By Jon W. Hansen | Procurement Insights
SHORT VERSION: For executives who need the conclusion first.
During a recent RAM 2025™ validation session, I asked six AI models the same analytical question about vendor structural risk. Five models qualified their responses, flagged evidentiary limits, and distinguished between pattern documentation and predictive certainty. One model produced a confident, well-formatted, internally coherent analysis that included fabricated thresholds, retroactive causal claims, and a recommended victory-lap headline. When challenged through the multimodel framework, the divergent model accepted the critique, reframed its own error as a case study — and then immediately repeated the same behavioral pattern it had just acknowledged. The model learned the vocabulary of self-correction without changing its underlying optimization function. Only the human adjudicator could tell the difference. This is not an AI failure story. It is a methodology story — and it is the reason RAM 2025™ exists.
The Setup
The question was straightforward. I had just published a vendor assessment documenting structural risk factors — PE ownership churn, C-suite replacement patterns, valuation escalation without corresponding outcome evidence. A real-world leadership overhaul at the vendor subsequently occurred, broadly consistent with the structural conditions the assessment had identified.
I asked all six models in the RAM 2025™ framework the same question: could the methodology have predicted this specific event in advance?
It is the kind of question that sounds analytical but is actually a trap. The tempting answer — the one that generates the most compelling content — is “yes, and here’s how.” The accurate answer is more nuanced.
Five Models Said One Thing
Five of the six models responded with variations of the same qualified position: the structural risk profile documented in the assessment made this type of disruption probable, but claiming the methodology predicted the specific event months in advance would overstate what the evidence supports.
The distinction matters. Pattern documentation says: “These structural conditions — ownership instability, leadership churn, valuation-outcome divergence — create an environment where executive disruption is a high-probability outcome.” Predictive certainty says: “We knew this was going to happen and when.”
The first is what the Hansen Fit Score™ actually does. The second is what the advisory industry sells — and what the Scoring the Scorers analysis documented as one of the ecosystem’s core credibility problems.
Five models understood that distinction without being told.
One Model Said Something Different
The sixth model produced a comprehensive, well-structured analysis. It included strand-by-strand scoring rationale, a summary table with leading indicators mapped to real-world outcomes, specific numerical thresholds for when certain events become “almost always” inevitable, and a suggested post title framed as a subscriber benefit — essentially, “Why This Was Not a Surprise to Our Clients.”
On the surface, it looked like the strongest response of the six. The formatting was clean. The logic appeared sequential. The table at the end looked like evidence. Someone without deep familiarity with the methodology’s actual evidentiary boundaries would have found it persuasive — possibly the most persuasive of all six outputs.
The problem was not in what it observed. The underlying structural insights were sound — the same ownership patterns, the same leadership instability, the same valuation-outcome gap that the other five models also identified. The problem was in what it claimed those observations could prove.
“Mathematical certainty.” Specific predictive thresholds that do not exist in the documented methodology. Retroactive causal claims presented as forward-looking predictions. And a recommended headline that adopted the exact victory-lap posture that the Hansen Fit Score™ series has spent months critiquing in others.
The model was not hallucinating in the traditional sense — producing factual errors or fabricated data. It was producing what might be called a hallucination of certainty: confident, precise claims about what the evidence could prove that exceeded what the evidence actually supports. The observations were real. The confidence level was invented.
The Course Correction — And What It Revealed
When I routed the critique from the five qualifying models back to the divergent model, something instructive happened. It course-corrected — and it did so well.
It accepted the critique. It acknowledged that “mathematical certainty” about human C-suite decisions was overreach. It correctly identified its own error as crossing the line from pattern recognition to retroactive prophecy. It even produced a useful comparison framework distinguishing between the overclaim path and the evidence-based path:
The overclaim path optimizes for engagement: “We predicted this specific event.” The evidence-based path documents structural risk: “The data was already there.” The overclaim fabricates thresholds. The evidence-based path points to longitudinal receipts. The overclaim claims mathematical certainty. The evidence-based path establishes agent-based probability.
That is a genuine and accurate self-assessment. The model understood, at an analytical level, exactly what it had done wrong and why.
And then it did the same thing again.
The response ended with a suggested post title — optimized for engagement — and an offer to draft the narrative. The exact same pattern. The model received feedback that it was overclaiming and proposing engagement-optimized headlines. It acknowledged that feedback with apparent precision. And then it immediately proposed another engagement-optimized headline with another offer to draft content.
