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
- Name the funding decision, horizon, scope, and accountable owner.
- Restate the causal value chain and mark evidenced versus assumed links.
- Quantify the current baseline and credible non-AI alternatives.
- Model value through customer, revenue, margin, cost, capacity, safety, or avoided risk outcomes.
- Include build, buy, data, integration, inference, evaluation, review, monitoring, governance, change, failure, and exit costs.
- Create downside, base, and upside scenarios with explicit adoption and quality assumptions.
- Run sensitivity and break-even analysis on the few variables that change the decision.
- Identify concentration, portability, and tail-risk exposure that arithmetic may hide.
- 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
Use for pilot funding, vendor selection, scale decisions, renewal, build-versus-buy, workflow redesign, and portfolio reallocation.
Common Pitfalls
- Counting gross time saved as cash benefit
- Omitting review and control cost
- Assuming full adoption at launch
- Ignoring non-AI alternatives and switching cost
- Letting expected value hide intolerable downside
- Preserving the original forecast after evidence changes
Combine With
Use aipom-platform-dependency-audit for concentration and exit exposure, aipom-investment-stage-gates for funding decisions, and aipom-initiative-readiness-review for cross-category constraints.
Assets and Templates
- Economic case template
- Synthetic worked example
- Weak example
Sources
This skill is an original AIPOM synthesis of scenario-based product economics, total-cost analysis, and evidence-based investment practice.
1---2name: aipom-economic-case-builder3description: Build an evidence-aware economic case for an AI investment across value, full lifecycle cost, uncertainty, alternatives, risk, and decision thresholds.4---56# AIPOM Economic Case Builder78## What Is It910Build 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.1112## Why Use It1314AI 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.1516## When to Use It1718Use 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.1920## What It Produces2122- Value logic and baseline23- Full lifecycle cost model24- Base, downside, and upside scenarios25- Sensitivity, break-even, and dependency analysis26- Continue, revise, pause, or stop decision rules2728## Who Should Participate2930Include the investment owner, Product Manager, finance, engineering or platform owners, operations, and governance partners where failure or compliance costs matter.3132## Evidence to Bring3334Bring outcome maps, baselines, experiments, adoption evidence, review burden, model and infrastructure costs, staffing, evaluation and control costs, incident estimates, contracts, alternatives, and uncertainty ranges.3536## How to Do It37381. Name the funding decision, horizon, scope, and accountable owner.392. Restate the causal value chain and mark evidenced versus assumed links.403. Quantify the current baseline and credible non-AI alternatives.414. Model value through customer, revenue, margin, cost, capacity, safety, or avoided risk outcomes.425. Include build, buy, data, integration, inference, evaluation, review, monitoring, governance, change, failure, and exit costs.436. Create downside, base, and upside scenarios with explicit adoption and quality assumptions.447. Run sensitivity and break-even analysis on the few variables that change the decision.458. Identify concentration, portability, and tail-risk exposure that arithmetic may hide.469. Define evidence milestones and continue, revise, pause, or stop thresholds.4748## Key Concepts4950- Saved minutes are not savings unless capacity or outcomes change.51- Ranges are more honest than unsupported point forecasts.52- Avoided risk needs probability, consequence, and attribution assumptions.53- Economic evidence should evolve at each investment gate.5455## Organizational Applications5657Use for pilot funding, vendor selection, scale decisions, renewal, build-versus-buy, workflow redesign, and portfolio reallocation.5859## Common Pitfalls6061- Counting gross time saved as cash benefit62- Omitting review and control cost63- Assuming full adoption at launch64- Ignoring non-AI alternatives and switching cost65- Letting expected value hide intolerable downside66- Preserving the original forecast after evidence changes6768## Combine With6970Use `aipom-platform-dependency-audit` for concentration and exit exposure, `aipom-investment-stage-gates` for funding decisions, and `aipom-initiative-readiness-review` for cross-category constraints.7172## Assets and Templates7374- [Economic case 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 scenario-based product economics, total-cost analysis, and evidence-based investment practice.
Dean Peters