Why Energy Operators Need AI Governance Before AI Becomes Operational Infrastructure
Energy operators are finding that tools reliant on AI are quickly becoming the norm in various systems across the energy sector. The question of how these tools are implemented and who is responsible for the decisions they make is one that is becoming ever more crucial for energy operators.
Just as it is in many other industries, artificial intelligence tools are becoming common practice across the worldwide energy sector. These tools have all sorts of applications, from automating tasks and saving man-hours to offering predictive solutions to problems before they arise. But as these tools start to make more decisions across the energy grid that were once made by human operators, the question of who is responsible for these decisions and what the limits of their decision-making are is becoming more and more important. In short, handling the governance of these AI tools is a problem that energy operators must solve before they become standard operational infrastructure.
Energy providers across the board, firms specializing in gas or oil and all sorts of utilities companies are coming to rely heavily on tools that are driven by AI. These tools have direct control or impact on safety, compliance and production, but while they might provide advantages in the market, they also leave energy operators exposed financially, operationally and regulatorily. This is because these AI systems are advancing at a pace that government structures and regulations are unable to match, but they will catch up eventually. Many energy operators are turning to third-party AI governance specialists like ModalPoint in order to ensure that they have strong records of accountability for their AI agents and tools, and that, before the energy sector becomes answerable for the decisions their AI tools are making, they already have the answers ready.
Let's take a closer look at why handling AI governance is so important for energy operators now, rather than after the regulatory environment catches up.
While the last decade or so has seen AI tools used to handle the automation of humdrum tasks or complex but fiddly work that eats up man-hours, this is no longer the only thing they can do. Newer AI tools are often used to predict when maintenance is most needed, monitor emissions, predict usage demands, optimize aspects of supply and demand, and even to help make fast operational decisions.
When oil and gas AI technology has such a strong influence on the way operators do things, it is becoming increasingly important that those decisions have an answerable human behind them. The use of AI in regulated industries like the energy sector is inevitable, but must still be done in such a way that there is accountability behind the decisions those tools make.
Mistakes at an operational level that are made within the energy sector can have such a heavy impact on the world that energy decision governance, especially when AI tools are involved, becomes crucial.
As more and more AI systems become embedded in operational roles, the operational AI risk rises. While these tools can make operations run more smoothly and efficiently, they can behave in unpredictable ways, and this can cause problems. While these tools are sure to be implemented, reliance on them should not be too heavy, and energy decision governance must be managed in such a way that human operators are aware of how and why AI tools make the decisions they do.
While a certain amount of operational AI risk is inevitable, energy operators should be asking themselves how much risk is acceptable. Implementing robust AI governance protocols can mitigate this risk. Having the correct AI governance in energy sectors means operators will be able to answer questions such as:
While these sorts of questions can be important in many different industries as AI tools become more standard, they are particularly important to AI governance in energy. Regulators of the sector need to know that oil and gas AI systems, and any other AI systems involved in the energy sector, are transparent and accountable.
While in most cases, the regulatory environment for how AI governance in energy is handled is at a point where, essentially, there is no regulatory environment for it, this is unlikely to remain true for long. Many different governments and regulatory bodies across the world are working to develop legislation for AI in regulated industries like energy, and tools like oil and gas AI that are used for operational decisions are sure to feel those regulatory changes.
But the solution to this is obvious: energy operators that are worried about operational AI risk and the possibility of regulations around energy decision governance being a problem for them later should act preemptively and position themselves to be better able to adapt to the coming AI regulations.
By working at achieving strong AI governance in energy operations now, operators will be able to limit their operational AI risk, and, before these AI tools become operational infrastructure as standard, will be able to confidently have governance over them.
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