What AI Cannot Replace in Software Development, Part 3

In a previous article in this series, I proposed a redefinition of legacy: legacy is not a property of the code itself, but a state of the relationship between the code and those expected to maintain it. A system becomes legacy the moment no one in the organization mentally inhabits what they are nonetheless expected to evolve. The problem lies less in the age of the technology than in the developer’s lack of deep cognitive engagement with their own code.

This definition raises an urgent question: if generative AI produces code in place of developers, and if those developers only supervise the output without having genuinely thought through the underlying reasoning, are we creating the conditions for legacy at scale from the very first generated line?

The purpose of this article is to examine this question and its organizational consequences. Partial integration of AI into the development process tends to create a structural instability, one that leads logically to what I call a tipping point: a discontinuity in how an organization must configure itself. Getting through this transition also requires a precise understanding of which organizational assets are truly durable, and the answer is less obvious than it appears.

Cognitive Engagement as a Condition of Maintainability

When a developer writes code, they do more than produce text: they build a mental representation of the problem, its constraints, edge cases, and trade-offs. This representation lives in procedural memory and enables effective maintenance: the ability to anticipate where things will break, to understand why a decision was made, and to assess the impact of a change without simulating the entire system.

When a developer merely supervises AI-generated code, this mental representation does not fully form. They can read the code, validate its apparent logic, and approve a pull request, but they have not traversed the reasoning that produced it.

Supervising AI-generated code without engagement in the reasoning that produces it is functionally similar to maintaining a legacy system: one understands what the code does, but not why it does it that way.

I am not criticizing generative AI as such. I am describing a structural property of the situation: intellectual ownership of code is a consequence of the creation process, not of reading the result, and review cannot supply it after the fact.

The Tipping Point: A Structural Discontinuity

Organizations that integrate generative AI without changing the structure of responsibility tend to drift into an unstable intermediate state, which typically becomes visible only during the first serious incidents.

Developers believe they are supervising, but have progressively lost the capacity to intervene autonomously. The system keeps growing, technically modern and cognitively orphaned. In several AI-assisted transitions I have observed, the pattern is consistent: productivity is high for months, velocity metrics look excellent, confidence is strong. Then comes the first non-trivial evolution (a scaling constraint, an architectural shift, or a subtle behavioral regression), and no one can confidently explain why the system behaves as it does.

The problem with gradual transition is precisely that it produces this situation without anyone having made the deliberate choice to accept it. The organization bears the costs of both models without benefiting from the advantages of either.

The relationship between human intellectual ownership and AI integration is not linear. Below a certain threshold of engagement, maintainability tends to collapse in a way that is difficult to reverse once the accumulated gap reaches critical mass. This threshold is the tipping point. It separates two internally stable configurations from a fragile intermediate zone.

The Two Stable Configurations

The Human-Author Model

Humans remain the cognitive authors of the code. Generative AI serves as an accelerator for boilerplate, refactoring, or exploration, but the developer still traverses the reasoning. They can explain every significant decision, debug unexpected behaviors, and anticipate the consequences of changes. This model preserves long-term maintainability and integrates naturally with existing quality practices. Its main constraint is speed: AI amplifies productivity but does not replace the human traversal of reasoning.

The Agent-Author Model

Agents become responsible for producing the code. Humans step out of the traditional technical loop. In exchange, the organization reconfigures itself deeply: code loses its status as a durable strategic asset and becomes a temporary instantiation of behavior defined elsewhere, rewritten on demand from specifications and behavioral tests, which now form the primary reference. Classical technical debt tends to be largely replaced by a different form of accumulated cost centered on specification quality and test completeness. Architecture becomes declarative: focused on what the system must do, not how.

Which of these assets, then, are truly durable over time? Current agent-oriented frameworks largely leave this question unanswered, and it deserves careful examination before committing to this model.

The Hierarchy of Durable Assets

Specification Drift and the Limits of Natural Language Stability

Current agent-oriented frameworks tend to treat specifications as a stable abstraction layer above the model. The assumption is that you write the spec once and can reliably regenerate the system across model versions and technology changes.

This assumption deserves scrutiny. Practitioners already observe prompt drift, LLM drift, and context drift: the gradual degradation in how a specification is interpreted as models evolve. A specification that worked reliably with one model version can produce subtly different behaviors in its successor, not because the spec changed, but because the model’s implicit understanding of its constructs has shifted. The long-term stability of a specification is coupled, implicitly, to the stability of the model that interprets it.

In systems meant to last years or decades, this specification drift becomes more than a prompt engineering inconvenience; it threatens the long-term reproducibility of the system itself. Subtle divergences can accumulate in the spaces between explicit requirements, even when tests continue to pass on covered behaviors. Current frameworks do not yet formally manage this coupling.

The Asset Hierarchy

This leads to a clear hierarchy of durability:

• Code is ephemeral by design, the least durable asset. It is regenerated, not evolved.
• Specifications are semi-stable. More durable than code, but subject to semantic drift relative to evolving models. They require active, periodic re-validation.
• Behavioral tests constitute the most stable anchor. A well-designed test yields a binary, objective verdict (it passes or it fails), independent of any model’s interpretation of intent.

However, the durability of tests ultimately rests on the quality of the human oracle that defines them. A poorly conceived test can protect incorrect behavior as reliably as a good one protects the right one. This brings us back directly to the oracle problem examined in the first article of this series: tests are only as strong as the human reasoning behind them.

In the agent-author model, the true organizational anchor is the behavioral test suite, but only insofar as it crystallizes genuine human understanding of expected behavior. Tests are the formal record of the oracle’s judgment, not a substitute for it.

Quality Assurance as a Foundational Discipline

This inversion is structural. In traditional development, QA is positioned as downstream validation. In the agent-author model, defining, maintaining, and evolving behavioral tests becomes the first step in the process, and the most consequential one.

The quality engineer who designs solid behavioral test suites is no longer in a support role. They steward the behavioral knowledge that makes reliable system regeneration possible across time, model versions, and technology generations. QA shifts from a verification function to a core strategic competency.

The Human Role in the Agent-Author Model

Agents operate within a bounded context, a necessary reduction of reality that always implies information loss. The real world is structurally larger than any context an agent can receive: regulatory constraints, internal political dynamics, implicit contractual commitments, strategic directions, stakeholder relationships. None of this is fully visible to a system operating within a defined context.

A concrete example: agents might correctly choose a technically superior tool within their context, yet overlook a token cost ten times higher, an unevaluated vendor dependency, or a strategic direction not yet formally communicated. These elements simply were not in their context.

The human role is therefore not to supervise code, but to maintain alignment between the space of agents and the real world that will never be entirely captured in a context. It is a translation role: transposing the constraints, objectives, and values of the real world into clear intentions for systems that operate within bounded spaces. Humans retain responsibility for the what and the why. Agents manage the how.

In this model, IT professionals do not disappear; they migrate. Their expertise moves from writing code to mastering specification quality, behavioral verification, and domain knowledge. The longstanding comprehension gap between business and IT dissolves here through a deeper reconfiguration of the IT role itself.

A Historical Precedent: The Transition to Deep Learning

The shift I describe has a direct parallel in AI’s own history. For several decades, symbolic expert systems emphasized full interpretability: every rule was explicit and traceable. The transition to deep learning abandoned this property. Neural networks produce impressive results through mechanisms their creators cannot fully explain. The debate was settled by results rather than philosophy: deep learning produced capabilities that symbolic systems could not reach.

AI became what it is today because the scientific community collectively accepted abandoning internal interpretability as a necessary condition of a system’s validity. Observable performance replaced transparency of mechanism as the primary evaluation criterion.

The parallel with the tipping point is direct. In the agent-author model, one abandons code comprehensibility as a condition of maintainability. It is replaced by the quality of specifications and, above all, the completeness of behavioral tests. Observable performance replaces internal understanding.

This precedent does not prove the tipping point is inevitable everywhere, but it shows that abandoning one form of comprehensibility for another form of validation can produce significant gains, and that resisting the shift can carry a competitive cost.

