AI in Oil & Gas: Adoption rising, expectations running ahead: European Technology/Software - Global Oil Services

Research evidence excerpt

AI in Oil & Gas: Adoption rising, expectations running ahead: European Technology/Software - Global Oil Services

uality data before they can deliver consistent results. Without these enablers,

returns may remain limited or delayed.

Overall, AI is emerging as a core driver of productivity and operational efficiency in oil and gas. We believe that the opportunity is

significant, but the path to value creation remains gradual and execution-dependent, requiring selective adoption and disciplined

investment.

INDUSTRY CONTEXT: EFFICIENCY UNDER CONSTRAINT

O&G companies operate in a structurally volatile environment, marked by fluctuating commodity prices, geopolitical uncertainty,

policy shifts, supply-chain pressure, and constrained capital spending. In this context, management teams are prioritizing returns

from existing assets rather than pursuing large-scale capacity expansion.

AI adoption is therefore driven by the need to manage this volatility while maintaining capital discipline. Companies are looking

for practical tools that can improve operating performance, protect margins, and strengthen resilience without increasing risk or

complexity.

AI is positioned as one of these tools, with value concentrated in three core areas:

• Improving asset utilization through better monitoring, optimization, and real-time decision-making

• Reducing operating costs via automation and predictive maintenance

• Enhancing safety by limiting human exposure to hazardous environments

These use cases directly support the sector’s core objective: maximizing output from existing infrastructure while controlling costs

and risk.

At the same time, the industry context reinforces that AI is not an end in itself. Its relevance depends on its ability to deliver