Cutting jobs for AI might boost profits today, but skipping on reskilling people could cost us the future we actually want.
We are celebrating the wrong victories.
Earnings calls have followed a familiar pattern this year. Leadership teams have stood before investors and analysts, proudly announcing how AI has driven efficiency gains and headcount reductions. As of June, 491 people, on average, lost their jobs to AI every single day. The press releases call it “optimization.” The board calls it progress. The stock price jumps.
But here’s what nobody is talking about: 55% of companies that laid off staff due to automation now regret the decision. That statistic should keep every CIO and CTO awake at night. Particularly as 41% of employers worldwide intend to reduce their workforce in the next five years due to AI automation.
I’ve spent enough time leading technology transformation to recognize when we are optimizing for the wrong metrics. Right now, too many companies are so focused on the immediate ROI of AI automation that they are missing the bigger picture. We are not just deploying technology, we are reshaping lives, communities and the social fabric that underpins our economy. Most of us are doing it without a plan for what comes next.
We are breaking the social contract
Here’s the uncomfortable truth: When you deploy AI to drive cost savings without investing equally in reskilling, you are not being efficient. You are being short-sighted. You are externalizing your costs onto society.
With the average age of technology employees rising, younger white collar workers are being increasingly locked out of entry level roles at unprecedented rates. This is not just business optimization with new technology. It’s the elimination of the entire bottom rung of the career ladder.
Every junior developer we replace with GitHub Copilot, every customer service rep we swap for a chatbot, every analyst we automate away are not just headcount reductions. They are human beings that might have families, mortgages or communities to support. They are also the future senior engineers, directors and CTOs who won’t exist if we don’t give them a chance to learn the skills required. This problem can have a generational impact.
Executives know that close to half of their workforce needs to reskill over the next three years as part of this wider technology shift, yet we are not building plans for that.
We know people need to reskill. We are just not doing it at the same pace as we are automating. That’s not a technology problem. It’s a leadership problem. The gap between knowing what we should do and actually doing it is massive.
What responsible leadership actually looks like
I’ve seen this movie before. Every major technology shift comes with the same promise: “This time it’s different. This time we’ll manage the transition better.”
But here’s the thing: This time is different. Not because the technology is smarter, but because the pace is faster and the scale is larger. Nearly half of employees say they want more formal training and believe it is the best way to boost AI adoption. They are telling us what they need. We are just not listening.
The companies getting this right understand something fundamental: AI isn’t here to replace humans. It’s here to amplify human capabilities. But amplification only works if you are investing in the human side of the equation.
The window of opportunity
Here’s what keeps me up at night: We have a narrow window now to get AI adoption right. McKinsey’s superagency report linked above shows 71% of employees trust their employers to act ethically as they develop AI. That trust is a gift. It won’t last forever. We need to use it.
That means making hard choices. It means telling your board that your AI efficiency gains need to be balanced with meaningful investments in people. It means extending timelines to ensure transitions are humane, not just economically optimal. It means accepting that the immediate ROI might be lower because you are investing in long-term sustainability.
I realize that’s not the message most executives want to hear. But consider the alternative.
We are technology leaders. We’ve spent our careers understanding that short-term optimization often leads to long-term technical debt. The same principle applies to people. Cut training budgets, ignore reskilling, automate without planning — that’s just a form of human capital technical debt. And like all technical debt, it comes due eventually. Sometimes with interest.
Recommendations from the trenches
The most forward-leaning companies are increasing their training and skills development budgets. Not because they are altruistic, but because they understand that their long-term success depends on having a workforce that can work alongside AI, not get replaced by it.
That investment changes everything. It changes how employees view transformation. It changes how they engage with new tools. Most importantly, it changes the ripple effect that our decisions have on their lives and the lives of their families.
Here are some things we learned along the way.
Match automation with education
For every dollar you invest in AI tooling, invest a dollar in training your people to use it effectively. Not token gestures. Real, meaningful programs that prepare people for new roles, not unemployment lines.
Use AI to close AI skill gaps
Here’s where it gets interesting: The same technology that’s creating this crisis might also be part of the solution. We can use AI to help people learn how to work alongside AI. It’s not without irony, but it might be necessary.
Generative AI can create personalized learning paths that adapt to individual learning styles. The best programs I’ve seen combine AI-driven personalization with what IDC’s Gina Smith calls “experiential learning”, labs, games, hackathons and other hands-on opportunities, customized to the individual.
Create pathways, not dead ends
When a role becomes automated, have a plan for where those people go next. Start building those bridges now, not after you’ve already cut the positions.
Be honest about the timeline
Entry-level job postings have dropped 15% year over year. If you know certain roles are going away, tell people early. Give them time to prepare, to learn, to transition. Transparency isn’t a weakness; it’s leadership.
The question we should be asking
Every time we have a conversation about deploying AI for cost savings, we should be asking one question: “What is our plan for our people?”
Because here’s the truth that nobody wants to say out loud: if we don’t answer that question thoughtfully and invest accordingly, we are not technology leaders. We are just accountants with laptops.
As I learned years ago, often the obstacle isn’t what gets in the way. The obstacle is the way forward. The barrier to getting AI transformation right isn’t technology. It’s our willingness to invest in people at the same pace that we are investing in automation.
So here’s my challenge to every CIO, CTO and technology executive reading this: Before your next AI deployment, before your next automation initiative, before your next efficiency drive — ask yourself what your plan is for the people. And if you don’t have a good answer, don’t deploy. Not yet. Because what matters is not just the metrics in our AI ROI dashboards. It’s the lives of the people that our decisions affect and in the generational impact those decisions can have.
Get it right and AI becomes a tool for building a better future. Get it wrong and we’ll all be dealing with the consequences for decades to come.
The choice is ours. Let’s choose wisely.
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