The Organizational Question: The Greenfield Model

For large organizations with decades of technological history, transforming the existing base is rarely feasible in the short term. Legacy systems (those for which intellectual ownership has already been lost) do not lend themselves to migration toward the agent-author model without massive prior investment in specification.

The coherent pattern is the greenfield model: creating a new entity, such as a division, a subsidiary, or a distinct organization, configured according to the agent-author model from day one. This entity develops the competencies in specification, declarative architecture, and behavioral testing while the existing organization continues under the human-author model.

This pattern is not without precedent. Large financial institutions have used it for digital transformation initiatives. Cooperatives have created subsidiaries to explore new business models without risking their operational core. When transformation requires a reconfiguration too deep to be achieved from within, one creates something new alongside.

The resulting coexistence, with units in human-author mode managing the existing systems and units in agent-author mode building the future, is stable when the boundary between the two models is explicit and deliberate. The danger lies in blurring the line between them, not in having two models.

Implications for Practitioners and Decision-Makers

Three practical observations emerge:

First, diagnose honestly. The relevant question is not “what percentage of our code is AI-generated?” but “can our developers intervene autonomously and confidently in the code that exists?” An uncertain answer signals that the organization is likely already in the unstable intermediate zone, without having made the deliberate choice to be there.

Second, choose deliberately. Maintaining the human-author model requires frameworks that preserve developers’ traversal of reasoning: not excluding AI, but ensuring they remain genuine authors of what they maintain. Moving to the agent-author model demands significant prior investment in specification quality and behavioral tests before the transition, not after.

Third, protect the right asset. Resist the temptation to treat specifications as the primary investment. The behavioral test suite, grounded in rigorous human judgment, is the more stable and strategically critical anchor. Specifications communicate intent to agents; tests verify that the intent was correctly understood. Only the latter survives model evolution without active re-validation.

The intermediate zone, where humans supervise code they have not genuinely inhabited without strong specifications and tests, is a trap rather than a transitional state. It is precisely where tomorrow’s legacy forms, at the accelerated speed of generative AI.

In the previous article in this series, I argued that the capacity to judge the correctness of a result is an irreplaceably human competency. Machines can evaluate outputs; what they cannot do is define what constitutes a correct output, because that requires an understanding of the real world that exceeds any formalizable context.

The tipping point does not contradict this thesis. It extends it upward. In the agent-author model, humans no longer judge the correctness of code. They define the behavioral tests that encode what correctness means, and they translate the real-world constraints that no bounded context will ever fully capture. The oracle does not disappear: it moves upstream and becomes the organization’s most critical competency.

AI is already transforming software development.
The question is whether your organization will deliberately choose its tipping point, or simply drift into one without realizing what it has surrendered.

Gabriel Mailhot
Organizational Engineering for the AI Era
Researcher (2005–2010): Image Processing & Creative AI
Business Expert / Domain Expert (2010-2026): Software Architecture, Software Development and Quality Practices

Series: What AI Cannot Replace in Software Development

An Operational Distinction

I have spent a significant part of my career working with organizations whose human foundation is unstable: high turnover, frequent reassignments, temporary teams assembled for a project and dissolved at its close. In these organizations, something specific happens to software systems over time. The knowledge of why the code is the way it is gradually disappears.

It is most visible with legacy systems. Take VB6 applications, COBOL modules, early Java codebases: the developers who built them have moved on, retired, or simply forgotten. What remains is the code itself, and a thin layer of documentation that rarely captures the decisions and constraints that shaped it. New developers inherit a system they can run but not fully understand.

I had assumed this was a problem of time, in which knowledge erodes because the people who held it leave. Then I started observing something different in teams using AI coding assistants intensively. The developers were still there, yet the knowledge was still eroding. The mechanism was disengagement rather than departure.

AI creates distance through engagement rather than through time, and that distinction changes how we should think about what developers must retain.

The Structural Advantages of AI: Memory and Computational Speed

AI has two advantages that do not depend on the quality of the model or its version: near-unlimited memory and processing speed that vastly exceeds human cognition.

Memory first. A large language model has been trained on billions of lines of code, millions of technical discussions, years of documentation. It can instantly retrieve the exact syntax of a rarely used API, the correct pattern for an obscure edge case, the exhaustive list of parameters for a given function. No developer, however experienced, can compete on this terrain.

Speed next. AI accomplishes in seconds what would take hours: generating boilerplate, converting an algorithm from one language to another, reformatting a data structure, producing variants. This is a real, measurable advantage, and it is not going away.

These two advantages are what they are: memory and performance, not judgment.

What the Developer Must Retain: Causal Understanding

The boundary between what AI does well and what it cannot do is more precise than a split between memory and reasoning in the abstract sense. It runs between pattern recognition and causal understanding.

An experienced developer who identifies a race condition in multithreaded code does more than recognize a familiar pattern. They understand why that pattern is problematic in this specific context: which timing constraint creates the risk, which property of the system makes the bug intermittent, which fix will resolve the problem without creating another.

AI can recognize the pattern. It does not possess the causal understanding that allows one to evaluate whether that recognition is relevant in this context. This understanding of the system, the domain, and the real constraints is what the developer must retain.

The concern has empirical support. A randomized controlled trial examining how AI coding assistance affects skill development found that developers using AI assistance scored 17 percentage points lower on comprehension and debugging tests than those coding manually (50% versus 67% on average). The contrast was even sharper by usage pattern: developers who delegated code generation to AI scored below 40%, while those who used AI for conceptual inquiry while coding independently scored 65% or higher. The engagement pattern mattered more than the tool itself.

This includes, as discussed in the first article of this series, the definition of expected behaviors and the design of tests. These activities call for judgment about what the system must do in the real world, not memorization.

Knowledge Erosion: An Underestimated Organizational Risk

When a developer delegates to AI not only memory and speed, but also understanding, a silent phenomenon takes hold: knowledge erosion.

The traditional form of this erosion is familiar. Developers leave, and with them goes the understanding of why the system is structured the way it is (the decisions, constraints, and trade-offs that shaped it). Call it a distance of time: the gap between the people who understood the system and those who now maintain it.

What AI introduces is structurally different. The developers are still there, but their engagement with the code has diminished. The cognitive effort of reading it, holding its structure in mind, and reasoning about its consequences no longer happens. They generate, accept, and move on, and the knowledge never forms.

This is a distance of engagement, and it is more insidious than the distance of time because it is invisible. The team is stable, the velocity metrics are green, the system runs. Yet the understanding that would allow someone to reason confidently about changing it has quietly failed to accumulate: anticipating the consequences of a modification, recognizing a subtle regression, formulating a test without looking at the code.

Teams using coding assistants intensively report that developers increasingly struggle to explain code they have recently written themselves. Understanding atrophies from lack of exercise, and a developer who does not fully understand their own system can no longer evaluate its quality. This closes the loop with the testing problem from the first article. Such developers can no longer verify that the system does what it should. They may not have lost sight of the intent, but they no longer understand the code well enough to reason about whether it delivers it.

The Optimal Division of Labor

The right question is therefore not “should we use AI?” but “for what should we use it?”

AI as a memory extension: syntax, APIs, known patterns, mechanical conversions, boilerplate. The developer formulates the request, evaluates the result, decides whether to integrate it. AI accelerates access to what they already know how to look for.

The developer as guardian of understanding: architecture, expected behaviors, edge cases, domain constraints, tests, decisions that commit the future of the system. These activities are not delegated. AI may well produce something that resembles them, but their value lies precisely in the human judgment that produces them.

This division is a stable structure rather than a temporary limitation, grounded in what causal understanding requires: lived experience of the system, knowledge of the domain, and accountability for the outcome.

Implications for Organizations and Training

This analysis has direct consequences for how organizations train their developers and evaluate the value of AI.

On training: the goal should not be to teach developers to use AI, but to ensure that AI use does not replace the development of understanding. Practices that require reading code, manual debugging, and peer review, precisely those that delivery pressure tends to eliminate, become more important.

