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IT leaders should be involved whenever a company attributes job cuts to AI. They are often the only executives who can say what deployed systems actually do, where they need human oversight, and whether the productivity gains used to justify a reduction have appeared in measured results. They should advise the decision, not make it alone. HR and operations leaders know the roles, the people, and the business obligations that a systems review cannot capture, and an executive who sponsored an AI investment has reasons to overstate its readiness. “AI washing,” meaning the labeling of cuts as AI-driven without evidence that AI is doing the work, is easiest to prevent when technical evidence and workforce accountability sit in the same review.
What “AI washing” means in a layoff context
AI washing describes an employer citing artificial intelligence as the reason for job cuts when the evidence for that reason is thin. The term does not by itself establish bad faith. A company can sincerely believe in an AI strategy and still be ahead of the results. The practical test is narrower: does available evidence show that deployed AI systems are performing the work the cut eliminated, and that the expected productivity gain has been measured?
That distinction matters because the phrase “we are cutting roles because of AI” can describe three very different situations: a tool that already handles the work, a tool that is being piloted, or a budget decision that uses AI as the story. Only the first is clearly supported by operational evidence, and only IT can usually confirm which situation applies.
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The IT contribution is technical evidence about the systems in question. In the reporting on this question, IT Pro, in its September 16, 2026 article on why IT leaders need to be involved in layoff decision-making, frames that contribution as operational evidence about AI deployments, their limitations, and realized productivity. Concretely, an IT review should be able to answer five things:
- What is in production. Separate systems that are live, systems in pilot, and systems that were purchased but not integrated. A licence is not a deployment.
- How it performs. Accuracy, error rates, escalation volume, and uptime for the specific task the eliminated roles performed.
- Where it breaks. The cases that still need a person, the data it cannot access, and the exceptions that land back on remaining staff.
- Dependencies. Whether removing a role leaves a workflow the system relies on without an owner.
- Measured versus projected gains. Whether the productivity figure in the business case was observed after deployment or estimated before it.
As IT Pro quotes Helen Fenner: “IT leaders know the difference between what an automation deck says and what the system actually does at 2 am on a Tuesday.” That gap between presentation and operation is the reason an IT view belongs in the room.
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Why IT should advise on layoffs rather than approve them
The case against giving IT sole authority is as important as the case for including it. An IT leader who sponsored an AI investment, or who is under pressure to show a return on it, has a stake in the answer. That does not make the IT leader dishonest, but it makes the assessment non-neutral. The same is true of any single sponsor, which is why the review needs people who did not champion the tool.
IT Pro also quotes David Fischer, chief revenue officer at Luware: “IT leaders absolutely need a seat at the table when businesses make workforce decisions linked to AI, but they shouldn’t be making those decisions alone.” Fenner makes the complementary point that excluding IT creates its own risk: “decisions made with an incomplete picture, with no one technically accountable when that picture turns out to be wrong.” The answer is shared decision rights with a named accountable owner, not IT’s exclusion or unilateral control.
What the survey figures say, and what they do not
IT Pro’s report cites Cornerstone research on how IT and HR work together on workforce change. The figures below are IT Pro’s reported numbers. The underlying Cornerstone report was not available for checking, so readers should treat them as secondary reporting rather than independently verified results.
| Figure reported by IT Pro | What it measures | Qualification |
|---|---|---|
| 13% faster | Pace of workforce changes where CIOs and CHROs jointly plan them | An association, not a proven cause. IT Pro’s report does not state the survey year. |
| 94% of 2,000 surveyed IT and HR leaders | Share who said a joint approach is becoming a priority | A stated priority, not a measure of practice. The survey year is not stated in IT Pro’s report. |
| 35% | Share who said AI-related decisions are being made together | Describes how often joint decisions occur, not how well they go. The survey year is not stated in IT Pro’s report. |
The gap between the 94% who call joint work a priority and the 35% who report making AI decisions together is the most useful takeaway. Intent is widespread; practice is still uncommon.
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How to test an AI attribution in a layoff announcement
Before accepting a company’s AI explanation, or a tracker’s classification of it, work through these questions in order:
- Who made the attribution? The employer, a reporter, an analyst, or a tracker. Each carries different incentives and different access to facts.
