Scaling AI for maximum impact in oil and gas

Moving from use cases to enterprise-wide AI is more than a technology challenge. It requires anchoring on value, feedback and innovation.

In brief

• Three key steps can help organizations scale their enterprise-wide AI efforts.
• Successful adoption of AI in oil and gas requires leader buy-in, culture change and constant feedback.
• Strategic collaborations, especially given the pace of AI innovation, can lend crucial support to oil and gas companies.

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.

2. 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, Technology Consulting, Ernst & Young LLP. “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.

For instance, though IT functions within international oil companies are maturing with regard to AI operations and capabilities, they will need to run agile testing, deployment and operating processes to scale. Meanwhile, other business units need AI stewards who understand how to extract value from AI outputs after scaling.

Similarly, many functions have responsibilities for the data fed to AI. Both IT and business groups, whether in direct revenue-generating functions, such as drilling and operations, or in supporting functions, such as procurement, need to constantly consider and update the quality of data that is fed to AI models. They also need appropriate methods to maintain that data feed.

In a recent EY survey, oil and gas executives reported the two highest opportunities for creating value from AI and emerging technology are: 1) predictive maintenance for heavy equipment and assets and 2) intelligent optimization of operations performance.

These functions will also need to work together to smartly utilize available resources — human, financial and data — to capture enterprise-wide impact. For example, does it make sense to use vendors for some AI projects but organically grow another function in-house?

Proper scaling of AI means developing a thoughtful strategy on the elements that will be vendor-led vs. organization-led. That means asking important — and sometimes difficult — questions around resourcing, intellectual property, and organizational skills and capabilities. Making the decision around what to build vs. what to buy will be more important than ever when it comes to the innovation around AI technology.

Ultimately, you may find that relying on outside resources to supplement internal staff can speed up your AI efforts, because you’ll have more total resources to apply to development and implementation.

3. 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.

In fact, employee education underpins cultural shifts. When employees understand what AI can do and how it can be applied to existing problems or opportunities, acceptance comes more quickly.

“You can’t keep AI strategy and development activities under wraps and then expect employees to embrace these tools when they come online,” said Himanshu Dabral, Senior Manager, Business Consulting, Ernst & Young LLP. “It’s important for companies to view their AI efforts as something greater than an IT project, because at some point they will need employees across the enterprise to adopt and adapt.”

Finally, successful AI implementation needs employee input to close the feedback loop. Did AI give correct answers? Did it save time or reduce busy work? Is there data that is missing or incomplete? AI calibrates itself and gets better over time, but it requires feedback — and constant data maintenance — to do so.

How to get started on scaling AI now

EY teams start by helping clients visualize what AI can do and the value it can deliver. From there, you can grow your understanding of AI’s potential in energy and the pathway to achieving maximum benefit.

It’s important to work across the business and the ecosystem to manage AI from end to end. What do you need for data? What do you need for technology? What resources do you have in-house, and where do you need outside support? We help clients understand what they have, what they’ll need and how to make it all come together.

In addition to our own experienced AI professionals with deep oil and gas knowledge, we also offer strategic alliance partnerships and collaboration with third-party vendors that enable us to build out complete fit-for-purpose systems that can be scaled rapidly.

Finally, our multifunctional team guides clients in achieving the three keys above, unlocking new operating models that deliver significant value.

Don’t wait — the hype is real. Contact us today.