AI opportunities accumulate quickly in a growing company. Every process looks like a candidate once the tools are easy to try. Evaluation is how you avoid building a museum of demos. The aim is not to reject AI. It is to spend attention on the few tasks where a model, a review step, and an existing system can produce a result a person already wants.
Five questions before a prototype
- ▸What is the job today, and who does it?
- ▸What does a good output look like, in a form another system could store?
- ▸What information is required, and what must stay out of scope?
- ▸What happens if the model is wrong?
- ▸Where does the accepted result go, and who is accountable for it?
If those questions have no answer, the organisation does not have an AI opportunity yet. It has an undefined process. Defining the process is the cheaper first step.
Prefer thin slices
A document type, a mailbox, a class of support question: these are better first projects than 'an assistant for the whole company'. Thin slices can be observed. They can also be stopped. Wide assistants tend to collect permissions, data, and expectations until nobody can say what good looks like.
Complexity should be earned
Custom models, large knowledge bases, and multi-agent designs are sometimes justified. They are rarely the first move. If a structured extraction plus a reviewer removes most of the grind, that is a complete system. Adding more machinery because it is available is how projects become difficult to operate.
CYPHOPS uses this evaluation as a filter. The work that proceeds is the work that can be designed, secured, and handed over as an operating workflow.
