Poorly timed AI training risks pushing older workers toward retirement

Research suggests that hastily designed AI training can overwhelm already-stretched older workers and accelerate their retirement rather than prevent it.

By Sama News Agency
August 30, 2026
An older man wearing glasses and a light blue pinstriped shirt works at a laptop at a wooden desk by a window with plants visible outside.
An older worker engages with technology at his desk, illustrating the challenge of integrating AI training into existing workplaces where employees nearing retirement age may face difficulty adapting to new systems. (Phys.org)
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Employers are increasingly investing in training to prepare employees for artificial intelligence, with 78% of Canadian businesses planning to maintain or increase training spending in 2026, according to a survey by the Canadian Federation of Independent Business. In March, 36% of Canadian workers reported using generative AI tools in their main job or business over the past year, while 83% said they wanted or needed upskilling to use the technology effectively, according to surveys cited in the source material.

But research suggests the approach may backfire for workers over 55, a growing segment of Canada's workforce. The share of workers aged 55 and older within organizations nearly doubled between 2001 and 2022, rising from 9.3% to 18.8%, according to Statistics Canada. Since 2000, the total number of mature workers in Canada has grown 184%, far outpacing growth in other age groups.

A study of 361 workers found that burnout weakens older workers' sense of their own work ability, pushing retirement intentions earlier. When training is poorly timed, overly complex or insufficiently supported, it can become an additional job demand that erodes workers' confidence and drives departures, according to research on the issue. The effect varies depending on how training is delivered: whether it is spread over time or compressed, broken into manageable pieces, and whether surrounding workload pressures are eased.

Employees experiencing burnout are more likely to consider leaving their jobs, and for older workers, leaving may mean retiring earlier than planned. Research on training transfer shows that whether employees apply new skills depends on their motivation and capacity, both of which decline under burnout. A training rollout that ignores how stretched employees already are can accelerate the departures employers are trying to prevent, according to the analysis.

Losing experienced workers early means losing institutional knowledge, mentorship and expertise that is difficult to replace. Before rolling out a new digital or AI program, employers should assess not only what employees need to learn, but whether they currently have the capacity to learn and apply it, researchers suggest.

Poorly timed AI training risks pushing older workers toward retirement | Sama News Agency