The right AI automation partner can explain the workflow in plain language, show how exceptions reach a person, and make ongoing ownership clear. Use the same questions with every provider so you can compare scope and risk on equal terms.
Ask for a workflow before a tool list
A strong discovery conversation begins with the work: what starts the process, what information is needed, who makes decisions, and what happens when something does not fit the expected path. Tools matter only after those details are understood.
- A current-state workflow map
- A proposed workflow with explicit limits
- A list of systems, access, and data dependencies
- A measurable review plan grounded in your baseline
Check safety and human control
Ask how the system uses approved business information, how it handles uncertainty, and who can pause or override it. For sensitive actions, ask what needs human review and how that review is recorded.
Request a demonstration with synthetic or anonymised data. Do not share customer data with a prospective provider until you have agreed the permitted use and handling.
Compare ownership and support
A proposal should state who owns workflow accounts and credentials, where logs and data are stored, how failures are reported, and what ongoing support includes. Confirm whether third-party usage, hosting, and messaging charges are separate.
A vendor lock-in question is also an exit question: can your team export the workflow, understand its configuration, and retain the documentation if the engagement ends?
Treat case studies and ROI claims carefully
Look for named, permissioned references and a clear description of what was measured. If a page is labelled an illustrative blueprint, treat it as a proposed design example—not evidence of achieved client results.
A calculator or projected uplift is only as reliable as its assumptions. Use your own operating data and agree a baseline before judging impact.
AIVOLVE’s working approach
AIVOLVE starts with an AI audit, scopes one or more workflows, and confirms integrations, access, human handover, and support before implementation. Its published solution stories are illustrative blueprints; no client outcome figures are presented as verified results.
Questions worth asking.
What should an AI automation proposal include?
It should describe the workflow, integrations and permissions, data handling, exceptions, human oversight, delivery scope, recurring costs, ownership, and support.
How can I compare agency results?
Ask for permissioned references and the method behind each reported measure. Treat illustrative examples and projected ROI as scenarios, not verified results.
Should I automate a whole department first?
Usually it is clearer to scope one bounded workflow first, review its behavior with the team, and then decide what to expand.