On value assessment: the usual metrics (lines of code produced, delivery speed, test coverage rates) do not capture knowledge erosion. An organization can see its metrics improve while its capacity to master its own system degrades. The indicators that matter are harder to measure: a developer’s ability to explain why an architectural decision was made, to recognize a subtle regression without running the tests, or to reason confidently about the consequences of a change they have not yet made.

AI does not save time; it changes the nature of work by shifting value toward what the machine cannot do. Organizations that understand this invest in human understanding. Those that do not let it atrophy without even noticing.

Legacy systems are already showing us what a system looks like when its knowledge has disappeared. The question is whether we are building the legacy systems of 2035 right now. If the specifications and tests that protect expected behavior are themselves produced by developers who no longer fully understand the system, the circle closes on itself, and the erosion spreads beyond the code to the knowledge that was supposed to protect it.

The distance of engagement is a choice, until it isn’t.

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: What AI Cannot Replace in Software Development

The Problem Nobody Names

A few years ago, I reviewed a software development contract between an organization and a vendor. The terms were standard: a fixed delivery date, a penalty clause for late delivery, and an obligation to maintain 80% code coverage. The intent was twofold. It would protect the organization from negligence, and guard against the kind of deliberate opportunism that governance frameworks exist precisely to prevent.

When the project fell behind schedule, the vendor found a way to satisfy the coverage requirement without slowing down: they had the AI generate unit tests after the developers had written the code. The coverage metric turned green, the delivery date was met, and the penalty was avoided.

Then came UAT, and the acceptance phase surfaced problem after problem. The tests had protected nothing, because they had been written to describe what the code did rather than what the system was supposed to do.

The contract had specified a metric, and the metric had been satisfied. The organization had paid, contractually and technically, for a test suite that institutionalized its own blind spots.

I am often asked whether it is a good idea to let AI generate unit tests. That experience is part of my answer. The complete answer is more interesting, because it touches on the nature of testing, the source of truth in a software system, and what AI actually changes in the work of a developer.

The Test Oracle Problem: What Research Already Knows

In the software engineering literature, the problem has a precise name: the test oracle problem. It designates the central challenge of any automated test generation. How do you automatically distinguish correct behavior from incorrect behavior?

Automated generation tools, including those based on large language models, produce what are called regression oracles: tests that capture what the code does today, not what it should do.

When you ask AI to generate tests from source code, you invert the causality of testing. The test becomes a mirror of the implementation, and if the implementation contains a bug, the test will capture it in order to protect it rather than flag it. Beyond introducing a bug into the system, you put in place an infrastructure that resists its correction.

Alan Turing demonstrated formally that one can never prove a program is free of all bugs. This is a consequence of undecidability: no general procedure can determine whether a program behaves correctly in all cases. In practice, this means there will always be incorrect behaviors the code produces without anyone having intended them. When AI takes the code as its source of truth, it freezes these invisible behaviors at industrial scale.

The test oracle problem is not new; what AI changes is its scale. A developer writing tests manually is constrained by the time it takes. An AI generating tests from code can institutionalize incorrect behavior at industrial speed, producing in seconds a test suite that would have taken days to write and that is wrong in exactly the same way throughout.

What Kent Beck Understood, and What Automated Generation Ignores

Kent Beck, in his Test-Driven Development philosophy, establishes a distinction that remains underappreciated: a test must speak to behavior, not implementation. Behavior is the very reason the code needs to exist. Unlike code, it is stable: it survives refactorings, technology changes, and complete rewrites.

A behavior-based test is a living document of the system’s intent. Future developers will refer to it to understand what the system must do, as opposed to what it happens to do today.

A test generated from code lies about that intent, documenting the accident rather than the design.

This has an organizational consequence the technical literature does not make explicit. An untested bug can be discovered and fixed freely. A bug protected by a test becomes a norm. Any attempt to correct it will cause the test to fail, creating institutional pressure not to fix it. At that point the problem is one of governance as much as of technique.

A test generated from code does not protect against bugs. It protects against change, including the change that would fix the bug.

The Limits of Specifications as a Substitute

One might think the problem is solved by asking AI to generate tests from specifications rather than from code. This is a direction academic research is actively exploring, enriching prompts with documentation, developer comments, and semantic context. It is better, but it is not enough.

A specification is always incomplete. Consider a typical example: a spec describes how a search feature should return results ranked by relevance. It says nothing about what happens when two users submit the same query simultaneously, or how the system should behave when the underlying index is being updated, or what “relevance” means when the user’s implicit intent differs from their explicit query. No negligent author forgot these edge cases. They belong to the class of conditions that only become visible when the system meets real users in a real environment.

Human judgment detects these gaps naturally, by confronting the spec with lived experience of how systems fail, how users actually behave, and what the production environment will impose. AI generates tests that are consistent with what is written, without the ability to signal what is missing.

A better model will not remove this limitation, because it is structural rather than temporary: understanding the real world, with its complexity, ambiguity, and unspoken assumptions, remains a capability current systems do not possess.

The Real Asset of a Software Organization

In a world where AI generates code, the real asset of an organization becomes its specifications and its tests.

The code itself will be rewritten, migrated, replaced. What endures is the knowledge of expected behavior, captured by humans who understand the problem in its full complexity.

This conclusion does not appear in the technical literature or in analyst reports, which measure the value of AI in terms of volume produced. It is a thesis about the nature of a software organization’s intellectual asset, and it radically changes what is worth investing in.

Organizations that have understood this invest in the quality of their specifications and in the human rigor of their tests. Those that delegate both to AI, working from existing code, leave their technical debt in place while making it harder to see and harder to correct.

The question of what the developer must retain, and what can legitimately be delegated, extends beyond the problem of tests alone. It touches on the cognitive division between human and machine, which is the subject of the next article in this series.

If the real asset of a software organization is not its code but its understanding of expected behavior, who in your organization is responsible for building and protecting that understanding? And what happens to it when the pressure to deliver is high enough?

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: What AI Cannot Replace in Software Development

Why I Am Writing This

I have been thinking about analogy since 2005, longer than I have been thinking about most things professionally.

At the time, I was working as a researcher in Québec City, trying to build a system that could assist creative professionals in finding visual inspiration. The technical problem was tractable. But solving it required first answering a question I had not expected: what is creativity, actually? Not as a personality trait or a cultural value, but as a cognitive mechanism.

The answer I arrived at led to an architecture, a patent specification, and a set of ideas that I could not discuss publicly for fifteen years due to contractual obligations.

By the time those constraints lifted, the world had changed considerably. LLMs had appeared, AI had become a public conversation, and the organizations I was working with were exhibiting exactly the pathologies I had been thinking about. I finally had both the freedom and the context to say so.

This essay is where those two threads meet.

The solutions to most organizational problems already exist. They were developed in other domains, under a different name.

Analogy versus Metaphor: A Distinction That Matters

There is a common confusion between metaphor and analogy that matters more than it might appear. A decorative metaphor makes an idea accessible; it creates recognition, sometimes resonance. A structural analogy reveals a real correspondence between two systems: it diagnoses, and it points toward solutions. The difference is epistemological, not stylistic.

Saying “an organization is like an organism” is a metaphor. It invites a certain way of seeing. Demonstrating that the mechanism of cellular apoptosis and the dysfunction of project management share exactly the same causal structure (a local unit disconnected from systemic feedback, optimizing for its own survival at the cost of the whole) is a structural analogy. The first inspires; the second diagnoses.

The core formulation is this: human creativity is combinatorial, not generative ex nihilo. The only way to produce something genuinely new within a bounded experiential space is to discover a structural correspondence between two distant domains that are already known. Novelty is a function of semantic distance between domains. Utility is a function of the match with the problem and with the evaluator’s own criteria.

This formulation connects to a tradition of research in cognitive science that has approached the same question with more formal rigor. Dedre Gentner’s structure-mapping theory (1983) established that what gets mapped in an analogy is not object attributes but relational structure: the causal and functional architecture of a domain, independent of its surface content. Douglas Hofstadter and Emmanuel Sander, in Surfaces and Essences (2013), argued that analogy is not a special cognitive operation but the very engine of thought, the mechanism by which every act of categorization and understanding operates. Arthur Koestler, in The Act of Creation (1964), called the productive collision of two incompatible frames of reference “bisociation,” the mechanism behind both humor and discovery.

