Most ‘AI readiness’ work is information reachability work. The phrase ‘AI readiness’ makes it sound as though the company is getting ready to receive a model. That’s usually the easy part. Models are already available at retail prices. What most companies still can’t do is answer basic questions about themselves, even to themselves.
Their books are in one system. Customer history is somewhere else. Operating metrics live in a third place. Vendor history is scattered across inboxes and attachments nobody can search. Reconciling the whole picture depends on a small number of people running spreadsheets on their laptops, and the spreadsheets disappear from the workflow the moment those people take leave. A new model doesn’t fix that. The model can’t reach the information, and neither can the company unless the right people are online that week.
What gets funded as ‘AI readiness’ is often a tools project. Teams spend months choosing infrastructure for retrieval and storage while the underlying information stays fragmented and hard to reach. Six months later the tools are in place and the original block is still there. The company didn’t have a model problem. It had an information access problem it had been living with for years.
AI is finally making that problem expensive enough to face directly. That’s the useful part of the moment. The company is being forced to pay the cost of making its own information reachable. The payoff is much bigger than AI alone: faster close, better forecasts, cleaner reporting, less key person risk. A useful AI layer sits on top of that work. It’s the bonus for finally doing the operational cleanup the business should’ve done anyway, rather than the main prize.