Using AI in Energy to Counter the Skills Gap

By Tim Fleet, Vice President, Business Development 

Energy organisations globally are wrestling with a growing skills gap. The fragmentation and often limited accessibility of information is both hindering learning and development and risking the loss of vital knowledge to retirements or layoffs. Multi-disciplinary collaboration is undermined by departmental boundaries and energy workers often struggle to access information that could hold the key to improving infrastructure development or transforming operational safety. 

With a major surge in demand for energy infrastructure, the unprecedented skills shortage is hindering progress.  170 new nuclear plants are planned or under construction globally and old plants are being restarted or kept open longer. More new renewable energy capacity is to be added in the next five years than in the previous 100, including a rapid increase in Liquified Natural Gas (LNG) projects. 

Despite this extraordinary demand, there is a risk that innovation is being stifled by an ageing workforce, increasing early retirements and a safety-conscious, conservative culture. The nuclear industry, for example, has taken over a decade just to move from paper to digital records. The industry has also been slow to adopt new innovations such as AI, with nuclear workers the least likely of any energy sector to use Artificial Intelligence (AI) in their current job role. 

Industry Specific AI

Organisations have a chance to leverage AI not only to transform operations but also attract the new skillsets required to support digitalisation and decarbonisation. Generative AIs such as ChatGPT have raised awareness of the power of Large Language Models (LLMs) to learn the patterns behind data. They have, however, also become infamous for creating misinformation due to being trained on unchecked internet data. 

Industry-specific LLMs are changing the equation. Trained on vast amounts of industry specific, verified data, these tools can transform everything from regulatory compliance and maintenance to innovation and workforce skills. 

• Compliance: LLMs trained on reliable, industry-specific data such as verified energy safety records, operational best practices and regulations can rapidly synthesise and summarise data to auto-generate multilingual training materials, drawing on the latest lessons learned, regulations and best practices. 
• Skills: LLMs can scour records to identify the biggest skills deficits and deficiencies across an organisation and amass the leading expertise in those areas, helping both find and fill skills gaps. In future, AIs could even suggest novel new forms of training or improvements to everything from design to operations. 
• Collaboration: The ability to rapidly amalgamate and translate millions of records for human consumption means that LLMs can also fuel cross-departmental knowledge sharing and help create a more multidisciplinary workforce.
• Knowledge: LLMs can place enormous cognitive resources at the fingertips of workers and unearth vital new insights into everything from energy operations to design. 
• Operational Efficiency: Using an LLM to identify and explain the common design faults across would help to develop smarter future energy plant designs. An LLM could be asked to check records of all plant maintenance between April-June 2024 and instantly produce digestible, on-demand summaries referencing all the relevant ISO standards or regulations. A nuclear worker could then qualify this by asking the AI to identify defects or delays during the process and it will re-analyse and summarise the records in that context.
• Innovation: By deriving new innovations from patterns in existing data, LLMs could in future auto-generate project templates or even infrastructure designs. 

Data is the Key

Such successful AI deployment is underpinned by the quality and availability of industry data. Engineering information management systems already widely used in the energy industry can now automate key data quality processes to achieve the high level of accuracy required to support LLMs:

• Version control and tag extraction
• Document management standards
• Audit trail of changes
• Automatic revision control and auto-generated version histories

By transforming industry knowledge and skills into a globally accessible resource that could be unlocked for new generations of workers, organisations can transform workforce training and level up knowledge across organisations. Unlocking skills bottlenecks on the energy transition, the adoption of LLMs will enable the energy industry to realise the full potential of its immense data resources and accelerate vital innovation and development.