Jon Hansen | Hansen Models™ | February 2026
The Short Version For Busy Executives
During a routine vendor assessment update, one of our independent AI models identified a mathematical inconsistency between a published composite score and the illustrative calculation formula shown in the methodology section.
The inconsistency was not material to the outcome — but it was material to credibility.
Instead of ignoring it, we ran a structured multimodel stress test.
The result wasn’t a correction of the score. It was a clarification of governance.
This is what AI governance actually looks like in practice.
The Trigger
A vendor assessment report included three diagnostic dimension scores, a composite Hansen Fit Score (HFS), and an illustrative weighted formula showing how the dimensions relate conceptually.
One model flagged a tension:
If someone runs the published formula mechanically, the output will not equal the composite score shown.
That observation was correct.
And this is where governance begins.
What A Non-Governed System Would Have Done
A traditional single-model environment would likely do one of four things: ignore the discrepancy, adjust the math retroactively, recalibrate the formula to force alignment, or quietly update the score.
All four responses would reduce intellectual integrity. And all four would create long-term credibility risk.
What Actually Happened
The multimodel architecture produced three resolution paths:
Option A: Change the published scores to match the formula output.
Option B: Change the formula to match the published scores.
Option C (Selected): Clarify that the dimension scores are diagnostic inputs informing a calibrated composite judgment — not a mechanical arithmetic generator.
Each option was evaluated against analytical integrity, legal defensibility, transparency, methodological consistency, and reputational risk.
Option C was selected. Not because it protected the score. Because it most accurately reflected reality.
The Governance Principle
The composite score is not the output of a calculator.
It is the result of dimension scoring, evidence weighting, cross-model review, archive validation, structured disagreement resolution, and calibration under defined interpretive bands.
The formula was illustrative. The governance was operative.
The revision removed the appearance of false mechanical precision and replaced it with explicit calibration transparency.
Think of the three dimensions as the diagnostic panels; the HFS is the physician’s documented conclusion, with an auditable rationale.
Why This Matters Beyond One Report
This episode demonstrates three critical governance principles:
1. Model Disagreement Is Expected
Disagreement is not a failure mode. Unacknowledged disagreement is.
2. Mechanical Precision Can Create False Confidence
A visible formula that appears definitive but does not reflect actual scoring logic creates legal exposure. Transparency must reflect operational reality.
3. Governance Is Proven Under Pressure
Governance is not what you publish. It’s what you do when challenged.
In this case: the inconsistency was surfaced, alternatives were debated, the methodology was clarified, and the composite score remained evidence-based. That is reconstructable decision architecture.
Regulatory Relevance
Under the EU AI Act, organizations must demonstrate:
- Transparency of decision systems
- Human oversight
- Traceable reasoning
- Accountability for outputs
This case provides a micro-level illustration of what that looks like operationally. The system did not hide tension. It logged it. Examined it. Resolved it. Documented it.
That is governed AI collaboration.
What We Did Not Do
We did not:
- Reverse-engineer math to match a conclusion
- Collapse judgment into a formula
- Remove the composite score
- Change interpretive bands
- Suppress disagreement
Governance is not about being right. It is about being auditable.
The Larger Point
AI governance is not a policy document. It is a live discipline.
It happens when a model challenges the math, a human evaluates the risk, multiple analytical agents weigh resolution paths, and the outcome is calibrated and documented.
This was not a crisis. It was a test. And the architecture passed.
Why This Case Matters For Boards And C-Suite Leaders
Most organizations believe they have AI governance because they have a policy, they have a committee, they have a dashboard.
But governance only becomes real when a system challenges itself — and survives the correction without losing integrity.
That is the standard we are building toward.
Not smarter models. Stronger decisions.
This is the first true proof “published” artifact that RAM 2025™ is not conceptual. It is operational. That’s different.
Hansen Models™ — Exposed. Explainable. Repeatable.
-30-
Procurement Insights
How AI Transparency And Governance Works In The Real World: A RAM 2025™ Multimodel Case Study
Posted on February 11, 2026
0
Jon Hansen | Hansen Models™ | February 2026
The Short Version For Busy Executives
During a routine vendor assessment update, one of our independent AI models identified a mathematical inconsistency between a published composite score and the illustrative calculation formula shown in the methodology section.
The inconsistency was not material to the outcome — but it was material to credibility.
Instead of ignoring it, we ran a structured multimodel stress test.
The result wasn’t a correction of the score. It was a clarification of governance.
This is what AI governance actually looks like in practice.
The Trigger
A vendor assessment report included three diagnostic dimension scores, a composite Hansen Fit Score (HFS), and an illustrative weighted formula showing how the dimensions relate conceptually.
One model flagged a tension:
That observation was correct.
And this is where governance begins.
What A Non-Governed System Would Have Done
A traditional single-model environment would likely do one of four things: ignore the discrepancy, adjust the math retroactively, recalibrate the formula to force alignment, or quietly update the score.
All four responses would reduce intellectual integrity. And all four would create long-term credibility risk.
What Actually Happened
The multimodel architecture produced three resolution paths:
Option A: Change the published scores to match the formula output.
Option B: Change the formula to match the published scores.
Option C (Selected): Clarify that the dimension scores are diagnostic inputs informing a calibrated composite judgment — not a mechanical arithmetic generator.
Each option was evaluated against analytical integrity, legal defensibility, transparency, methodological consistency, and reputational risk.
Option C was selected. Not because it protected the score. Because it most accurately reflected reality.
The Governance Principle
The composite score is not the output of a calculator.
It is the result of dimension scoring, evidence weighting, cross-model review, archive validation, structured disagreement resolution, and calibration under defined interpretive bands.
The formula was illustrative. The governance was operative.
The revision removed the appearance of false mechanical precision and replaced it with explicit calibration transparency.
Why This Matters Beyond One Report
This episode demonstrates three critical governance principles:
1. Model Disagreement Is Expected
Disagreement is not a failure mode. Unacknowledged disagreement is.
2. Mechanical Precision Can Create False Confidence
A visible formula that appears definitive but does not reflect actual scoring logic creates legal exposure. Transparency must reflect operational reality.
3. Governance Is Proven Under Pressure
Governance is not what you publish. It’s what you do when challenged.
In this case: the inconsistency was surfaced, alternatives were debated, the methodology was clarified, and the composite score remained evidence-based. That is reconstructable decision architecture.
Regulatory Relevance
Under the EU AI Act, organizations must demonstrate:
This case provides a micro-level illustration of what that looks like operationally. The system did not hide tension. It logged it. Examined it. Resolved it. Documented it.
That is governed AI collaboration.
What We Did Not Do
We did not:
Governance is not about being right. It is about being auditable.
The Larger Point
AI governance is not a policy document. It is a live discipline.
It happens when a model challenges the math, a human evaluates the risk, multiple analytical agents weigh resolution paths, and the outcome is calibrated and documented.
This was not a crisis. It was a test. And the architecture passed.
Why This Case Matters For Boards And C-Suite Leaders
Most organizations believe they have AI governance because they have a policy, they have a committee, they have a dashboard.
But governance only becomes real when a system challenges itself — and survives the correction without losing integrity.
That is the standard we are building toward.
Not smarter models. Stronger decisions.
This is the first true proof “published” artifact that RAM 2025™ is not conceptual. It is operational. That’s different.
Hansen Models™ — Exposed. Explainable. Repeatable.
-30-
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