aipom-economic-case-builder

name	aipom-economic-case-builder
description	Build an evidence-aware economic case for an AI investment across value, full lifecycle cost, uncertainty, alternatives, risk, and decision thresholds.
type	component
category	strategy-and-economic-outcomes
phase	3
status	active
operating_level	["portfolio","initiative"]
audience	["CPO","VP Product","Product Operations","Product Manager","Finance","Engineering","Operations","AI Governance"]
best_for	["Funding validation or scale","Comparing AI with non-AI alternatives","Revising a case when evidence changes"]
evidence_required	["Outcome value map and baselines","Cost and capacity evidence","Adoption and evaluation evidence","Dependency risk and alternative estimates"]
produces	["AI economic case","Scenario and sensitivity model","Funding decision rules"]
assessment_questions	["STR-02","STR-03","STR-04","POR-02","POR-03"]
maturity_move	{"from":"repeatable","to":"operationalized"}
estimated_time	60-120 min
group_size	3-8
depends_on	["aipom-outcome-value-map","aipom-bet-charter"]
combine_with	["aipom-platform-dependency-audit","aipom-investment-stage-gates","aipom-initiative-readiness-review"]
sources	[]

AIPOM Economic Case Builder

What Is It

Build a decision-ready economic case connecting AI behavior and adoption to customer, operational, financial, safety, or risk value. Include full lifecycle cost, uncertainty, alternatives, dependencies, and explicit funding thresholds.

Why Use It

AI cases often count optimistic labor savings while omitting review, evaluation, data, inference, integration, governance, failure, switching, and change costs. A useful case exposes the causal and economic assumptions that must earn further investment.

When to Use It

Use before material validation, procurement, scale, or renewal decisions. Early cases should use ranges and learning milestones; do not demand false precision from discovery-stage evidence.

What It Produces

• Value logic and baseline
• Full lifecycle cost model
• Base, downside, and upside scenarios
• Sensitivity, break-even, and dependency analysis
• Continue, revise, pause, or stop decision rules

Who Should Participate

Include the investment owner, Product Manager, finance, engineering or platform owners, operations, and governance partners where failure or compliance costs matter.

Evidence to Bring

Bring outcome maps, baselines, experiments, adoption evidence, review burden, model and infrastructure costs, staffing, evaluation and control costs, incident estimates, contracts, alternatives, and uncertainty ranges.

How to Do It

1. Name the funding decision, horizon, scope, and accountable owner.
2. Restate the causal value chain and mark evidenced versus assumed links.
3. Quantify the current baseline and credible non-AI alternatives.
4. Model value through customer, revenue, margin, cost, capacity, safety, or avoided risk outcomes.
5. Include build, buy, data, integration, inference, evaluation, review, monitoring, governance, change, failure, and exit costs.
6. Create downside, base, and upside scenarios with explicit adoption and quality assumptions.
7. Run sensitivity and break-even analysis on the few variables that change the decision.
8. Identify concentration, portability, and tail-risk exposure that arithmetic may hide.
9. Define evidence milestones and continue, revise, pause, or stop thresholds.

Key Concepts

• Saved minutes are not savings unless capacity or outcomes change.
• Ranges are more honest than unsupported point forecasts.
• Avoided risk needs probability, consequence, and attribution assumptions.
• Economic evidence should evolve at each investment gate.

Organizational Applications