AIPOM Decision Cycle Redesign
What Is It
Redesign one recurring product decision from trigger through evidence, deliberation, authority, action, feedback, and revision. Use AI only where it improves decision quality, cycle time, learning, or consistency without obscuring judgment and accountability.
Why Use It
Adding AI to document production can accelerate noise while leaving the decision cycle unchanged. This workflow integrates context, behavior, evaluation, human judgment, authority, and feedback around the decision itself.
When to Use It
Use after mapping the current productive motion and establishing foundational controls. Choose a bounded recurring decision with an accountable owner and measurable baseline.
What It Produces
- Current decision-cycle diagnosis
- Future cycle and human-AI responsibilities
- Evidence, context, evaluation, authority, and fallback design
- Bounded pilot, comparison, measures, and adoption plan
- Playbook and improvement handoff
Who Should Participate
Include the decision owner, Team Lead, people performing and affected by the work, Product Manager, Product Operations, technical and data partners, and governance partners proportionate to consequence.
Evidence to Bring
Bring observed cases, baselines, decisions and outcomes, motion maps, context packages, behavior contracts, evaluations, work contracts, exceptions, incidents, and practitioner capacity evidence.
How to Do It
- Load supplied context into a ledger and define the decision boundary.
- Diagnose the current cycle: trigger, evidence, synthesis, deliberation, decision, action, feedback, and revision.
- Identify the constraint, failure modes, missing perspectives, and hidden labor.
- Set redesign outcomes and countermeasures.
- Allocate AI preparation, analysis, recommendation, execution, and monitoring separately.
- Assign human review, judgment, approval, accountability, escalation, and stop authority.
- Design authoritative context, provenance, behavior, evaluation, fallback, and incident paths.
- Run a bounded comparison against the baseline with representative cases.
- Decide adopt, revise, contain, or stop using evidence and preserved disagreement.
- Convert the proven cycle into a playbook with ownership and review cadence.
Facilitation Protocol
Support guided, context-dump, and best-guess modes. Ask first which recurring decision must improve. Reuse prior artifacts; do not re-interview the team about mapped facts. In best-guess mode reduce autonomy and scope, label assumptions, and prioritize evidence collection.
Decision Logic
- Repair the current cycle when unclear authority, missing context, or avoidable handoffs—not AI capability—cause the failure.
- Assist a bounded step when evidence supports value and consequences remain reviewable.
- Redesign the full cycle when several linked decisions, feedback, and context flows must change together.
- Contain or stop when critical behavior, data, authority, evaluation, or recovery gaps remain.
Prefer the smallest design that changes the decision outcome. Do not automate a decision merely because preparatory tasks can be automated.
Completion Criteria
Finish with the baseline, redesigned cycle, evidence and assumptions, human and AI responsibilities, authority, controls, pilot results or plan, measures, unresolved questions, adoption decision, owner, and review cadence.
Key Concepts
- Optimize the decision cycle, not document throughput.
- Faster preparation may increase downstream review.
- Feedback must revise context, behavior, or rules.
- Human accountability remains explicit even when execution is automated.
Organizational Applications
Use for opportunity selection, research synthesis, roadmap changes, experiment decisions, escalation routing, launch readiness, and portfolio reviews.
Common Pitfalls
- Starting with a tool feature
- Redesigning the happy path only
- Measuring output speed without decision quality
- Adding review without capacity
- Automating authority through vague approval
- Standardizing before a representative pilot
Combine With
Use context, evaluation, and accountability skills to deepen weak conditions; hand proven behavior into aipom-workflow-playbook-builder and workflow-to-skill-converter.
Assets and Templates
- Decision-cycle redesign template
- Synthetic worked example
- Weak example
Sources
This workflow is an original AIPOM synthesis of decision design, workflow improvement, human-AI collaboration, and evidence-based product practice.
