Decision 1 of 6
One workflow idea
Sample AI Workflow Audit
Pressure-test one workflow idea before anyone buys tools, writes prompts, or builds the wrong automation.
Step 1
Answer guided questions
Step 2
Get verdict
Step 3
Use result in strategy session
Decision Tool
Turn a workflow idea into a yes, no, or fix-first call.
3-5 minutes6 decisions
Workflow
Sample audit fields
Example output from the ADA method.
Workflow
Website contact form routing
Visible bottleneck
New inquiries arrive without urgency, source context, or clear owner assignment.
Source evidence
Form fields, page source, prior customer status, service category, territory, availability, and duplicate history.
AI can prepare
Inquiry summary, urgency flag, missing-info list, duplicate check, and owner recommendation.
Where a person owns it
Owner reviews high-value accounts, unclear requests, pricing questions, and customer-visible replies.
When the workflow pauses
When consent, identity, service fit, or ownership is unclear.
Metric
Time to first qualified owner response and wrong-route corrections.
What most companies skip
Most audits stop at whether a task can be automated. That misses what actually decides payback: which revenue, margin, speed, or capacity number AI is meant to move, whether the evidence to do it exists, who owns the result, and what happens when the output is wrong.
How ADA uses it
The audit separates preparation from action. AI can summarize, classify, draft, check, and route. A person still owns customer commitments, pricing, account ownership, and risky exceptions.
When the audit says not yet
If the evidence is missing, the owner is unclear, or the workflow has no measurable baseline, the right answer is usually process cleanup before automation.
Quality Bar
Use these checks before calling the workflow ready.
The audit names one workflow clearly enough that a team member could recognize it.
The required evidence exists in current systems or can be collected without a major platform project.
A named owner can review the output in minutes, not hours.
The first version avoids customer-visible commitments and system-of-record changes unless approved.
The success metric can be reviewed after real work runs through the process.
Where This Helps
Use it before build decisions get expensive.
Lead intake
Proposal review
Customer escalations
Onboarding handoffs
Weekly reporting
Research Basis
Built against practical AI risk and quality standards.
NIST AI RMFSupports a structured risk-management view before AI is used in an organization.NIST AI RMF PlaybookFrames AI work through Govern, Map, Measure, and Manage activities.NIST Generative AI ProfileAdds generative-AI-specific risk context for mapping, measuring, and managing workflow use cases.
Related Resources
Read this with the workflows and service pages it supports.
FAQ
What is an AI workflow audit?
It is a review of whether a repeated business workflow can actually be moved by AI: the revenue or capacity at stake, the evidence available, the owner, and the number that should improve.
Is this a technical audit?
Not first. It is an operating audit. Technical choices come after the workflow is clear enough to implement.
What does the audit produce?
A workflow readiness view, evidence gaps, review rules, stop rules, and the first measurable improvement target.
Next Step
Use this result on a real workflow.
Bring the bottleneck, current handoff, and the result you want to improve.