🧠 Food for thought

Implications, context, and why it matters.

SLB’s agentic AI (autonomous, task-oriented AI agents) lacks public proof of ROI or adoption

• SLB markets Tela as cutting decision times “from weeks to seconds” through a conversational interface. It automates log prediction (predicting downhole well-log properties) and equipment optimization. SLB has not named upstream users or shared pilots or published outcomes like lower non-productive time or production uplift 1.
• The seismic foundational model (a base AI model trained on subsurface seismic data) aims to surface hidden geophysical findings with speed and precision. Without benchmarks such as exploration hit rates or cost savings versus traditional methods, the claims stay hard to verify 2.
• SLB says Tela’s rig activity scheduler (a planning tool that sequences rig operations and moves) delivers a “20% reduction in rig moves.” Deployment across varied drilling settings is unproven, so real-world performance is unclear 3.

Partner ecosystem opens doors for Independent Software Vendors (ISVs) and system integrators to build Tela extensions

• Tela’s agentic framework (an architecture for AI agents that act on tasks with limited human input) lets teams build and manage their own agents and plug in partner tools. That creates room for Independent Software Vendors (ISVs) to ship workflow-specific agents, data connectors, or governance add-ons on SLB’s Lumi data and AI platform 4.
• The Digital Platform Partner Program gives ISVs testing paths and commercial workflows to launch on the platform. Once live, apps appear instantly for customers, which avoids delays tied to vendor approval cycles 5.
• System integrators (SIs) can use Tela’s flexible deployment in cloud or on-premises in a customer’s own environment to offer migration work, custom agent orchestration (coordinating multiple agents), or integration layers that connect Tela to existing energy management systems and non-SLB workflows 46.