Someone is building an AI assistant. Someone else is wondering whether they can upload a client document. Both work in the same organisation.
Thinking in seasons can help you decide what needs attention. In spring, explore small experiments. In summer, build repeatable practices with agreed tools, reliable information and clear responsibilities. In autumn, evaluate the results. In winter, retire weak experiments and fix the foundations.
This is a planning metaphor. Teams can experience different seasons at the same time, and revisit them as their needs change.
To assess maturity, look beyond individual enthusiasm. Can colleagues explain the process, name its owner and show how errors are caught? Does useful knowledge survive when the person who introduced the tool goes on leave?
Consider people, processes, data, oversight and measurement together. A sophisticated tool cannot compensate for unreliable inputs or unclear responsibilities.
Our existing three-page AI Maturity Workbook takes the assessment further, helping you review eight areas and choose your next practical step.