Abstract
As artificial intelligence becomes embedded in workplace tools, concerns persist that older workers may be left behind. Drawing on survey data from workers age 50 and older, employer surveys, and LinkedIn skills and learning data, this article examines how older workers are experiencing, using, and preparing for AI. The findings suggest that low AI use reflects misalignment between training, job tasks, and workplace practices rather than worker resistance or inability. Older workers demonstrate strong interest in learning and increasing engagement with technology-focused skills, and they occupy roles where experience and judgment remain critical, highlighting opportunities for more inclusive AI adoption.
Key Words
Artificial Intelligence; older workers; technology
The United States labor market is currently navigating a dual transformation: the accelerated integration of artificial intelligence (AI) and the rapid aging of its workforce. AI is no longer a niche workplace technology. It has become part of everyday work for millions of Americans – sometimes highly visible, sometimes embedded quietly into the tools workers already use. At the same time, the U.S. workforce is aging. As of 2022, workers age 55 and older make up 24% of the U.S. workforce, up from just 10% in the mid 1990s, and constitute the fastest-growing age group in the labor force (U.S. Census Bureau, 2025).
These two trends—rapid AI adoption and workforce aging—are colliding. Public discussion often treats this collision as a problem, deepened by age-based assumptions and discrimination, and casts older workers as vulnerable to displacement or ill-equipped to adapt. Yet early labor market evidence paints a more complicated picture. While AI has reshaped tasks and workflows, its employment effects have not been evenly distributed across age groups. In occupations with high exposure to AI, employment has declined for workers in their early 20s but grown for workers age 30 and older, including those age 50 and older (Brynjolfsson et al., 2025). At the same time, most workers—across all age groups—report limited direct use of AI: 63% of U.S. workers say they use AI either not much or not at all (Lin & Parker, 2025).
Against this backdrop, understanding how older workers themselves experience and prepare for AI, and how employers are responding, becomes essential. Drawing primarily on three complementary data sources—survey data from workers age 50 and older, survey data from employers, and LinkedIn data on older worker skills and learning—this article examines how older workers are navigating AI in practice. The findings suggest that the central challenge is not resistance or ability, but alignment between technology, training, and the realities of work across the life course.
Older Workers and AI: Awareness Is Rising, Use Is Still Limited
Among workers age 50 and older, knowledge of AI is moderate but increasing. In the most recent wave of AARP’s survey of older workers, 53% say they are knowledgeable about AI generally, up from 48% in 2024, and 52% say they are familiar with the use of AI in the workplace, up 13 percentage points since 2024. These gains reflect the growing visibility of AI tools and their integration into common workplace software (Perron, 2026a).
When older workers do use AI, they tend to use it for specific, practical purposes rather than complete task replacement.
Yet familiarity does not necessarily translate into extensive use. Only 23% of workers age 50 and older say they use AI to a great or some extent in their work, while 73% say they use it not too much or not at all (Perron, 2026a). This pattern mirrors national findings across all ages, where just 16% of workers say at least some of their work is done with AI, and only 2% say most or all of their work involves AI (Lin & Parker, 2025).
When older workers do use AI, they tend to use it for specific, practical purposes rather than complete task replacement. Among older AI users, the most common uses include finding information (67%), creating content such as text or images (37%), and analyzing data or information (40%; Perron, 2026a).
These are supportive, augmentation-oriented uses, showing AI as a tool that helps workers do their jobs more efficiently rather than one that completely redefines or replaces the job itself.
Opportunity and Risk in Parallel
Older workers do not view AI in simple terms. Instead, their perceptions reflect a careful weighing of benefits and risks, influenced at least in part by individuals’ knowledge of AI and how AI is framed by their employers. As such, when looking forward 5 years, 31% of workers age 50 and older say AI is both a threat and an opportunity in their line of work. Another 30% see it primarily as a threat, while 19% see it mostly as an opportunity. Importantly, from an educational and employer perspective, 20% say they don’t know whether AI is a threat or an opportunity, indicating a potential knowledge gap about what AI is and can do (Perron, 2026a).
Concerns about job disruption coexist with some optimism about productivity. When asked how AI will affect the future of work, older workers most frequently respond that it will replace workers (66%), automate repetitive tasks (49%), threaten data privacy (46%), enhance productivity (37%), and make work easier (36%; Perron, 2026a).
Notably, worries about fairness are also present. Twenty-four percent of older workers believe AI could increase age bias in hiring, compared with just 5% who believe it could decrease age bias (Perron, 2026a). These concerns are grounded in lived experience, as many older adults had encountered age discrimination well before AI entered the picture. Data show that having seen or experienced age discrimination, currently reported by 64% of older adults, remains stubbornly persistent over time (Perron, 2025).
