If this were genuinely a consultant's writing, they would need to present a modeling hypothesis for why failure occurs in organizations and support it with metrics. But claiming their own observation of 0% is not a hypothesis.

Of course, they can write it on a personal blog. That's not inherently bad.

But when they're rejecting client requests while offering no opinion on how to model the metrics that would define success—that's the problem. This type of writing is just an illusionary piece that claims to offer insight. It's a bad form of human writing. I like to call this style the 'Rhonda Byrne Secret type.'

At the very least, a consultant should provide modeling of their hypothesis and actionable advice.

Explaining everything with a single reason—that people are afraid of being laid off if they don't adopt AI—is just a cop-out. There's no hypothesis, no reasoning behind why that claim holds. It's trying to explain everything with just that one assertion. I'm not even sure if that holds up organizationally

The OP offers no logical reasoning about how decisions are made or why they break down.

A proper piece of writing would have looked like this:

Why did the client want to adopt LLMs?

What problems arose during the client's process of adopting LLMs?

How does this connect to the organization's internal issues and disrupt its pipeline?

You need at least that level of description. This is just an emotional post that seems to say 'I wish LLMs would fail.

If they had really explained organizational dynamics, they would have talked about the asymmetry of expected costs.

Professional managers and mid-level managers prioritize career defense above all else, so they're extremely risk-averse. They would have explained the costs of adopting AI versus the costs of failing to adopt AI.

If they reject AI adoption, shareholders or the board would say they're 'failing to read the trend.' If they adopt AI and fail, they can package it as 'early market entry costs'—a defensible risk.

Providing that kind of defensive logic to decision-makers is exactly what consultants do.

It's described as being held at gunpoint, but it's really just a prisoner's dilemma. In a market where competitors and vendors are all lying and saying 'we've achieved 100x productivity with AI,' the moment someone tells the truth and says 'we don't need AI for our business,' they become a political target both inside and outside the organization. In the end, everyone knows it's useless, but they approve budgets for 'fake AI projects' anyway. I can offer that kind of cheap insight too—it's a Nash equilibrium where the system has been driven to a race to the bottom.

And I'm not even a consultant