Custom AI software should start with a real workflow, not a generic feature list.
Map who enters data, who reviews it, what exceptions appear, and what outcome matters.
Custom Software
How to Scope Custom AI Software Around Real Workflows
How to scope custom AI software around workflows, reviewers, exceptions, and measurable outcomes.
Key takeaways
- Scope the workflow first
- Define review paths
- Measure operational outcomes
FAQ
Should we start with the hardest problem or the easiest?
Start with the most measurable, frequent bottleneck, which is usually neither the hardest nor the trivial. You want a target painful enough to justify adoption but narrow enough to define and ship quickly.
How detailed does the workflow map need to be?
Detailed enough to include the exceptions and workarounds, not just the ideal path. Those side channels are where most projects get surprised, so they belong in the scope from the start.
When do we decide which AI technology to use?
Last. Once the workflow, the bottleneck, and the human-review points are clear, the technology follows from the problem definition rather than driving it.
How long should the first phase be?
Short enough to test your core assumptions before committing further — typically a focused slice that ships real value with humans still reviewing output. The point is to learn from reality early, not to deliver everything at once.