AI optimism and IT systems investment conflict for oil and gas executives

Optimistic conversations are colliding with tight capital spending, threatening E&Ps ability to realize value from upstream AI. While executives rhapsodize about AI-powered optimization and automation, they're operating on legacy systems that simply cannot support these advanced capabilities. 

Bain & Company’s 2025 Energy Agenda survey of over 700 executives found that 72% feel positively about the business case for AI and digital tools in the coming 5-10 years. Yet more than 60% of surveyed executives acknowledge they need to overhaul their Enterprise Resource Planning (ERP) systems within the next three years – a tacit admission that their current infrastructure is inadequate for the AI future they envision.

Such energy companies are building AI castles on data quicksand. 

This gap between ambition and execution reveals a deeper strategic problem. For decades, the industry prioritized acreage acquisition and drilling expansion over technological modernization. As Troy Ruths of PetroAI notes, AI implementation "requires a rich cultural change" and "is not cheap." Yet many companies approach digital transformation piecemeal, seeking tactical fixes rather than the comprehensive overhaul their aging systems demand.

The consequences of this misalignment are particularly acute in land management and resource optimization. The Permian Basin – once the crown jewel of American production – exhibits troubling signs of maturation, with diminishing returns from increasingly marginal acreage. Here, AI's potential to identify optimal drilling locations and maximize recovery from existing wells could prove transformative. However, without robust data infrastructure and integrated systems to capture, process, and analyze geological and production data, these benefits remain theoretical.

Executives need to recognize that AI isn't merely a technological overlay but requires fundamentally information architecture. As Peter Harding of Kelvin AI observes, companies truly committed to AI transformation are "getting incredible returns," while others merely "dabble." The difference lies in systematic infrastructure investment, not just AI point solutions. Early adopters like Saudi Aramco and ADNOC understood this principle, building comprehensive digital foundations before deploying advanced AI applications.

Capital project cost inflation compounds this challenge. With more than three-quarters of executives reporting increased project costs, the industry faces difficult allocation decisions. Too often, digital infrastructure investments lose out to traditional capital projects, despite their potential to drive efficiency across operations. Executives recognize the need for improved capital allocation, but hesitate to direct sufficient resources toward foundational systems modernization.

The path forward requires reconciling AI optimism with infrastructure reality. Before companies can realize the transformative potential of artificial intelligence, they must commit to overhauling their land data management, ERP systems, and sensor networks. This means substantial investment, robust executive sponsorship, and patience – a comprehensive technological retooling rather than incremental improvements. Without this foundation, the industry's AI aspirations will remain just that – aspirations, not achievements.