87%), research and development (61%), marketing (60%) and manufacturing and production (56%). This focus on manufacturing and production is 15 percentage points greater than the global average and likely stems from the need to optimise complex, large-scale operations, improve equipment efficiency, and enhance predictive maintenance to reduce downtime and costs. By leveraging AI in these areas, energy organisations can drive greater operational reliability, safety, and productivity; all critical factors in maintaining supply stability and meeting growing energy demands.

In addition, the sector is more likely to adopt AI across supply chain operational areas when compared to the global average (51% vs an average of 39%).