Across two keynote sessions and a closing panel, one theme cut through every discussion: the competitive frontier in oil & gas is no longer about what a company owns, it's about what it can act on. The companies capturing the most value are not necessarily the largest, they're the ones that have built the organizational and data capability to actually operationalize AI, rather than simply pilot it.
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1. Reframing the Competitive Edge: Ramya Sethurathinam (BCG) opened by mapping the scale of pressure facing operators today, rising regulatory complexity, portfolio repositioning, capital allocation trade-offs, cost discipline, and the need to rebuild investor confidence. Against that backdrop, BCG's analysis puts real numbers behind the opportunity: AI could deliver value equivalent to 30–70% of current EBIT across upstream, refining, and fuel retail by 2030, with real-world examples already showing dramatic gains, from cutting unplanned downtime from 27 days a year to 6, to compressing seismic interpretation from 12 months to 2 weeks. Yet Ramya was clear that most companies still struggle to scale these wins past isolated use cases, and that the real bottleneck isn't the algorithm. Per BCG's 10-20-70 framework, successful AI transformation is only 20% technology and 10% algorithm, 70% comes down to people, organization, and process.​
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2. Building the Foundation Before the Intelligence: Denis Petrakov (SLB) offered the operator's-eye view of what that 70% actually looks like on the ground. Using SLB itself as the case study, a company shaped by decades of acquisitions into three distinct operating models with three incompatible data languages, Denis made the case that most organizations instinctively reach for full standardization, and that this instinct is precisely what fails. The principle that works instead: harmonize only what must be common, master data, shared services, core governance, while letting business-specific systems stay different by design. His central warning for the industry: AI is a payoff, not a strategy. Chase the model before the foundation is solid, and the result is a portfolio of pilots that never scale.
 
3. Why Partnerships Stall Between Pilot and Scale: The closing panel, featuring leaders from Chevron, AWS, Microsoft, and Baker Hughes, turned the conversation toward collaboration itself, and surfaced a candid, shared diagnosis. The technology to power AI transformation largely exists; what's missing is trust and governance clarity between operators and technology partners. Panelists pointed to recurring friction points: ambiguity over how value is actually split when one side brings infrastructure and the other brings domain expertise, data-sharing hesitancy and procurement bottlenecks on the operator side, and a persistent tendency for technology partners to underestimate the operational complexity operators are managing.
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The result: industry-wide AI investment remains concentrated in a narrow set of use cases, with few partnerships making it past initial pilots. The throughline across all three sessions was consistent: the industry has the appetite, the technology, and increasingly the evidence to justify AI investment. What it still lacks, in most organizations, is the foundation and the collaborative trust to turn that appetite into scale. Closing that gap, through disciplined data governance, clearer partnership models, and cross-industry alignment, is now the defining challenge for oil & gas companies competing in the AI era.