index - why us? why now?
- index
- Feb 11
- 2 min read
Most organisations don’t have an AI problem. They have a knowledge truth problem.
GenAI is massively increasing the volume of content businesses produce: policies, procedures, work instructions, customer guidance, internal comms, “quick drafts” that quietly become “official.” That’s the part everyone celebrates. The part they miss is what happens next: the more content you create, the harder it becomes to know what’s correct, current, approved, and safe.
That gap creates a specific kind of enterprise mess: duplicates that look different but mean the same thing, near-identical procedures with one critical step changed, outdated guidance that still ranks highly in search, “local variants” that are valid in one context and dangerous in another, and whole areas of knowledge with no clear owner or review cadence. Humans can often compensate with judgement and experience. AI can’t. It retrieves what it can see and produces confident output, even when the underlying knowledge is conflicting.
And as copilots evolve into agents that take actions across systems, this gets more serious. The risk isn’t just a hallucinated answer. It’s an incorrect workflow triggered, a policy breached, a customer misinformed, or an operational decision made from the wrong version of the truth. In other words: your knowledge estate becomes part of your control environment, whether you planned for it or not.
That’s why the next wave of enterprise AI isn’t just about better models or better prompts. It’s about knowledge health: being able to measure where the knowledge is broken, prioritise what matters, fix it through governed workflows, and keep it from drifting again.
The winners won’t be the organisations with the most AI features. They’ll be the organisations that can prove trust, continuously.
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