Every major technology shift creates a familiar response.
New capabilities emerge. Organizations scramble to adapt. And almost immediately, the work begins of redefining people around the technology.
New titles. New roles. New org charts.
The industry is doing it again with AI.
Scroll through any AI roles breakdown circulating on LinkedIn and you will see dozens of new titles being created to support the technology. Chief AI Officer. Head of Applied AI. AI Strategy Manager. AI Compliance Analyst. AI HR Business Partner. The list runs to forty entries on a typical posting and grows weekly.
That part is not surprising.
What is surprising is how quickly the industry forgets that this conversation has been held before, with different vocabulary, in every prior technology era.
In 2007, Procurement Insights published a piece on procurement’s expanding role and asked a structural question that has not aged: were organizations truly evolving the function, or were they reshaping people to fit a model that had not been designed for how work actually happens?
Procurement’s Expanding Role and the Executive of the Future — Procurement Insights, 2007
The pattern was clear then. It is clearer now.
Seven technology eras across thirty years. Each era created new role categories in response to the technology of its moment. The failure rate held within the same 55–75% range throughout. The role count keeps going up. The failure rate remains high.
When systems do not align with real-world decision-making, organizations respond by creating roles to compensate, layering on governance to manage the gap, and adding oversight to catch what the system itself was never designed to handle. And eventually, when outcomes fail to materialize, talent gets blamed for failures the system architecture made inevitable.
AI does not change that pattern. It amplifies it. Decisions move faster. Errors scale faster. Misalignment becomes visible sooner. The cost of getting the structural relationship wrong compounds in compressed timeframes.
The deeper structural inversion is the one nobody is naming.
Organizations are once again adapting people to the technology rather than building technology that adapts to how work actually happens. The talent-war framing circulating on LinkedIn — that 94% of CEOs treat AI skills as their top priority while 90% of enterprises cannot find the talent — assumes the constraint is human capacity. The constraint has been, in every prior era, the structural mismatch between how the technology was designed and how decisions actually get made under real-world conditions.
This is the iceberg argument applied to talent. The roles are the visible ten percent — the algorithms-and-titles layer that gets the org chart attention. The structural conditions that determine whether any role configuration can produce capability are the ninety percent the conversation keeps treating as a constant. Adding more roles to the visible ten percent does not change what is happening in the ninety percent the architecture has not addressed.
The AI Compliance Analyst exists because the system cannot ensure compliance by design. The AI HR Business Partner exists because the deployment was never aligned with how HR decisions actually get made. Each new role is a structural workaround given a job title.
So the question is not what new AI roles do organizations need.
The question is whether organizations are designing systems that reflect how decisions actually get made under real-world conditions — or whether they are designing roles to compensate for the absence of that design.
If it is the latter, the outcome is already documented. More structure. More complexity. Same results.
The industry does not need more roles.
It needs to stop building systems that require those roles to compensate for what was never designed in.
AI will not fix misaligned systems. It will make their consequences unavoidable regardless of the titles you create.
Phase 0™ · Hansen Fit Score™ (HFS™) · ARA™ · RAM 2025™ · Real-World Condition Substrate™
Hansen Models™ · Founder: Jon W. Hansen · hansenprocurement.com
-30-
Procurement Insights
We’ve Seen This Before. And It Didn’t End Well.
Posted on May 3, 2026
0
Every major technology shift creates a familiar response.
New capabilities emerge. Organizations scramble to adapt. And almost immediately, the work begins of redefining people around the technology.
New titles. New roles. New org charts.
The industry is doing it again with AI.
Scroll through any AI roles breakdown circulating on LinkedIn and you will see dozens of new titles being created to support the technology. Chief AI Officer. Head of Applied AI. AI Strategy Manager. AI Compliance Analyst. AI HR Business Partner. The list runs to forty entries on a typical posting and grows weekly.
That part is not surprising.
What is surprising is how quickly the industry forgets that this conversation has been held before, with different vocabulary, in every prior technology era.
In 2007, Procurement Insights published a piece on procurement’s expanding role and asked a structural question that has not aged: were organizations truly evolving the function, or were they reshaping people to fit a model that had not been designed for how work actually happens?
Procurement’s Expanding Role and the Executive of the Future — Procurement Insights, 2007
The pattern was clear then. It is clearer now.
Seven technology eras across thirty years. Each era created new role categories in response to the technology of its moment. The failure rate held within the same 55–75% range throughout. The role count keeps going up. The failure rate remains high.
When systems do not align with real-world decision-making, organizations respond by creating roles to compensate, layering on governance to manage the gap, and adding oversight to catch what the system itself was never designed to handle. And eventually, when outcomes fail to materialize, talent gets blamed for failures the system architecture made inevitable.
AI does not change that pattern. It amplifies it. Decisions move faster. Errors scale faster. Misalignment becomes visible sooner. The cost of getting the structural relationship wrong compounds in compressed timeframes.
The deeper structural inversion is the one nobody is naming.
Organizations are once again adapting people to the technology rather than building technology that adapts to how work actually happens. The talent-war framing circulating on LinkedIn — that 94% of CEOs treat AI skills as their top priority while 90% of enterprises cannot find the talent — assumes the constraint is human capacity. The constraint has been, in every prior era, the structural mismatch between how the technology was designed and how decisions actually get made under real-world conditions.
This is the iceberg argument applied to talent. The roles are the visible ten percent — the algorithms-and-titles layer that gets the org chart attention. The structural conditions that determine whether any role configuration can produce capability are the ninety percent the conversation keeps treating as a constant. Adding more roles to the visible ten percent does not change what is happening in the ninety percent the architecture has not addressed.
The AI Compliance Analyst exists because the system cannot ensure compliance by design. The AI HR Business Partner exists because the deployment was never aligned with how HR decisions actually get made. Each new role is a structural workaround given a job title.
So the question is not what new AI roles do organizations need.
The question is whether organizations are designing systems that reflect how decisions actually get made under real-world conditions — or whether they are designing roles to compensate for the absence of that design.
If it is the latter, the outcome is already documented. More structure. More complexity. Same results.
The industry does not need more roles.
It needs to stop building systems that require those roles to compensate for what was never designed in.
AI will not fix misaligned systems. It will make their consequences unavoidable regardless of the titles you create.
Phase 0™ · Hansen Fit Score™ (HFS™) · ARA™ · RAM 2025™ · Real-World Condition Substrate™
Hansen Models™ · Founder: Jon W. Hansen · hansenprocurement.com
-30-
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