Edge computing. AI. Simulation. Big data.
These tools are widely available. But which ones are energy companies actually using?
This was a question posed at the FutureFlow 2025 panel, where energy leaders shared what’s working and where gaps remain. Hosted by CMG, the session explored the reality of technology adoption across energy companies, including how organizations are investing, what they’re seeing, and what it takes to make progress.
According to EY’s 2025 Future of Energy survey, nearly half of executives in the resource sector plan to invest significantly in digital technologies. This marks a 20-point increase since 2020. But many still report underwhelming results. Only 27 percent say that tools like cloud computing, AI, or machine learning are meeting expectations.
That gap highlights deeper issues like poor integration, unclear use cases, and limited workforce readiness. It also shows where the real opportunity is. The companies making progress are matching the right tools with the right people and applying them in the right ways.
At FutureFlow, panelists provided a practical view from the ground. They spoke to the tools that are working, the conditions required for implementation, and the leadership behaviours that support real outcomes.
Watch the full panel discussion on what energy leaders are doing differently now
Energy leaders invest in data first, then advanced tech
Oil and gas producers, Gran Tierra Energy Inc. and Strathcona Resources Ltd., are both still in the early stages of their digital transformation journeys. They’re also both taking a highly deliberate approach to tech adoption.
Gran Tierra’s COO, Sebastien Morin, said the company is focused on building a strong technology foundation before layering in more advanced tools.
“We’re at the start of the journey … right now the company is working on a data hub, and making sure that the data is scrubbed and simple.”
A single, clear tech goal helps. Morin said it makes it easier for staff who are less comfortable with digital change to engage.
“Saying you’re going from spreadsheets to AI-driven summaries of large databases, it can be a little bit overwhelming,” he said.
Kim Chiu, President of SCR Cold Lake at Strathcona Resources, said a similar principle is being applied within his organization.
Chiu said they also started with a focus on getting the data in the right format, “in a viewpoint that we can make decisions upon.”
Now that his team is confident that reservoir data is housed in the right format and is accurate, they can progress to the next step of transformation, explained Chiu.
“We’ve formed a new team … it’s an operational instrumentation and data team, and they’re looking at automation and … AI utilization.”
McDaniel, an organization that provides evaluation services to oil and gas companies, is further along on their digital transformation journey, said President and CEO Brian Hamm.
The company started centralizing data and developing evaluation dashboards more than a decade ago, he said. Since then, they’ve advanced the tool to handle more automation and even have decision-making capabilities.
“We’re at the point now where we’re actually using the tool internally to start to do some of the functions that our reservoir engineers would otherwise do,” said Hamm. “We are doing three, four or five times as much work per person now as we were 20 years ago.”
Watch the full panel discussion on what energy leaders are doing differently now
To successfully implement new tech, you need the right talent and industry-specific models
Implementing tech that delivers results takes more than investment and time. It requires the right people to make it work, explained Pramod Jain, CEO of Computer Modelling Group (CMG).
For Jain, finding talent outside of oil and gas has been important.
“You have to have the right talent who is also coming from outside the industry, and giving a perspective to say ‘we have solved these problems through AI and machine learning, and then this is how we can apply for oil and gas’,” he said.
While outside talent can help bring new ideas, you also can’t simply apply machine learning models from one industry to another.
“There’s going to be a commonality of different asset types, but there’s also going to be differences,” Jain said.
As a company that focuses on the subsurface, CMG solutions include both physics-based models and simulation, as well as AI.
“Physics keeps it real,” Jain said. “Physics keeps it accurate. AI makes it efficient. AI gives us productivity. And if you can marry the two together, I think we can add much more value internally and also to our customers.”
Energy companies face real pressure to adopt new tools. The ones making progress are moving with focus and in manageable steps.
Leaders are focusing on simplicity, the right adoption structure and talent investment to make sure new technologies actually stick.
Watch the full FutureFlow 2025 panel to see how energy teams are applying new tools in practice. To continue reading, explore how energy teams are working differently, how leadership approaches are shifting, and what executives think is coming next for the industry.
