What smoking, procurement, and a logistics podcast have in common
Something happened this weekend that I need to document — not because it was planned, but because it wasn’t.
Within a 48-hour window, three unrelated events converged in a way that demonstrates exactly how Hansen Strand Commonality™ works — and why the methodology behind the Hansen Fit Score™ produces outcomes in the 93–97% longitudinal accuracy range across measured implementation success vs. failure outcomes.
Let me walk you through it.
The Convergence
On Friday, CIO Magazine published an analysis of Salesforce’s new Agentic Work Unit (AWU) metric. Analysts from Moor Insights, Constellation Research, Greyhound Research, and The Futurum Group independently concluded the same thing:
AWU measures activity, not outcomes.
In sharing his article on LinkedIn, the author — Anirban Ghoshal — cited the Hansen Method™ by name alongside those analyst firms.
On Sunday evening, a senior procurement executive I’ve known for over a decade — someone with direct experience across Fortune 500 transformations — sent me a message. Chris Sawchuk, Principal and Global Procurement Advisory Practice Leader at The Hackett Group, had just posted on LinkedIn about agentic AI in procurement. His key phrase: organizations face challenges in “readiness, governance, and team awareness.”
Their note: “I’ve known him 10 years. Never heard him mention readiness before.”
At the same time, in a completely unrelated corner of my feed, Harshida Acharya — a logistics partner and podcast co-host — was writing about delivery and retention. Her observation: “Many leaders make it a priority to experience their own operations directly. Seeing the process as a customer often reveals gaps that metrics alone do not.”
She was describing the 4 o’clock question — the diagnostic moment from our 1998 Department of National Defence engagement — without knowing the term, the methodology, or the history behind it.
Two posts. Two industries. One diagnostic pattern — emerging independently, without coordination.
That is what strand commonality looks like in real time.
The Longer Pattern
These aren’t isolated signals. They are data points on a trajectory that has been building for four decades — and that the industry has been unable or unwilling to see.
To understand why, it helps to look at the only other modern case where overwhelming evidence failed to change institutional behavior for decades — and what finally broke the pattern.
In 1950, researchers published the first clinical evidence linking smoking to lung cancer. Smoking rates didn’t drop. They increased — peaking at nearly 57% by 1955. It took 14 years for the Surgeon General to issue an official report. It took 48 years for the tobacco industry to concede through the Master Settlement Agreement. It took 75 years for smoking rates to fall from 45% to 10%.
The evidence existed in 1950. The behavior didn’t change until ignoring the evidence became structurally expensive — through litigation, regulation, taxation, and accountability.
Enterprise technology is following the same curve.
Since the early 1980s, implementation failure rates have held at 70–80%. That number has not moved through four decades of technological advancement — not through ERP, not through cloud, not through digital transformation, not through AI. The platforms improved dramatically. The failure rate stayed the same.
During those same four decades, reliance on the instruments designed to guide vendor selection — Gartner’s Magic Quadrant, analyst rankings, capability assessments — increased from near zero to market dominance. And the failure rate held.
The most revealing observation on this timeline is not that the failure rate stayed flat. It is that as instrument reliance increased, outcomes did not improve. The industry didn’t abandon the instrument when it failed to produce results. It doubled down — exactly as public health institutions doubled down on measurement before litigation and regulation finally forced behavioral change in smoking.
What was missing was not better technology, but a diagnostic gate that made starting the wrong initiative more expensive than delaying the right one.
At scale, persistent transformation failure is no longer an operational issue — it is a governance failure.
Smoking rates bent only after ignoring the evidence became structurally untenable. Enterprise technology failure rates are approaching that same inflection — and readiness diagnostics like Phase 0™ are the instrument designed to accelerate the decline.
What the Extended Projection Shows
Using the smoking trajectory as a structural analog, RAM 2025™ multi-model consensus projects a three-phase shift:
2026–2035: The plateau. Failure rates hold at 70–80%. The narrative shifts to “AI is different.” The reality is the same decision structures producing faster failure. Gartner-style influence peaks and begins to flatten. This is the industry’s pre-Surgeon General period — the evidence is now public, the convergence is happening, but institutional behavior hasn’t changed yet.