1---2name: aipom-decision-cycle-redesign3description: Redesign a recurring product decision cycle around evidence, context, human judgment, AI assistance, authority, feedback, and measurable learning.4---56# AIPOM Decision Cycle Redesign78## What Is It910Redesign one recurring product decision from trigger through evidence, deliberation, authority, action, feedback, and revision. Use AI only where it improves decision quality, cycle time, learning, or consistency without obscuring judgment and accountability.1112## Why Use It1314Adding AI to document production can accelerate noise while leaving the decision cycle unchanged. This workflow integrates context, behavior, evaluation, human judgment, authority, and feedback around the decision itself.1516## When to Use It1718Use after mapping the current productive motion and establishing foundational controls. Choose a bounded recurring decision with an accountable owner and measurable baseline.1920## What It Produces2122- Current decision-cycle diagnosis23- Future cycle and human-AI responsibilities24- Evidence, context, evaluation, authority, and fallback design25- Bounded pilot, comparison, measures, and adoption plan26- Playbook and improvement handoff2728## Who Should Participate2930Include the decision owner, Team Lead, people performing and affected by the work, Product Manager, Product Operations, technical and data partners, and governance partners proportionate to consequence.3132## Evidence to Bring3334Bring observed cases, baselines, decisions and outcomes, motion maps, context packages, behavior contracts, evaluations, work contracts, exceptions, incidents, and practitioner capacity evidence.3536## How to Do It37381. Load supplied context into a ledger and define the decision boundary.392. Diagnose the current cycle: trigger, evidence, synthesis, deliberation, decision, action, feedback, and revision.403. Identify the constraint, failure modes, missing perspectives, and hidden labor.414. Set redesign outcomes and countermeasures.425. Allocate AI preparation, analysis, recommendation, execution, and monitoring separately.436. Assign human review, judgment, approval, accountability, escalation, and stop authority.447. Design authoritative context, provenance, behavior, evaluation, fallback, and incident paths.458. Run a bounded comparison against the baseline with representative cases.469. Decide adopt, revise, contain, or stop using evidence and preserved disagreement.4710. Convert the proven cycle into a playbook with ownership and review cadence.4849## Facilitation Protocol5051Support guided, context-dump, and best-guess modes. Ask first which recurring decision must improve. Reuse prior artifacts; do not re-interview the team about mapped facts. In best-guess mode reduce autonomy and scope, label assumptions, and prioritize evidence collection.5253## Decision Logic54551. **Repair the current cycle** when unclear authority, missing context, or avoidable handoffs—not AI capability—cause the failure.562. **Assist a bounded step** when evidence supports value and consequences remain reviewable.573. **Redesign the full cycle** when several linked decisions, feedback, and context flows must change together.584. **Contain or stop** when critical behavior, data, authority, evaluation, or recovery gaps remain.5960Prefer the smallest design that changes the decision outcome. Do not automate a decision merely because preparatory tasks can be automated.6162## Completion Criteria6364Finish with the baseline, redesigned cycle, evidence and assumptions, human and AI responsibilities, authority, controls, pilot results or plan, measures, unresolved questions, adoption decision, owner, and review cadence.6566## Key Concepts6768- Optimize the decision cycle, not document throughput.69- Faster preparation may increase downstream review.70- Feedback must revise context, behavior, or rules.71- Human accountability remains explicit even when execution is automated.7273## Organizational Applications7475Use for opportunity selection, research synthesis, roadmap changes, experiment decisions, escalation routing, launch readiness, and portfolio reviews.7677## Common Pitfalls7879- Starting with a tool feature80- Redesigning the happy path only81- Measuring output speed without decision quality82- Adding review without capacity83- Automating authority through vague approval84- Standardizing before a representative pilot8586## Combine With8788Use context, evaluation, and accountability skills to deepen weak conditions; hand proven behavior into `aipom-workflow-playbook-builder` and `workflow-to-skill-converter`.8990## Assets and Templates9192- [Decision-cycle redesign template](template.md)93- [Synthetic worked example](examples/worked-example.md)94- [Weak example](examples/weak-example.md)9596## Sources9798This workflow is an original AIPOM synthesis of decision design, workflow improvement, human-AI collaboration, and evidence-based product practice.
Dean Peters