Dilip Kumar Astik Independent AI Investment Risk Assessor Chartered Accountant - MIT Professional Courses in Data Science

AI Investment Decision Readiness Checklist

An evidence-focused screen that helps you decide whether to commit capital to AI initiatives—and whether to continue funding them when progress stalls. This checklist structures judgment, not replaces it.

How to Use This Checklist

Use when: Considering new AI investment approval · Reviewing additional capital requests · Evaluating stalled or ambiguous projects · Comparing competing AI proposals

If multiple questions cannot be answered clearly, pause is a valid decision.

Part I

Before Capital Commitment (Go / No-Go)

A. Problem Definition & Business Intent

If the problem is unclear, the solution is irrelevant.

Question Director's Lens
1. What specific business outcome will improve if this works? Avoids funding technical ambition
2. What happens if this AI component does not work at all? Reveals downside containment
3. What is the baseline performance today, without AI? Prevents improvement illusion
4. How will success be judged in business terms, not technical metrics? Aligns incentives
5. What outcome would justify stopping the initiative early? Defines walk-away threshold

Board Signal: If more than two questions lack clear answers, consider No-Go or defer until clarity improves.

B. Evidence & Learnability

Not all problems are learnable—many only appear so.

Question Director's Lens
6. What evidence shows this problem is learnable from available data? Prevents assumption-based investment
7. How stable is the relationship between inputs and outcomes? Reveals non-stationarity risk
8. How will evaluation distinguish true learning from overfitting? Tests analytical rigor
9. What specifically differs between the training environment and production? Exposes simulation gap
10. How was the data validated for quality and relevance? Prevents foundation failures

Board Signal: A gap >5% between training and real-world performance demands further scrutiny before approval.

C. Governance & Control

Ambiguity in governance is a leading indicator of loss.

Question Director's Lens
11. Who has explicit authority to pause or stop deployment? Prevents escalation bias
12. What specific indicators trigger intervention or review? Removes ambiguity
13. What is the maximum acceptable loss before reassessment? Protects capital
14. How frequently will independent review occur? Counters internal bias
15. How will unintended behavior be detected and contained? Addresses incentive drift

Board Signal: Absence of predefined stop authority represents a governance control gap that should be resolved before approval.

Part II

When Progress Stalls (Continue / Stop)

Early Warning Signs

Two or more warrant a formal Continue / Stop discussion:

  • Success criteria keep shifting
  • Updates focus on effort, not outcomes
  • New data or dependencies repeatedly emerge
  • Comparisons avoid baseline or alternatives
  • "Almost there" persists across review cycles

CONTINUE Only If ALL Are True

  • Root cause of stall is clearly identified
  • Original investment thesis remains valid
  • Remediation plan is time-bound and testable
  • Opportunity cost is explicitly acceptable
  • Team capability matches revised challenge

STOP If ANY Are True

  • No credible path to business value exists
  • The problem has materially changed
  • Continuation is driven by sunk cost
  • Better risk-adjusted alternatives exist
  • Evidence consistently lags confidence

Stopping is not failure—it is risk containment.

Part III

Board-Level Questions

For Initial Approval

  • What is the single assumption that would make this investment fail?
  • What must we learn in 90 days to justify continuation?
  • How does this compare to non-AI alternatives?
  • Who independently validates the evidence presented?

For Ongoing Reviews

  • What do we know now that we didn't at approval?
  • Has our confidence increased or decreased, and why?
  • What would immediately convince us to stop?
  • Are we measuring progress or activity?

Part IV

Risk Prioritization & Scoring

Highest Capital Risk Focus

  • Incentive misalignment
  • Simulation–reality gap
  • Evaluation blind spots
  • Governance ambiguity

Lower Priority (Often Over-Discussed)

  • Model accuracy improvements
  • Feature additions
  • Infrastructure upgrades
  • Team expansion requests

Interpreting Red Flags

0–2 concerns: Proceed with standard oversight
3–5 concerns: Enhanced monitoring or independent review
6+ concerns: Independent assessment recommended

A checklist is a spotlight—not a substitute for judgment. Perfect answers are suspicious; transparency beats confidence.

Derived from documented failure patterns in The 98% Win Rate That Failed
Version: Director Edition v1.1 | December 2025