Published on the 16/04/2026 | Written by Heather Wright

Skills gaps limiting productivity promise…

How is your employee AIQ? That’s AI quotient and, according to Forrester, it’s probably nothing to brag about with new research showing that while AI tools are rolling out at speed across enterprises, workforce readiness is barely moving.

Put another way, organisations are buying the AI sports car, but most employees still don’t have a driver’s licence.

“Organisations that treat AI literacy as a strategic priority, not a box-ticking exercise, will unlock meaningful productivity gains and long-term competitive advantage.”

AIQ measures the readiness of individuals, teams and organisations to adapt to, collaborate with, trust and generate business results from genAI and other forms of the technology. The Forrester report AIQ 2.0: Employees (Still) Aren’t Ready to Succeed with Workforce AI, finds that only 16 percent of employees achieved a high AIQ in 2025, up marginally from the 12 percent seen in 2024. That’s despite widespread deployment of generative AI tools such as Microsoft 365 Copilot, Google Workspace and enterprise copilots now embedded into daily workflows.

The disconnect matters. JP Gownder, Forrester VP, principal analyst, says the lack of AIQ is becoming a clear bottleneck to productivity and ROI. Employees with low AIQ are slower to adopt AI tools or use them incorrectly, increasing errors, rework and frustration. In many cases, the time and effort required to use AI outweighs the value generated, preventing organisations from reaching the productivity ‘crossover point’ where AI meaningfully improves work outcomes.

Forrester’s AIQ framework looks at four dimensions of employee readiness: Understanding AI, skills and training, confidence and motivation, and ethics, risk and privacy awareness.

While 68 percent of organisations report using generative AI in production applications, only a small minority of employees show a strong ability to work effectively with the tools, with AI skills ‘stagnating’ despite the widespread deployment.

“This slow progress indicates that enterprise AI rollouts are outpacing investments in employee capability, creating a readiness gap that limits productivity gains and increases operational risk,” the research firm says.

Training gaps a key constraint

Training is a major weak spot. Just over half of organisations provide AI training to non-technical staff and fewer than a quarter offer prompt engineering training, which Forrester dubs a ‘foundational skill’ for effectively using tools like Microsoft 365 Copilot and Google Workspace.

The report dubs a ‘mere’ four-point gain in the percentage of individual contributes who said they received formal training as a ‘shocking lack of progress’. “It’s clear that employers haven’t stepped up to enable their employees through learning initiatives.”

It notes that GenAI tools are probabilistic, rather than the deterministic computing we’re used to. Where with deterministic tools the same input produces the same output each time, following fixed rules and logic, with GenAI’s probalistic outcomes ‘we don’t know exactly what they’re going to say and do’, Forrester says. That changes how employees must work with technology, requiring them to understand that AI outputs are suggestions, rather than definitive answers and making the need to know when to question the output of AI ‘crucial’.

There’s also the human factor. Many employees still worry AI will replace their jobs, even though Forrester’s forecasts show limited job displacement to date. Poor communication and vague messaging about AI’s role are fuelling anxiety, reducing trust and quietly sabotaging adoption efforts.

Gaps in employee confidence and motivation are also highlighted. Only 37 percent of employees report feeling confident in adapting to AI-driven work and fewer than half are motivated to build AI-related skills.

Ethical readiness is another area of concern with just 44 percent of employees feeling confident using AI responsibly and ethically. Forrester warns these gaps increase organisational risk, particularly as AI is increasingly used in customer-facing, decision-making and regulated environments.

“Employers aren’t giving their people the skills, understanding or ethical grounding they need to succeed with AI,” Gownder says. “Our research shows most organisations are rolling out AI tools without investing in employees’ ability to use them effectively.”

Here’s how to close the AIQ gap

The good news is the research is equally clear on what works. High-performing organisations treat AI literacy as a capability to be built over time, not a one-off training exercise.

“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,” Gownder says. “This isn’t about replacing workers – it’s about enabling them to work smarter with AI.”

Formal learning plays a surprisingly small role in raising AIQ, with linear training quickly forgotten, Forrester says. Organisations making progress are investing in continuous learning models that combine formal instruction with practical, hands-on experience. This allows employees to apply AI skills directly within day-to-day work and build capability over time. “Better is a weekly email with tips, videos and best practices form inside your organisation and ideally inside your division.”

Social learning – with weekly office hours with a subject matter expert – are also key. Approaches such as AI champions programmes, shared experimentation and peer support networks have been shown to be more effective than traditional training alone. These models help reinforce learning and support sustained adoption across teams. Middle managers need to signal with their attendance the behaviour they want to see from direct reports. Online discussion forums offering real-time help and testimonial videos of successes and failures from within your organisation have more legitimacy, Forrester says, than bland, online training.

Third, organisations need to ensure training reaches non-technical employees, who make up the majority of the workforce using generative AI tools. Practical training in areas such as prompt development and use-case application is critical to improving baseline AI capability.

Forrester also highlights the need to address employee concerns and build confidence. Clear communication about how AI will be used, what it will and will not automate, and where human judgement remains essential can reduce anxiety and support adoption.

Finally, the report stresses that ethical, risk and privacy considerations must be integrated into workforce AI education. Employees need guidance on responsible use, particularly as AI systems are embedded into sensitive and regulated workflows.

Forrester’s conclusion is clear: Technology deployment, as is so often the case, is not enough on its own. Without corresponding investment in employee readiness, organisations will struggle to realise the full benefits of workforce AI.

“The organisations 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 says.