In a recent conversation with Digitalisation World, I challenged one of the most common assumptions in enterprise AI right now. That agents are plug-and-play. When businesses rush to deploy AI agents without first aligning the definitions, decision logic, and context that sit underneath them, results in agents fighting against the very thing they’re supposed to be addressing by masquerading as efficiency boons. Unfortunately, they only accelerate inconsistency. Every system that was already pulling in a different direction now pulls faster.
In this interview, I unpack what that misalignment looks like in practice, why it's so widespread, and what it takes to fix it. Building the shared layer of meaning that makes agents trustworthy in the first place, as opposed to a better agent.
That's the work we do at Valliance. Helping organisations structure their data, define what things mean across the business, and create the conditions where AI can operate reliably at scale.
Watch the full conversation below.
