• 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 and senior executives. AI spending has more than tripled, to 3.3% of revenue, and 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 and everyone else has moved up the stack, to whether a company's data is current, consistently defined, trustworthy, and managed with clear ownership. It tells companies to keep their definitions, business rules and decision logic in a layer they control and 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, and 2% name building and 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 and 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, and 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, and 16% engineer one on purpose.

📊 The Format:

• The numbers: BCG's spending and ownership data, and 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, and what it means for anyone about to fund one.

🤝 Who Should Attend:

• Enterprise Executives: See where AI budgets and accountability sit at the 7.5% of companies BCG calls future-built (all of them have clear ownership of the AI agenda), and 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, and 22% expect to consolidate onto one model provider's stack.
• Product, Data, and Operations Leads: Compare notes on the work underneath. 46% of respondents say their teams spend more than a quarter of their time maintaining and integrating tools, and 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 and where the bad data came from.