2035–2050: The inflection. Variance increases. Early adopters of readiness-first diagnostics begin separating from the pack. Regulatory pressure on AI governance, board-level accountability for transformation failures, and accumulated evidence from documented Phase 0™ outcomes create the forcing function. Failure rates begin their structural decline — not because technology improves, but because ignoring readiness becomes financially and operationally untenable.
2050–2075: The readiness era. The analyst ecosystem doesn’t disappear — it repositions as one input among many, rather than the dominant instrument. Readiness-first diagnostics replace capability-first rankings as the primary decision architecture. Failure rates decline to approximately 28% — never zero, because organizational behavior is structurally stickier than individual habits. But less than half of where they sat for four decades.
Why This Matters Now
Phase 0™ is not predicting the future. It is diagnosing the present — using a methodology built on government-funded research (Canada’s SR&ED program, 1998), maintained through the most comprehensive independent practitioner-provider archive in the industry (Procurement Insights, 2007–present, 3,300+ published documents), and stress-tested through five independent AI models in structured dissent.
The strand commonality identified this weekend — Chris Sawchuk using readiness language for the first time in 10 years, a logistics professional independently describing diagnostic-first thinking, CIO Magazine article author citing the Hansen Method™ alongside major analyst firms in his LinkedIn post — is not a coincidence. It is the convergence point on the timeline, happening in real time.
The evidence against enterprise implementation failure has existed for four decades. What’s missing isn’t data — it’s a forcing function that makes ignoring it impossible.
Phase 0™ is that forcing function.
Smoking didn’t decline because the evidence got better. It declined when the cost of ignoring it exceeded the cost of acting on it.
The enterprise technology industry is approaching the same inflection.
Hansen Models™ · Jon Hansen, Founder · HPT@HansenProcurement.com Independent since 1998 · No vendor funding · No referral fees · No implementation revenue
-30-
Procurement Insights
Evidence Doesn’t Change Behavior — Until It Does
Posted on March 2, 2026
0
What smoking, procurement, and a logistics podcast have in common
Something happened this weekend that I need to document — not because it was planned, but because it wasn’t.
Within a 48-hour window, three unrelated events converged in a way that demonstrates exactly how Hansen Strand Commonality™ works — and why the methodology behind the Hansen Fit Score™ produces outcomes in the 93–97% longitudinal accuracy range across measured implementation success vs. failure outcomes.
Let me walk you through it.
The Convergence
On Friday, CIO Magazine published an analysis of Salesforce’s new Agentic Work Unit (AWU) metric. Analysts from Moor Insights, Constellation Research, Greyhound Research, and The Futurum Group independently concluded the same thing:
AWU measures activity, not outcomes.
In sharing his article on LinkedIn, the author — Anirban Ghoshal — cited the Hansen Method™ by name alongside those analyst firms.
On Sunday evening, a senior procurement executive I’ve known for over a decade — someone with direct experience across Fortune 500 transformations — sent me a message. Chris Sawchuk, Principal and Global Procurement Advisory Practice Leader at The Hackett Group, had just posted on LinkedIn about agentic AI in procurement. His key phrase: organizations face challenges in “readiness, governance, and team awareness.”
Their note: “I’ve known him 10 years. Never heard him mention readiness before.”
At the same time, in a completely unrelated corner of my feed, Harshida Acharya — a logistics partner and podcast co-host — was writing about delivery and retention. Her observation: “Many leaders make it a priority to experience their own operations directly. Seeing the process as a customer often reveals gaps that metrics alone do not.”
She was describing the 4 o’clock question — the diagnostic moment from our 1998 Department of National Defence engagement — without knowing the term, the methodology, or the history behind it.
Two posts. Two industries. One diagnostic pattern — emerging independently, without coordination.
That is what strand commonality looks like in real time.
The Longer Pattern
These aren’t isolated signals. They are data points on a trajectory that has been building for four decades — and that the industry has been unable or unwilling to see.
