Across disciplines, capable people often appear to disagree when they are in fact describing the same thing in different languages. The cost of that untranslated difference is most of why organizations misfire.
Over the past several months, I have noticed something in the conversations I keep having with people across very different fields — procurement, enterprise architecture, transformation, supply chain, AI governance, academic research, systems thinking. At first they appear to be talking about entirely different things. Spend an hour in a room with them and you would conclude they disagree.
Listen more closely and the opposite turns out to be true. They are describing the same thing, from different doors, in different vocabularies — and the vocabulary is hiding the agreement.
The estate, the ecosystem, the operating model, the Metaprise
A transformation practitioner recently captured part of it in a single line: in a brownfield environment, the estate decides the order. Canda Rozier, a long-time and widely respected CPO and an advisor on our board, responded with the readiness corollary: without a foundation of readiness, transformation — brownfield or otherwise — is likely to fail. Between them, the two statements name the whole of it.
A word on the terms, because they matter. A greenfield environment is the luxury of starting from scratch: if we could design this today, how would we build it? A brownfield environment is the reality almost everyone actually lives in — existing systems, processes, contracts, integrations, workarounds, people, history, politics, technical debt, and dependencies, all already in place. You do not get to start over. You inherit what exists. And the “estate” is the whole of that inheritance: systems, data, processes, contracts, people, governance, dependencies.
So the estate decides the order means: before deciding what to change, understand what already exists and how it is connected, because the reality of the environment determines the sequence in which anything can be changed.
When I read that line, it named something I have described for more than two decades as the Metaprise — the entire operating environment in which an outcome is actually determined: people, systems, suppliers, customers, regulators, incentives, decision rights, AI agents, workflows, governance, external dependencies. It is the same reason the inferior tool so often outlasts the superior one — what governs the outcome was never the technology, but the conditions it entered. Different language. The same observation.
And the same observation keeps surfacing under different names. Enterprise architecture calls it the estate. Transformation calls it the operating model. Procurement calls it the ecosystem. Supply chain calls it the network. AI governance calls it the human-AI system. Systems thinkers call it a complex adaptive system. Each discipline arrives through its own door and names what it finds in its own dialect. In a great many cases, they are standing in the same room.
Why organizations misfire
This is not a linguistic curiosity. It is, I think, one of the most underdiagnosed reasons organizations underperform — and it is worth being precise about the mechanism.
Language shapes action. When different groups describe the same environment through different frameworks, they do not merely use different words. They pursue different objectives, measure different outcomes, and optimize different incentives — each internally coherent, each pointed somewhere slightly different. The result is familiar to anyone who has watched a transformation stall: projects drift, technology underperforms, stakeholders grow frustrated, and everyone reaches for a culprit. Leadership failed. Adoption failed. Governance failed. Change management failed.
A defence-sector engagement I worked on in the late 1990s shows the trap in miniature. The contract called for 90% next-day parts resolution; delivery was running at 51%, and I was asked to automate procurement to fix it. But the breakdown did not start in procurement. The field technicians were measured on service-call volume, and ordering parts after each call slowed them down — so they sandbagged, holding their orders until the end of the day to protect their call targets. Rational, in their own frame. Except late orders arrived after customs cutoffs and, on volatile commodities, at many times the morning price — which is what crippled delivery. And the part nobody traced: the same delay the technicians created to protect their call volume came back around as parts that never arrived in time, leaving the calls they had rushed to diagnose open. Their own call-closure rates were being wrecked by the very behavior they adopted to hit their numbers. They never saw the connection — because no one was looking at the strand that linked the technician’s incentive to procurement to the technician’s own outcome. (The full account is here.)
As stated earlier, the culprit is rarely a failure of leadership, adoption, or governance. It is that the participants were solving different versions of the same problem without realizing it. One group optimizes for speed-to-contract while another optimizes for risk containment; both goals are legitimate, and without a shared view of the conditions underneath, the two work quietly against each other. Goals become misaligned because the underlying assumptions were never aligned. Incentives diverge because success was defined differently by each group. Communication breaks down because identical words carry different meanings across disciplines. The problem was not disagreement. It was the absence of translation.
That distinction matters, because the two problems have opposite remedies. If the problem were disagreement, you would resolve it by debate — argue until someone is persuaded. If the problem is untranslated difference, debate makes it worse: each side argues harder in its own dialect, and the gap widens. The more eloquent each side becomes in its own language, the less the other understands. What is needed is not a winner. It is a translation layer.
The signal beneath the language
This is, in part, why I developed Strand Commonality™. Its purpose was never to prove one discipline right and another wrong. It was to identify the determining variables that persist regardless of the terminology used to describe them — to ask, beneath the estate and the ecosystem and the operating model, a single question: what conditions are actually governing this outcome within the Metaprise?
