Sample AI Workflow Audit
A sample audit showing how ADA reviews a workflow before recommending AI, automation, or a simpler process fix.
Direct Answer
A useful AI workflow audit does not start with tools. It starts by naming the workflow, the bottleneck, the owner, the source evidence, the review point, the risk boundary, and the metric that should improve.
How to use this
- Step 1: Pick one repeated workflow, not a department-wide transformation.
- Step 2: Write the current trigger in plain language: what starts the work today?
- Step 3: List the evidence a competent employee already checks before acting.
- Step 4: Separate what AI can prepare from what a person must approve.
- Step 5: Name the stop rule before choosing software.
- Step 6: Choose one baseline metric that already matters to the business.
Sample audit fields
- 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.
- Human review point: Owner reviews high-value accounts, unclear requests, pricing questions, and customer-visible replies.
- Stop rule: Pause when consent, identity, service fit, or ownership is unclear.
- Metric: Time to first qualified owner response and wrong-route corrections.
Worksheet prompts
- Workflow trigger: What event starts the workflow, and how often does it happen?
- Evidence inventory: Which forms, calls, CRM fields, emails, policies, examples, or files does the workflow need?
- Owner review: Who can tell whether the output is correct, useful, and safe enough to use?
- Risk boundary: What should AI never send, promise, change, approve, delete, merge, or decide?
- Exception path: When evidence is missing or contradictory, where should the workflow stop?
- Measurement: What should improve: response time, rework, missed follow-up, cycle time, owner time, or error rate?
What most companies skip
Most audits stop at whether a task can be automated. That misses the part that causes failed deployments: who owns the result, what evidence is trusted, 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
- 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
Research basis
- NIST AI RMF: Supports a structured risk-management view before AI is used in an organization.
- NIST AI RMF Playbook: Frames AI work through Govern, Map, Measure, and Manage activities.
- NIST Generative AI Profile: Adds generative-AI-specific risk context for mapping, measuring, and managing workflow use cases.
Related Resources
FAQ
- What is an AI workflow audit?: It is a review of whether a repeated business workflow is ready for AI assistance, including inputs, owner, review point, risk, and measurement.
- 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.