Layoffs by Press Release, Not by Data: The AI Excuse Meets Its Match

Is AI really to blame for all of the layoffs attributed to it in 2025? Thanks to a new bipartisan proposal, the AI‑Related Jobs Impact Clarity Act, we may soon find out. The C‑suite will soon have to prove when workforce changes are actually caused by AI-not just say so in a press release.

2025 Layoffs And The AI Excuse

By November of last year, companies logged more than 1.1 million announced job cuts in the U.S., the highest level since the early pandemic years. In statement after statement, companies cite “AI” or “automation” as a reason, often alongside generic phrases like “restructuring” or “strategic realignment.” Yet there is still no consistent way to tell which of those cuts were truly driven by AI and which were simply cost decisions wrapped in trendy language.

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As someone who’s day job is all about workforce and hiring data, I can tell you that gap matters because it blurs the line between deliberate technology strategy and basic expense management. Without real data, boards, regulators, and employees are left guessing whether AI is transforming work or just providing cover for headcount reduction. The AI‑Related Jobs Impact Clarity Act is designed to close that gap by forcing companies to measure and disclose AI’s actual workforce impact. And honestly, it’s about time.

What The AI Jobs Impact Clarity Act Does

The AI‑Related Jobs Impact Clarity Act would require major companies and federal agencies to file quarterly reports on how AI is affecting jobs, not just profits. Under the bill, covered organizations must submit data on “any artificial intelligence‑related job impact” shortly after each quarter ends, with the information going to the Department of Labor.

The reports get into some pretty specific detail including:

• how many workers were laid off because of AI systems,

• how many new roles were created as a result of AI adoption,

• how many positions were left unfilled due to automation, and

• how many people were retrained to work with AI.

The Labor Department is then tasked with compiling, analyzing, and making these trends public so that lawmakers and the general public can see how AI is reshaping the workforce across sectors. I think there will be some noise in the numbers, at least initially. But what is the silver lining is this move takes AI out of the realm of slogans and turns it into a set of numbers that can be inspected and challenged.

Why This Is Huge For HR

For years, HR has been caught in the crossfire whenever layoffs happen, even when those decisions originate in the finance office or the boardroom. I have been in those crosshairs several times throughout my 20+ years in HR and it’s never a good feeling. I had to remind myself I had an ethical responsibility to the employees and fiduciary responsibility to the organization. This was a balance that was often hard to manage.

When AI is mentioned, HR teams are often expected to explain complex automation decisions without a clear data model for what was genuinely AI‑driven versus what was broader restructuring. This bill gives HR a much clearer framework to say, “Here is what counts as AI‑related and here is what does not,” backed by standardized metrics.

This shift also creates calls for HR leaders to step into a more strategic, data‑driven role, something many CHROs have been pushing toward for years. Instead of only administering layoff processes, HR can help define AI impact categories, oversee reskilling strategies, and provide the C‑suite with credible reporting. Done well, this positions HR as a core interpreter of AI workforce data and not just a messenger of bad news.

Why The C‑Suite Should Treat This As A Wake‑Up Call

For CXOs, the biggest change is accountability: “AI made us do it” will no longer stand on its own without evidence. Once the law is in force, any claim about AI‑related restructuring will need to match the numbers filed with the Department of Labor and, eventually, the trend reports that come out of that data

That creates real reputational risk if the story and the metrics diverge. Imagine telling investors that AI eliminated a large chunk of roles while your quarterly report shows only a handful of layoffs tied to automation and a modest investment in retraining. Media, analysts, and employees will be able to compare what you say about AI with what you actually report about AI, and that gap will shape how your leadership is perceived.

Key Questions Leaders Will Face

• Can you clearly distinguish AI‑driven workforce changes from general cost cutting in your own data?

• Do your press releases, earnings calls, and internal town halls match what you will be required to disclose?

• Are you prepared to explain not only which jobs were lost but which jobs were created and which people were reskilled because of AI?

This will become the new baseline and framework to approach layoffs and reporting going forward. As someone who is consistently asked if AI is affecting the workforce, I am glad we will finally have standards to distinguish noise from reality.

What Companies Will Need To Track

The bill outlines several core categories that companies will have to monitor and report on a regular basis. Treat these as the minimum data set your internal systems should be able to produce:

• Layoffs directly caused by AI replacing human tasks or roles.

• New hires associated with AI adoption, such as data trainers, AI operations specialists, or prompt engineers.

• Positions left intentionally unfilled because AI or automation tools now handle that work

• Employees who are retrained or upskilled to work with AI systems or to manage AI‑enabled workflows.

On top of that, companies will need to classify these changes by industry and function, often through NAICS or comparable codes. That means your HRIS and finance systems must align on how roles are labeled and how workforce events—hiring, layoffs, attrition, redeployment—are tagged if AI is part of the reason.

The Department of Labor will use this structured information to build an aggregate picture of AI’s employment impact by sector, which means your data contributes directly to the national story about AI and jobs. If what you report looks wildly different from peers in your industry, expect questions from regulators, investors, and employees.

Private Companies Should Not Assume They’re Exempt

The current version of the proposal clearly covers major companies and federal agencies, but it also gives the Secretary of Labor room to decide. The Secretary can decided if entities should fall under the rules, based on size, revenue, and economic significance. There is a 180 days window after enactment for the Labor Department to define exactly who counts as a “covered entity.”

That flexibility means large private companies, private‑equity‑backed firms, and influential regional employers may all eventually be pulled into the reporting net. Even if you never file a 10‑K, you may still find yourself required to disclose AI‑related job impacts if your workforce decisions ripple across a local or national labor market. Practically, it is safer to assume you will be asked to show your data than to bet on being ignored.

This Is About Clarity, Not Punishment

It is easy to frame any regulation touching AI as a penalty, but this proposal is fundamentally about measurement and transparency. Research from groups like the World Economic Forum already show that a large share of companies expect AI to reduce headcount in some areas while creating demand for new skills elsewhere. Consistent reporting will help separate realistic plans from speculative talking points.

If you sit in the C‑suite or lead HR, now is the time to ask one simple question: if this bill became law tomorrow, could you defend your AI story with hard numbers? If the honest answer is “not yet,” the best time to start fixing that is before your first AI‑related workforce report is due.