What this research tradition establishes, and what the diagnostic method proposed here builds on, is that structural correspondence is the operative criterion. The question is not whether “these domains feel similar” but whether “these domains share the same relational architecture.” The implication for organizational diagnosis is direct: the value of an analogy is structural rather than rhetorical, and it can be evaluated as such.

A Taxonomy of Analogical Domains

Not all distant domains are equivalent as diagnostic tools, and not all combinations of domains are equally powerful. The patent specification formalized a taxonomy for a metaphor-based search system, and that taxonomy maps directly onto the diagnostic method proposed here.

Three types of relationships between analogical domains matter:

Alternative domains cover the same structural dimension of the problem. They are interchangeable: selecting one or the other yields the same diagnostic contribution. Using both is redundant. They compete rather than combine.

Complementary domains cover distinct structural dimensions of the problem. They are combinable, and their combination produces a structural coverage that neither achieves alone. This is where the power of the method concentrates: finding two or three domains that together cover all dimensions of the problem.

Linked domains are complementary domains that also share an internal overlap with each other. That coherence strengthens the combination and makes it more than the sum of its parts.

The selection criterion for a strong set of analogical domains is therefore maximum structural coverage with maximum internal coherence, not quantity. Three well-chosen complementary domains with strong internal links produce a richer diagnostic than ten loosely related ones.

This taxonomy disciplines the method. Without it, analogical reasoning risks becoming what it seeks to transcend: the accumulation of decorative metaphors that feel illuminating without actually diagnosing anything.

The Constraint of the Judge

A structurally rigorous analogy is not automatically an adopted solution. Its value is co-constructed between the structural correspondence and the human evaluator who receives it.

The 2005 theory was precise on this point: utility is a function of the match with the problem and with the evaluator’s own criteria. For organizational diagnostics, this introduces a constraint that the method cannot dissolve but can make visible: the judges of a proposed solution are often precisely the people whose reward system is aligned with the status quo that the solution diagnoses.

A chief risk officer evaluating a proposal to extend project manager accountability beyond delivery dates is simultaneously the person whose career was built on the existing governance model. A board reviewing a recommendation to shift from process-driven to principle-driven governance is the same board that approved the processes now being questioned.

The method does not resolve this. What it does is name it with precision, which is the first condition for treating it. It also shifts the framing: the resistance is structurally predictable rather than irrational. That means it can be anticipated and designed around, and sometimes dissolved by finding the frame in which the solution resonates with the judge’s own criteria.

This is itself an analogical problem: finding the presentation of the solution that achieves maximum structural coverage of the judge’s own conceptual framework. The method recurses.

The Demonstration: Project Management and the Short Horizon

The Observation

A technology asset with a multi-year lifecycle is managed through a sequence of fixed-budget, fixed-date projects. Each project team is constituted for the duration of the project and evaluated on its ability to deliver within its commitments. Decisions are made to protect delivery dates and budget envelopes, and those decisions degrade the asset over the longer term. The team’s focus has shifted from the organization’s interest to the project’s success, which are not the same thing.

The pathology is consistent and well-recognized by practitioners. What is less well-understood is its causal structure, and therefore what kinds of interventions might actually address it rather than merely managing its symptoms.

Applying the Taxonomy: Three Complementary Domains

The search for analogical domains begins with a structural characterization of the dysfunction: a local unit optimizes for its own constraints, producing decisions whose costs are externalized to the larger system and to the future. The local unit has no mechanism to perceive or weight these externalized costs. Its reward system reinforces the optimization.

Domain 1, Biology (apoptosis): the mechanism

A cancer cell is not malfunctioning within its own frame of reference. It is optimizing perfectly for its own reproduction, which is precisely what makes it pathological for the organism. What is absent is apoptosis: the signal received from the organism that tells the cell when to stop reproducing, when to sacrifice itself for the integrity of the whole. Cancer is cellular disconnection rather than cellular rebellion: the rupture of the feedback channel through which the organism communicates systemic state to its constituent units.

The project team is in the structurally identical situation. It is not irresponsible by nature; it is disconnected from the signals that would allow it to integrate the systemic impact of its decisions. The organizational system has not built the equivalent of apoptosis, the mechanism by which a local unit receives information about what its decisions cost the larger whole.

What this domain contributes: the dimension of mechanism. The pathology is architectural, not motivational. The solution direction is not better incentives alone but the construction of a feedback channel.

Taxonomy check: this domain covers the mechanism dimension. It does not address who bears responsibility for the effects, nor why the disconnection is individually rational.

Domain 2, Law (deferred responsibility): the governance gap

Law has progressively recognized that retrospective responsibility is insufficient when effects are diffuse and temporally distant. The evolution from simple liability to fiduciary duty, from shareholder primacy to stakeholder obligation, from individual fault to systemic accountability, represents a legal tradition grappling with exactly the problem at hand: how to assign responsibility for effects that materialize outside the perimeter and after the horizon of the actor who caused them.

The project manager’s perimeter of responsibility does not correspond to the perimeter of impact of their decisions. That is a governance design problem rather than a competence problem, and law has spent two centuries developing frameworks for precisely this mismatch.

What this domain contributes: the dimension of governance. It reframes the question from “how do we motivate better behavior” to “how do we design a responsibility structure whose perimeter matches the impact perimeter of the decisions being made.”

Taxonomy check: complementary to biology. Biology covers mechanism; law covers governance. Their structural dimensions are distinct, so they combine instead of competing.

Domain 3, Neuroeconomics (reward systems): the deep cause

The human brain discounts the future at a high rate: a certain reward today is structurally worth more than an uncertain consequence in eighteen months. This is a cognitive architecture, not a character flaw. And organizational reward systems, when they encode short evaluation horizons, amplify this natural discounting rather than counteracting it.

Nassim Taleb’s skin in the game formulates the structural response: the person who makes the decision must bear a portion of the risk of its future consequences, as an alignment mechanism rather than a punitive measure. When the decision-maker’s long-term interest is structurally coupled to the long-term consequences of their decisions, the cognitive discounting of the future is partially corrected by the system design.

What this domain contributes: the dimension of deep cause. The disconnection is not merely architectural (biology) or ungoverned (law); it is also individually rational given the reward structure.

Taxonomy check: linked to biology. Both neuroeconomics and biology address adaptive mechanisms, one at the cellular level and one at the cognitive level. This internal overlap strengthens the coherence of the combination without making them redundant. Together, all three domains cover: mechanism + governance + deep cause.

The Synthetic Diagnostic

The local unit is disconnected from systemic feedback (biology). The organizational system has no mechanism to assign it responsibility for its deferred effects (law). And its reward system gives it no reason to seek reconnection (neuroeconomics). Each domain alone produces a partial diagnosis. Together, they produce a complete one.

Solution Directions: What the Source Domains Suggest

The biological analogy suggests constructing a systemic feedback channel: a technology asset health metric that follows the project manager after delivery, visible to them during the project as a forward-looking indicator of the consequences their decisions are accumulating.

The legal analogy suggests redefining the responsibility perimeter. The next team that inherits the asset should have a formal mechanism to evaluate and record the state in which they received it, and that evaluation should be part of the delivering team’s record. Accountability extends to the handoff instead of ending at the launch.

The neuroeconomic analogy suggests realigning the reward system with the asset’s horizon rather than the project’s: evaluating the project manager’s performance at 24 months post-delivery, weighted toward asset quality indicators, as incentive alignment rather than punishment.

These three solution directions are as complementary as the domains that generated them. Each addresses a dimension the others do not, and their combination amounts to a governance redesign rather than a management improvement.

What the Method Does, and What It Does Not

The method produces precise diagnostics and solution directions that the source domains have already explored. It does not produce turnkey solutions: the transposition to the specific organizational context remains a work of judgment that the method cannot replace.

