The End of AI Pilot Purgatory? What Last Year's AI in Energy Week Told Us About Oil & Gas Priorities

The oil and gas industry has largely proven that AI can work. The challenge now is turning successful pilots into repeatable operational capability. After years of pilots, proofs-of-concept and innovation initiatives, many organizations, even the leading operators, are still struggling to translate AI investments into measurable operational value. While the technology continues to advance rapidly, scaling successful use cases across complex operations remains a major challenge. 

At last year's AI in Energy Week Summit, this reality was impossible to ignore. Conversations across 34 sessions and 131 senior energy leaders revealed an industry moving beyond the excitement of experimentation and confronting the practical realities of deployment. The discussions were less about algorithms and more about governance, workforce readiness, workflow integration and the organizational foundations needed to scale AI successfully.  

The result is a clear picture of the priorities separating AI ambition from execution, as oil and gas leaders focus on scaling adoption, strengthening operational foundations and delivering measurable value at scale. 

Trusted Data is Becoming a Competitive Advantage 

If there was one message that appeared consistently throughout the Summit, it was that successful AI depends on trusted data. 

Live audience polling found that 30% of attendees identified data quality, foundations and governance as their biggest challenge when executing AI strategies, making it the most cited obstacle among respondents. 

30% of attendees identified data quality, foundations and governance as their biggest challenge when executing AI strategies.

Rather than talking exclusively about models and algorithms, speakers repeatedly emphasized the importance of information underneath AI systems. The consensus was clear: AI amplifies whatever sits beneath it. Therefore, it is good data that creates opportunity and poor data that creates risk. 

Discover what the industry's AI leaders are prioritizing today. Explore the latest insights on data governance, workforce readiness and the operational foundations of successful AI adoption.

As Raymond Mitten, Vice President of Advanced Digital Technologies at Imperative Chemical Partners, explained:  

"The true power isn't AI. It's what data is underneath."  

Leading operators are increasingly approaching governance not as a compliance exercise but as an enabler of scale. Data stewardship, AI-assisted data cleansing and trust thresholds are becoming operational requirements rather than future aspirations.

The AI Reality Check  

• 30% cite data governance as their biggest AI challenge
• 18% struggle to scale beyond pilots
• Only 17% report their workforce is highly prepared for AI
• 50% prioritize workforce productivity and augmentation 

Operationalization is Replacing Experimentation

Despite significant investment and years of experimentation, many operators are still grappling with a familiar question: how can successful AI initiatives be translated into consistent operational value across assets, functions and the enterprise as a whole? 

This challenge has become increasingly apparent as organizations move beyond the proof-of-concept stage. While pilots can demonstrate what's possible, scaling AI across large, asset-intensive businesses requires a different set of capabilities altogether. Data governance, workforce adoption, integration with existing systems and operational ownership often prove just as important as the technology itself. 

The industry's growing focus on these realities was reflected in attendee polling, where 18% of respondents identified scaling beyond pilots as the biggest challenge in executing their AI strategies.  

18% of respondents identified scaling beyond pilots as the biggest challenge in executing their AI strategies. 

Importantly, many senior energy leaders no longer view this as a sign that AI has failed to live up to expectations. Rather, it reflects a broader shift in maturity. Organizations are becoming more disciplined in how they evaluate AI investments, recognizing that long-term success depends on establishing the operational foundations required to support adoption at scale.  

In practical terms, this means moving beyond experimentation as a measure of progress. Increasingly, success is defined by whether AI can be embedded into existing workflows, adopted by frontline teams and consistently deliver outcomes across the business. 

As Erin Boyd, Vice President of Digital Technology at AES Corporation, observed: 

"If AI remains ornamental, boards and investors will treat it as a discretionary spend." 

Ultimately, the discussion pointed to a wider industry realization: competitive advantage is unlikely to come from launching more pilots. It will come from operationalizing proven use cases faster and more consistently than peers. 

AI Delivers Value When It Becomes Part of Everyday Operations 

Across the AI in Energy Week Summit, speakers repeatedly stressed that AI creates the greatest impact when it is embedded directly into operational workflows. 

Notably, many of the examples discussed were no longer focused on prediction alone. Instead, organizations are increasingly embedding AI into operational workflows where recommendations can trigger maintenance decisions, inspection priorities and intervention planning. 

Real-world examples highlighted during the event included: 

• Early anomaly detection in refining operations at Marathon Petroleum 
• Computer vision applications supporting transmission and distribution inspections at Eversource Energy 
• Edge AI deployments for pipeline monitoring at Chevron Corporation 

These examples shared a common theme. AI was not delivered as a standalone analytics tool, it was integrated directly into day-to-day operational processes. 

This aligns with investment priorities identified during audience polling. Nearly 50% of respondents cited workforce productivity, efficiency and augmentation as the primary driver behind AI investment.

Read the benchmarking report to uncover why 95% of AI initiatives still fail to deliver value and why the problem often has little to do with the technology itself  

Workforce Readiness May Be the Industry's Toughest Challenge 

As much as technology dominated many discussions, people remained central to the conversation. 

Only 17% of attendees reported that their organizations are highly prepared for AI adoption, while more than 70% described their companies as only moderately or somewhat prepared. 

Only 17% of attendees reported that their organizations are highly prepared for AI adoption. 

Participants highlighted workforce challenges ranging from retirement driven knowledge loss to change fatigue across already stretched organizations. However, the prevailing message was less about hiring large numbers of AI specialists and more about enabling existing employees to work effectively alongside AI enabled tools. 

More than 70% described their companies as only moderately or somewhat prepared. 

As Nathan Church, Data Science Lead at Murphy Oil Corporate, explained: 

"What we need to do is make our existing people know how to do AI."  

Increasingly, leaders are treating workforce readiness and change management as core transformation priorities rather than supporting activities.  

AI Is Becoming Operational Infrastructure 

Perhaps the most significant insight from AI in Energy Week 2026 was that leading organizations no longer view AI as a technology experiment, they are increasingly treating AI as operational infrastructure. 

Across discussions, 4 foundational themes surfaced repeatedly: data governance, workflow integration, workforce readiness and governance. Organizations making measurable progress are treating these capabilities as a connected system rather than independent initiatives.  

This marks an important shift in how oil and gas companies evaluate AI success. The focus is no longer on launching the most pilots or deploying the most sophisticated models. Success is increasingly defined by the ability to operationalize AI consistently, responsibly, and at scale.  

For oil and gas leaders, the message was clear: success will no longer be measured by the number of pilots launched or models deployed. Increasingly, it will be defined by an organization's ability to embed AI into operational workflows, scale adoption responsibly and translate digital capability into measurable business performance. 

The industry's leading organizations are already moving in this direction. Rather than treating AI as a standalone innovation initiative, they are beginning to treat it as operational infrastructure underpinning future competitiveness. 

Join the Conversation at AI in Energy Week 2027 and learn from energy operators, digital leaders and technology experts as they share practical lessons, deployment strategies, and real-world outcomes shaping the future of AI across oil and gas, utilities and critical energy infrastructure.