AI Readiness Assessment

Yes — take the two-minute check. Five questions on your data, your processes and the work your team repeats by hand, and you get an honest read at the end of it plus the sensible next move. It is free, there is nothing to book, and plenty of people stop there. The full assessment is for when you want the detail behind that answer before you spend on a build.

It is a short, fixed-scope engagement — a matter of weeks, not months, priced up front. You get a clear recommendation you can act on at the end of it, not an open-ended consulting retainer. We confirm the exact scope and cost before we start.

No. The assessment is honest input, not a sales funnel. The recommendation is yours to act on however you like — with us, with your own team, or not at all if the honest answer is 'not yet'. We would rather tell you the truth than sell you a build that stalls.

That is a valid and common outcome, and it is worth knowing before you spend on a build. Where there is a readiness gap, the recommendation lays out exactly what it takes to close it — usually getting your data connected and your processes defined, which an Apex engagement can put in place.

You do not need clean data to start, only access to the data you already have. We look at the systems your team works in every day, such as your accounting package, ERP, CRM or job system, plus the spreadsheets and inboxes that fill the gaps between them. Messy data is normal and is part of what the assessment measures. What helps is someone who can show us where each number comes from and who keys it in. If most of the information lives in people's heads, that is a finding too, and it tells you where to start before any AI work.

Fixing 'not ready' usually means connecting two or three systems and writing down a few rules, not a big programme. The common gaps are data split across systems that do not talk to each other, a spreadsheet doing a job a system should do, and a process each person runs their own way. Closing them might mean an integration between your ERP and CRM, retiring a shared spreadsheet, or a short session with your operations lead to agree how a job should be done. The recommendation sizes each gap so you can see the effort before you commit, and you can close them with your own team.

After the assessment you decide what to do with the recommendation, and there are usually three paths. If a use case is ready, you can build it, either with your own team or with us as a fixed-scope pilot. If there are gaps, you can close them first and come back to AI once the data is joined up. If the honest answer is that AI does not pay yet, you keep the report and spend the money elsewhere. Either way, you walk out knowing which job to start with, what it needs and what to measure once it is running.

A strategy deck tells you AI is important. This tells you, specifically, which jobs in your business AI can take on, which it cannot yet, and what stands in the way. It is grounded in the actual state of your data and systems, not in generic trends.

Whoever owns the operations and the data — usually an operations lead and someone who understands the systems. We keep the time demand light: a couple of working sessions and access to see how information actually flows, not a committee.

Yes. The report, the roadmap, and everything in it are yours to keep and to act on, with us or with anyone else. There is no obligation to build with CodeFish afterwards, and the assessment is written so another team could pick it up and act on it.