88% of Organizations Get This Wrong About AI and Jobs- Kyle Forrest

TL;DR: Is AI actually the reason companies are cutting jobs?

Short answer: No, not in the way headlines suggest. AI is reshaping work, but the layoffs getting blamed on it are mostly restructuring decisions dressed up in a more fundable story.

Key stats you need to know:

• Deloitte's own client data shows that of every AI initiative dollar spent from summer 2025 through fall 2025, roughly 93 percent went to technology and only about 7 percent went to redesigning the actual work or roles, according to Deloitte Future of HR leader Kyle Forrest, who shared the figure on my podcast.

• In Deloitte's 2026 Global Human Capital Trends report, 88 percent of 9,000 respondents across 70 countries said orchestrating people and AI together matters, but only 7 percent said their organization is doing it well. That 81-point gap was the largest in the entire study (Deloitte).

• 55 percent of business leaders who made employees redundant because of AI now say it was the wrong call (Orgvue).

The leadership takeaway: if your organization announced an AI-related headcount cut without redesigning a single workflow first, you didn't automate anything. You just moved the cost of poor planning onto the people who are left.

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Is AI causing the layoffs everyone keeps citing?

No. What's causing the layoffs is organizations reallocating the capital they used to invest in people into AI infrastructure, then reaching for AI as the explanation because it sounds more forward-looking than "we restructured badly."

I sat down with Kyle Forrest, who leads Deloitte's future of HR research and spends his days with CHROs across every sector, and asked him directly whether he's seen a single company credibly prove a layoff was because agents replaced humans. His answer: he hasn't seen one either. What he has seen is his own firm's client data. Across the back half of 2025, 93 percent of AI initiative spend went to the technology itself, and only 7 percent went to redesigning work, roles, or change management. Roll the tools out first, figure out the people part later, if at all. That ordering problem is the story, not the technology.

It shows up in real jobs, not theory. Orgvue's research backs this up: 55 percent of leaders who cut headcount citing AI now regret it, because the work didn't disappear, it moved. Someone still has to do it, usually the people who survived the cut, now verifying AI output on top of their own workload without the training that output needed in the first place. That's not efficiency. That's borrowed time with interest.

The World Economic Forum's macro data reinforces the pattern at scale: its Future of Jobs Report 2025 projects 92 million existing roles displaced by 2030 against 170 million new ones created, a net gain, not a net loss (World Economic Forum). Jobs are shifting, not evaporating. If your organization's layoff announcement can't survive that math, it wasn't really about AI.

What's really happening when a company blames AI for a layoff?

I call it an Alibi Layoff: a headcount reduction driven by ordinary business pressure, then relabeled as an AI decision because that framing plays better to the board and Wall Street than admitting the org chart was bloated or the strategy shifted.

Three things are usually happening underneath the alibi: the company is genuinely investing in AI tools without touching the underlying workflow or role design, leadership wants a headcount story that sounds strategic rather than reactive, and the workforce sees a layoff headline land near an internal AI initiative and connects dots nobody at the top actually explained.

In the boardroom, I'd say it this way: you don't get to claim the AI efficiency story unless you can show the workflow you redesigned and the capacity you actually freed up. Otherwise you're restructuring your headcount and renting AI's reputation to explain it. That distinction matters for trust, for retention, and eventually for the filing when someone asks for the receipts.

How should C-suite leaders respond to the AI layoff narrative?

C-suite leaders and CHROs should respond by getting explicit, fast, before the workforce fills the silence with its own worst-case story.

1. Name the real driver out loud. If the decision is a restructuring, say restructuring. If it's genuinely AI-driven capacity, show the before-and-after workflow that proves it, using Deloitte's own assisted, augmented, or powered framework as the language (Deloitte).

2. Close the orchestration gap before you cut. With an 81-point gap between organizations that say orchestration matters and those doing it well, most companies aren't ready to claim AI efficiency gains yet. Map where humans and AI actually hand off work before counting anyone's job as redundant.

3. Plan for the demographic math, not just the AI math. Every baby boomer hits 65 by 2030, and the 2025 U.S. high school graduating class was the largest ever, with more graduating seniors than incoming kindergartners. Cutting experienced workers today while the entry-level pipeline shrinks solves a quarter's problem by creating a decade's problem.

4. Give people a real answer, not silence. Deloitte's trust research ties workforce buy-in directly to transparency and follow-through. Show where reskilling actually happened and where redeployment actually landed. Silence erodes trust fastest of all.

5. Invest in the humans staying, not just the tool. Everyone will eventually have access to the same models. The organizations that win will be the ones that built their people's capability alongside it.

Summary: what this means for you as a leader

AI is rewarding organizations that redesign work deliberately and exposing the ones that skipped straight to headcount decisions. The tension isn't AI versus jobs. It's discipline versus a convenient story.

Leaders who do the unglamorous work of mapping roles, closing the orchestration gap, and telling the workforce the truth will build the kind of trust that compounds. Leaders who keep reaching for AI as the alibi will keep discovering, a year later and 55 percent of the time, that they made the wrong call.

Ready to build a workforce plan that can survive a board question? Start with the demographic cliff piece on what CHROs and the C-suite must do next on AI layoffs, then map your own orchestration gap before your next planning cycle.

FAQ

Is AI actually creating or destroying jobs right now?

Both, but not evenly. The World Economic Forum projects 92 million roles displaced against 170 million created by 2030, a net gain overall (World Economic Forum). The disruption is real inside individual roles even when the aggregate number looks fine, which is why role-level redesign matters more than headcount math.

Why do companies keep blaming layoffs on AI when the data doesn't back it up?

Because "AI efficiency" is a more fundable story than admitting to overhiring or a bad restructuring. Deloitte's Kyle Forrest told me directly he hasn't seen a company credibly prove a layoff was AI-driven with real before-and-after data, and Orgvue found 55 percent of leaders who made that claim now regret it (Orgvue).

Why is there a growing premium for people who can redesign work around AI, not just use it?

Because the tools are becoming commodity and the redesign skill isn't. Deloitte's 88 percent versus 7 percent orchestration gap shows nearly every organization knows this matters and almost none have built the muscle yet (Deloitte).

How does closing the orchestration gap improve job quality?

It stops the burnout pattern where cut survivors inherit both their own workload and the job of verifying AI output the system was never trained to handle. Redesigning the human-AI handoff before headcount decisions prevents that quiet overload, a pattern I've broken down in Stop Restructuring Around AI Until You Read This.

What is an Alibi Layoff in simple terms?

An Alibi Layoff is a headcount cut driven by ordinary business pressure, then relabeled as an AI decision because it plays better to the board. If leadership can't show the specific workflow AI took over, the label doesn't hold up, a distinction I dig into further in what McKinsey's AI workforce signal really means for HR.

What should CHROs and CEOs do first before announcing an AI-related workforce change?

Map the workflow and role changes before making any headcount claim tied to AI. If you can't point to the tasks that moved, the capacity that opened up, and the plan for the people affected, you're not ready to announce, a point covered further in Gen Z, AI Layoffs, and the Truth About the 2026 Job Market.