Observe the real work
Interview the owner and the employees who run the process. Follow real inputs, handoffs, exceptions, and workarounds—not the idealized SOP.
A dependable AI workflow starts with ownership, boundaries, exceptions, and evidence. Technology is selected only after we understand how the result should be produced and controlled.
Interview the owner and the employees who run the process. Follow real inputs, handoffs, exceptions, and workarounds—not the idealized SOP.
Define what AI may interpret or execute, what remains deterministic, and where a person must review or decide.
Test one workflow with realistic examples, explicit success criteria, and production-shaped controls before expanding scope.
Launch in stages, train employees on the new responsibility split, and make it easy to pause or override the workflow.
Monitor real outcomes, study exceptions, update business rules, and adapt when systems, formats, or teams change.
It is the deliberate placement of judgment where it creates the most value and controls the most risk.
Payments, deletions, binding commitments, and consequential changes stay behind explicit control.
Thresholds can require review based on customer value, order size, risk, or policy.
Uncertain extraction or interpretation moves to a person with the evidence highlighted.
The team can stop the workflow and continue manually when inputs or systems behave unexpectedly.
A practical first step
Bring us a workflow that is repetitive, cross-system, exception-heavy, or simply too dependent on one employee. We will help you decide whether AI belongs in it.