Assessment Framework
A governance lens that prioritizes capital preservation and decision clarity over technical validation. This is not an engineering review—it is a governance assessment designed to answer the questions boards must resolve before and during AI investment.
What This Assessment Is (and Is Not)
This Is
- An independent evaluation of AI investment risk
- Focused on capital protection and decision clarity
- Designed for board-level Go/No-Go and Continue/Stop decisions
- Structured around observable risk activation, not technical syntax
This Is Not
- A technical design audit
- Model optimization or tuning
- A consulting engagement to improve performance
- An implementation review
Independence means the assessment stands on its own—free of delivery incentives or advocacy.
Two Decision Moments This Framework Serves
Before Capital Commitment (Go/No-Go)
Used when:
- A new AI initiative is proposed
- Funding approval is required
- Strategic narratives are strong but evidence is uneven
Does this investment have a defensible probability of delivering real business value?
After Project Initiation (Continue/Stop)
Used when:
- Capital is already deployed
- Progress is slow or ambiguous
- Metrics look acceptable but outcomes remain unclear
Is continued investment justified, or are we compounding risk?
Stopping is not failure—it is risk containment.
Risk Dimensions Boards Must See
The framework evaluates AI initiatives across five governance dimensions:
1. Problem Framing Risk
- Is the AI system solving the right problem?
- Are business objectives translated into operational terms?
- Are constraints explicit and codified?
Board Trigger: Is the problem articulated in measurable business outcomes, not technical outputs?
2. Learning & Incentive Risk
- What exactly is the system being rewarded for?
- Could it optimize for proxies while undermining business logic?
- Are incentives aligned with real-world outcomes?
Board Trigger: Are we rewarding outcomes that drive capital value—not statistical artifacts?
3. Evaluation & Evidence Risk
- Do reported metrics reflect reality or laboratory conditions?
- Are failure modes visible—or systematically hidden?
- Is performance robust across regimes and scenarios?
Board Trigger: Do the metrics connect to business value, not just internal consistency?
4. Governance & Control Risk
- Who owns intervention decisions?
- Are stop-losses, overrides, and rollback mechanisms defined?
- Is there clarity on when the project should stop?
Board Trigger: Is there an explicit governance pathway for Stop decisions?
5. Data & Learning Input Governance
- Are data inputs and learning design treated as capital decisions?
- Is the irreversibility of learning understood at the executive level?
- Are senior governance checkpoints in place for retraining and data changes?
Board Trigger: Are learning inputs governed by capital risk logic, not delegated to implementation layers?
The Assessment Process
The assessment proceeds through structured review stages, each producing explicit findings rather than recommendations.
At each stage:
- Evidence is examined
- Assumptions are surfaced
- Risk exposure is articulated in decision language
The output is not a technical score, but a decision-grade risk narrative.
What the Board Receives
The final assessment provides:
- Clear articulation of capital risk drivers
- Identification of structural failure modes
- Explicit Go/No-Go or Continue/Stop rationale
- Early warning indicators for future review cycles
All findings are presented without technical jargon—the board needs economic clarity, not engineering detail.
Why This Framework Exists
Most AI failures are only obvious in hindsight.
This framework exists to surface those risks before momentum and sunk cost make them invisible.
It is designed for:
- Independent Directors
- Board members
- CEOs and CFOs responsible for capital stewardship
There is no technical score that replaces governance judgment—only early visibility of risk patterns does.
Boundary Conditions
To preserve independence:
- No implementation responsibility is accepted
- No success fees are linked to outcomes
- No pressure exists to justify continuation
The assessment stands on its own.
Key Board Questions
To assist governance deliberations:
Before Capital
- What assumptions would make this investment worthless if wrong?
- What business outcome will this change measurably?
During Execution
- Have we validated metrics against reality?
- Who has the authority to stop this initiative?
- Are data and learning inputs governed at the right level?
"Are we investing in learning—or in an illusion of progress?"