In 2022, I was hired to build AI operations at a health-tech startup. At the time, we were pioneering the use of AI in healthcare, which required significant human oversight until one day, this did not happen. GPT-4 started and within a short period of time, I realized that my role was no longer meaningful. My employer came to the same conclusion. There was no plan to retrain me or repurpose my skills into a new version of the job. My job just disappeared.
I say this not as a cautionary tale, but as a context. When I see a wave of mass cuts being justified as an AI transformation, I’m not remotely reading about it. I’m on the other side of that decision.
What I did not fully see then was that my employer had not changed. They were adapting. Layoff offers neat math. They provide a simple story for boards eager to see immediate cost savings and returns on AI investments. What they don’t provide is capacity building, creative gains, or new types of work. I lost a cost. Inherent Capability Question – What should this job be? – was never asked.
When companies like Meta and Microsoft cut thousands of employees, many leaders frame it as a necessary step to become more “AI-native.” I know exactly what’s going on. They are choosing the fast path to efficiency rather than the hard path to reinvention. They’re closing their own way to conversion because it’s easier than reinventing how things are done. I know the difference between those two things.
Today I lead AI operations at Pearl, an AI company for independent professionals, where we’ve taken a different path: empowering employees, reshaping roles, and having uncomfortable conversations earlier than most companies want. One of those conversations stands out.
I work closely with a tech writer who recently asked the question many employees are silently thinking: “AI can do a lot of my work for me – so what’s my job now?” She realized that much of the value she provided—drafting, editing, and refining documents—was now available to anyone using AI effectively. I understood that moment immediately. I lived it.
The difference this time was that we didn’t ignore the question. We answered together. Today, she works as an entire technical writing department with a team of AI agents who help proofread, edit and standardize her content. He also has our internal intranet, a function that often fails because it relies on constant manual updates. Instead of following teams around for updates, she uses AI to collect, organize, and refresh content across departments—turning a typically stale system into a living source of truth. She has reduced the time required to maintain that system by 95% – completely her own.
The reason this works is because we were already talking clearly and early on about how AI is changing work. Programs like our AI Champions initiative—which gives all department leaders 10% time to explore and build AI-powered workflows—have helped normalize use and facilitate honest conversations about where roles evolve.
This opportunity is missed by companies. When leaders avoid redefining roles early on, they create a moment where layoffs feel inevitable. Teams wake up with hundreds of people whose old jobs no longer exist and no clear plan for what happens next. At that point, layoffs become a response to inaction. That is a failure of leadership, not the result of AI.
Companies that are truly transforming with AI are doing much more difficult than simply issuing headcount cuts. They are acknowledging that work itself is changing and actively designing for it. They are retraining employees, redeploying them into new roles, and redefining what “good” work looks like in an AI-enabled environment.
It’s not easy, especially at scale. It is too simplistic to tell each department to cut 20% of their staff and “figure it out”. Large organizations are adapted for that type of instruction. And when boards demand results in a single quarter, leaders often default to layoffs because they feel urgent and decisive.
But there is a deeper risk: Layoffs create a downward spiral. AI will continue to improve, so if each new wave of capabilities is met with another round of headcount reductions, companies will continually shrink themselves, relying more on the technology until there is nothing left to change. These companies will survive but not evolve. They become smaller versions of themselves, able to do the same amount of work with fewer people, while more adaptive organizations expand their scope and output with the same teams.
We are still in the early stages of this transition, but a clear divide is emerging. On one side are companies that treat AI as a justification for workforce cuts. At the other are companies that treat it as a catalyst for reinvention. The difference will be whether leaders choose to change over short-term pressures through long-term capacity building.
Companies that navigate this well will never be the ones to face disruption. They will be the ones who learned from it – and built structures to handle the next wave before it arrives.
AI doesn’t just reduce labor. It multiplies what organizations can achieve when people are given the structure to grow with it. I know because I had to find that structure for myself—and because I’ve now helped someone else find it. You can close your path to conversion and hope efficiency will carry you forward. Or you can do the hard work. I know where the former goes.
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This story was originally featured on Fortune.com
