Tag Archives: Layoffs

Reading Between the Layoffs: Is AI the Culprit or the Scapegoat?

Artificial intelligence may get most of the attention, but it’s not the only reason jobs are disappearing.

In a year packed with headlines about tech-fueled disruption, a closer look shows that only a small fraction of layoffs are being directly tied to AI. According to recent reporting in Forbes, only 75 out of 286,000 job cuts so far this year explicitly resulted from artificial intelligence. The rest were largely due to more familiar drivers such as cost reduction, automation, overhiring, and restructuring.

Still, AI is playing a role, showing up in strategy meetings, altering jobs, and forcing both employers and employees to reevaluate how work gets done. Focusing on AI alone, however, misses the bigger picture.

What’s happening now has less to do with futuristic technology and more to do with company values and what organizations are willing to invest in.

More Precision, Fewer Promises

Job security used to be tied to experience, consistency, and loyalty, but that’s changing. Companies are hiring with more scrutiny, spending less time filling roles that once seemed essential while moving away from blanket hiring strategies in favor of targeted placements with measurable returns.

AI may contribute to this, but this transformation didn’t start with generative tools – it all began when companies started prioritizing flexibility over structure and leaning harder on productivity benchmarks. Many are using AI to fine-tune that process not to replace entire teams, but to consolidate, reassign, or simply do less with more.

This is the pattern workers should be watching – not whether AI will take their job, but whether their role still aligns with what the business needs right now.

The True Function of AI

In practice, AI is mostly being used to reduce manual labor, surface information faster, and assist in low-risk decision-making. It helps streamline workflows and fill gaps, but still relies on human input.

That means jobs aren’t just being automated away, but redesigned. Tasks that once required multiple steps or several people are being minimized, while others are being redistributed to those who can handle more complexity.

The result is fewer entry points, teams with less management layers, and higher demand for roles that blend technical understanding with strategic judgment.

Where HR Comes In

For HR leaders, this is not the time to over-index on AI policy or panic over every new tool, but to gain a clear understanding on how roles are evolving and what the organization is trying to build.

A few key priorities stand out:

  • Review job functions with fresh eyes. Are people doing work that still needs to be done? Are there new needs that aren’t reflected in existing roles?
  • Focus less on AI skills in isolation. Broader abilities like problem framing, decision-making, and adaptability carry more long-term value than tool-specific knowledge.
  • Clarify what AI is and isn’t being used for. Silence creates uncertainty, so give employees the facts.
  • Invest in transition support. Some jobs will change, and some won’t return – preparing people for that reality should not be put off.

What Employees Should Know

The best way to stay relevant in the current environment isn’t necessarily to learn every AI tool, but to build the kind of judgment and awareness that can’t be outsourced.

That means understanding how your work connects to outcomes, knowing what problems your role is solving, and having the ability to adjust when priorities change.

Employees who can see the full picture including where value is being created and where it’s being lost won’t just survive these changes, they’ll navigate them with purpose.

The Path Ahead

Organizations that treat AI as a reason to cut headcount without rethinking design will see short-term savings but long-term gaps. Tools don’t replace systems, and systems don’t run without people who know how to use them.

Success comes from intention, not automating everything – from deciding what’s worth doing manually, what can be delegated to machines, and where human capability still sets the standard.