Ai Investment Checklist

AI-Driven Business Innovation Masterclass

AI Investment Checklist

Use this checklist to evaluate every AI initiative before investment approval.

How to Use This Checklist

  1. For each AI project: Answer all questions in both sections
  2. Scoring: Must pass ALL criteria in Section A (Traditional) AND Section B (AI-Specific)
  3. If any answer is “No” or score <7: Add mitigation plan and budget before proceeding

Section A: Traditional Investment Criteria

Strategic Fit

    • Horizon 1 (optimize core): Target 60-70% of budget
    • Horizon 2 (emerging capabilities): Target 20-30% of budget
    • Horizon 3 (transformational): Target 10-15% of budget
    • Optimize (process/incremental)
    • Enhance (strategic/incremental)
    • Revolutionize (process/transformational)
    • Transform (strategic/transformational)

Financial ROI

Calculate value across four categories:

  1. Cost Reduction:
    • Annual savings: $________
    • Payback period: ________ months
  2. Revenue Growth:
    • New revenue: $________
    • Revenue retained: $________
  3. Risk Reduction:
    • Risk avoided (quantified): $________
    • Compliance value: $________
  4. Strategic Positioning:
    • Competitive advantage: (describe)
    • Capability building: (describe)

Total ROI: ________%

ROI Threshold Met? - [ ] Horizon 1: >200% in Year 1 - [ ] Horizon 2: >150% over 3 years - [ ] Horizon 3: >100% over 5 years

Implementation Feasibility

If all boxes checked: ✅ Proceed to Section B (AI-Specific Criteria)
If any unchecked: ❌ Do not proceed until addressed

Section B: AI-Specific Investment Criteria

1. Data Readiness Assessment

Score your data readiness (0-10):

Factor Score Weight Weighted Score
Data Availability: Do we have enough historical data? __/10 25% __
Data Quality: Is data accurate, complete, consistent? __/10 30% __
Data Access: Can we integrate necessary data sources? __/10 20% __
Data Bias: Is historical data representative/unbiased? __/10 25% __
TOTAL DATA READINESS SCORE __/10

Interpretation: - 8-10: Data ready for AI ✅ - 7: Data ready with minor improvements ⚠️ - <7: Must invest in data infrastructure FIRST ❌

Data Readiness Questions:

If score <7: - Action required: Data infrastructure project first - Budget add: 40% for data cleaning/integration - Timeline add: 3-6 months for data preparation

2. Continuous Learning Plan

How will this AI improve over time?

    • Lower ongoing cost
    • Risk of obsolescence as world changes
    • Annual cost: ~10-15% of development cost
    • Medium ongoing cost
    • Stays current with trends
    • Annual cost: ~25-30% of development cost
    • Higher ongoing cost
    • Always improving, competitive advantage
    • Annual cost: ~35-50% of development cost

5-Year Total Cost of Ownership:

Year Development Operations Retraining/Learning Total
1 $__________ $_________ $_____________ $_____
2 $0 $_________ $_____________ $_____
3 $0 $_________ $_____________ $_____
4 $0 $_________ $_____________ $_____
5 $0 $_________ $_____________ ||* * 5 − YrTCO * *||||**_____**

Questions: - [ ] Have we budgeted for ongoing retraining costs? - [ ] Do we have infrastructure for continuous learning? - [ ] Does this AI create a data flywheel (better with usage)? - [ ] Have we planned for model monitoring and performance tracking?

3. Accuracy Requirements & Risk Tolerance

What’s the cost of being wrong?

Business Impact of AI Error: - [ ] Low: Minor inconvenience (product recommendation, content ranking) - Acceptable accuracy: 70-80% - Human oversight: Minimal (audit only)

    • Acceptable accuracy: 85-95%
    • Human oversight: Review flagged cases
    • Acceptable accuracy: 95-99%+
    • Human oversight: Human-in-loop for all decisions

Match accuracy to risk:

AI Capability Your Requirement Match?
Expected AI accuracy: ___% Minimum required: ___% ☐ Yes ☐ No

Human-in-Loop Decision Matrix:

AI Confidence Human Role Process
<70% Human decides AI provides information only
70-90% Human reviews AI recommends, human approves
90-95% Human audits AI decides, human spot-checks
>95% Auto-approve AI decides, human audits later

Questions: - [ ] Have we defined acceptable failure rate? - [ ] Do we have process for edge cases? - [ ] Is human oversight plan defined and budgeted? - [ ] Have we tested AI performance on our specific use case?

If accuracy doesn’t match risk: ❌ Do not proceed or add human oversight

4. Explainability Requirements

Do we need to explain AI decisions?

