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Blog July 7, 2026

What I Heard at Reuters Data-Driven Oil & Gas (DDOG) in Houston: AI ambition versus data reality

Whitney Olmsted
Author
Whitney Olmsted

I spent time this week with operators, technologists, and data leaders at Reuters Data-Driven Oil & Gas (DDOG) in Houston. Fitting, given the name, because nearly every conversation came back to data as the dividing line between companies pulling ahead and those falling behind. A few themes stuck with me, and they line up with something I've believed for a long time: technology only matters when it solves a real businessproblem.

I had the privilege of speaking on the panel "Data Dilemma: Harnessing AI to Bridge Legacy and Data Gaps." It's a challenge every operator in the room recognized: decades of legacy systems, hard-won domain knowledge, and data spread across formats and eras that were never designed to talk to one another. The through-line of our conversation was clear: AI is most valuable not when it replaces that legacy foundation, but when it helps bridge it, connecting old and new, filling gaps, and making institutional knowledge usable again. And that only works if the data underneath is trustworthy. Which brings me to the themes that ran through the whole event.

Image 26
Image 26

I had the privilege of speaking on the panel "Data Dilemma: Harnessing AI to Bridge Legacy and Data Gaps." It's a challenge every operator in the room recognized: decades of legacy systems, hard-won domain knowledge, and data spread across formats and eras that were never designed to talk to one another. The through-line of our conversation was clear: AI is most valuable not when it replaces that legacy foundation, but when it helps bridge it, connecting old and new, filling gaps, and making institutional knowledge usable again. And that only works if the data underneath is trustworthy. Which brings me to the themes that ran through the whole event.

AI has crossed from interesting to valuable.

The conversation in energy has moved past "what could AI do someday?" to "what is it doing for us right now?" The value is real, and it's compounding. As the models get better, they're earning their way into higher-stakes decisions, the kind of work where being right matters, where safety, capital, and uptime are on the line. That's a meaningful shift for an industry that has rightly been cautious about where it lets new technology operate.

The business can simply do more.

Between better models and a maturing ecosystem of tools, teams can take on work that wasn't practical a year or two ago. The ceiling on what a team can accomplish has moved up, not because anyone added headcount, but because the tools are starting to match the ambition. What was once shadow IT is coming out of the shadows and into the mainstream. Business users are building and doing more on their own, and the task now is helping companies put the right guardrails in place so that newfound capability stays safe, governed, and aligned to the business.

 

But the gap between leaders and laggards is widening, and data governance is the fault line.

This is the tension I kept coming back to. As AI gets more powerful, the payoff for organizations with clean, well-governed, trustworthy data grows. So does the penalty for those without it. Poor data governance doesn't just slow you down; it limits how far you can safely push AI into high-stakes work. The better the technology gets, the larger that chasm becomes. Two companies can buy the same tools and end up in very different places, and the difference is rarely the model. It's the data foundation underneath it.

"From Data Chaos to Clarity" reframed what that foundation really is. One of the sharpest exchanges of the event came from the "From Data Chaos to Clarity" panel with Brian Jones, Kelli Law, and Harish Chhokra, moderated by Tina Johnson. The insight I haven't stopped thinking about: data chaos is less about volume and more about lost context. In most large organizations, the structured data already exists. What's missing is the surrounding meaning: the assumptions, decisions, and rationale that live in meetings, emails, and informal exchanges but rarely get captured or connected. The differentiator isn't the toolset; it's whether teams can turn data into shared understanding, responsible action, and durable workflows. When taxonomy, domain knowledge, and operational context come together, systems, including AI, can operate much closer to how teams actually reason and decide. Put simply: data without context scales ambiguity. Context, explicitly modeled and operationalized, is what makes it strategic. That is the governance conversation maturing in real time: from "is the data accessible?" to "is it interpretable, trusted, and usable?"

The Takeaway

For energy and resources leaders, the winning move isn't chasing the newest model. It's getting the fundamentals right so you can actually use it: governed data, clear ownership, and a disciplined focus on the business problems worth solving. The tools are ready to do more. The question is whether your foundation lets them.  The conversation consistently pivoted back to the growing gap between AI ambition and data reality.  Everyone seems to be aligned that AI can create value, but companies have to address readiness concerns to unlock that value.  The industry understands where it wants to go with AI, but many are still doing the foundational work to get there.

 

Grateful for the conversations in Houston. If you're wrestling with how to turn AI potential into operational reality in this sector, I'd love to compare notes.

Whitney Olmsted

Energy, Resources, & Manufacturing Portfolio Lead

Whitney leads CapTech’s Energy, Resources, & Manufacturing sector, collaborating closely with clients to identify industry-specific challenges and drive the development of technology solutions that yield substantial returns on investment. With over three decades of expertise in technology and strategy, Whitney leads her teams in implementing innovative solutions that deliver tangible, real-world value.

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