AI and Digitalisation: From Hype to Hard Value

Artificial intelligence and digitalisation are rapidly evolving from industry buzzwords into business-critical capabilities across the energy, refining, and chemicals sectors. While public narratives often portray AI as a disruptive, entirely new force, the reality is both more grounded—and more familiar.

AI Is Already Part of the Industry
Despite today’s pressure to “adopt AI,” the industry has relied on forms of artificial intelligence for decades. Linear programming, predictive modelling, optimisation, and advanced process control have long underpinned complex operational decisions—analysing vast datasets, forecasting outcomes, and identifying optimal actions within defined constraints.
What has changed is not the underlying principle, but the scale, accessibility, and visibility of modern AI tools. Advances in computing power, cloud platforms, and machine-learning techniques have extended these capabilities well beyond specialist teams, placing them firmly within day-to-day business operations.

From Experimentation to Measurable Returns
As AI adoption matures, organisations are becoming more disciplined. The question is no longer whether to invest in AI, but how to ensure it delivers tangible value.
Cost reduction, productivity gains, risk mitigation, and competitive advantage are now the benchmark. In a tightening capital environment, initiatives that cannot demonstrate clear and measurable returns are increasingly difficult to justify—and unlikely to survive.

Engineering and Execution Move Centre Stage
Some of the most immediate and compelling benefits are emerging in engineering and project execution. EPCs are deploying AI-driven tools to streamline workflows, improve design quality, reduce rework, and compress delivery schedules—benefits that increasingly flow through to asset owners.
A major opportunity lies in integrating engineering workflows with business operations into unified digital platforms. This integration enables better project de-risking, improved decision-making, and reductions in both OpEx and CapEx.
Agentic AI is already transforming tasks such as technical and commercial bid evaluations, compressing work that once took weeks into hours—while maintaining rigour, traceability, and auditability.

Data Unlocks Value—but Legacy Assets Constrain It
Data-driven thinking is deeply embedded in the sector’s DNA, particularly among companies with roots in subsurface analytics. Extending this mindset across surface assets and refineries, however, requires significant investment in data readiness—including modern instrumentation, sensors, and scalable data infrastructure.
Many facilities were built decades ago for a very different era of analytics. Fixed architectures and physical constraints limit data generation and availability, often restricting how much value even advanced AI can unlock. In many cases, infrastructure—not algorithms—is the binding constraint.

AI as an Enabler, Not a Substitute
While AI can process vast volumes of data at speed and scale, human expertise remains essential for interpretation, judgement, and accountability. The most effective applications position AI as an enhancement tool—amplifying human decision-making, not replacing it.
Successful organisations are those that combine strong domain knowledge with digital tools, embedding AI into established workflows rather than treating it as a stand-alone solution.

Pragmatism Will Define Success
AI and digitalisation are no longer optional. But long-term success will depend on disciplined deployment, robust data foundations, and a relentless focus on value creation.
Those who balance ambition with realism—and who align digital innovation with operational excellence—will be best placed to convert digital potential into durable competitive advantage.