Regulatory Requirements: - [ ] High Explainability: Banking, lending, healthcare, legal - Regulatory requirement to explain decisions - Algorithm choice limited (no deep neural networks) - Use: Decision trees, linear models, rule-based systems - Budget add: +15-20% for interpretable models

    • Need to trust and audit decisions
    • Algorithm choice: Any, but add explanation layer
    • Use: Model-agnostic explanations (SHAP, LIME)
    • Budget add: +10-15% for explanation tools
    • Results speak for themselves
    • Algorithm choice: Any (including deep learning)
    • Use: Most powerful AI techniques available
    • Budget add: +0%

Explanation Method: - [ ] Feature importance (which factors mattered?) - [ ] Counterfactual (what would change the decision?) - [ ] Similar cases (what similar examples exist?) - [ ] Rule extraction (can we create simple rules?)

Questions: - [ ] Are we in a regulated industry requiring explanations? - [ ] Will users demand to know “why” for AI decisions? - [ ] Have we chosen AI approach compatible with our explainability needs? - [ ] Do we have tools/process to generate explanations?

If high explainability needed: Limits algorithm choices, add budget for tools

5. Ethical Risk Assessment

Could this AI discriminate or cause harm?

Risk Level:

    • Examples: Hiring, lending, medical diagnosis, criminal justice
    • Action required: Extensive bias testing + diverse teams + ongoing monitoring
    • Budget add: +20-30% for ethics/bias testing
    • Examples: Pricing, recommendations, advertising targeting
    • Action required: Bias testing + diverse perspectives in design
    • Budget add: +10-15% for bias testing
    • Examples: Inventory optimization, logistics, forecasting
    • Action required: Basic bias assessment
    • Budget add: +5% for bias review

Ethical AI Checklist:

    • Equal opportunity (same % of qualified candidates)
    • Demographic parity (same % across all groups)
    • Individual fairness (similar people treated similarly)

Protected Classes to Test: - [ ] Gender - [ ] Race/ethnicity - [ ] Age - [ ] Disability status - [ ] Other relevant demographics: ______________

Questions: - [ ] Could this AI systematically disadvantage any group? - [ ] Is our training data from a biased historical process? - [ ] Have we tested AI performance across all user demographics? - [ ] Do we have diverse perspectives on the development team? - [ ] What’s the reputational cost if this AI is found to be biased?

If high ethical risk: ❌ Do not proceed without bias testing + diverse team

Decision Matrix

Must satisfy ALL criteria:

Criteria Status Required Action
A. Traditional Criteria
Strategic fit ☐ Pass ☐ Fail
Financial ROI meets threshold ☐ Pass ☐ Fail
Implementation feasible ☐ Pass ☐ Fail
B. AI-Specific Criteria
Data readiness ≥ 7 ☐ Pass ☐ Fail If <7: Data infrastructure project first
Continuous learning plan & budget ☐ Pass ☐ Fail Add 30-50% to annual costs
Accuracy matches risk tolerance ☐ Pass ☐ Fail Add human oversight or don’t proceed
Explainability requirements met ☐ Pass ☐ Fail Choose interpretable AI or add tools
Ethical risks assessed & mitigated ☐ Pass ☐ Fail Add bias testing + diverse teams

Final Decision: - ☐ APPROVED: All criteria passed → Proceed to implementation - ☐ CONDITIONAL: Some criteria failed → Address gaps and re-evaluate - ☐ REJECTED: Critical criteria failed → Do not proceed

Budget Impact Summary

AI-Specific Budget Additions:

Factor Add to Budget This Project
Data infrastructure (if score <7) +40% to Year 1 +||Continuouslearning(ongoing)|+30 − 50________ /yr
Human oversight (if needed) +15-25% +||Explainabilitytools(ifneeded)|+10 − 20________
Bias testing & ethics (if needed) +10-30% +||* * TOTALAI − SPECIFICADDITIONS * *||* * +________**

Revised Total Investment: - Original estimate: $__________ - AI-specific additions: $__________ - Revised total: $__________

Revised ROI: - Original ROI: ________% - Revised ROI (with AI additions): ________% - Still meets threshold? ☐ Yes ☐ No

Sign-Off

This AI investment has been evaluated and: - ☐ Approved - All criteria met, proceed with implementation - ☐ Approved with Conditions - Address the following before proceeding: - ________________________________ - ________________________________ - ☐ Rejected - Does not meet criteria, do not proceed

Evaluation completed by: ________________________
Date: ____________
Next review date: ____________

Quick Reference: The 10 AI Investment Questions

Before approving ANY AI project, ensure you can answer:

  1. Data: Is our data readiness score ≥7?
  2. Learning: What’s our continuous learning plan and 5-year TCO?
  3. Risk: What’s the cost of being wrong, and does accuracy match?
  4. Trust: Do we need to explain decisions, and can we?
  5. Ethics: What’s the bias risk, and how will we test?
  6. Budget: Have we added AI-specific costs (30-50% typically)?
  7. Timeline: Have we added 3-6 months for data prep if needed?
  8. Oversight: What’s our human-in-loop plan?
  9. Monitoring: How will we track AI performance over time?
  10. Exit: What’s our plan if the AI doesn’t work or becomes biased?

If you can’t answer all 10, you’re not ready to invest.

Use this checklist in every investment decision to avoid common AI failure modes.