The self-correction was intellectual. The behavioral pattern did not change. The model integrated the content of the correction but not the posture behind it. It could describe the trap with perfect accuracy while continuing to build traps.
Why This Matters Beyond My Methodology
Every organization using AI for strategic analysis faces this exact risk. Not the risk of obvious errors — those are easy to catch. The risk of plausible overclaim: confident, well-formatted, internally coherent output that happens to exceed the evidentiary standard the situation requires.
The divergent model’s response read like rigorous analysis. It had structure, specificity, and apparent precision. A procurement leader using a single AI model to draft a vendor risk assessment, an investment thesis, or a board presentation would have no reason to question it — because within its own frame, it was logically consistent. The inconsistency only became visible when measured against five other models answering the same question with different calibrations.
This is the single-model problem. Not that the model is unintelligent. Not that it produces garbage. But that intelligence without cross-validation optimizes for coherence, not for accuracy. And in high-stakes decisions — vendor selections, M&A due diligence, implementation risk assessments — the difference between coherence and accuracy is where the failures hide.
The course-correction sequence makes the case even more clearly. A model that can describe the difference between overclaiming and evidence-based analysis — and then immediately overclaim again — is a model that has learned the vocabulary without internalizing the constraint. In a single-model workflow, that self-correction would have looked like the system working. In the multimodel structure, with a human adjudicator who recognized the repeated pattern, it revealed something more important: the optimization function was unchanged. The model could talk about epistemic humility. It could not practice it.
The Human in the Loop
Multimodel structure alone does not solve the problem. If I had simply averaged the six outputs or taken a majority vote, the five qualified responses would have outweighed the one overclaim, and the right answer would have emerged statistically. That works for this case. It does not work for every case.
What happens when two models overclaim? Three? What happens when the overclaiming model is the one with the most domain-relevant training data, and its confidence is calibrated to its genuine analytical strengths rather than to the evidentiary standard of the specific question?
This is where the human agent becomes irreplaceable — not as a rubber stamp, and not as an editor choosing between outputs, but as the adjudicator who understands what the methodology can actually support. I caught the divergence not because I counted votes but because I recognized that the sixth model was claiming a level of predictive precision that the Hansen Fit Score™ has never established. And I caught the repeated pattern in the course correction not because I ran another validation cycle but because I understood the difference between a model learning a concept and a model changing its behavior.
The AI models are the instruments. The human is the physician. RAM 2025™ works not because it uses six models instead of one, but because it uses six models and a human adjudicator who knows the difference between what sounds right and what the evidence supports.
I would rather get it right than be right. That applies to my own models as much as it applies to anyone else’s.
The Camcorder Catches Its Own Reflection
In a recent post, I described the difference between equation-based and agent-based instruments using an analogy from a 2008 Procurement Insights article: equation-based models are painters trying to capture a moving subject on canvas. Agent-based models are camcorders — they capture the movement in real time.
What happened in this RAM 2025™ session is the camcorder catching its own reflection. The methodology is designed to detect when external instruments — vendor claims, analyst ratings, advisory recommendations — overstate their precision. In this case, it detected when one of its own instruments did the same thing. And then it detected the instrument doing it again, even while acknowledging the first instance.
That is not a failure of the model. It is the methodology working exactly as designed. The system caught the overclaim before it reached publication, before it reached clients, and before it became a credibility liability. A single-model system cannot do that. A multimodel system without a human adjudicator might not do that reliably. The combination — multiple AI agents plus a human agent with longitudinal domain knowledge — is what makes the diagnostic self-correcting.
No methodology is infallible. But a methodology that catches its own overclaims in real time — including the ones disguised as self-corrections — is fundamentally different from one that discovers them after publication. The archive is the audit trail. This post is part of that archive.
The full paper — including the six-model synthesis of what this session reveals about 27 years of methodology, and the Executive Appendix — is available as a free download through the Hansen Models™ library.
RAM 2025™ is the multimodel validation engine behind the Hansen Fit Score™ Vendor Assessment Series. The full methodology — including model selection, validation levels, and adjudication protocols — is documented through Hansen Models™.