What it avoids: reinventing wheels that other domains have already manufactured, and remaining imprisoned in the managerial vocabulary that masks the real structure of the problem. A problem named in managerial terms produces managerial solutions. The same problem named in biological, legal, and neuroeconomic terms produces a radically different solution space.

The method also has direct implications for agentic AI systems. An agent that reasons purely by similarity, retrieving the nearest known pattern and applying it, is executing the organizational equivalent of process-following. An agent capable of structural analogy (identifying correspondence across distant domains, composing complementary representations, evaluating coverage rather than proximity) would be executing something closer to genuine reasoning. In AI systems, the gap between similarity and analogy is the gap between a sophisticated retrieval engine and a system capable of diagnosis.

Organizations do not lack solutions. They lack the ability to recognize them across domains.

The method remains constant. The domains change. And each time, the question is the same: what is the structural correspondence, who has already solved this problem, and what does that suggest here?

If the pathologies of organizations have already been solved (in biology, in law, in physics, in software design), what are we waiting for to transpose those solutions? And what does it say about our field that we keep reinventing the same diagnoses without looking next door?

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: Structural Analogy as an Organizational Diagnostic

The first three articles in this series examined specific symptoms: the instability of the People-Process-Tools model under high human variability, the strategic liability of tool positioning in a fast-moving AI market, and the asymmetric risk that regulated organizations are structurally failing to measure. This fourth article steps back to name the underlying condition, the diagnosis from which those symptoms follow.

AI is not just a technological disruption. It is an organizational architecture disruption.

A Mismatch That Went Unnoticed Until Now

For most of the industrial and information age, there was a comfortable relationship between the pace of technological change and the pace of organizational adaptation. Technology evolved, but slowly enough that organizations could absorb each shift, stabilize their processes around it, and build institutional knowledge before the next wave arrived. The lag between technological possibility and organizational practice was an inconvenience, not a structural threat.

That relationship has inverted. For the first time in the history of organized work, the speed of technological evolution is structurally outpacing the speed of organizational adaptation. Rather than a temporary disruption caused by one particularly fast technology cycle, this is a change of regime: a permanent shift in the relative velocities of technological change and organizational response.

The evidence is visible in every domain. Organizations complete a tool evaluation cycle and find the market has already moved. They design a training program for a capability that AI has partially automated by the time the program launches. They build processes around a set of constraints that are redefined by regulatory guidance six months later. The gap is widening rather than closing, and the organizational models built for a slower world are showing the strain.

Why Processes Were the Right Answer, and What They Assumed

Consider what processes actually are, and why they represented such a powerful organizational technology for so long. Beyond being a list of steps, a process is an encoded solution to a recurring problem, a crystallization of organizational learning into a repeatable, transferable, auditable form. When an organization documents a process, it is converting tacit knowledge into institutional memory, individual judgment into scalable behavior.

This is genuinely valuable, and it rests on three assumptions that held for most of the twentieth century and well into the twenty-first: that the problem being solved is sufficiently stable to justify encoding, that the conditions under which the process operates will remain predictable enough for the steps to stay valid, and that repeatability is itself a source of value (doing the same thing reliably, at scale, is better than improvising every time).

AI attacks all three assumptions simultaneously. It renders stable problems unstable by continuously shifting what is technically possible and economically rational. It introduces permanent uncertainty into operating conditions. And it transforms repeatability from an asset into a fragility: the ability to execute the same steps reliably becomes a liability when those steps are precisely what needs to change.

Processes are not so much wrong as misaligned, optimized for a world that is receding faster than most organizations have recognized.

Processes Are Becoming Legacy Systems

In technology, we use the term ’legacy system’ to describe something specific: not a system that is broken, but a system that was built for a set of constraints that no longer match the environment in which it now operates. A legacy system can be technically functional, even well-maintained. What makes it legacy is the growing gap between its underlying assumptions and current reality.

This is precisely the condition of process-driven organizational architectures in the face of AI-driven technological turbulence. The processes themselves may be executing correctly. The people following them may be doing so diligently. The problem is architectural. The way the organization governs work (through predefined steps, stable roles, documented procedures, sequential approvals) was designed for a world of slower change, and that world is no longer the operating environment.

The recognition is uncomfortable because process-driven governance is deeply embedded in how organizations understand accountability, compliance, and quality. Audits verify process adherence. Performance frameworks reward process execution. Regulatory frameworks often mandate specific processes. Saying that processes are becoming legacy systems does not mean that accountability, compliance, and quality no longer matter. It means that the mechanism through which they have been achieved is reaching the limits of its fitness for purpose.

Principle-Driven Systems as the Architectural Response

If processes assume stability, principles assume change. This is the essential distinction, and it points toward a different architecture for organizational governance.

A principle defines what must be true: the quality threshold that cannot be compromised, the constraint that is non-negotiable, the outcome that must be achieved. It does not prescribe how. It governs by results rather than by steps. Teams operating under a principle-driven system follow guardrails and pursue defined objectives, but they adapt their methods continuously as the environment evolves, while the principle itself stays stable.

This is not a novel idea in theory. Agile movements, outcome-based management, and mission-command doctrine in military organizations have all pointed in this direction. What is new is the urgency and the mechanism. The urgency comes from the acceleration of technological change. The mechanism comes from agentic AI architectures: systems that are natively coupled to objectives and constraints rather than to specific tools or procedures, and that can adapt to changing operating conditions without requiring the governance layer to be redesigned every time the tooling shifts.

Agentic architectures are not simply a more efficient way to execute processes. They are an architectural alternative to them, embedding adaptability at the level of the system itself rather than relying on human judgment to compensate for procedural rigidity.

Consider a concrete illustration. A logistics team operating under a process-driven model has a documented approval workflow for any change to its supply chain routing tools, a cycle that takes weeks and requires sign-off from architecture, security, and procurement. When a new AI-powered routing tool becomes available, the process triggers in full, regardless of how minor the change. Under a principle-driven model, the governing constraint is different: “all routing decisions must maintain delivery reliability above X%, cost variance below Y%, and full auditability.” An agent operating under those principles can evaluate and adopt a new routing tool autonomously, because what is being governed is the outcome rather than the mechanism. The principle holds, and the approval cycle dissolves.

The shift from process-driven to principle-driven governance is therefore primarily an architectural decision rather than a technology one. It concerns where stability should live in an organization. In a world of permanent technological turbulence, the answer is that stability must live in principles and outcomes, not in procedures and tools.

Organizations Must Be Engineered for Technological Turbulence

None of this argues for improvisation, or for abandoning the rigor that processes have historically provided. The argument is that rigor itself must be rearchitected. The question to settle is what to govern and at what level of abstraction, not whether to govern.

The organizations that handle the coming decade well will not be the ones that execute their current processes better, but the ones that have made a deliberate architectural choice: to invest in stable, well-defined principles rather than brittle, step-by-step procedures; to build agentic systems capable of working with continuously shifting tooling; and to develop adaptive governance frameworks that measure outcomes rather than adherence.

Engineering an organization for technological turbulence means building a particular kind of resilience. Bouncing back from disruption is one form of it; being structurally designed to absorb continuous change without losing coherence is another. The distinction matters, because the first is a recovery capability and the second is an architectural property.

The organizations that possess this property will not be immune to the challenges of an AI-accelerated environment. But they will face those challenges with a structural advantage: the ability to adapt their methods while holding their principles constant, and to absorb technological change as a normal operating condition rather than as an exceptional disruption.

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: Organizational Engineering for the AI Era

A Time-Tested Model Confronting an Unprecedented Context

The People, Process, Tools (PPT) model has shaped technology governance for decades. Its founding premise rests on the stability of human capital: skilled individuals, carrying a shared culture, apply defined processes using adapted tools. In its mature form, this model has always recognized that processes and tools encode organizational knowledge, with the human dimension serving as the activation vector.

But in an environment defined by high resource turnover, systematic reliance on external consultants, and the proliferation of teams assembled for short-term needs, that premise of stability no longer holds. The PPT model remains theoretically valid. What is in question is whether it can operate effectively when its human pillar is structurally unstable.

