Trusted Data, Sovereign AI: Making Energy’s AI Outputs Defensible
Round table hosted by Context Labs & BP Energy Partners
AI adoption in energy has outpaced data readiness. Gartner predicts that through 2026, organizations will abandon 60 percent of AI projects because their data is not ready for AI. The model is rarely the only problem. Data is scattered across systems, often without the context or pedigree needed to verify its source and value.
Companies that use AI outputs to support claims to regulators, counterparties, or risk and investment partners need a traceable record behind every submission, not just each figure. This roundtable looks at how energy companies can ground AI in verified data they control and move from compliant to competitive disclosure.
Speaker: Dan Harple, Chief Executive Officer at Context Labs
Moderator: Claire Curry, Head of Technology, Industry & Innovation at BloombergNEF
This is an invite-only session held under the Chatham House Rule. To request an invitation, contact Max Landy at BloombergNEF.
About Context Labs
Powering data for sovereign AI in energy markets
Energy companies need more options across production, transportation, and supply chains. Those options bring more complexity, and more data to understand. Context Labs AI Distillery helps organizations bring their own data together, give it usable context, and prepare it for sovereign AI. The goal is better decisions about risk and resilience, without giving up control of proprietary data.
Get your data ready for sovereign AI
Energy operations data sits across historians, ERPs, maintenance systems and emissions inventories — each with its own gaps. Context Labs AI Distillery resolves those inconsistencies and attaches the context a model needs: which asset, which conditions, which source. Every answer has a traceable basis in your own records.
Make your business understandable to AI
The same compressor might be “C-14” to a field crew, “Southfield Unit 14” in a maintenance log, and asset 40017 in the ERP. Context Labs AI Distillery records your naming conventions and relationships as you use them, then translates to industry-standard terms where regulators or partners need them. Your vocabulary, your operations — legible to the outside world.
Manage complexity without surrendering control
Adding suppliers, routes and partners multiplies the data you have to reconcile — much of it too sensitive for an external model provider. Context Labs AI Distillery keeps it inside your own environment, with governance, classification and access rights applied. Analyze a more complex supply chain with AI, while controlling who sees what.
Partnerships & Customers
