
This episode examines why operators, service companies, investment organizations, and consultants keep hitting that wall, and why the answer usually isn’t a bespoke application. Building custom software means signing up for data plumbing, security reviews, ongoing maintenance, and the risk that institutional knowledge walks out the door when one key developer leaves. For most organizations, that’s a poor trade. The harder problems, including governed data access, analytics that scale from a single wellbore to an entire basin, and workflows practitioners actually trust, have already been solved in platforms teams use today.
The conversation covers what it takes to move AI from prototype to production in subsurface workflows: where GenAI genuinely helps decision-making right now, where it’s overhyped, and how a platform-based approach serves organizations with very different needs. A multi-basin operator screening thousands of wells, a PE-backed team evaluating acreage without a data science bench, and a small consultancy delivering client work all face the same underlying question of whether to build from scratch or launch from something proven. This episode makes the practical case for the latter, with concrete examples of AI-assisted workflows spanning interpretation, field development, and production analytics, and a realistic view of what getting started actually looks like.





















