AIPOM Workflow Playbook Builder
What Is It
Capture a tested human-AI productive motion so another qualified team can run, inspect, adapt, and improve it without losing its evidence, judgment, controls, or exception behavior.
Why Use It
Informal workflows drift, depend on a few experts, and preserve happy paths while forgetting failure recovery. A playbook makes the operating practice visible; observed use and improved outcomes—not the document—establish maturity.
When to Use It
Use after the workflow has been mapped, responsibilities are explicit, and a bounded pilot has produced evidence. Do not standardize an untested redesign or package one team’s workaround as universal practice.
What It Produces
- Purpose, trigger, scope, prerequisites, and outcome
- Roles, decisions, context, steps, and handoffs
- Examples, behavior boundaries, controls, escalation, and fallback
- Measures, review cadence, owner, version, and retirement rules
Who Should Participate
Include practitioners, workflow and decision owners, product and technical partners, a new-user reviewer, and governance partners proportionate to consequence.
Evidence to Bring
Bring motion maps, work contracts, context packages, evaluations, run records, before-and-after measures, exceptions, incidents, user feedback, and revision decisions.
How to Do It
- Confirm that the workflow is sufficiently tested to reuse and name remaining limits.
- State purpose, trigger, outcome, scope, prerequisites, and non-goals.
- Define roles, decision rights, accountability, and required competence.
- Specify authoritative inputs, context assembly, provenance, and exclusions.
- Write the smallest usable happy path with visible handoffs and review.
- Add representative examples, weak examples, exceptions, escalation, fallback, and stop rules.
- Connect behavior and evaluation requirements to decision points.
- Define outcome, cycle, rework, burden, safety, and adoption measures.
- Assign an owner, review cadence, versioning, change, and retirement process.
- Forward-test with a qualified user who did not design it.
Key Concepts
- Reusable means judgment and failure behavior travel with the steps.
- A playbook is a governed operating artifact, not proof of adoption.
- Minimum viable guidance should be runnable without hiding prerequisites.
- Exceptions are first-class design inputs.
Organizational Applications
Use to scale research synthesis, support preparation, product reviews, launch checks, portfolio analysis, and other recurring AI-assisted decisions.
Common Pitfalls
- Publishing before testing actual use
- Describing tools without the decision or outcome
- Hiding context and competence prerequisites
- Documenting only the happy path
- Omitting ownership, measures, and retirement
- Copying across teams without checking local consequence
Combine With
Use workflow-to-skill-converter when the playbook should become an agent-facilitated skill, aipom-context-package-builder for reusable inputs, and aipom-behavior-contract-builder for testable behavior.
Assets and Templates
- Workflow playbook template
- Synthetic worked example
- Weak example
Sources
This skill is an original AIPOM synthesis of workflow standardization, human-AI operating design, and continuous-improvement practice.
1---2name: aipom-workflow-playbook-builder3description: Turn a tested human-AI workflow into an inspectable playbook with context, roles, decisions, examples, controls, measures, fallback, and improvement ownership.4---56# AIPOM Workflow Playbook Builder78## What Is It910Capture a tested human-AI productive motion so another qualified team can run, inspect, adapt, and improve it without losing its evidence, judgment, controls, or exception behavior.1112## Why Use It1314Informal workflows drift, depend on a few experts, and preserve happy paths while forgetting failure recovery. A playbook makes the operating practice visible; observed use and improved outcomes—not the document—establish maturity.1516## When to Use It1718Use after the workflow has been mapped, responsibilities are explicit, and a bounded pilot has produced evidence. Do not standardize an untested redesign or package one team’s workaround as universal practice.1920## What It Produces2122- Purpose, trigger, scope, prerequisites, and outcome23- Roles, decisions, context, steps, and handoffs24- Examples, behavior boundaries, controls, escalation, and fallback25- Measures, review cadence, owner, version, and retirement rules2627## Who Should Participate2829Include practitioners, workflow and decision owners, product and technical partners, a new-user reviewer, and governance partners proportionate to consequence.3031## Evidence to Bring3233Bring motion maps, work contracts, context packages, evaluations, run records, before-and-after measures, exceptions, incidents, user feedback, and revision decisions.3435## How to Do It36371. Confirm that the workflow is sufficiently tested to reuse and name remaining limits.382. State purpose, trigger, outcome, scope, prerequisites, and non-goals.393. Define roles, decision rights, accountability, and required competence.404. Specify authoritative inputs, context assembly, provenance, and exclusions.415. Write the smallest usable happy path with visible handoffs and review.426. Add representative examples, weak examples, exceptions, escalation, fallback, and stop rules.437. Connect behavior and evaluation requirements to decision points.448. Define outcome, cycle, rework, burden, safety, and adoption measures.459. Assign an owner, review cadence, versioning, change, and retirement process.4610. Forward-test with a qualified user who did not design it.4748## Key Concepts4950- Reusable means judgment and failure behavior travel with the steps.51- A playbook is a governed operating artifact, not proof of adoption.52- Minimum viable guidance should be runnable without hiding prerequisites.53- Exceptions are first-class design inputs.5455## Organizational Applications5657Use to scale research synthesis, support preparation, product reviews, launch checks, portfolio analysis, and other recurring AI-assisted decisions.5859## Common Pitfalls6061- Publishing before testing actual use62- Describing tools without the decision or outcome63- Hiding context and competence prerequisites64- Documenting only the happy path65- Omitting ownership, measures, and retirement66- Copying across teams without checking local consequence6768## Combine With6970Use `workflow-to-skill-converter` when the playbook should become an agent-facilitated skill, `aipom-context-package-builder` for reusable inputs, and `aipom-behavior-contract-builder` for testable behavior.7172## Assets and Templates7374- [Workflow playbook template](template.md)75- [Synthetic worked example](examples/worked-example.md)76- [Weak example](examples/weak-example.md)7778## Sources7980This skill is an original AIPOM synthesis of workflow standardization, human-AI operating design, and continuous-improvement practice.
Dean Peters