Stanford’s Foundation Model Transparency Index evaluates a critical factor: how transparent the AI model itself is— its data sources, governance, responsible use, and documentation.
IBM Granite scored 96%, the highest ever recorded. That is a real moat. A trustworthy engine matters.
But here’s the part no transparency index measures yet:
Whether the buyer is structurally ready to succeed with the technology.
For 35 years, procurement and enterprise tech have shared the same pattern:
- Highly capable platforms
- Highly transparent models
- 80% failure rate in real-world adoption
Not because the models are opaque… But because the organizations buying them are.
That’s where the Hansen Fit Score (HFS) and the RAM 2025 6-Model/5-Level framework come in.
They measure a different domain of transparency:
Readiness transparency — the clarity of incentives, alignment, governance, decision-making, and absorptive capacity.
This is the “hidden layer” that determines whether the organization can actually use the transparent model they just purchased.
Think of it this way:
- IBM Granite shows you how the engine works.
- HFS/RAM shows you whether you can drive the car without crashing it.
Google’s Patrick Marlow put it more colorfully in an exchange we had last year:
“Imagine buying a new Corvette, running it into the lake, then blaming Chevy for it not being able to float. 😅 Replace Corvette/Chevy with LLM/any model provider, and that’s exactly what is happening across the industry. People are attempting to deliver projects with tech they haven’t taken the time to truly understand.”
His advice? “Stop driving your Corvette into the lake.”
Both matter. One protects you from black-box AI. The other protects you from black-box organizations.
And here’s the industry’s uncomfortable truth:
Most AI failures aren’t caused by the model. They’re caused by misalignment, unreadiness, and governance gaps that no model transparency score can detect.
That’s why the two transparencies belong together — and why the comparison above matters.
The future won’t be won by the best model alone. It will be won by the organizations that pair:
- Transparent Models (IBM, etc.)
- Transparent Readiness (HFS, RAM 2025)
One is a moat. The other is the moat no one else is measuring yet.
Together, they reduce the 80% failure rate the industry has accepted for far too long.
#AI #Procurement #DigitalTransformation #Transparency #ProcureTech
Procurement Insights
IBM just topped Stanford’s global transparency index — and it’s a meaningful win. But there’s a second kind of transparency the industry still isn’t measuring… and it’s the one that determines whether organizations actually succeed with AI.
Posted on December 11, 2025
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Stanford’s Foundation Model Transparency Index evaluates a critical factor: how transparent the AI model itself is— its data sources, governance, responsible use, and documentation.
IBM Granite scored 96%, the highest ever recorded. That is a real moat. A trustworthy engine matters.
But here’s the part no transparency index measures yet:
Whether the buyer is structurally ready to succeed with the technology.
For 35 years, procurement and enterprise tech have shared the same pattern:
Not because the models are opaque… But because the organizations buying them are.
That’s where the Hansen Fit Score (HFS) and the RAM 2025 6-Model/5-Level framework come in.
They measure a different domain of transparency:
Readiness transparency — the clarity of incentives, alignment, governance, decision-making, and absorptive capacity.
This is the “hidden layer” that determines whether the organization can actually use the transparent model they just purchased.
Think of it this way:
Google’s Patrick Marlow put it more colorfully in an exchange we had last year:
His advice? “Stop driving your Corvette into the lake.”
Both matter. One protects you from black-box AI. The other protects you from black-box organizations.
And here’s the industry’s uncomfortable truth:
Most AI failures aren’t caused by the model. They’re caused by misalignment, unreadiness, and governance gaps that no model transparency score can detect.
That’s why the two transparencies belong together — and why the comparison above matters.
The future won’t be won by the best model alone. It will be won by the organizations that pair:
One is a moat. The other is the moat no one else is measuring yet.
Together, they reduce the 80% failure rate the industry has accepted for far too long.
#AI #Procurement #DigitalTransformation #Transparency #ProcureTech
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