| name | aipom-decision-cycle-redesign |
| description | Redesign a recurring product decision cycle around evidence, context, human judgment, AI assistance, authority, feedback, and measurable learning. |
| type | workflow |
| category | product-team-workflows |
| phase | 3 |
| status | active |
| operating_level | ["product-team","initiative"] |
| audience | ["Product Operations","Product Manager","Team Lead","Design","Engineering","Research","Data","AI Governance"] |
| best_for | ["Improving a slow or low-quality recurring decision","Integrating multiple Phase 2 practices","Testing whether AI changes learning rather than output"] |
| evidence_required | ["Productive-motion map and baseline","Decision records and outcomes","Context behavior and evaluation artifacts","Authority and incident evidence"] |
| produces | ["Redesigned decision cycle","Pilot and measurement plan","Governed workflow handoff"] |
| assessment_questions | ["WFL-01","WFL-02","WFL-03","WFL-04","WFL-05"] |
| maturity_move | {"from":"repeatable","to":"operationalized"} |
| estimated_time | 2-4 hours across multiple sessions |
| group_size | 4-10 |
| depends_on | ["aipom-productive-motion-map","human-aipom-work-contract","aipom-workflow-playbook-builder"] |
| combine_with | ["aipom-context-package-builder","aipom-evaluation-strategy-advisor","aipom-accountability-charter"] |
| sources | [] |
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
