Forrester: fear of AI-driven layoffs is slowing AI adoption in companies

Consulting firm Forrester has reached an uncomfortable conclusion for business: companies are already implementing generative AI in workflows, but employees are not ready to follow this wave. The main reasons are fear of layoffs and lack of practical training.

The Gap Between Stakes and Readiness

In the AIQ 2.0 report, published by Forrester on March 23, 2026, researchers assessed the readiness of employees, teams, and organizations to work with AI in the United States, United Kingdom, Germany, France, and Australia. The picture turned out to be contradictory. On one hand, generative AI has already moved beyond the experimental stage: companies are embedding it in real processes and expecting efficiency gains. On the other hand, employees themselves have made almost no progress in the skills and confidence needed to use such tools without resistance and chaos. Key figures from the study show how sharply management expectations diverged from people's actual readiness:

• 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 specialists on AI, though in 2024 this figure was 47%
• Only 23% of organizations provide training in prompt engineering
• 43% of employees expect automation to lead to the loss of many jobs in the next five years

These figures are important not in themselves. They show that business is already betting on AI as a work tool, but is not building a clear operational environment around it. When management has a deployment plan but employees lack both clear use scenarios and basic skills, new tools begin to be perceived not as help, but as a signal of threat.

Why People Are Slowing Down

The first problem is a typical training deficit. In most companies, AI training either doesn't exist or is limited to general presentations without connection to specific roles. For non-technical employees, this is especially critical: they need to understand where AI speeds up routine work, where it requires result verification, and where it cannot be used at all. Prompt engineering training is failing separately—a skill that in many office scenarios has effectively become new digital literacy. If a person can't formulate requests correctly, they quickly get poor results and lose trust in the tool.

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

"Some of our employees fear losing their jobs and therefore completely

turn away from AI."

These concerns have a rational basis. Previous surveys showed that 51% of British company managers view AI as a way to reduce personnel investments. Another study found that 43% of managers expect cuts to entry-level positions within a year in favor of AI, and half directly link AI with staff reductions. In such an environment, resistance to implementation looks not like irrationality, but a natural reaction of people who hear one signal from HR and another entirely from top management.

What Forrester Recommends

Forrester's prescription is not simply to add another corporate course. Researchers recommend raising employee AIQ through training and engagement simultaneously: explaining why the company needs AI, which specific tasks it changes, and how it will affect people's daily work. The tone of communication is also important. If the company sells implementation as a personnel cost-saving program, employees will resist. If it shows AI as a tool for productivity growth and role expansion, the chances of acceptance are notably higher.

From the report's conclusions, several practical steps follow for companies:

• Train not just engineers, but sales, support, marketing, and back-office teams on AI
• Provide not general lectures, but scenarios tailored to specific work tasks
• Teach separately how to work with prompts, verify answers, and understand model limitations
• Explain to employees where AI helps speed things up and where decisions still remain with people
• Create internal examples and knowledge sharing rather than relying solely on formal courses

In publications based on the report, another nuance is emphasized separately: formal training itself plays a surprisingly small role if there is no proper exchange of practice within the team. In other words, an LMS and a couple of webinars are not enough. People more readily master AI when they see how colleagues actually use it in similar tasks, what mistakes they make, and how they verify results. For management, this is an inconvenient conclusion because it requires not procuring another tool, but changing everyday work culture.

What This Means

In enterprise AI, the main bottleneck turned out to be not the models or budgets, but employee trust. While business promises efficiency gains and people hear the threat of layoffs, adoption will proceed slower than leaders expect.