Forrester finds AI training gap stalls workplace gains

Forrester has published research showing that most employees are not prepared to use workplace AI effectively. Only 16% achieved a high Artificial Intelligence Quotient in 2025.

The findings highlight a gap between the spread of generative AI tools and the readiness of the people expected to use them. While 68% of organisations reported using generative AI in deployed production applications, the share of workers with a high AIQ rose only slightly, from 12% in 2024 to 16% in 2025.

Forrester defines AIQ as a measure of employee readiness across four areas: understanding of AI, hard skills and training, confidence and motivation, and ethics, risk and privacy awareness. The framework is designed to assess whether workers can adapt to AI tools, use them in daily work and apply them responsibly.

The research suggests training remains uneven, especially outside technical teams. Just 51% of organisations provide AI training to non-technical staff, while only 23% offer training in prompt engineering, which Forrester identified as a basic skill for employees using tools such as Microsoft 365 Copilot, Google Workspace and other workplace copilots.

That shortfall is reflected in employee attitudes as well as technical preparedness. Only 37% of employees said they felt confident adapting to AI-driven work, fewer than half said they were motivated to build AI skills, and 44% said they felt confident using AI responsibly and ethically.

Training Gap

The mismatch between deployment and workforce preparation is affecting productivity and return on investment. Employees with low AIQ are more likely to adopt tools slowly or use them incorrectly, leading to errors, repeated work and frustration rather than efficiency gains.

The study also found that anxiety about AI remains high, even though job losses linked to the technology have been limited. Weak communication from leadership and poor transparency about how AI will change work are contributing to fears about job displacement and resistance to adoption.

This is particularly significant as companies deploy AI in customer-facing roles, decision-support systems and regulated workflows. In those settings, weak understanding of ethics, risk and privacy can create operational and compliance concerns, alongside questions of employee trust.

JP Gownder, Vice President and Principal Analyst at Forrester, said many employers were treating deployment as the main objective while neglecting workforce development.

"Employers aren't giving their people the skills, understanding, or ethical grounding they need to succeed with AI - and it's becoming a clear bottleneck to productivity and ROI. Our research shows most organizations are rolling out AI tools without investing in employees' ability to use them effectively.

"To close the gap, businesses must move beyond surface-level training and build continuous, hands-on learning that demystifies AI, addresses employee concerns, and develops real capability. This isn't about replacing workers - it's about enabling them to work smarter with AI.

"The organizations that treat AI literacy as a strategic priority, not a box-ticking exercise, will be the ones that unlock meaningful productivity gains and long-term competitive advantage," Gownder said.

Beyond One-Off Courses

The report argues that conventional training alone is not enough. Instead, it points to continuous learning models that combine formal instruction with peer support, practical experimentation and workplace use.

Examples include peer-based learning, AI champions programmes, prompt libraries and regular hands-on practice. These approaches are proving more effective than one-off courses in helping staff build confidence and stay engaged over time.

The broader implication for employers is that spending on software licences and deployment may not deliver the expected returns if workers do not know how to use the tools well. In that sense, workforce readiness is as much a management issue as a technical one.

Forrester's data suggests the pressure is no longer limited to early adopters of AI. As workplace systems increasingly include generative AI functions as standard features, organisations face growing pressure to ensure staff can judge outputs, understand risks and integrate the tools into routine work without creating new inefficiencies.