The Real Reason Behind "AI Layoffs"

Many big companies are laying off workers, and they all say the same thing: "AI made us too productive, so we don't need these people anymore." But if you look closer, this is mostly a clever excuse. Experts call it "AI washing" (Forrester Research).

Here is what is actually happening behind the scenes:

Before layoffs, focus on efficiency first and consider where AI can add value.

1. The Stock Market Trick

If a CEO says, "Our sales are dropping, so we must cut jobs," investors panic and stock prices fall. But if they say, "We are using AI to become faster," Wall Street gets excited. Blaming AI shifts the narrative from corporate failure to innovation, keeping stock prices stable or even driving them up (CBS News). It turns bad news into good news.

2. The Over-Hiring Hangover

During the pandemic, tech companies grew too fast and hired way too many people. Now, with high interest rates and lower demand, they need to fix that mistake. Instead of admitting they made poor hiring decisions, executives use AI as a convenient cover story for cuts they had to make anyway (MIT Analysis).

3. The Reality of AI Washing

When a company claims ChatGPT or automation replaced their staff, it is usually not true yet.

• The tools aren't ready: Many companies firing workers do not even have mature AI systems fully integrated into their business (Forrester Research).

• It’s about budget, not efficiency: A Gartner survey showed that 80% of executives cut headcount regardless of whether their AI projects actually worked. Often, they fire people just to free up cash to buy expensive AI software.

• Workers get squeezed: Because the AI cannot actually do the whole job, the remaining staff are forced to absorb the extra workload. They end up doing the work of two or three people, just with an AI tool helping them a little bit.

How We Should Respond: Rethinking the Future of Work

Companies and workers need to change how they approach this transition. Leaders should start using AI to build true efficiency for the technology and people they already have.

To do this right, organizations need a clear roadmap:

• Map the Value Stream: You must have a crystal-clear view of how value flows through your business. Where do things actually get stuck?

• Identify Needed Competences: Once you understand the workflow, figure out exactly what kind of skills your team needs to master to enhance that specific stream.

• Target Revenue and Profit Drivers: Don't waste time automating random tasks. Build competencies directly on your revenue-making, profit-generating streams first.

• Start Small and Iterate: Implement small-scale, high-impact, incremental changes that are manageable for the organization rather than trying to change everything overnight.

This means shifting focus from cutting costs to building actual digital competences and capabilities. By training the existing workforce to properly master these tools, businesses can create a more resilient culture. Technology will amplify human talent rather than replace it—turning AI into a real tool for growth.

Next time you see a headline about AI stealing jobs, remember: it is often just a shield to hide normal budget cuts and corporate mistakes.

But it does not have to be this way

When a company does this properly—by mapping its value streams, upskilling its people, and focusing on real efficiency—it builds genuine, long-term trust with investors. True innovation isn't about replacing humans; it's about building a sustainable, future-proof business that the stock market can believe in.