In early April, we recapped the ML in Oil & Gas Conference in Houston – a hybrid event with 60+ people in the room and 350+ online, marking a rare chance to meet in person again and compare notes on where ML is creating value. The energy in the room was palpable and the discussion clustered around six recurring themes.
1) AI/ML is here – most companies are running projects, yet many have struggled to turn pilots into results. The focus is shifting from proofs of concept to repeatable delivery.
2) Data is king – without accessible, reliable data, there is no ML; pipelines and governance matter as much as algorithms.
3) Top-down support – executive understanding and sponsorship remain foundational for budgets, staffing, and runway.
4) Hybrid teams – the best models blend data science with domain experience to ask the right questions and interpret outcomes.
5) Explainability – if stakeholders can’t understand the model, sustained support is hard to earn.
6) Problem selection – pick use cases where the expected value clearly exceeds the cost of the project.
As our CEO Travis Nadelhoffer noted:
"We have masks and are seated 6 feet plus apart. But it feels so good to talk and meet with other humans face to face in a work environment."
That sentiment captured both the moment and the momentum around ML adoption in oil & gas.
These six themes form a practical checklist for teams planning their next ML steps: ensure data readiness, secure sponsorship, pair data scientists with domain experts, make models explainable, and choose problems with measurable impact. We’re excited to see how the community builds on this in the months ahead.
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