Asked that way, the terminology matters less. Readiness, architecture, estate, governance, substrate, ecosystem (the Metaprise) — call it whatever your field calls it. The question cuts underneath the words to the thing they are all circling.
It also reframes something I wrote about recently. When independent observers from unrelated disciplines keep arriving at the same conclusion, the convergence itself is the signal. I have been treating that as people reaching similar judgments. The deeper version is this: the disciplines are converging because they are studying different parts of the same phenomenon, and the underlying problem is forcing them toward the same observations whether or not they share a language for it. The terminology multiplies. The pattern beneath it does not. Which is, fittingly, an instance of Strand Commonality™ describing itself — different observers, different fields, different words, one signal.
The future belongs to the translators
The AI era raises the stakes on this, and quickly. Organizations are now introducing human and non-human agents into the same operating environment, and every agent — person or model — arrives with its own assumptions, objectives, capabilities, limits, and decision logic. The challenge is no longer only aligning people who use different words. It is aligning increasingly diverse agents, some of which do not use words at all, around a common outcome.
In that environment, the scarce capability is not technical fluency. It is translation: the ability to identify the conditions actually governing an outcome regardless of the language — or the architecture — used to describe them. Translation is the mechanism; the real objective is to locate the common reality beneath the competing vocabularies. The technologies will keep changing. The disciplines will keep multiplying their vocabularies. The determining variables beneath them are likely far more consistent than the words we use for them suggest.
The Tower of Babel was not a story about people who disagreed. It was a story about people who could no longer understand one another while building the same thing. That is the problem worth solving — and it begins by asking, beneath whatever language is in the room, what is actually deciding the outcome.
Truth Is Believing. Accuracy Is Knowing.
Related reading
-30-
Jon Hansen is the creator of Implementation Physics™, a research-based framework developed over nearly three decades to explain why technology initiatives succeed or fail regardless of the technology being deployed. His work spans six technology generations — from ERP through Agentic AI — and includes the Metaprise™ model first articulated in the late 1990s. His research forms the foundation for the Hansen Method™, Hansen Fit Score™ (HFS™), Phase 0™ Readiness Assessment, and the ARA™ RAM 2025™ multimodel verification architecture. He currently serves as a Board Member of the CIPS Americas Chapter.
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The Tower of Babel Problem: Why Smart People Keep Talking Past Each Other
Posted on June 23, 2026
0
Across disciplines, capable people often appear to disagree when they are in fact describing the same thing in different languages. The cost of that untranslated difference is most of why organizations misfire.
Over the past several months, I have noticed something in the conversations I keep having with people across very different fields — procurement, enterprise architecture, transformation, supply chain, AI governance, academic research, systems thinking. At first they appear to be talking about entirely different things. Spend an hour in a room with them and you would conclude they disagree.
Listen more closely and the opposite turns out to be true. They are describing the same thing, from different doors, in different vocabularies — and the vocabulary is hiding the agreement.
The estate, the ecosystem, the operating model, the Metaprise
A transformation practitioner recently captured part of it in a single line: in a brownfield environment, the estate decides the order. Canda Rozier, a long-time and widely respected CPO and an advisor on our board, responded with the readiness corollary: without a foundation of readiness, transformation — brownfield or otherwise — is likely to fail. Between them, the two statements name the whole of it.
A word on the terms, because they matter. A greenfield environment is the luxury of starting from scratch: if we could design this today, how would we build it? A brownfield environment is the reality almost everyone actually lives in — existing systems, processes, contracts, integrations, workarounds, people, history, politics, technical debt, and dependencies, all already in place. You do not get to start over. You inherit what exists. And the “estate” is the whole of that inheritance: systems, data, processes, contracts, people, governance, dependencies.
So the estate decides the order means: before deciding what to change, understand what already exists and how it is connected, because the reality of the environment determines the sequence in which anything can be changed.
When I read that line, it named something I have described for more than two decades as the Metaprise — the entire operating environment in which an outcome is actually determined: people, systems, suppliers, customers, regulators, incentives, decision rights, AI agents, workflows, governance, external dependencies. It is the same reason the inferior tool so often outlasts the superior one — what governs the outcome was never the technology, but the conditions it entered. Different language. The same observation.
And the same observation keeps surfacing under different names. Enterprise architecture calls it the estate. Transformation calls it the operating model. Procurement calls it the ecosystem. Supply chain calls it the network. AI governance calls it the human-AI system. Systems thinkers call it a complex adaptive system. Each discipline arrives through its own door and names what it finds in its own dialect. In a great many cases, they are standing in the same room.
Why organizations misfire
This is not a linguistic curiosity. It is, I think, one of the most underdiagnosed reasons organizations underperform — and it is worth being precise about the mechanism.
