Free AI workflow risk tool

Find how an AI workflow can fail before a customer does.

Describe one workflow, the data it touches, what the AI can do, and who is affected. Get a practical failure-mode map with controls, human checkpoints, and stop conditions you can use before a pilot goes live.

No signupRuns in your browserDecision-ready output

Workflow context

Define the operating surface.

Workflow

Primary ownerHighest action

Data used

Who is affected?Current review model

Conditions

Workflow may touch sensitive, private, or restricted information.Workflow output may reach someone outside the company.Workflow depends on another system, API, CRM, inbox, or database.Inputs are often incomplete, inconsistent, or ambiguous.

Failure-mode map

Control the predictable failures.

Moderate control burden

Use this before launch

Every failure needs a detection path and a recovery path.

The useful question is not whether an AI workflow is “accurate.” It is whether the business can detect a bad outcome, stop propagation, recover cleanly, and identify who owns the decision when the normal path breaks.

01Detect.How will anyone know the failure occurred?

02Contain.What prevents one bad output from creating downstream damage?

03Recover.What manual or automated fallback restores the workflow?

04Learn.How does the failure become a new test case or control?

Next step

Turn the controls into a bounded pilot.

Once the failure modes are explicit, use the pilot brief builder to define the trigger, owner, systems, human review, stop conditions, and success criteria.

Build the pilot brief →Assess the team →