Prioritise AI Work by Evidence and Executability

Cybergizer implementation notes · Practical guide

Describe candidate work precisely

Replace labels such as sales AI or finance automation with a concrete input, task and accepted output. Name the process owner and affected users. Capture frequency, current effort and the cost of errors where reliable evidence exists. A clear description allows the team to compare opportunities without mistaking the breadth of an idea for its potential value.

Test feasibility alongside value

Check source access, integration routes, review capacity and required permissions. Consider whether the workflow can be piloted independently or depends on a larger system change. Use simple confidence labels for estimates and record their sources. A promising benefit should not erase a critical delivery dependency, and a convenient technical demo should not outrank a real business need.

Sequence learning deliberately

Select one bounded project whose results will resolve important uncertainty. Define its stop conditions and the decision it should support. Reserve capacity for data and adoption work, not just implementation. Revisit priorities after observing the first workflow in use. Prefer explicit tradeoffs and a small portfolio of executable decisions to a long ranked list that no owner can act on.