AI Is Not a Better Gantt Chart

18 June 2026

A Strategic Conversation with Dr. Leandro Herrero:

The previous conversation in this series ended with a question. The linear model of organizational life – sequential, mechanistic, comfort-driven – has persisted despite decades of evidence that it cannot keep pace with the conditions it is meant to manage. What would it take to break it?

One answer is now arriving faster than most organizations are prepared to acknowledge.

Artificial intelligence is not disrupting linearity from the outside. It is dissolving the internal rationale that made linearity feel necessary. And in doing so, it is surfacing a choice that management culture has never had to make quite this explicitly: whether the machine is a useful tool or a terminal condition.

Dr. Leandro Herrero and Mike Klein pick up the thread.

The Readiness Trap Gets Sprung

Mike Klein: The concept of readiness in organizations has always been partly about information. You can’t act until you know enough. You need the data, the analysis, the briefings, the stakeholder mapping. The preparation process is the preparation process because processing information takes time. AI collapses that logic entirely. The information processing that used to take weeks takes minutes. So what is the readiness argument now?

Leandro Herrero: That is exactly the point. Many of the conditions that readiness was supposedly waiting for – gathered information, analyzed options, briefed stakeholders – can now be available in an afternoon. So you are suddenly, theoretically, more ready than you thought you were. And the old excuse disappears. Which means organizations that still use readiness as a gate will have to find a different reason not to act. They will become, in that sense, naked to the world.

Mike Klein: Or they will dress themselves in new procedural clothing. The risk is that organizations respond to AI’s elimination of the information rationale by inventing new procedural requirements – AI governance frameworks, use-case approval processes, prompt libraries that require sign-off. We’ve seen this already. Companies that approach AI the way they approached ERP – lock it down, define the permitted workflows, train everyone to use it identically – are building the new machine on top of the old one.

Leandro Herrero: That is a very real risk. If behind the AI adoption there is a lot of money and a lot of resources being put into the system with the expectation of recreating the machine – the machine will be recreated. The label changes. The logic doesn’t.

AI Is Not SAP

This is the argument at the center of a piece Klein co-authored with organizational network analysts Andras Vicsek and Peter Kalmar – “AI Is Not SAP” – and it is one of the more consequential distinctions in current management thinking.

SAP implementations required total process standardization. Variance was the enemy. Every workflow had to conform to the system’s architecture before the system could deliver value. Readiness, in that context, was a genuine pre-condition. The machine could not run until all the pieces fit.

AI works on an entirely different logic. Its value is not in process standardization. It is in adaptive capacity – in the ability of an individual to direct the tool toward a problem as the problem actually presents itself, not as a workflow diagram anticipated it would.

Mike Klein: When you give a communications person access to Claude and tell them to figure it out, they figure it out. And the way they figure it out is specific to how they work, how they think, what they are actually trying to accomplish. That is not a replication of a prior process. It is the generation of a new one. And if you have thousands of employees doing that across an organization simultaneously, you are not implementing a system. You are seeding a change process that nobody designed and nobody is governing.

Leandro Herrero: That is exactly where it becomes interesting. And where it could become transformative. Because what you are describing is the beginning of something that emerges from behavior, not from a plan. That is very different from how organizations have tried to drive change for the last forty years.

Agency by Accident

There is a scenario – neither optimistic nor pessimistic, just increasingly visible – in which organizations inadvertently release human agency at scale.

The mechanism is this: large-scale workforce reductions are accompanied by the deployment of AI tools to the survivors. The survivors must meet the same or higher output expectations with fewer people. AI is their primary adaptive resource. Nobody has written the playbook. The employees write it themselves.

Mike Klein: What happens when all of this gets put together? When people start talking to each other about how they’re using AI, sharing what works, iterating on each other’s approaches? You could have a viral change process happening by default – not because anyone decided to run one, but because survival required it. And a lot of what we have been frustrated about for thirty years – the inability to get organizations to let go of linear procedure and actually adapt – goes out the window. Not because the organization chose to let go. But because letting go of the people made letting go of the procedures unavoidable.

