Forrester: Fear of AI-Driven Layoffs Is Slowing AI Adoption at Companies

Consulting firm Forrester has reached a conclusion that business leaders will find unwelcome: companies are already building generative AI into their workflows, but employees are not ready to follow. The main reasons are fear of layoffs and a lack of practical training.

The gap between the bets and the readiness

In the AIQ 2.0 report, published by Forrester on March 23, 2026, researchers assessed the AI readiness of employees, teams and organizations in the US, the UK, Germany, France and Australia. The picture is contradictory. On one hand, generative AI has moved past the experimental stage: companies are embedding it in real processes and expect efficiency gains from it. On the other, workers themselves have made almost no progress over the past year in the skills and confidence needed to use these tools without resistance and chaos.

The key figures from the study show how far management's expectations and people's actual readiness have diverged:

• 68% of organizations already use generative AI in production applications
• 81% of decision-makers consider AI assistants important for improving employee efficiency
• Only 51% of companies train non-technical staff in AI, up from 47% in 2024
• Just 23% of organizations offer prompt engineering training
• 43% of employees expect automation to cause the loss of many jobs within the next five years

These numbers matter less on their own than for what they imply. Business is already betting on AI as a working tool, but is not building a clear operating environment around it. When leadership has a rollout plan while employees have neither clear use cases nor basic skills, new tools start to look less like help and more like a threat.

Why people are holding back

The first problem is a plain lack of preparation. In most companies, AI training is either absent or limited to generic presentations not tied to a specific role. This is especially critical for non-technical staff: they need to understand where AI speeds up routine work, where its output needs checking, and where it should not be used at all. Prompt engineering training is failing in particular, even though in many office scenarios the skill has effectively become a new form of digital literacy. If a person cannot phrase a request properly, they quickly get a poor result and lose trust in the tool.

The second problem runs deeper: people see AI not just as new software but as a potential replacement for themselves. Forrester links this directly to a general atmosphere of anxiety in the job market. According to the company, 43% of employees fear that automation will cause mass job losses in the next five years, and one in four expects AI to affect their own position. Against this backdrop, any talk of efficiency gains sounds ambiguous, especially when some executives openly talk about cutting staff costs.

"Some of our employees are afraid of losing their jobs, and so they

turn away from AI completely."

These fears have a rational basis. Earlier surveys showed that 51% of executives at British companies see AI as a way to reduce investment in staff. Another study found that 43% of executives expect entry-level positions to be cut within a year in favor of AI, and half directly link AI to headcount reductions. In such an environment, resistance to adoption looks not irrational but like a natural reaction from people who hear one message from HR and a very different one from top management.

What Forrester recommends

Forrester's recipe is not simply to add one more corporate course. The researchers advise raising employees' AIQ through training and engagement together: explain why the company needs AI, which specific tasks it changes, and how it will affect people's daily work.

The tone of communication matters too. If a company sells the rollout as a headcount-saving program, employees will defend themselves. If it presents AI as a tool for higher productivity and broader roles, the chance of acceptance is noticeably higher.

The report's findings translate into fairly down-to-earth steps for companies:

• train not only engineers in AI, but also sales, support, marketing and back-office teams
• offer scenarios for specific work tasks rather than generic lectures
• teach prompting, answer verification and model limitations separately
• explain to employees where AI helps them move faster and where the decision still rests with a human
• create internal examples and peer exchange instead of relying only on formal courses

Coverage of the report highlights one more nuance: formal training plays a surprisingly small role on its own if there is no real exchange of practice within the team. In other words, one LMS and a couple of webinars are not enough. People pick up AI more readily when they see how colleagues actually use it on similar tasks, what mistakes they make and how they check the results. This is an uncomfortable conclusion for management, because it demands not the purchase of yet another tool but a change in everyday work culture.

What this means

In office AI, the main bottleneck turned out to be neither models nor budgets, but employee trust. As long as business promises efficiency gains and people hear the threat of layoffs, adoption will move more slowly than executives expect.