How to choose your first AI workflow
Compare workflow value, readiness and risk before committing to an AI deployment.
Find repeated work with an observable outcome.
Look for a process that happens often enough to study: document intake, report preparation or service triage. Describe the current effort and the result the next person needs. Prefer a workflow with a clear beginning and end over a broad ambition such as improving productivity.
Check access before estimating delivery.
Identify the source systems, owners, permissions and data quality. A promising idea may depend on an unavailable API, undocumented records or a procurement decision. These dependencies belong in the feasibility assessment, not in a footnote after a deployment date has been announced.
Compare value and consequences together.
Estimate the labor involved using your own assumptions. Also consider review effort, integration work, model usage and ongoing operations. Ask what happens when the system is wrong and whether a human can catch the error before it has an external effect. Economic potential alone is not a release criterion.
Choose one owner and one next decision.
An accountable process owner can supply examples, settle exceptions and accept the release. Agree a first milestone that tests the important uncertainty. If the evidence is weak, the useful output may be a better process or a smaller integration rather than an AI deployment.
Put the next decision to work.
ANY Scan turns this review into a paid assessment and a prioritized implementation roadmap.
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