What every technology era inherits — and what it leaves behind.
Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
Yesterday I published a graph and one question. Four technology eras, four research organizations with no connection to one another, spanning a decade. What do you see, and what effect did 2016 have on 2018, 2025 and 2026?
Ellie L D. gave the sharpest answer, and she gave it in numbers rather than opinion. She read the columns back — 73% invested or planning in big data, 15% to production; 72% expecting to start RPA, 3% at fifty or more robots; 88% using AI, 7% fully scaled; 80% calling procurement AI transformational, 12% at large scale — and then made the observation that matters:
“What changed each time was the label on the tech.”
What didn’t change, in her reading, was whether organizations built the governance and capability to industrialize it.
That is a good answer, and I want to push one part of it further.
Governance may be downstream
Governance and capability almost certainly explain some of the gap. But before an organization can govern a technology or industrialize it, something has to happen first.
It has to establish what the technology is actually being introduced into.
Not the process map. Not the target operating model. The actual conditions on the ground — including the conditions inherited from the last time this exercise was run.
Because here is what the four columns quietly record. Big data did not disappear when RPA arrived. RPA did not disappear when AI arrived. Each new era inherited the systems, the data, the processes, the assumptions and the workarounds of the one before it.
The 85% who had not reached production with big data in 2016 did not stop existing. They had budgets, projects, people assigned — and then the industry moved on to the next wave without anyone going back to ask what became of them.
They are still there. They are the operation the 2026 agent is being deployed into.
Legacy Drag
I call the compounding effect Legacy Drag.
It is not simply technical debt, and it is not only dirty data. It is the accumulated condition an initiative inherits from every prior initiative — the unfinished integrations, the workarounds that became procedure, the master data that four departments name four different ways for four good reasons, and the organizational memory of the last program that was announced and never landed.
Drag is the right word because it behaves like drag. It is a force acting against the current initiative, continuously, and it scales with speed. The faster the thing you are deploying, the more the unresolved conditions cost you.
Which is why it does not decay between waves. Each generation adds its own residue while still pulling against the last one’s.
And it is worth being precise about the limit here: the conditions are identifiable, but the rate is not measured. Nobody has published a figure for how quickly an unresolved condition propagates through an agentic environment, and I am not going to invent one. What can be said is that the conditions are present, and that nothing in the record suggests they were cleared.
Legacy Draft
The opposite of drag is not the absence of drag. It is Legacy Draft.
In racing, drafting is what happens when the vehicle in front reduces the resistance for the one behind. The follower goes faster on less effort, because of the conditions the leader created.
Legacy Draft is structuring what you build today so that tomorrow’s initiative arrives into conditions that help it rather than hold it back.
That is a different design objective from the one most programs carry. The usual objective is: deliver this initiative. The Legacy Draft objective is: deliver this initiative and leave the operation in a state that makes the next one faster.
Those are not the same thing, and the second is rarely anyone’s success measure. Nobody’s bonus depends on how easy they made the 2030 program.
Which is precisely why every era starts from behind.
This is not a new observation
In January 2008 I published an independent assessment of SAP’s procurement offering for the public sector. Buried in the middle of it is a sentence I did not have a name for at the time:
Many organizations may very well be embarking on a new undertaking from one or two steps back. This is an important factor that has to be both quantified and addressed prior to moving forward with any strategy.
Quantified and addressed prior to moving forward. Eighteen years ago.
The same paper noted why it compounds rather than resets. An organization that has been through a misaligned implementation carries more than the technical residue. It carries the stakeholder response to it — visible, as I wrote then, as cynicism at the operational levels. That travels forward into the next program too, and no data-cleansing exercise touches it.
So Legacy Drag is not a term I coined to describe 2026. It is a name for a condition documented in my own work in 2008 and observable in every column of that graph.
What this changes about the question
Ellie’s reading and mine are not in conflict. Governance and capability matter. But the sequence matters more than the list.
You cannot govern what you have not established. You cannot industrialize into an operation you have not traced. And you cannot assess readiness for a new technology without accounting for what the last three left behind.
So the question underneath the recurring gap may not be why does this keep happening.
It may be: what do we carry forward each time that allows it to recur — and what would we have to leave behind differently for the next era to start from ahead rather than behind?
The first half of that is Legacy Drag. The second half is Legacy Draft.
One of them is what you inherit. The other is what you choose.
Keep the human at the wheel. Everything else is just faster.
-30-
This analysis draws on the Procurement Insights archive — an independent record carrying zero vendor sponsorships, published openly since 2007 and consolidating documented client work, lectures, and articles reaching back to 1998. Every claim is held to the Provenance Ledger™: a verify-before-publish discipline that traces each assertion to a primary source and never quietly edits the record once posted. That record is the evidence base for two working lenses — Invariant Physics™, the constant that however far the technology advances the operating logic must be in place first, and Implementation Physics™, its per-engagement application. Phase 0™ identifies and examines the unique and collective attributes within a wide range of seemingly disparate strands — the Strand Commonality™ theory, funded by the Government of Canada’s Scientific Research and Experimental Development program.
Getting it right rather than being right.