To understand why, it helps to look at the only other modern case where overwhelming evidence failed to change institutional behavior for decades — and what finally broke the pattern.
In 1950, researchers published the first clinical evidence linking smoking to lung cancer. Smoking rates didn’t drop. They increased — peaking at nearly 57% by 1955. It took 14 years for the Surgeon General to issue an official report. It took 48 years for the tobacco industry to concede through the Master Settlement Agreement. It took 75 years for smoking rates to fall from 45% to 10%.
The evidence existed in 1950. The behavior didn’t change until ignoring the evidence became structurally expensive — through litigation, regulation, taxation, and accountability.
Enterprise technology is following the same curve.
Since the early 1980s, implementation failure rates have held at 70–80%. That number has not moved through four decades of technological advancement — not through ERP, not through cloud, not through digital transformation, not through AI. The platforms improved dramatically. The failure rate stayed the same.
During those same four decades, reliance on the instruments designed to guide vendor selection — Gartner’s Magic Quadrant, analyst rankings, capability assessments — increased from near zero to market dominance. And the failure rate held.
The most revealing observation on this timeline is not that the failure rate stayed flat. It is that as instrument reliance increased, outcomes did not improve. The industry didn’t abandon the instrument when it failed to produce results. It doubled down — exactly as public health institutions doubled down on measurement before litigation and regulation finally forced behavioral change in smoking.
What was missing was not better technology, but a diagnostic gate that made starting the wrong initiative more expensive than delaying the right one.
At scale, persistent transformation failure is no longer an operational issue — it is a governance failure.
Smoking rates bent only after ignoring the evidence became structurally untenable. Enterprise technology failure rates are approaching that same inflection — and readiness diagnostics like Phase 0™ are the instrument designed to accelerate the decline.
What the Extended Projection Shows
Using the smoking trajectory as a structural analog, RAM 2025™ multi-model consensus projects a three-phase shift:
2026–2035: The plateau. Failure rates hold at 70–80%. The narrative shifts to “AI is different.” The reality is the same decision structures producing faster failure. Gartner-style influence peaks and begins to flatten. This is the industry’s pre-Surgeon General period — the evidence is now public, the convergence is happening, but institutional behavior hasn’t changed yet.
2035–2050: The inflection. Variance increases. Early adopters of readiness-first diagnostics begin separating from the pack. Regulatory pressure on AI governance, board-level accountability for transformation failures, and accumulated evidence from documented Phase 0™ outcomes create the forcing function. Failure rates begin their structural decline — not because technology improves, but because ignoring readiness becomes financially and operationally untenable.
2050–2075: The readiness era. The analyst ecosystem doesn’t disappear — it repositions as one input among many, rather than the dominant instrument. Readiness-first diagnostics replace capability-first rankings as the primary decision architecture. Failure rates decline to approximately 28% — never zero, because organizational behavior is structurally stickier than individual habits. But less than half of where they sat for four decades.
Why This Matters Now
Phase 0™ is not predicting the future. It is diagnosing the present — using a methodology built on government-funded research (Canada’s SR&ED program, 1998), maintained through the most comprehensive independent practitioner-provider archive in the industry (Procurement Insights, 2007–present, 3,300+ published documents), and stress-tested through five independent AI models in structured dissent.
The strand commonality identified this weekend — Chris Sawchuk using readiness language for the first time in 10 years, a logistics professional independently describing diagnostic-first thinking, CIO Magazine article author citing the Hansen Method™ alongside major analyst firms in his LinkedIn post — is not a coincidence. It is the convergence point on the timeline, happening in real time.
The evidence against enterprise implementation failure has existed for four decades. What’s missing isn’t data — it’s a forcing function that makes ignoring it impossible.
Phase 0™ is that forcing function.
Smoking didn’t decline because the evidence got better. It declined when the cost of ignoring it exceeded the cost of acting on it.
The enterprise technology industry is approaching the same inflection.
Hansen Models™ · Jon Hansen, Founder · HPT@HansenProcurement.com Independent since 1998 · No vendor funding · No referral fees · No implementation revenue
-30-
Share this:
Related