This week the enterprise AI market published two opposite fixes for the same problem, twelve days apart, in the same outlet. One said buy a governed context layer. The other said fix the data engineering. Three vendors shipped context machinery while that argument ran. Meanwhile the disagreement is already documented in software you pay for: HubSpot publishes a page explaining why its numbers will never match Google Analytics, and Baremetrics lists eight reasons your MRR reads differently in Stripe. Same data, different answers, by design. Brought to you by Entrecore.

For two years the whole industry chased bigger models. This year the biggest names all landed on the same sentence: the models are fine, what is missing is context. Then they spent the year fighting over who owns the fix and renaming it every few months. Meanwhile the people actually running businesses are still losing the first ten minutes of every meeting to an argument over whose number is right. Episode 1 of Ground Truth walks through the year, the agent reality check, and the gap between how vendors talk and how operators actually hurt. In this episode: - How "AI has a context problem" went from contrarian take to industry consensus in one year - Why the fix kept getting renamed, and what an honest version of the fancy term actually is - The agent reality check: 88 to 95 percent of pilots stalled, and only 5 percent of enterprises trust the agents they already run - The language gap: "semantic layer" versus "three different answers to the same question" - Why the same pain shows up in hotels, plants, and factories that sell no software at all Brought to you by Entrecore.