The ‘People’ Pillar as an Unstable Variable

When resource turnover is high, organizational knowledge disperses instead of accumulating. Every departure takes with it decision-making context, tacit practices, and domain understanding that formal artifacts only partially capture. Internal culture loses its coherence when a significant proportion of contributors operates within a temporary horizon. Practices imported from outside, heterogeneous by nature, complicate governance and introduce variability that neither documented processes nor available tools can adequately contain.

Under these conditions, structuring quality around a constantly renewing pool of human expertise exposes the organization to real risks: operational discontinuity, practice drift, and erosion of technological maturity. What this calls for is a shift in ratio, not an inversion of the model. The human factor remains central, but it can no longer serve as the sole foundation.

Repositioning the Foundation: Processes, Tools, and Platform Engineering

Faced with this reality, it becomes necessary to shift the model’s foundation toward elements that remain constant: processes, tools, and now the platform. This reconfiguration does not marginalize the human role; it relieves it of a responsibility it can no longer bear alone in a context of high variability. Individuals stop being the source of coherence and become its beneficiaries: they join a structured environment that guides them from day one, regardless of their background.

The natural evolution of this approach is platformization. An internal platform encodes organizational knowledge in a reusable ecosystem of normalized pipelines, pre-configured environments, project templates, automated controls, and standardized practices. It substitutes a human memory that erodes with turnover for an integrated, persistent operational memory.

That said, this approach is demanding. Building and maintaining a quality internal platform requires a competent, stable, and well-governed team. The structural irony is real: to reduce dependence on interchangeable individuals, you create an infrastructure that requires highly specialized individuals over the long term. Many organizations underestimate this cost. A poorly designed or poorly governed platform can become an even greater obstacle than having no platform at all. This repositioning is no magic solution but a long-term investment, with its own governance requirements.

Artificial Intelligence as a Lever and as a Responsibility

The integration of artificial intelligence represents a significant acceleration of this logic. Within a heavily tooled system, AI can transform static processes into adaptive mechanisms: automated code analysis, test generation, continuous review, technical drift detection, and domain knowledge assistance. These capabilities reduce dependence on individual expertise and strengthen the system’s resilience against team turnover.

However, it would be inaccurate to present AI as a frictionless solution. Its effective deployment raises several challenges:

• AI system governance (prompt engineering, continuous output evaluation, data lineage management, bias monitoring) itself creates dependence on specialized competencies (MLOps, LLMOps, Platform AI) that are often rare and expensive.
• AI shifts human dependencies; it does not eliminate them. It generates new layers of specialization, often more demanding than those it replaces.
• The cognitive debt risk is real: junior profiles who become overly reliant on AI suggestions may develop impoverished reasoning, undermining precisely the human capital the system seeks to make less critical.

An organization that adopts AI as a pillar of its quality engineering must simultaneously invest in the governance of that pillar. Without it, you are simply substituting one fragility for another.

Scope of Application and Model Limitations

This repositioning is particularly well-suited to high-variability organizations: fast-growing scale-ups, technology consulting firms, product teams with a significant proportion of contractors, or IT service delivery environments. In these contexts, platformization and AI augmentation represent high-ROI levers for operational resilience.

In high-criticality domains such as aerospace, healthcare, heavily regulated finance, and critical infrastructure, this logic must be applied with considerable care. Individual accountability and accumulated expertise remain non-substitutable pillars in these fields. Automation and standardization can play a supporting role, but they cannot render the human factor ‘interchangeable’ without introducing risks of a fundamentally different nature.

A Hybrid Model for an Asymmetric Environment

The repositioning proposed here aims to rebalance the PPT model rather than invert it. In an environment where human variability has become the norm, operational excellence rests on an asymmetric combination: a solid platform infrastructure, processes that enforce coherence, and well-governed AI that increases resilience. As an indispensable complement, it also needs a core of highly skilled individuals who maintain and evolve the system and embody its vision.

This model does not guarantee excellence on its own. It creates the structural conditions under which excellence becomes reproducible and less dependent on the luck of individual trajectories. What you gain is predictability. You do not eliminate the need for exceptional people; their role concentrates where it is least substitutable, in judgment, innovation, and governance of the system itself.

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: Organizational Engineering for the AI Era

In a previous article, I argued that the People-Process-Tools model needs to be rebalanced toward platform engineering in contexts of high resource variability. This article extends that argument one step further: in a world where AI is redefining the available tools every few months, even the ‘Tools’ layer of that platform deserves to be rethought.

The Paradox of Tool Positioning

In large organizations, the process of selecting, evaluating, approving, and deploying a new tool is rarely a lightweight exercise. Between security reviews, architecture assessments, procurement cycles, integration planning, and training programs, the full lifecycle from initial evaluation to production readiness commonly spans six to eighteen months. For highly regulated environments, that timeline can extend further.

This investment was once rational. When technology moved slowly, the cost of a rigorous positioning process was amortized over years of standardized usage. The approved tool became part of the organizational fabric, embedded in workflows, documentation, onboarding paths, and institutional memory.

That calculus has changed. The current cycle of AI-driven tool evolution is measured not in years but in months, sometimes weeks. By the time an organization completes its positioning process for a given tool, the competitive situation may have shifted, the tool itself may have been substantially transformed, and a new generation of alternatives may have emerged. The organization ends up optimized for a world that no longer exists.

Why This Was Rational, and Why It No Longer Is

It would be unfair to dismiss tool positioning processes as bureaucratic excess. They exist for legitimate reasons: security and compliance requirements, interoperability constraints, support and vendor stability considerations, and the very real cost of fragmented tooling across teams. These concerns have not disappeared.

What has changed is the nature of the coupling between an organization and its tools. In a slower-moving environment, investing deeply in a specific tool (building expertise, integrations, and institutional knowledge around it) generated durable returns. That depth was an asset.

In today’s environment, that same depth increasingly functions as a liability. Deep coupling to a specific tool creates switching costs that slow adaptation. Institutional knowledge built around a particular interface or workflow becomes obsolete as AI transforms the tools themselves. The more thoroughly an organization has embedded a tool into its operating fabric, the more painful the inevitable transition becomes.

The issue lies less in the tools themselves than in the strategic weight organizations assign to tool selection. When tool positioning is treated as a long-horizon strategic commitment, the organization assumes a stability that the environment no longer provides.

The Pivot: From Tools to Principles

If tools are no longer a stable strategic variable, what is? I would argue for principles: the articulation of expected behaviors, quality standards, operational constraints, and governance guardrails that define how work should be conducted, independently of which tool happens to be executing it.

This shift goes deeper than it may initially appear. Principle-driven governance asks organizations to invest in defining what good looks like (what outcomes are expected, what boundaries must be respected, what quality thresholds must be met) rather than prescribing the specific mechanism through which those outcomes are achieved. The principle is stable; the tooling that enacts it is fluid.

Consider the difference between these two governance postures:

Tool-centric: “All code reviews must be conducted using platform X, following workflow Y.”

Principle-centric: “All code changes must demonstrate coverage above threshold T, pass static analysis at severity level S, and include a human review sign-off before merging.”

The second posture is tool-agnostic. It can be enacted by a human reviewer, a CI/CD pipeline, an AI-assisted review agent, or any combination thereof. As tooling evolves, the principle remains intact. Only the mechanism of enactment changes, and that change becomes an optimization rather than a disruption.

Agentic Architectures as a Tooling Abstraction Layer

This is precisely where agentic AI architectures become strategically relevant, as a structural response to tooling volatility and not merely as a productivity lever.

A well-designed agent is coupled to an objective, a set of constraints, and a definition of acceptable outputs, rather than to a specific tool. Given those parameters, the agent selects, orchestrates, and adapts its tooling dynamically. The tool becomes an implementation detail, visible to the agent but invisible to the governance layer.

