Pick a workflow, not a tool
Successful AI projects start from a specific, repetitive workflow — triaging emails, extracting fields from documents, routing leads — not from a desire to “use AI”. Describe the steps, the inputs and what a correct output looks like.
Check volume and cost of the manual process
Estimate how many times a month the task happens and how long it takes. Low-volume tasks rarely justify automation; high-volume tasks with a clear pattern often do.
Check your data
Can the inputs be accessed through an export, an API or an inbox? Are they consistent enough to learn from? Messy data is not a blocker, but it changes what is realistic.
Decide where a human stays in control
Anything that affects customers, payments, legal commitments or regulated data should have a review step. Design the fallback before the automation.
Agree how you will measure it
Compare a pilot with the manual process on accuracy, time per item and failure cases. If the pilot doesn’t beat the manual process, stop or redesign it.
Plan for maintenance
Policies, products and data change. Someone has to update knowledge sources and prompts and review logs. Budget for this from the start.
Written by the ARQENSIAL team. General guidance only — your situation may differ.
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