Companies have spent billions rolling out AI tools to their workforces. Yet the investment is gathering dust. New research from Forrester shows the problem isn't the technology itself. It's that employees are too frightened to use it.
Low employee readiness is the main thing holding back business success with workforce AI programs, according to the advisory firm's latest report. The numbers reveal a blunt reality: employees in the US, UK, Germany, France, and Australia failed to progress meaningfully over the past year on AI readiness metrics.
The skill gap is glaring. Just 23 percent of organisations say they offered training in prompt engineering, a key skill for using generative AI tools. Training provision has barely budged. While a strong majority of AI decision-makers state that their organisations use AI applications, only half report offering AI training for nontechnical employees.
But underskilling is only half the story. Fear is the real barrier. While few jobs were lost to AI in 2025 and future job losses are not expected to constitute a job apocalypse, worker anxiety regarding this is pervasive, Forrester found.
That anxiety is not irrational. Just over half of UK business leaders (51 percent) saw AI as a way to cut investment in staff. More broadly, 43 percent of business leaders expect to reduce entry-level roles in favour of AI, while 50 percent "specifically" said AI is helping them reduce headcount. The result: 43 percent of employees are concerned that many people will lose their jobs to automation over the next five years, while a quarter suspect it will impact their own job during that period.
The CEO Problem
Some of this worker anxiety stems directly from corporate leadership. When CEOs publicly announce that AI-driven job cuts are the point of deployment, they poison the well for anyone tasked with driving adoption. Employee fears are exacerbated by public statements from their own CEOs, who engage in what some call "AI washing" by blaming financially driven layoffs on AI replacement when AI isn't the cause.
There is a legitimate tension here. Forrester itself has forecast that AI will augment 20% of jobs over the next five years, making it essential for businesses to invest in employee training and upskilling. Yet over half of layoffs attributed to AI will be quietly reversed as companies realise the operational challenges of replacing human talent prematurely. In other words, many businesses are over-automating today, discovering it doesn't work, and retreating.
For employees watching this unfold, the case for embracing AI tools is not compelling. Why invest energy learning a system your employer plans to use to replace you, particularly when the company hasn't given you meaningful training in the first place?
Where the Money Goes But Returns Don't
The confidence gap between what companies are investing and what they're getting back is staggering. More than half of CEOs report seeing neither increased revenue nor decreased costs from AI, despite massive investments in the technology, according to a recent PwC survey of 4,454 business leaders. Only 12 percent reported both lower costs and higher revenue, while 56 percent saw neither benefit.
In Australia, the evidence points in the same direction. Despite two-thirds of Australian companies employing AI tools, a mere 5% derive substantial value, resulting in a $44 billion loss per year in potential value. 53 percent of small businesses lack a budget or strategy for AI training, yet they're expected to extract value from the tools anyway.
The pattern is unmistakable. Companies are running "isolated, tactical AI projects" that "often don't deliver measurable value"—a condition some call Pilot Purgatory, where AI gets used enough to feel like progress, but never deeply enough to create results.
The Vanguard Exception
There is a small cohort performing differently. Companies achieving both additional revenues and lower costs from AI are furthest ahead in building strong foundations and are applying AI more extensively across different areas of the business. For example, 44% of those in the vanguard have applied AI to their products, services, and experiences, compared to only 17% for other companies.
The distinction matters. The successful minority is integrating AI into what they sell and how they generate revenue, not merely deploying it as an internal cost-cutting tool. They're also investing in the human side of the equation. CEOs whose organisations have established strong AI foundations—such as Responsible AI frameworks and technology environments that enable enterprise-wide integration—are three times more likely to report meaningful financial returns.
Forrester's analysis aligns with this finding. Formal learning "plays a surprisingly small role in raising AIQ," and organisations that instead get social learning right tend to succeed with workforce AI. The implication is clear: throwing a one-hour training session at employees isn't the answer. Building a workplace culture where people see AI as a tool that makes their jobs better, not threatens their livelihood, is what separates winners from the rest.
A Question of Leadership Credibility
The gap between companies claiming to value their people and companies using AI to eliminate roles is widening. Forrester quotes one business leader:
"Some of our employees fear job loss, and it turns them away from AI altogether."That statement could define the problem facing any organisation trying to drive AI adoption today.
If a company wants employees to engage seriously with new technology, it needs to first answer a basic question: are we using this to make your work better or to eliminate your job? The answer should be credible, backed by action, and reinforced consistently by leadership. Right now, for many organisations, employees are answering that question for themselves. And they're stepping back.
The economic stakes are real. Over 26% of the Australian economy, equating to nearly $600 billion, faces imminent disruption from generative AI technologies. Companies that fail to solve the adoption problem will miss the productivity gains available to competitors who do. But solving it requires more than deploying better tools. It requires building the trust and capability to use them.