Aipom workflow opportunity advisor
Skill deanpeters/ai-product-operating-model-skills/skills/aipom-workflow-opportunity-advisor
Identify which product-team decision or productive workflow should be redesigned with AI first, based on outcome value, friction, evidence, consequence, and readiness.From its SKILL.md
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SKILL.md
4.5 KB, 694 tokens by cl100k_base, as published. Nobody here has run it
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.
- Redesign before automation.
Organizational Applications
Use for discovery synthesis, evidence review, prioritization, roadmap learning, customer-feedback routing, and decision preparation.
Common Pitfalls
- Selecting the easiest document to generate
- Automating an unclear or broken process
- Ignoring review burden and affected users
- Measuring usage rather than the decision or outcome
- Attempting an end-to-end transformation as the first test
Combine With
Use aipom-productive-motion-map to understand current work, human-aipom-work-contract to assign responsibilities, and aipom-workflow-playbook-builder after the redesigned motion works.
Assets and Templates
Sources
This advisor is an original AIPOM synthesis of workflow redesign and evidence-based product practice.
What ships with it: 3 files
1.2 KB alongside SKILL.md
examples/
- weak-example.md260 B
- worked-example.md540 B
- template.md382 B
Gives 0 of the 12 instructions most automation workflows skills give in 694 tokens
Counted across 813 of the 1,214 authors here whose files we hold, read 2026-09-06
- Use conventional commit message formatin 38 of 813, across 34 files
- Write tests before implementing codein 26 of 813, across 12 files
- Achieve at least 80 percent test coveragein 24 of 813, across 11 files
- Mock external dependencies for unit testsin 22 of 813, across 10 files
- Read product marketing context before asking questionsin 21 of 813, across 6 files
- Implement rollback plans for every deploymentin 21 of 813, across 9 files
- Follow the arrange-act-assert patternin 20 of 813, across 9 files
- Delete branches after mergingin 20 of 813, across 18 files
- Test all edge cases and error scenariosin 20 of 813, across 8 files
- Use semantic selectors for UI testsin 19 of 813, across 7 files
- Define sequence type and audience contextin 18 of 813, across 5 files
- Monitor feature drift and prediction distribution driftin 18 of 813, across 5 files
Said here and by no other author read
- Identify the decision each workflow enables
- Compare workflows by outcome importance and friction
- Present strongest options with fit and risk
- Recommend one bounded starting point
- Define baseline and success evidence
- Redesign workflows before automating them
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.