AI Is Teaching Us That Consciousness Is About Structure

One of the strangest things happening in the AI era is that organizations are rediscovering the importance of structure.

For years, in my job as a program manager in large technology companies, I watched experienced teams bypass process because talented humans are remarkably good at compensating for ambiguity.

In many corporate situations, employees treat process as necessary but annoying operational overhead. Process design and optimization was often not seen as particular glamourous part of the business, and more of an annoying backroom activity that is inherently less valuable than sales, innovation, finance or marketing.

This is despite the fact that when business processes break because processes and systems are weak, the results can be catastrophic to the bottom line. Weak systems occur where knowledge is undocumented, requirements are vague, workflows are missing, standards are not consistently applied, ownership is unclear, and so on. Human experience is great at compensating for weak systems, as we are highly adaptive and fill gaps intuitively.

AI changes that equation completely: machines cannot reliably fill gaps in processes or weak systems intuitively, so suddenly structure becomes critical rather than optional. At my last role at Cisco, as AI increasing began to be used within our organization, I watched a sudden increase in the interest in workflows, process mining, structured context, specification quality, architectural maps, execution guardrails - the list goes on and on.

Why is this? Without structure, AI hallucinates and often produces less than optimum results. The irony is fascinating. Humans often resist rigid process because human cognition is flexible enough to compensate for ambiguity. In technical terms, humans instinctively reduce prediction hallucination by instinctively applying compensating controls. But AI exposes the hidden dependency we always had on human interpretation, forcing us to reexamine the importance of structure.

The deeper trend here is that AI is forcing organizations to selectively externalize cognition itself. And cognition, even when it often doesn't feel like it, is a highly structured process. I suspect this has implications far beyond the software engineering and technology fields.

Over the last several years, my own research into meditation, predictive processing, and consciousness has led me toward a similar realization: Consciousness itself can be considered to operate far more structurally than we have previously assumed. For a long time, I kept my personal research into these fields very separate from my work life as I didn't see much overlap. One was a personal study using my regular mindfulness practice to explore the structure of my own mind. The other was my day job, organizing people, things and events to deploy complex technology for major corporations. But because AI is changing the role of cognition within organizations, my personal experiences and my work experiences are rapidly converging.

Historically, psychology focused heavily on meaning: what emotions mean, what experiences mean, what beliefs mean. However, modern neuroscience increasingly suggests the brain operates more like a layered predictive system — constantly generating models, simulations, and structured representations of reality - remarkably similar to how some AI models work.

Creating a new word for a new way to thinking

Identifying this shift from meaning to structure led me to new (human!) cognitive capacity I have started calling the ability to abstractify:

Abstractify verb: to deliberately observe an experience from a higher level of structural abstraction within consciousness.

For example: Normally, anger feels personal and immersive. We first experience it, then we might attempt to interpret it semantically and find out its meaning. We analyze causes and stories. But when you instead process anger structurally — treating it as a predictive routine running in the nervous system — your relationship to that emotion changes immediately.

Not because the emotion was “solved.” But because your observational and processing structure changed. How to master and apply the ability to abstractify emotion and other sensory input is the skill you will learn in the Neutralize course.

Oddly enough, this feels increasingly similar to what we are learning with AI systems. Large language models are extraordinarily powerful pattern engines. But their outputs are highly dependent on context setting, workflow sequencing, defining constraints, memory architecture and probability weighting, which are all structural elements that define how the model processes inputs and decides on outputs.

In AI systems, weights determine how strongly certain patterns influence future predictions. Human consciousness may operate in a similar way, where emotional experiences that receive repeated attention appear to gain “weight” in the brain’s predictive models, making them more likely to shape future perception and behavior.

In other words, both artificial intelligence and human consciousness appear deeply sensitive to structure. That may become one of the defining ideas of the next decade. Not just in AI engineering — but in leadership, organizational design, emotional regulation, learning systems, and human performance.

We humans may eventually realize that intelligence is not just about meaning. It is about how systems organize prediction, attention, and structure to create our world.