In a multi-agent architecture, this abstraction scales. Individual agents can specialize (one handling code analysis, another managing test generation, another monitoring technical debt), each operating against the same set of organizational principles while independently managing their tooling layer. As new, more capable tools emerge, the agents can incorporate them without requiring a governance review of the tool itself. What gets reviewed is whether the agent’s outputs continue to satisfy the principles.

This represents a meaningful inversion of the traditional governance model. Rather than controlling quality by controlling tools, the organization controls quality by controlling outcomes. The locus of governance shifts from inputs to outputs, from “what tool are you using” to “are the results meeting the standard.”

The implications for large organizations are significant. Tool evaluation cycles shorten dramatically. Shadow tooling, a persistent governance challenge in organizations with slow approval processes, largely dissolves as a concern: if the agent is producing compliant outputs, the underlying tooling becomes an operational detail rather than a governance risk.

Limits and Conditions for Success

This architecture relocates governance complexity rather than eliminating it. Several challenges deserve honest acknowledgment.

Defining good principles is harder than it appears. Principle-driven governance requires organizations to articulate, with precision, what they actually care about. Vague principles produce inconsistent agent behavior. Overly rigid principles reproduce the brittleness of tool-centric governance in a different form. Getting the level of abstraction right requires significant organizational investment and ongoing calibration.

Output evaluation at scale is a partially unsolved problem. For well-defined, measurable outcomes such as test coverage, static analysis scores, or deployment frequency, this is tractable. For more qualitative dimensions like architectural coherence, domain appropriateness, and long-term maintainability, it remains genuinely difficult.

Agent maturity remains a limiting factor. Current agentic systems are capable and improving rapidly, but they are not infallible. Human oversight, particularly for high-stakes decisions, cannot be fully removed from the loop, at least not yet.

Regulated environments require special treatment. In domains where regulatory compliance is tied to specific tools, processes, or audit trails, the fluidity of agentic tooling introduces compliance risks that cannot be resolved through principle-definition alone.

A Governance Model Built for the Speed of Change

The organizations best equipped for the current period of technological acceleration are not necessarily those with the best tools. They are those with the clearest principles, along with the architectural flexibility to let those principles be enacted by whatever mechanisms become available.

This is a direct extension of the argument made in the first article of this series. If the People-Process-Tools model needs to be rebalanced toward platform and process in a context of human variability, then the tooling layer of that platform needs to be rebalanced toward principles and agents in a context of technological volatility.

The strategic question for technology leaders has shifted from “which tools should we standardize on” to “which principles are non-negotiable, and how do we build systems, human and agentic, that reliably enact them, regardless of what the tooling landscape looks like six months from now.”

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: Organizational Engineering for the AI Era

The first two articles in this series examined how to rebalance technology governance toward platform engineering in contexts of high human variability, and why tool positioning is becoming a strategic liability in a world of accelerating AI. This third article steps back from the technical layer to address a more basic challenge: the organizational resistance that makes any of these transformations genuinely difficult, and the asymmetric risk of not moving fast enough.

Organizations have spent decades building systems to control technological risk. They have not built systems to detect technological irrelevance.

An Organization Does Not Have Processes: It Is Its Processes

There is a common way of framing organizational change that sounds reasonable but is misleading: the idea that an organization has processes, in the same way it has office space or software licenses, assets that can be evaluated, updated, or replaced through a sufficiently deliberate management decision.

The reality is more unsettling. An organization does not merely possess its processes. In a meaningful sense, it is its processes. They define how decisions get made, how work gets coordinated, how risk gets assessed, how value gets delivered. They encode the accumulated responses to past failures, regulatory pressures, and hard-learned lessons. Rather than a layer sitting on top of the organization, they are its operating identity.

This is the central insight behind Allen Holub’s work on systems of work: you cannot remove or change one element without affecting the entire system, and the system will actively resist changes that threaten its coherence, pulling the organization back toward its previous state. Organizational resistance to change is therefore not a failure of leadership courage or a deficit of innovation culture; it is the system functioning exactly as designed. The challenge is not to overcome that resistance through force of will, but to understand which parts of the system need to transform, and how to do so without collapsing the coherence that holds everything together.

AI Is Not Waiting at the Door: It Is Already Inside

Regulated organizations (financial institutions, healthcare systems, energy companies, public sector bodies, legal and professional services firms) have historically managed technological adoption through deliberate, controlled processes. This was rational. In a stable technological environment, moving carefully reduced integration risk, ensured regulatory compliance, and preserved operational continuity.

That posture is increasingly misaligned with the current moment. AI does not present itself as a discrete technology that organizations can choose to evaluate and adopt on their own timeline. It is infiltrating through every layer of operations: the productivity tools individual contributors are already using, the platforms vendors are embedding AI capabilities into, the competitors who are quietly compressing delivery timelines and cost structures, the talent market where expectations about AI-augmented work are shifting rapidly.

In financial institutions, the question is no longer whether AI will affect credit risk modeling, fraud detection, regulatory reporting, or client-facing services. It already does, whether through internal adoption or through the competitive and regulatory environment that surrounds the institution. The relevant question has shifted from should we adopt AI? to at what rate are our current processes becoming a competitive and operational disadvantage, and are we measuring that rate?

The Asymmetry of Risk That Does Not Appear in Governance Reports

This is the most structurally important challenge for regulated organizations, and it is one that their governance frameworks are poorly designed to surface.

The risk of moving too fast with AI is visible and auditable. It manifests as compliance failures, model risk incidents, data governance violations, operational disruptions. It appears in risk committee reports, regulatory examinations, and audit findings. Regulated organizations have spent decades building sophisticated machinery to detect, quantify, and contain exactly this type of risk, and they are genuinely good at it.

The risk of not moving fast enough is structurally different. It is diffuse, cumulative, and largely invisible to conventional risk measurement frameworks. It does not generate an incident or trigger a regulatory finding. It accumulates silently as a widening gap between the organization’s operational capabilities and those of its environment: competitors, technology providers, talent expectations, and eventually regulators themselves.

By the time this second type of risk becomes visible, it typically manifests not as a discrete event but as a condition: an inability to attract engineering talent with relevant AI skills, a growing gap in delivery speed relative to competitors, a technological debt so deep that transformation becomes structurally prohibitive.

Consider a concrete illustration. A major bank’s regulatory reporting process relies on manual data consolidation across legacy systems. The cycle takes three to four weeks per quarter, absorbs dozens of analyst hours, and produces results that are already partially stale by the time they reach the board. An AI-augmented equivalent could compress that cycle to days, reduce error rates, and free the analysts for higher-judgment work. The risk of implementing such a system is visible and manageable (model validation, auditability, change management). The risk of not implementing it accumulates invisibly: mounting operational cost, growing talent frustration, and an expanding gap relative to institutions that have already made the transition. The first risk has a line in the risk register; the second does not.

The asymmetry is this: the governance machinery of regulated organizations is optimized for the first type of risk and structurally blind to the second.

Regulation as Constraint, and as Underestimated Lever

In conversations about AI adoption in regulated industries, regulatory constraints are frequently invoked as the primary justification for caution. This framing is not wrong; regulatory constraints are real and consequential.

But it is increasingly incomplete, and in some cases it has become a rhetorical convenience that obscures something less comfortable: the pace of adoption is often constrained less by regulation than by organizational inertia dressed in regulatory language.

Regulators themselves are not standing still. Financial supervisory authorities, including the AMF, OSFI, the European Banking Authority, and others, are actively developing AI governance frameworks, publishing guidance on responsible AI use, and in some cases explicitly encouraging institutions to build AI capabilities in areas such as real-time compliance monitoring, fraud detection, and operational risk management. The implicit message from regulators is not “wait until we have figured this out” but “develop the governance maturity to use these tools responsibly.”

An organization that waits for the regulatory framework to be fully stabilized before investing in AI capabilities is making a specific bet: that the cost of early compliance investment outweighs the cost of late adoption. In a faster-moving environment, that bet is becoming harder to justify.

Regulation, properly understood, is a constraint on the speed and form of AI adoption, not a justification for its absence.

