Organizational Engineering for the AI Era

On why large organizations structurally produce technical debt, select for political behavior, and reliably defeat every attempt at reform, including Agile. I have watched a pattern repeat itself across organizations too many times to believe it is accidental. A technically competent person does careful work, raises legitimate concerns about architectural shortcuts, pushes back on timelines that will generate problems downstream. They are right. Their concerns are documented. And they are quietly sidelined while the project proceeds, delivers on schedule, and the manager who overrode their concerns is promoted. ...

Part IV of a series on rethinking technology governance foundations, and a pivot toward what comes next 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. ...

Rethinking the foundations of technology governance in an era of structural human variability 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. ...

Part II of a series on rethinking technology governance foundations 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. ...

Part III of a series on rethinking technology governance foundations 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. ...

On Creativity, Metaphor, and What AI Still Needs to Learn 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. ...