Practical resource
Practical AI Workflow Guide
A six-step guide for turning AI interest into a useful, reviewable, and measurable recurring workflow. The work starts with the job and the evidence, not with a platform purchase or an automation target.
Six workflow-design stages
1. Pick a real recurring job
Choose work that happens often enough to matter and is narrow enough to observe end to end.
- What is slow, repetitive, or duplicated today?
- What useful result should change if the workflow improves?
2. Map and baseline the current work
Identify the inputs, decisions, handoffs, system of record, and failure points before selecting a tool, and record the evidence the team can support today.
- Where does trustworthy source information come from?
- What can the team support about volume, cycle time, rework, quality, and exceptions?
3. Define evidence and review
Separate sourced facts, inference, and actions that still require a person to approve them.
- What must the workflow cite or preserve?
- Which errors or external actions require a human stop?
4. Pilot on bounded live work
Run the assisted workflow on a small real set and compare it with the current process.
- How will time, rework, unsupported output, and duplicate entry be measured?
- What condition should stop or narrow the pilot?
5. Train the owner and make responsibility explicit
Document who runs the workflow, reviews exceptions, maintains the standard, and decides whether it should expand.
- Where is completion recorded and verified?
- What does the owner need to practice before the workflow handles more volume?
6. Measure adoption and decide the next move
Review use, quality, exceptions, time or capacity signals, and the agreed business outcome before expanding.
- Is the workflow being used as intended, and is the result more useful?
- Should the team standardize, revise, integrate, expand, or stop?
External context
Risk-management starting points
- NIST AI Risk Management Framework
A voluntary framework for managing AI risks across design, development, use, and evaluation.
- NIST Generative AI Profile
Cross-sector guidance for identifying and managing risks specific to generative AI.
Have one workflow in mind?
Assess that workflow in a paid, fixed-price Sprint over 7 to 10 business days. Identify where AI fits and leave with a roadmap, with custom implementation and an ongoing AI Advisor retainer available as separately scoped next steps.