Navigating the Transition: Changing Without Breaking

The response to this challenge is not a wholesale transformation that attempts to change everything at once. Holub’s insight about systems of work applies with particular force here: an organization that tries to revolutionize its entire operating model in response to an external pressure is as likely to collapse its coherence as to improve its capabilities.

This requires organizations to develop a capability that is itself relatively new: the ability to map their own processes according to their strategic value in an AI-augmented environment. Not all rigidity is equivalent. Some processes derive their value precisely from their stability and resistance to change: regulatory compliance mechanisms, audit trails, risk controls, fiduciary obligations. These are not candidates for AI-driven transformation. They are anchors that must be protected even as the surrounding system evolves.

Other processes derive value from their efficiency and adaptability: knowledge management, delivery pipelines, onboarding, tooling, internal reporting, operational monitoring. These are precisely the areas where AI augmentation offers the highest return, and where the cost of inaction is accumulating most rapidly. Treating them with the same conservatism as core compliance processes is a category error, not prudent risk management.

The practical implication is a dual governance posture: rigorous protection of the processes whose value is their stability, combined with active transformation of the processes whose value is their performance. The organizations that handle this transition most effectively will be those that can hold both imperatives simultaneously, and resist the temptation to use the first as cover for avoiding the second.

This is not a call to abandon prudence, but to redefine where prudence actually applies.

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: Organizational Engineering for the AI Era

A Theory of Creativity First

Before building any system, I needed to understand what creativity actually is.

Here is the formulation I arrived at around 2005, working as a researcher in Québec City:

Humans cannot create something entirely new from nothing.

We can only perceive reality through our senses. You cannot imagine a color that does not exist. You cannot conceive a sound you have never heard. The entire domain available to human creativity is bounded by accumulated sensory experience.

Creative thinking is therefore combinatorial rather than generative ex nihilo. The only way to produce something genuinely new within a bounded experiential space is to discover a structural correspondence between two distant domains that are already known.

When that correspondence holds, it is a metaphor.

But novelty alone is insufficient. A creative idea must also be useful, and usefulness is not intrinsic to the idea itself. It is evaluated by a human observer with a specific problem and a specific set of objectives. The same association can be brilliant in one context and irrelevant in another.

This leads to a precise formulation:

A creative idea is an association between two distant known domains whose relevance is judged by a human evaluator as both novel and useful within a specific context.

Novelty is a function of semantic distance between domains.

Utility is a function of the match with the problem and with the evaluator’s own criteria.

From this theory, an architecture follows naturally.

The Architecture (2005–2010)

If creativity is the identification of structural correspondences across distant domains, then a system designed to assist creativity should not search for what is similar. It should search for what is structurally equivalent across distance.

Between 2005 and 2010, working at a private imaging company in Québec City, I designed and implemented such an architecture.

Each object in the system (initially images, but the framework was modality-independent) was described through three structured vectors:

• A composition vector capturing spatial organization, perspective, and luminosity
• An object-property vector encoding detected geometries, shapes, and physical attributes
• A semantic vector containing qualitative keyword descriptors

These vectors were computed automatically using classical computer vision techniques: color histograms, wavelet decompositions, geometric segmentation, texture analysis, spatial frequency distributions. Each image received a multidimensional signature, which we called a “genetic code,” stored in a database and queryable in real time across a collection of 100,000 images.

In today’s terminology: a hand-crafted feature-embedding pipeline feeding a structured vector database, with sub-second query response time. Built in 2008, before those words existed in this context.

But the architectural intent was quite different from similarity search.

The system did not ask: What is nearest?

It asked:

• Which objects share equivalent structural overlaps with a reference? (alternative objects: substitutable, not combinable)
• Which objects contribute complementary, non-overlapping components of a reference? (complementary objects: combinable to form a stronger metaphor)
• Which combinations increase analogical coherence rather than surface resemblance? (linked objects: internal reinforcement as a tiebreaker)

The metaphorical strength of a combination was computed as the aggregate overlap across structured dimensions. Combinations were ranked by analogical coverage, not by proximity.

A red apple and a white tablecloth, taken separately, are only partial metaphors for the Japanese flag. Combined, they cover it completely: one contributes the red circle, the other the white rectangle. Their combination scores higher not because they resemble the flag, but because together they account for its structure. That is analogy, not similarity.

Similar questions were being explored across content-based retrieval and computational creativity research during that period. The problem was recognized; what was missing were the representational tools to address it at scale.

Why This Distinction Still Matters

Modern embedding systems, including the best multimodal foundation models, are extraordinarily capable. They organize high-dimensional spaces where related concepts cluster together. Distance minimization in embedding space has proven to be a remarkably powerful paradigm.

But the analogical operation itself remains largely latent in these systems.

Current architectures optimize for distance, retrieve nearest neighbors, and interpolate within learned manifolds. They do not explicitly evaluate which pairs or sets of representations together form a structurally stronger mapping to a reference than any single element alone.

Foundation models can produce metaphor-like outputs through statistical association, sometimes impressively so. But the mechanism is emergent rather than structured, which means it cannot be controlled, composed, or inspected.

Making analogy a first-class, explicit, steerable operation in AI systems remains an open architectural problem.

What AI Still Needs

Revisiting this research today, three limitations of current AI systems stand out, each rooted in the same theory of creativity I was working from in 2005.

1. Implicit context inference

Human creativity does not operate in a vacuum. The relevance of any idea depends heavily on the context in which the human is operating: their constraints, objectives, mental state, environment. A skilled collaborator reads this context without being explicitly told.

Current AI systems depend almost entirely on what the user explicitly provides. They do not perceive the context in which the human evolves. For AI to become a genuine creative partner, it needs access to richer contextual signals, along with the capacity to infer meaning from them without requiring the human to articulate everything. That is a problem of situated cognition rather than retrieval.

2. Memory as a knowledge reservoir

In my theory of creativity, the quality of associative reasoning is directly proportional to the richness of available memory. More diverse experience means more potential connections across distant domains.

Current AI systems have impressive parametric knowledge, but their memory architecture is largely static between training runs. The strategic management of episodic, contextual, and long-term memory, treated as a dynamic, queryable reservoir that evolves with use, remains underdeveloped. Memory, in this view, is the substrate of association and not just storage.

3. Emotional memory

This is perhaps the most significant gap, and the one most likely to trigger an objection from researchers in reinforcement learning or affective computing.

The objection is predictable: AI systems do incorporate affective signals, through reward modeling, reinforcement learning from human feedback, and preference learning. This is true, and the work is serious.

But it addresses a different problem. These approaches treat affect as an external training signal: feedback injected during learning to shape behavior. Human emotional memory is structurally different. Rather than a signal applied to knowledge from the outside, it is woven into knowledge itself.

The neuroscientist Antonio Damasio demonstrated this precisely. Emotion is not separate from rational cognition; it is constitutive of it. Patients with damage to emotional memory centers do not become more rational. They lose the capacity to make decisions at all, because they can no longer assign differential weight to recalled experiences.

Every human memory carries affective encoding (fear, joy, discomfort, wonder) that influences which associations surface, which combinations feel meaningful, and which ideas resonate. This weighting is embedded in the retrieval structure itself rather than applied post-hoc.

Current AI systems process semantic relationships without this integrated affective architecture. The gap may therefore be architectural rather than merely incremental.

Building systems where affective structure is embedded in memory, instead of applied to it from outside, may be one of the most important open problems in AI research on creativity and reasoning.

Why I Am Writing This Now

The research described here was conducted between 2005 and 2010 and was not publicly discussed at the time due to contractual obligations.

Revisiting it now is less an exercise in retrospection than a recognition that the underlying questions remain open, and that the technological conditions to investigate them meaningfully finally exist.

I am not writing to claim priority. Parallel discovery is the norm in research, and I have no interest in relitigating timing. I am writing because the problem is still in front of us.

If you are working on reasoning architectures, compositional intelligence, contextual AI systems, or human–AI collaboration, I would like to talk.

The research described above is documented in an unpublished patent specification and a research poster produced in 2008.

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: Organizational Engineering for the AI Era