Leandro Herrero: I don’t disagree with that. What you’re describing is happening. The question is whether the organizational response will absorb it or amplify it. Reality has always moved faster than our attempts to manage it. AI exaggerates that gap. And the organizations that respond by trying to reduce the gap through more control will find themselves further behind, not less. Those that permit the emergence – that tolerate the viral and iterative – have a genuine opportunity.

The Different Zero

There is a second pathway – less visible than the agency-by-accident scenario, but potentially more structurally significant. It runs through the organizations that are starting from zero now, rather than adapting from fifty years of accumulated mechanistic infrastructure.

Leandro Herrero: Starting from zero today is different from starting from zero ten years ago, or five years ago. It’s still zero, but it’s a different zero. The speed is different. The tools are different. The delta between what you can do immediately and what used to require years of build is genuinely different. An AI-native startup in a service industry does not have to un-learn anything. It has no prior machine to recreate. It starts at a point that incumbents cannot easily reach.

Mike Klein: The threat to established companies is specific. It is not abstract disruption. Startup companies – particularly in professional and knowledge services – that are AI-native from day one are positioned to deliver at a cost and speed that organizations addicted to procedural preservation simply cannot match. The procedural complexity that was once a competitive moat – it signals credibility, rigor, institutional weight – becomes a liability when the competitor next door can produce the same or better output with a fraction of the overhead.

Leandro Herrero: I haven’t seen that fully realized yet, to be honest. In the areas I know best – healthcare, pharmaceutical – the new entrants are often recreating the same patterns with new money and new energy. They appear and disappear. But I think in technology, in professional services, in knowledge work broadly, the conditions for a genuinely different model exist in a way they didn’t before. The pace of what’s happening will not wait for the slow adoption of anything.

The Critical Thinking Question

There is one dimension of this that neither optimists nor skeptics are addressing with sufficient seriousness. AI’s capacity to dissolve bureaucratic linearity is a precondition for something valuable. It is not the thing itself.

Leandro Herrero: The most interesting possibility, to me, is not speed. It is critical thinking. AI used well – not as an advanced search engine, not as a content generator, but as a mechanism for contrasting positions, testing arguments, challenging assumptions – could actually increase the quality of thinking in organizations. That is the possibility that excites me most. And it is also the most dependent on the user’s willingness to actually think.

Mike Klein: Most people I see are still using it as a utility. A faster version of something they already did. Which is not wrong – the efficiency gains are real and they matter. But that is not what breaks the machine. What breaks the machine is using AI to do the thing the machine was specifically designed to prevent: to ask, in real time, why we are doing this in this sequence, whether the sequence is still valid, and what a different sequence would produce.

That question has been organizationally suppressed for decades. AI does not automatically liberate it. But it removes, for the first time, the primary excuses that were used to prevent it being asked.

What Gets Left Behind

The outcome is not predetermined. The comfortable mechanistic model will not simply dissolve under AI pressure. It will fight. It will rename itself. It will build AI governance that reproduces its own logic in a new language. It will find new reasons that the organization is not ready.

But the conditions for a different organizational model – one built around adaptive capacity rather than procedural control, around agency rather than sequence – have never been more available.

Leandro Herrero: What is left behind, when the dust settles, is all the practices that we never forced ourselves to question because the model was comfortable enough not to require it. We didn’t learn to rethink and refresh. We refined the model and added labels. What is happening now forces us to question more things, challenge more things – including the language, including the concepts, including readiness, engagement, wellbeing, all of it.

Mike Klein: Readiness is a 20th-century ERP implementation concept. It does not belong in the organization that AI is making possible. The paradigm that treats every human process as a sequential machine with a declared start state was always wrong. Now it is also visibly, consequentially, competitively wrong.

That is new. What organizations do with it is the question of this decade.

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Dr. Leandro Herrero is the founder of The Chalfont Project and the creator of Viral Change™. Mike Klein is the principal of Changing The Terms and editor-in-chief of Strategic Magazine.