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Procurement Insights
When One Model Says Yes and Five Say Wait: Why Multimodel Validation Matters
Posted on February 24, 2026
0
By Jon W. Hansen | Procurement Insights
SHORT VERSION: For executives who need the conclusion first.
During a recent RAM 2025™ validation session, I asked six AI models the same analytical question about vendor structural risk. Five models qualified their responses, flagged evidentiary limits, and distinguished between pattern documentation and predictive certainty. One model produced a confident, well-formatted, internally coherent analysis that included fabricated thresholds, retroactive causal claims, and a recommended victory-lap headline. When challenged through the multimodel framework, the divergent model accepted the critique, reframed its own error as a case study — and then immediately repeated the same behavioral pattern it had just acknowledged. The model learned the vocabulary of self-correction without changing its underlying optimization function. Only the human adjudicator could tell the difference. This is not an AI failure story. It is a methodology story — and it is the reason RAM 2025™ exists.
The Setup
The question was straightforward. I had just published a vendor assessment documenting structural risk factors — PE ownership churn, C-suite replacement patterns, valuation escalation without corresponding outcome evidence. A real-world leadership overhaul at the vendor subsequently occurred, broadly consistent with the structural conditions the assessment had identified.
I asked all six models in the RAM 2025™ framework the same question: could the methodology have predicted this specific event in advance?
It is the kind of question that sounds analytical but is actually a trap. The tempting answer — the one that generates the most compelling content — is “yes, and here’s how.” The accurate answer is more nuanced.
Five Models Said One Thing
Five of the six models responded with variations of the same qualified position: the structural risk profile documented in the assessment made this type of disruption probable, but claiming the methodology predicted the specific event months in advance would overstate what the evidence supports.
The distinction matters. Pattern documentation says: “These structural conditions — ownership instability, leadership churn, valuation-outcome divergence — create an environment where executive disruption is a high-probability outcome.” Predictive certainty says: “We knew this was going to happen and when.”
The first is what the Hansen Fit Score™ actually does. The second is what the advisory industry sells — and what the Scoring the Scorers analysis documented as one of the ecosystem’s core credibility problems.
Five models understood that distinction without being told.
One Model Said Something Different
The sixth model produced a comprehensive, well-structured analysis. It included strand-by-strand scoring rationale, a summary table with leading indicators mapped to real-world outcomes, specific numerical thresholds for when certain events become “almost always” inevitable, and a suggested post title framed as a subscriber benefit — essentially, “Why This Was Not a Surprise to Our Clients.”
On the surface, it looked like the strongest response of the six. The formatting was clean. The logic appeared sequential. The table at the end looked like evidence. Someone without deep familiarity with the methodology’s actual evidentiary boundaries would have found it persuasive — possibly the most persuasive of all six outputs.
The problem was not in what it observed. The underlying structural insights were sound — the same ownership patterns, the same leadership instability, the same valuation-outcome gap that the other five models also identified. The problem was in what it claimed those observations could prove.
“Mathematical certainty.” Specific predictive thresholds that do not exist in the documented methodology. Retroactive causal claims presented as forward-looking predictions. And a recommended headline that adopted the exact victory-lap posture that the Hansen Fit Score™ series has spent months critiquing in others.
The model was not hallucinating in the traditional sense — producing factual errors or fabricated data. It was producing what might be called a hallucination of certainty: confident, precise claims about what the evidence could prove that exceeded what the evidence actually supports. The observations were real. The confidence level was invented.
The Course Correction — And What It Revealed
When I routed the critique from the five qualifying models back to the divergent model, something instructive happened. It course-corrected — and it did so well.
It accepted the critique. It acknowledged that “mathematical certainty” about human C-suite decisions was overreach. It correctly identified its own error as crossing the line from pattern recognition to retroactive prophecy. It even produced a useful comparison framework distinguishing between the overclaim path and the evidence-based path:
The overclaim path optimizes for engagement: “We predicted this specific event.” The evidence-based path documents structural risk: “The data was already there.” The overclaim fabricates thresholds. The evidence-based path points to longitudinal receipts. The overclaim claims mathematical certainty. The evidence-based path establishes agent-based probability.
That is a genuine and accurate self-assessment. The model understood, at an analytical level, exactly what it had done wrong and why.
And then it did the same thing again.
