This essay as told is based on a conversation with Max Votek, co-founder and managing partner of Customertimes, a business consulting firm that helps Fortune 500 companies implement AI. This essay has been edited for length and clarity.

Companies are quick to blame layoffs on artificial intelligence. But from my perspective, advising Fortune 500 companies on AI adoption, that’s often not the whole story.

I am the co-founder and managing partner of Customertimes, a 1,000-person consulting firm that helps companies implement AI. Before that, I spent almost a decade in the pharmaceutical industry leading technology transformation projects. I work with CFOs, CIOs, and CEOs every week, and what I hear behind closed doors often doesn’t match the public narrative.

This is what people get wrong about AI layoffs.

Companies must be honest about AI

AI has an image problem and companies are making it worse by refusing to explain what is happening.

When companies announce layoffs, report record profits, and never explain where the savings went, people naturally assume the worst. If companies leave If there is an information gap, someone else will fill it.

People want transparency. They want companies to explain how they use AI, how they protect customer data, and whether the benefits accrue to employees or customers.

That’s why I think companies should publicly disclose how AI savings are used. If AI-driven profitability translates into employee bonuses, lower prices for customers, or business-enhancing investments, companies should say so.

A simple ledger showing where those profits go would eliminate a lot of suspicion.

Right now, too many companies are silent. And when they do, they end up writing the conspiracy theory themselves.

Where the savings really go

Our survey found that 86% of adults believe companies that save money through AI should reduce prices for consumers. I think that’s a fair expectation.

Many people assume that companies lay off people, pocket the savings, and give executives bigger bonuses.

It’s more complicated than that.

Behind the scenes, companies receive huge bills from AI vendors. CFOs and CIOs regularly tell me that they underestimated the nominal costs, and many organizations have already exhausted their AI budgets much faster than expected.

Some executives even talk about “token maxxing” – exhausting their allocated spending on AI in a matter of months without achieving the productivity gains they originally forecast.

Additionally, companies are investing heavily in protecting their internal knowledge. They don’t want proprietary business processes or trade secrets flowing into large public language models, so they are building additional AI infrastructure to keep that information within their organizations.

These investments are not cheap.

The public often doesn’t understand where AI savings actually go. In many cases, they do not simply go toward executive compensation. They are being absorbed by infrastructure, token costs, AI licensing and building secure internal systems.

Companies have always sought efficiency

Long before generative AI, companies used robotic process automation to eliminate repetitive work. The goal hasn’t changed: find inefficient business processes, automate routine tasks, and free people from work they don’t like to do over and over again.

I rarely hear executives say they are replacing people with AI. I talk to CFOs, CIOs, and CEOs every week, and that’s simply not how these conversations are framed internally.

Instead, I think many companies are using AI to explain restructuring decisions they were already planning to make.

In many cases, AI masks an underlying inefficiency. A company identifies a process that no longer makes sense, restructures it, and then wraps the decision in the language of AI.

There is nothing unusual about the restructuring. Companies have always reorganized to be more efficient. The problem is that many are not honest with employees or shareholders about what is really driving those decisions.

The reality is also more nuanced than many headlines suggest. AI is great at handling repeatable tasks, but it is no substitute for accountability. You can automate a CEO’s presentation with an AI avatar, but you can’t automate the responsibility that comes with leading a company.

I’ve also seen many employees adapt. At my company, we invest heavily in AI training, and testers and business consultants can learn new AI skills in just a few weeks.

Instead of becoming less valuable, they often become capable of offering much more.