AI readiness and applied AI
The pressure to “do something with AI” is real, and most of what it produces is noise: pilots that never leave the demo, tools bought before anyone defined the problem, and assistants answering questions from data nobody governs. None of that fails because the model is weak. It fails because the ground under it is.
Applied badly, AI accelerates disorder: it automates a bad process faster than anyone can catch the errors. The work here is to find where process, data, access, and ownership are already clear enough, apply AI there first, and strengthen the ground everywhere else before betting on it.
We assess readiness where it actually lives: the process, the data, who has access, and who would own the outcome. Case by case, not company-wide slogans.
We choose the cases where AI pays for itself first, define what it may and may not touch, and design the workflow around the people who stay responsible.
We build prototypes the team can try on real work, and turn the ones that earn their place into supported workflows and automation.
Models, tools, and rules change fast. We review what the AI does in practice, adjust it as the business changes, and retire whatever stops earning its place.
Businesses that want AI applied where it will hold, and an honest answer about where the foundations need work first.
Adopting AI to have something to announce. Automating a process nobody understands. We do not sell AI tools or take vendor commissions, so if the honest answer is “not yet”, that is the answer you get.