- Did the employer name AI as a reason, or only discuss AI investment? Mentioning a technology strategy is weaker evidence than stating that the strategy is why roles were removed.
- Did the same source state the number of affected jobs? A cut described without a figure is harder to aggregate and easier to overstate.
- Are other causes stated? Look for cost pressure, weaker demand, restructuring, or post-pandemic over-hiring.
- What is the scope and date of the dataset? Which company, which region, and which period the claim covers.
Different trackers answer different questions, so their totals should not be treated as interchangeable. Compare the definitions first.
Worked example: monday.com’s July 2026 restructuring filing
monday.com Ltd.’s Form 6-K, dated July 22, 2026, is a useful test case because it is a primary document. In the SEC filing, the company says its restructuring plan aimed to align its organization with its AI Work Platform strategy and to reduce its workforce by approximately 20%, while continuing to hire in key strategic areas.
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Applying the five questions: the employer made the attribution; it named an AI strategy as the rationale; it stated an approximate size for the reduction; and the passage reported here does not describe other causes, so readers should check the full filing for any. The dataset is a single document dated July 22, 2026.
What the filing establishes is the company’s stated rationale. What it does not establish is that AI systems took over the tasks of the affected workers. That second claim is exactly what an IT review inside the company should be able to confirm or refute, and it is the claim that outside readers cannot check from the filing alone.
How large are AI-linked layoff totals?
Counts of AI-linked job losses vary widely with definition. The AI Layoffs working paper, a source-cited register analysis rather than an official government count, sorts its entries by how the attribution was made. Its register version 1.31, archived October 5, 2026, covers reported cuts from May 2023 to September 2026. The working paper’s methodology explains the two standards.
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| Measure in the register | Jobs | Share of all reported figures | Definition |
|---|---|---|---|
| All figures reported | 358,974 | Baseline | Any AI-linked layoff figure in the register |
| Stricter counted figures | 59,454 | 17% | The employer both named AI and stated the number of jobs |
The gap between the two rows is the definitional problem in one table. A reader who sees the larger number should ask which standard produced it before treating it as the number of jobs lost to AI.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What broader labor evidence can and cannot tell you
Economy-wide evidence is mixed. A Federal Reserve-hosted summary dated April 14, 2026, based on corporate executive evidence, and a Stanford Institute for Economic Policy Research policy brief from July 2026 both describe nuanced results. Neither establishes broad, near-term aggregate job losses caused by AI, and neither rules out localized disruption or changes that have not yet appeared in the data.
For a single company, aggregate findings are a weak substitute for company-level evidence. National patterns cannot show whether a specific system replaced a specific role, which is why the review process below asks for system-level data.
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A review process that gives IT a real role without sole control
What IT should produce
- A written inventory of AI systems in production, with the workflows each one touches.
- Performance data for the tasks affected by the proposed cut, measured over a defined period.
- A list of known limitations, exception volumes, and dependencies that would be left unowned.
- A statement of which productivity figures were observed and which were projected.
What HR and operations should produce
- Role-level analysis of what the affected employees do, including work the system does not cover.
- Workload and service-level forecasts for the remaining team.
- Legal, notice, and redeployment considerations, and any hiring plans in strategic areas that offset the cut.
What the accountable decision owner should sign
- A written reason for the decision that states whether AI is a cited cause, and whether the cited AI capability has been observed doing the work.
- The other causes considered, such as cost, demand, or restructuring.
- A named owner for the post-decision check, scheduled against the measured performance figures.
The review should also record whether the IT and HR assessments agreed. Disagreement is useful information and should be documented, not resolved by whichever executive has the most authority.
The same statement, “AI is why these roles are gone,” can be true, partly true, or unsupported. The review process is the mechanism that distinguishes between those outcomes before a company announces them.
Once the team has established what the system does, the questions for the decision owner become straightforward: does the evidence show the work is done, does the organization have the people to cover what the system does not, and who answers if the gains do not appear.
Figures in this article are as reported by their sources at the dates stated. The IT Pro figures are secondary reporting of Cornerstone research.
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