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The AI Briefing is a recurring AI deep-dive for decision-makers.
Each month, we select one high-signal industry report, circulate it in advance, & meet to cut through the noise.
This month's source: The Formula for Agentic AI Value: Applied AI Index 2026 (Boston Consulting Group - Sep 2026)
Secondary sources:
5 Emerging Trends in Enterprise Data & AI (The Modern Data Company - Aug 2026)
Agentic context layers (VentureBeat Intelligence - Oct 2026)
Most of the money behind enterprise AI no longer runs through IT.
BCG surveyed 1,330 CxOs & senior executives. AI spending has more than tripled, to 3.3% of revenue, & 2.8 points of it sit outside the enterprise IT budget. Business units generate about two-thirds of the value AI creates.
So who owns the data their agents run on?
BCG says the gap between leaders & everyone else has moved up the stack, to whether a company's data is current, consistently defined, trustworthy, & managed with clear ownership. It tells companies to keep their definitions, business rules & decision logic in a layer they control & could move to another vendor.
VentureBeat's August survey points the other way. 98% of companies running retrieval in production use at least one system someone else built, & 2% name building & owning their context layer as their likely direction. 64% have already caught an AI agent giving a confidently wrong answer because of their own company's data.
The Mission: Deciding who owns the definitions, rules & context your AI runs on
This series has spent the year on readiness: how fast enterprises adopted, where leadership's confidence outran results, how governance fell behind agents, & whether the workforce was ready. October's discussion turned to a question none of those sessions answered: who owns the data layer?
We'll test BCG's advice against what data teams report from the inside. 61% call a reliable context layer critical for AI agents, & 16% engineer one on purpose.
The Format:
The numbers: BCG's spending & ownership data, & where a self-reported executive survey stops being evidence.
Cross-pollinated debate: Who owns it in your org? IT, the data team, the business unit paying for the agent, or the model vendor whose stack holds the context.
The semantic layer problem: In VentureBeat's survey, companies running or building a semantic layer were about twice as likely to report a confidently wrong agent answer (78% vs. 37%). We'll work through why, & what it means for anyone about to fund one.
Who Should Attend:
Enterprise Executives: See where AI budgets & accountability sit at the 7.5% of companies BCG calls future-built (all of them have clear ownership of the AI agenda), & decide whose name goes on the data layer before a vendor's does.
Founders & Builders: Hear what buyers mean when they ask about lock-in. 37% of VentureBeat's respondents plan to keep standalone tools alongside the model, & 22% expect to consolidate onto one model provider's stack.
Product, Data, & Operations Leads: Compare notes on the work underneath. 46% of respondents say their teams spend more than a quarter of their time maintaining & integrating tools, & 8.4% say the data feeding their AI is trustworthy enough for production.
The Prep: This is a working session, not a lecture. Read the report here. Bring one wrong answer your AI gave & where the bad data came from.
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