Language shapes action. When different groups describe the same environment through different frameworks, they do not merely use different words. They pursue different objectives, measure different outcomes, and optimize different incentives — each internally coherent, each pointed somewhere slightly different. The result is familiar to anyone who has watched a transformation stall: projects drift, technology underperforms, stakeholders grow frustrated, and everyone reaches for a culprit. Leadership failed. Adoption failed. Governance failed. Change management failed.
A defence-sector engagement I worked on in the late 1990s shows the trap in miniature. The contract called for 90% next-day parts resolution; delivery was running at 51%, and I was asked to automate procurement to fix it. But the breakdown did not start in procurement. The field technicians were measured on service-call volume, and ordering parts after each call slowed them down — so they sandbagged, holding their orders until the end of the day to protect their call targets. Rational, in their own frame. Except late orders arrived after customs cutoffs and, on volatile commodities, at many times the morning price — which is what crippled delivery. And the part nobody traced: the same delay the technicians created to protect their call volume came back around as parts that never arrived in time, leaving the calls they had rushed to diagnose open. Their own call-closure rates were being wrecked by the very behavior they adopted to hit their numbers. They never saw the connection — because no one was looking at the strand that linked the technician’s incentive to procurement to the technician’s own outcome. (The full account is here.)
As stated earlier, the culprit is rarely a failure of leadership, adoption, or governance. It is that the participants were solving different versions of the same problem without realizing it. One group optimizes for speed-to-contract while another optimizes for risk containment; both goals are legitimate, and without a shared view of the conditions underneath, the two work quietly against each other. Goals become misaligned because the underlying assumptions were never aligned. Incentives diverge because success was defined differently by each group. Communication breaks down because identical words carry different meanings across disciplines. The problem was not disagreement. It was the absence of translation.
That distinction matters, because the two problems have opposite remedies. If the problem were disagreement, you would resolve it by debate — argue until someone is persuaded. If the problem is untranslated difference, debate makes it worse: each side argues harder in its own dialect, and the gap widens. The more eloquent each side becomes in its own language, the less the other understands. What is needed is not a winner. It is a translation layer.
The signal beneath the language
This is, in part, why I developed Strand Commonality™. Its purpose was never to prove one discipline right and another wrong. It was to identify the determining variables that persist regardless of the terminology used to describe them — to ask, beneath the estate and the ecosystem and the operating model, a single question: what conditions are actually governing this outcome within the Metaprise?
Asked that way, the terminology matters less. Readiness, architecture, estate, governance, substrate, ecosystem (the Metaprise) — call it whatever your field calls it. The question cuts underneath the words to the thing they are all circling.
It also reframes something I wrote about recently. When independent observers from unrelated disciplines keep arriving at the same conclusion, the convergence itself is the signal. I have been treating that as people reaching similar judgments. The deeper version is this: the disciplines are converging because they are studying different parts of the same phenomenon, and the underlying problem is forcing them toward the same observations whether or not they share a language for it. The terminology multiplies. The pattern beneath it does not. Which is, fittingly, an instance of Strand Commonality™ describing itself — different observers, different fields, different words, one signal.
The future belongs to the translators
The AI era raises the stakes on this, and quickly. Organizations are now introducing human and non-human agents into the same operating environment, and every agent — person or model — arrives with its own assumptions, objectives, capabilities, limits, and decision logic. The challenge is no longer only aligning people who use different words. It is aligning increasingly diverse agents, some of which do not use words at all, around a common outcome.
In that environment, the scarce capability is not technical fluency. It is translation: the ability to identify the conditions actually governing an outcome regardless of the language — or the architecture — used to describe them. Translation is the mechanism; the real objective is to locate the common reality beneath the competing vocabularies. The technologies will keep changing. The disciplines will keep multiplying their vocabularies. The determining variables beneath them are likely far more consistent than the words we use for them suggest.
The Tower of Babel was not a story about people who disagreed. It was a story about people who could no longer understand one another while building the same thing. That is the problem worth solving — and it begins by asking, beneath whatever language is in the room, what is actually deciding the outcome.
Truth Is Believing. Accuracy Is Knowing.
Related reading
-30-
Jon Hansen is the creator of Implementation Physics™, a research-based framework developed over nearly three decades to explain why technology initiatives succeed or fail regardless of the technology being deployed. His work spans six technology generations — from ERP through Agentic AI — and includes the Metaprise™ model first articulated in the late 1990s. His research forms the foundation for the Hansen Method™, Hansen Fit Score™ (HFS™), Phase 0™ Readiness Assessment, and the ARA™ RAM 2025™ multimodel verification architecture. He currently serves as a Board Member of the CIPS Americas Chapter.
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