Dilip Kumar Astik Independent AI Investment Risk Assessor

AI Investment Decision Readiness Checklist

This checklist structures judgment, not replaces it. Derived from documented failure patterns in adversarial financial markets.

When to Use

Use this checklist when:

  • Approving an AI-related capital investment
  • Reviewing requests for additional funding
  • Evaluating stalled or ambiguous initiatives
  • Comparing competing AI proposals for the same business objective

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

Director signal: If answers rely on optimism, narratives, or future clarity → No-Go or defer.

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

Director signal: Vague data plans are capital risk.

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

Director signal: If governance is "to be decided later," risk already exists.

Part II

When Progress Stalls (Continue / Stop)

Early Warning Signals

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

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 (Director View)

Highest Capital Risk

  • 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 Signals

  • 0–2 concerns: Proceed with standard oversight
  • 3–5 concerns: Tighten review cycles
  • 6+ concerns: Independent assessment recommended

Perfect answers are suspicious. Transparency beats confidence.

Derived from documented failure patterns in The 98% Win Rate That Failed
Version: Director Edition v1.0 | December 2025