| name | aipom-workflow-opportunity-advisor |
| description | Identify which product-team decision or productive workflow should be redesigned with AI first, based on outcome value, friction, evidence, consequence, and readiness. |
| type | interactive |
| category | product-team-workflows |
| phase | 1 |
| status | active |
| operating_level | ["product-team"] |
| audience | ["Product Operations","Product Manager","Team Lead","Design","Engineering","Research"] |
| best_for | ["Choosing where AI should change product-team work","Avoiding tool-first adoption","Prioritizing one workflow redesign"] |
| evidence_required | ["Workflow inventory","Decision and outcome evidence","Delay and rework evidence","Context and governance constraints"] |
| produces | ["Priority workflow recommendation","Fit and readiness diagnosis","Next mapping or experiment action"] |
| assessment_questions | ["WFL-01","WFL-02","WFL-03","WFL-04","WFL-05"] |
| maturity_move | {"from":"emerging","to":"repeatable"} |
| estimated_time | 30-60 min |
| group_size | 2-8 |
| depends_on | [] |
| combine_with | ["aipom-productive-motion-map","human-aipom-work-contract","aipom-workflow-playbook-builder"] |
| sources | [] |
AIPOM Workflow Opportunity Advisor
What Is It
Choose the product-team decision or productive motion where redesign with AI could create meaningful value and responsible learning. Start with work and outcomes, not a tool looking for a task.
Why Use It
Automating low-value artifact production can increase volume while leaving slow decisions, weak evidence, and rework untouched. This advisor directs redesign toward consequential friction.
When to Use It
Use when teams have many AI workflow ideas, adoption is tool-led, or the organization needs one bounded redesign target.
What It Produces
- Candidate workflow comparison
- Recommended first redesign and rationale
- Readiness, consequence, and evidence gaps
- Baseline and next mapping or pilot action
Who Should Participate
Include people who perform and receive the work, Product Operations, a Product Manager, and design, research, engineering, or governance partners as needed.
Evidence to Bring
Bring actual workflows, decisions, cycle time, wait time, rework, quality failures, user impact, context inputs, exceptions, and current measures.
How to Do It
- Recognize supplied workflows and desired outcomes.
- Identify the decision each workflow enables—not merely its artifacts.
- Compare outcome importance, frequency, friction, evidence loss, rework, consequence, and context readiness.
- Exclude work where the problem is unclear, authority is unsafe, or a simpler non-AI fix dominates.
- Present the strongest options and recommend one bounded starting point.
- Define baseline, owner, first mapping action, and success evidence.
Facilitation Protocol
Use guided, context-dump, or best-guess mode. Ask only questions that change selection. Present numbered candidates with fit, risk, and learning value; accept combined or custom choices.
Decision Logic
Prefer workflows with an important repeated decision, observable friction, accessible evidence, manageable consequence, and a bounded path to learning. Defer workflows with unclear purpose, missing authority, unavailable context, or irreversible consequences. Recommend process simplification when AI adds no material advantage.
Completion Criteria
Finish with one priority motion, alternatives considered, evidence and assumptions, baseline, human owner, next mapping or pilot step, and unresolved readiness gaps.
Key Concepts
- Productive motion means a repeatable pattern that improves a decision or outcome.
- Artifact speed is not decision quality.
- AI fit depends on context and consequence, not task popularity.
