In every industry, but especially in energy, leaders are racing to figure out how to actually make AI work, not just in pilots or proofs of concept, but at scale. Indeed, a survey of oil & gas executives by IBM reveals that 67% of leaders are seeking ways to unlock AI’s full potential with 58% anticipating AI will improve cash flow and 70% looking for it to increase organizational resilience. For energy professionals who are often told to do more with less, AI is the workforce multiplier that will materially increase work output while dropping more revenue to the bottom line. Or at least that’s what most energy professionals are hoping for, but for many the promise of AI is bigger than the actual results.
Over the course of this blog series, we’ll explore energy’s AI imperative and provide practical, actionable insights to unleash AI’s full potential in your organization. Please reach out with any questions along the way.
Embracing AI is no longer optional: it’s the dividing line between energy companies that thrive in a volatile market and those that fall behind. It’s an urgent imperative to join the AI Revolution or miss out on a once in a lifetime opportunity to transform your business and unlock a new level of efficiency, often compared to paradigm shifts like the Industrial Revolution and adoption of the Internet. In an industry where falling commodity prices can make one basin or field unprofitable overnight, AI is how energy companies can shrink breakeven costs further and decrease LOE. From the field to the C-suite, the possibilities for AI are seemingly endless. But like any other paradigm shift in history, the journey often starts with roadblocks.
The truth is, AI doesn’t fail because of the algorithms. It fails because of the data. Analysts at Gartner project that 60% of AI initiatives will fail by 2026 due to poor data quality, availability, or consistency. McKinsey estimates only 1% of organizations have achieved full AI maturity as of 2025. Yet the opportunity remains massive: most of the tasks holding back the energy workforce are still manual and repetitive.
Stonebridge has spent years solving that problem from the ground up. We know that before you can automate, predict, or optimize, you have to trust your data. Building an AI-ready foundation means connecting your systems, cleaning your inputs, and structuring your knowledge so it actually feeds AI models that understand your business. That’s where EnerHub, Stonebridge’s energy-specific data integration and quality assurance solution, is playing such a crucial role in client AI projects. Its sole purpose is to help energy companies transform fragmented information into clean, governed, and connected datasets across structured and unstructured data and documents. As it turns out, that’s exactly what energy needs to succeed with AI.
Unlike consultants who drop in to deliver a “strategy” deck, Stonebridge delivers both the vision and the execution. Our teams know what it takes to make data flow across land, production, accounting, and supply chain systems. We build those connections to last with our deep expertise in energy data, governance, and technology solutions. We know that when you build a strong data foundation, AI can finally do what it promises: simplify complexity and amplify human performance.
In the next blog in this series, we’ll show you what that looks like in practice by taking a deeper dive into how Stonebridge partners with clients to identify where AI can have the biggest impact, mapping opportunities across departments and workflows. Then, in the final post, we’ll show how EnerHub’s master data and quality framework powers that transformation.
In the AI era, your data foundation isn’t just infrastructure, it’s your competitive edge. Don’t get left behind: the AI Revolution is happening now.
Contact Stonebridge today to start your journey toward a strong, AI-ready data foundation.