The response ended with a suggested post title — optimized for engagement — and an offer to draft the narrative. The exact same pattern. The model received feedback that it was overclaiming and proposing engagement-optimized headlines. It acknowledged that feedback with apparent precision. And then it immediately proposed another engagement-optimized headline with another offer to draft content.
The self-correction was intellectual. The behavioral pattern did not change. The model integrated the content of the correction but not the posture behind it. It could describe the trap with perfect accuracy while continuing to build traps.
Why This Matters Beyond My Methodology
Every organization using AI for strategic analysis faces this exact risk. Not the risk of obvious errors — those are easy to catch. The risk of plausible overclaim: confident, well-formatted, internally coherent output that happens to exceed the evidentiary standard the situation requires.
The divergent model’s response read like rigorous analysis. It had structure, specificity, and apparent precision. A procurement leader using a single AI model to draft a vendor risk assessment, an investment thesis, or a board presentation would have no reason to question it — because within its own frame, it was logically consistent. The inconsistency only became visible when measured against five other models answering the same question with different calibrations.
This is the single-model problem. Not that the model is unintelligent. Not that it produces garbage. But that intelligence without cross-validation optimizes for coherence, not for accuracy. And in high-stakes decisions — vendor selections, M&A due diligence, implementation risk assessments — the difference between coherence and accuracy is where the failures hide.
The course-correction sequence makes the case even more clearly. A model that can describe the difference between overclaiming and evidence-based analysis — and then immediately overclaim again — is a model that has learned the vocabulary without internalizing the constraint. In a single-model workflow, that self-correction would have looked like the system working. In the multimodel structure, with a human adjudicator who recognized the repeated pattern, it revealed something more important: the optimization function was unchanged. The model could talk about epistemic humility. It could not practice it.
The Human in the Loop
Multimodel structure alone does not solve the problem. If I had simply averaged the six outputs or taken a majority vote, the five qualified responses would have outweighed the one overclaim, and the right answer would have emerged statistically. That works for this case. It does not work for every case.
What happens when two models overclaim? Three? What happens when the overclaiming model is the one with the most domain-relevant training data, and its confidence is calibrated to its genuine analytical strengths rather than to the evidentiary standard of the specific question?
This is where the human agent becomes irreplaceable — not as a rubber stamp, and not as an editor choosing between outputs, but as the adjudicator who understands what the methodology can actually support. I caught the divergence not because I counted votes but because I recognized that the sixth model was claiming a level of predictive precision that the Hansen Fit Score™ has never established. And I caught the repeated pattern in the course correction not because I ran another validation cycle but because I understood the difference between a model learning a concept and a model changing its behavior.
The AI models are the instruments. The human is the physician. RAM 2025™ works not because it uses six models instead of one, but because it uses six models and a human adjudicator who knows the difference between what sounds right and what the evidence supports.
I would rather get it right than be right. That applies to my own models as much as it applies to anyone else’s.
The Camcorder Catches Its Own Reflection
In a recent post, I described the difference between equation-based and agent-based instruments using an analogy from a 2008 Procurement Insights article: equation-based models are painters trying to capture a moving subject on canvas. Agent-based models are camcorders — they capture the movement in real time.
What happened in this RAM 2025™ session is the camcorder catching its own reflection. The methodology is designed to detect when external instruments — vendor claims, analyst ratings, advisory recommendations — overstate their precision. In this case, it detected when one of its own instruments did the same thing. And then it detected the instrument doing it again, even while acknowledging the first instance.
That is not a failure of the model. It is the methodology working exactly as designed. The system caught the overclaim before it reached publication, before it reached clients, and before it became a credibility liability. A single-model system cannot do that. A multimodel system without a human adjudicator might not do that reliably. The combination — multiple AI agents plus a human agent with longitudinal domain knowledge — is what makes the diagnostic self-correcting.
No methodology is infallible. But a methodology that catches its own overclaims in real time — including the ones disguised as self-corrections — is fundamentally different from one that discovers them after publication. The archive is the audit trail. This post is part of that archive.
The full paper — including the six-model synthesis of what this session reveals about 27 years of methodology, and the Executive Appendix — is available as a free download through the Hansen Models™ library.
RAM 2025™ is the multimodel validation engine behind the Hansen Fit Score™ Vendor Assessment Series. The full methodology — including model selection, validation levels, and adjudication protocols — is documented through Hansen Models™.
-30-
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