The energy intelligence revolution

The emergence of AI into the mainstream represents a genuine paradigm shift, as tech moves from its role in the background as an enabler, to a source of dynamic agency.

It begins to impact decision making, creativity, and value creation, redefining how individuals and enterprises interact with information, with each other, and how we innovate and compete.

A plateau?

Each new dazzling innovation - the cloud, open source, big data, the Internet of Things, software as a service (SaaS) - promises much, but even when taken together have led to only modest efficiency gains. We’ve been tuning the engine - fuel injection, a bigger exhaust, lighter rotating parts - when what we need is a new vehicle, perhaps one that we don’t even drive ourselves.

Few energy companies have successfully delivered a digital transformation across the value chain at meaningful scale and with material and lasting performance gains. Data and knowledge in our industry continues to move slowly by way of manual hand-offs across disciplines, leading to sequential decision-making, frequent context loss, and significant value leakage.

Enter Agentic AI

Meanwhile, the industry is facing challenges - both cyclical and structural. Pressure to deliver lower cost and lower carbon barrels from aging and increasingly complex assets and infrastructure with a shrinking global talent pool sees us grappling with a classic “Nexus of Forces.”

With Agentic AI, tech is no longer a passive and reactive tool, but a dynamic, proactive teammate. From supporting users on individual tasks by using machine learning or domain-informed models, to agents that can string tasks together and interact with their environment, these agents will make decisions will be made and action will be taken towards full workflow-scale autonomy, either with humans-in-the-loop or with humans freed up for the things they do best, such as problem-solving and collaborating.

From assistants to autonomous agents

For most of human history, intelligence was a rare commodity. With the rise of AI, intelligence is no longer scarce. It’s abundant. It’s accessible to every professional, in every discipline, in every organisation.

When AI can function as a force multiplier in technical domains, engineers and geoscientists can be elevated to the role of ‘systems thinkers,’ becoming curators of judgment and decisions, ensuring AI outputs are reliable and contextually correct.

They can leave much of the heavy lifting to AI, freeing themselves up to do the things that humans do best: framing questions in context, and collaborating with one another.

The dynamic oilfield

Systems thinking becomes practical at scale. A competency shift occurs as we begin to see and govern the oilfield as a living, dynamic system, not just a chain of isolated technical puzzles. Closed-loop processes and new levels of system autonomy at many scales become possible, giving us a more holistic view across the value chain. Better and faster decisions then give rise to significant performance gains such as reduced lifting costs, greater recovery, and lower carbon.