This as-told-to essay is based on a conversation with Max Votek, the cofounder and managing partner of Customertimes, an enterprise consulting firm that helps Fortune 500 companies implement AI. This essay has been edited for length and clarity.
Companies are rushing to blame layoffs on artificial intelligence. But from where I sit, advising Fortune 500 companies on AI adoption, that’s often not the whole story.
I’m the cofounder and managing partner of Customertimes, a 1,000-person consulting firm that helps enterprises implement AI. Before that, I spent nearly 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.
Here’s what people get wrong about AI layoffs.
Companies need to be honest about AI
AI has an image problem, and companies are making it worse by refusing to explain what’s happening.
When businesses announce layoffs, report record profits, and never explain where the savings went, people naturally assume the worst. If companies leave an information vacuum, someone else will fill it.
People want transparency. They want companies to explain how they’re using AI, how they’re protecting customer data, and whether the benefits are flowing back to employees or customers.
That’s why I think companies should publicly disclose how AI savings are being used. If AI-driven profitability is flowing into employee bonuses, lower client pricing, or investments that improve the business, companies should say so.
A simple ledger showing where those gains go would eliminate a lot of suspicion.
Right now, too many companies stay silent. And when they do, they end up writing the conspiracy theory themselves.
Where the savings are really going
Our survey found that 86% of adults believe companies saving money through AI should lower prices for consumers. I think that’s a fair expectation.
A lot of people assume companies lay people off, pocket the savings, and hand executives bigger bonuses.
It’s more complicated than that.
Behind the scenes, companies are getting enormous bills from AI providers. CFOs and CIOs regularly tell me they underestimated token costs, and many organizations have already burned through their AI budgets much faster than expected.
Some executives even talk about “token maxxing” — exhausting their allotted AI spending within a matter of months while failing to achieve the productivity gains they originally forecast.
On top of that, companies are investing heavily in protecting their internal knowledge. They don’t want proprietary business processes or trade secrets flowing into public large language models, so they’re building additional AI infrastructure to keep that information inside their organizations.
Those investments aren’t cheap.
The public often misunderstands where AI savings actually go. In many cases, they aren’t simply flowing into executive compensation. They’re being swallowed by infrastructure, token costs, AI licensing, and building secure internal systems.
Companies have always chased efficiency
Long before generative AI, companies were using 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 enjoy doing over and over.
I rarely hear executives say they’re 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 is masking an underlying inefficiency. A company identifies a process that no longer makes sense, restructures it, and then wraps the decision in AI language.
There’s nothing unusual about restructuring. Companies have always reorganized to become more efficient. The problem is that many aren’t being honest with employees or shareholders about what’s actually driving those decisions.
The reality is also more nuanced than many headlines suggest. AI is very good at handling repeatable tasks, but it doesn’t replace 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 plenty of 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 delivering much more.
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