The energy industry has embraced artificial intelligence (AI) as a lever for enhancing productivity and decision-making, and companies of all sizes are investing significant resources to develop and deploy AI tools.
In recent years, oil and gas companies have focused on standing up the data foundations necessary to properly deploy AI. But now, the industry is increasingly hungry to move beyond pockets of success to meaningful, enterprisewide impact. This means scaling AI platforms that deliver consistent benefits. The acceptance gap between traditional operations and AI tools remains an issue, as does “model drift” — when AI decisions lose preciseness as new data is incorporated — due to inattention to cloud system maintenance and a lack of focus on continuous model improvement.
Evolving from proof of concept on small projects to scale is a complex undertaking, and it’s the last mile that proves most daunting.
Three key steps to unlocking the full potential of AI for oil and gas companies
- The leadership team must believe in AI — and anchor on the value it provides.
As with all corporate initiatives, executive buy-in and support is critical. The road to full AI integration will require significant resource investments. And there will be missteps, especially as teams work to understand what data adds quality to decisions and what is just noise.
Functional leaders and organizations who anchor on value as the decision mechanism and pragmatically assess whether the problem is worth the investment in AI, considering its high costs, will be more likely to succeed. Past industry digital transformation efforts demonstrate the critical need for a value feedback loop.
Oil and gas leaders obviously don’t need to be able to build AI tools themselves, but they must be able to confidently communicate how AI can help the organization. And they must share that vision frequently, while being vocal in their support of development and deployment efforts.
- Scaling AI means expanding people, processes, data, and technology.
“Scaling AI is not like cloud computing, where you can solve issues by adding more power,” said Matt Russell, manager of technology consulting at Ernst & Young. “If you want a more robust or insightful AI, you need more robust, insightful information feeding it, and that increases operational complexity as well. Everything must scale exponentially — people, processes and technology. Scaling your AI tools — and keeping them on track as your data inputs grow — requires a real organizational commitment.”
AI isn’t a one-time implementation; it requires constant monitoring of tools, data and systems to confirm that predictions are still accurate and AI models aren’t drifting over time. AI also offers endless opportunities and challenges to keep pace with innovation around the technology.
Scaling IT requires detailed planning and design, along with a commitment to deploy needed resources — both human and financial — as well as necessary and ever-evolving data to achieve scale. It also requires understanding and assurance across multiple functions in complex organizations.
- The last mile requires a culture change
The importance of culture change in scaling AI can’t be overstated.
There is often cultural resistance to using AI tools or trusting their output. People who have done their jobs a certain way for years are often hesitant to learn new technology, especially with something like AI that changes how work is done. Cultural resistance could be the biggest risk that oil and gas companies face in covering the last mile.
It’s not enough to just build AI tools; people must adopt them, incorporate them into daily activities and maintain them. A change management effort — one that focuses on communicating AI strategy and benefits and how work will be transformed — is vital.