Finding AI Initiative Candidates
Start with work that already has an owner and a recognizable outcome. Capture candidate work in Tasks, but reserve Workflows for a process whose repeated steps and controls are already understood. Avoid beginning with a model, agent type, or integration and then searching for a problem that justifies it. For user-level examples, compare Choose Tasks AI Can Do Well.
Look for Repeatable Friction
Good candidate areas often include:
- repeated reading, comparison, extraction, or classification,
- fragmented knowledge that slows a decision,
- recurring drafts that follow a known structure,
- high-volume requests that require the same first-pass analysis,
- workflows where people repeatedly copy context between systems,
- review queues where evidence can be prepared before human approval.
Write a Candidate Brief
Use one page and record:
| Field | What to capture |
|---|---|
| Problem | Observable delay, error, cost, or risk |
| Users | People who perform or review the work |
| Outcome | Decision or deliverable the work must support |
| Inputs | Required documents, records, events, and systems |
| Output | Expected structure and where it goes next |
| Actions | Read and write operations the system may perform |
| Controls | Access, approvals, verification, and retention requirements |
| Owner | Business owner and delivery owner |
| Evidence | Baseline and pilot success measures |
Check the Fit
A strong candidate is language-heavy, has approved inputs, produces a reviewable output, and can begin with a limited pilot. A weak candidate has an undefined owner, depends on unavailable data, requires perfect autonomous judgment, or creates irreversible impact without a human checkpoint.
Narrow the First Version
Prefer one department, one approved Data collection, one output format, and one decision point. Remove external write actions until the read-only result is useful. Add tools and automation only after the team can explain how failures will be detected and handled.
The output of this exercise is not approval to build. It is a candidate ready for Prioritizing AI Initiatives and a feasibility review.