Jon W. Hansen, FCIPS — Procurement Insights | Hansen Models™
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Legacy Drag and Legacy Draft,
Posted on September 2, 2026
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What every technology era inherits — and what it leaves behind.
Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
Yesterday I published a graph and one question. Four technology eras, four research organizations with no connection to one another, spanning a decade. What do you see, and what effect did 2016 have on 2018, 2025 and 2026?
Ellie L D. gave the sharpest answer, and she gave it in numbers rather than opinion. She read the columns back — 73% invested or planning in big data, 15% to production; 72% expecting to start RPA, 3% at fifty or more robots; 88% using AI, 7% fully scaled; 80% calling procurement AI transformational, 12% at large scale — and then made the observation that matters:
What didn’t change, in her reading, was whether organizations built the governance and capability to industrialize it.
That is a good answer, and I want to push one part of it further.
Governance may be downstream
Governance and capability almost certainly explain some of the gap. But before an organization can govern a technology or industrialize it, something has to happen first.
It has to establish what the technology is actually being introduced into.
Not the process map. Not the target operating model. The actual conditions on the ground — including the conditions inherited from the last time this exercise was run.
Because here is what the four columns quietly record. Big data did not disappear when RPA arrived. RPA did not disappear when AI arrived. Each new era inherited the systems, the data, the processes, the assumptions and the workarounds of the one before it.
The 85% who had not reached production with big data in 2016 did not stop existing. They had budgets, projects, people assigned — and then the industry moved on to the next wave without anyone going back to ask what became of them.
They are still there. They are the operation the 2026 agent is being deployed into.
Legacy Drag
I call the compounding effect Legacy Drag.
It is not simply technical debt, and it is not only dirty data. It is the accumulated condition an initiative inherits from every prior initiative — the unfinished integrations, the workarounds that became procedure, the master data that four departments name four different ways for four good reasons, and the organizational memory of the last program that was announced and never landed.
Drag is the right word because it behaves like drag. It is a force acting against the current initiative, continuously, and it scales with speed. The faster the thing you are deploying, the more the unresolved conditions cost you.
Which is why it does not decay between waves. Each generation adds its own residue while still pulling against the last one’s.
And it is worth being precise about the limit here: the conditions are identifiable, but the rate is not measured. Nobody has published a figure for how quickly an unresolved condition propagates through an agentic environment, and I am not going to invent one. What can be said is that the conditions are present, and that nothing in the record suggests they were cleared.
Legacy Draft
The opposite of drag is not the absence of drag. It is Legacy Draft.
In racing, drafting is what happens when the vehicle in front reduces the resistance for the one behind. The follower goes faster on less effort, because of the conditions the leader created.
Legacy Draft is structuring what you build today so that tomorrow’s initiative arrives into conditions that help it rather than hold it back.
That is a different design objective from the one most programs carry. The usual objective is: deliver this initiative. The Legacy Draft objective is: deliver this initiative and leave the operation in a state that makes the next one faster.
Those are not the same thing, and the second is rarely anyone’s success measure. Nobody’s bonus depends on how easy they made the 2030 program.
Which is precisely why every era starts from behind.
This is not a new observation
In January 2008 I published an independent assessment of SAP’s procurement offering for the public sector. Buried in the middle of it is a sentence I did not have a name for at the time:
Quantified and addressed prior to moving forward. Eighteen years ago.
The same paper noted why it compounds rather than resets. An organization that has been through a misaligned implementation carries more than the technical residue. It carries the stakeholder response to it — visible, as I wrote then, as cynicism at the operational levels. That travels forward into the next program too, and no data-cleansing exercise touches it.
So Legacy Drag is not a term I coined to describe 2026. It is a name for a condition documented in my own work in 2008 and observable in every column of that graph.
What this changes about the question
Ellie’s reading and mine are not in conflict. Governance and capability matter. But the sequence matters more than the list.
You cannot govern what you have not established. You cannot industrialize into an operation you have not traced. And you cannot assess readiness for a new technology without accounting for what the last three left behind.
So the question underneath the recurring gap may not be why does this keep happening.
It may be: what do we carry forward each time that allows it to recur — and what would we have to leave behind differently for the next era to start from ahead rather than behind?
The first half of that is Legacy Drag. The second half is Legacy Draft.
One of them is what you inherit. The other is what you choose.
Keep the human at the wheel. Everything else is just faster.
-30-
This analysis draws on the Procurement Insights archive — an independent record carrying zero vendor sponsorships, published openly since 2007 and consolidating documented client work, lectures, and articles reaching back to 1998. Every claim is held to the Provenance Ledger™: a verify-before-publish discipline that traces each assertion to a primary source and never quietly edits the record once posted. That record is the evidence base for two working lenses — Invariant Physics™, the constant that however far the technology advances the operating logic must be in place first, and Implementation Physics™, its per-engagement application. Phase 0™ identifies and examines the unique and collective attributes within a wide range of seemingly disparate strands — the Strand Commonality™ theory, funded by the Government of Canada’s Scientific Research and Experimental Development program.
Getting it right rather than being right.
Jon W. Hansen, FCIPS — Procurement Insights | Hansen Models™
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