Decision anatomy
Accountable owner
Chief Product Officer or accountable product leader
Recurrence
Continuous intake with monthly or quarterly portfolio decisions
Reversibility
Medium before build; progressively lower after staffing, commitments, and architecture changes.
Cost of error
High through opportunity cost, team churn, strategic delay, and accumulated complexity.
Evidence
- customer evidence
- strategic fit
- expected value
- cost and capacity
- technical and market risk
- dependencies
Alternatives
- candidate initiatives
- sequence variants
- fund, defer, or decline
- experiments before commitment
Objectives
- customer outcome
- business value
- learning
- strategic leverage
- delivery confidence
Constraints
- team capacity
- architecture
- dependencies
- budget
- commitments
- risk appetite
Uncertainty
- demand
- solution efficacy
- effort
- adoption
- opportunity cost
How AI may participate
This is design guidance for the archetype, not a universal maturity requirement or a claim about a particular product.
- Assemble customer evidence, strategy, dependencies, effort ranges, opportunity cost, and delivery risk.
- Compare fund, sequence, explore, defer, or stop alternatives under explicit objectives and constraints.
- Show which recommendation changes when adoption, effort, or strategic assumptions move.
Authority and retained human judgment
Use AI for evidence synthesis, scenario comparison, and proposal. Portfolio commitment and roadmap promises remain with accountable product, engineering, and business leaders.
What accountable people still decide
- Which strategic bets deserve exploration despite weak historical evidence.
- How to balance customer need, platform health, risk reduction, revenue, and learning.
- When stakeholder commitments or architectural dependencies outweigh a local score.
Outcome observability
Delivery is visible quickly; product and business outcomes require longer causal evaluation.
Evaluation and trace
- Compare forecast adoption, effort, timing, and strategic contribution with observed results.
- Track whether the decision improved focus and learning, not merely whether the item shipped.
- Retain rejected alternatives and later priority changes so outcome attribution is not rewritten after the fact.
Failure and abstention conditions
- Abstain when alternatives, decision owner, strategy, or material dependencies are not explicit.
- Do not manufacture precision from incomparable discovery evidence and delivery estimates.
- Escalate when the recommendation depends on unresolved security, reliability, legal, or platform constraints.
Assess your version of this decision
An archetype cannot determine the right authority boundary by itself. Decisagent Fit separately assesses useful AI participation and supportable action authority from your decision context.
Reviewed relevant products
No product relationship has crossed human review for this decision. Candidate seed links are intentionally excluded.