At the same time, interest in learning remains high. Despite relatively low participation in AI training (12%), 49% of workers age 50 and older say they are interested in training or classes to learn more about using AI at work (Perron, 2026a). Additional AARP data show that older workers are both willing and eager to acquire new skills: 86% of older workers say they wouldn’t accept a new job unless it offered them the opportunity to learn something new and 79% of workers age 50 and older actively seek out opportunities to learn new skills (Choi-Allum, 2023b). When asked specifically about reskilling, 60% of older workers state they are willing to learn new skills if requested by an employer. Perhaps most importantly for thought leaders, the primary motivation for this training is “personal interest in gaining new skills” (34%), which far exceeds the pressure of mandatory employer requirements (11%; Choi-Allum, 2023a).
Employers: Widespread AI Adoption, Broad Training Claims
From the employer perspective, AI is already deeply embedded in organizational operations. In the AI and Multigenerational Workforce study (Perron et al., 2025), 88% of employers say their organization currently uses AI, and 92% say they personally use AI in their role. Employers overwhelmingly see AI as beneficial to employees: 78% describe AI as a minor or major opportunity to workers themselves, including 55% who call it a major opportunity. Employers rely heavily on their older workers related to AI use and implementation: 46% of employers report that most or all of their strategic decisions regarding where and how to use AI are made by employees age 40 and older (Perron, 2026b).
Training appears, at least on paper, to be widely available. Seventy-five percent of employers report offering internal AI-related training, and 29% report offering external training, while 69% encourage employees to seek AI training on their own. Among employers that offer AI training, 97% say they take steps to ensure training is accessible and inclusive across ages, including offering content at different skill levels and in multiple formats (Perron, 2026b).
The Training Gap: Availability vs. Uptake
Despite these employer-reported efforts, older workers’ training participation remains modest. In the AI and Older Worker survey, only 12% of workers age 50 and older say they have taken AI-related training at work. In fact, while reports of employer training appear high, older workers themselves largely disagree that employers are doing enough to train workers to use AI in their work (63%; Perron, 2026a). This gap between employer reports and worker experience suggests that availability alone does not guarantee engagement (Perron, 2026b).
Several factors likely contribute. Training may be offered but not clearly connected to specific job tasks; properly conveying the utility to the worker can smooth the way to training. It may be framed as optional or remedial rather than developmental, needing a direct link to growth for it to be worth the time. Or training may be delivered in ways that do not align with how older workers prefer to learn, like including all-age examples and hands-on experience during the training.
Nearly half of older workers express interest in learning more about AI, indicating unmet demand rather than resistance.
Importantly, this gap does not reflect lack of motivation: Nearly half of older workers express interest in learning more about AI, indicating unmet demand rather than resistance. This is precisely why we asked employers about professional development conversations and whether AI is included in these meetings. While nearly all companies report holding professional development meetings, a quarter of these discussions are informal, and only 38% consistently include dialog about AI-related skills or training (Perron, 2026b).
Most organizations report that the majority of employees at all levels are eligible for AI-related training, but eligibility is not universal, especially for entry-level roles. Mid-level employees are most likely to be eligible (94%), compared with senior-level employees (85%) and entry-level employees (77%), creating a 17-percentage-point gap between mid- and entry-level eligibility. Put differently, 23% of organizations say entry-level employees are not eligible, versus 15% for senior-level and 6% for mid-level employees. This highlights a meaningful subset of workers still excluded from access (Perron, 2026b).
These gaps point to a significant opportunity for employers to more intentionally and thoughtfully provide training opportunities to all employees, regardless of age.
What Learning Behavior Shows
LinkedIn Learning data provide an important counterbalance to survey responses. While older workers historically participated less in online learning than younger workers, that gap has narrowed dramatically. By 2025, the difference in LinkedIn Learning participation between younger and older workers had shrunk to just 1.6 percentage points, down from 13.5 points in 2022 (Perron et al., 2025).
Older workers are also shifting toward technology-focused learning. Between 2022 and 2025, the share of older workers’ LinkedIn Learning sessions devoted to technology topics grew from 19.5% to 26.6%, substantially narrowing the age gap in tech learning. This trend suggests that older workers are not avoiding technical skills; they are selectively investing in skills that complement their experience (Perron et al., 2025).
Skills data reinforce this pattern. While workers 50-plus list traditional tech skills less frequently than younger workers, they have increased their share of disruptive tech skills[1] by 25% over the past 5 years, compared with 13.5% among younger workers. At the same time, they maintain strong representation in leadership, communication, and strategic skills—areas that are difficult to automate and increasingly valuable in AI-enabled workplaces (Perron et al., 2025).
Experience and AI Resilience
LinkedIn data also show that 49.4% of workers age 50 and older are in roles considered insulated from generative AI disruption, compared with 42.2% of younger workers. These roles tend to rely on judgment, coordination, and human interaction—capabilities that AI currently supports rather than replaces (Perron et al., 2025).
These data align with broader labor market research showing that AI’s impact is more task-specific than occupation-wide. Even in roles with high AI exposure, employment has grown for midcareer and older workers as AI augments productivity rather than eliminating jobs. In this context, experience becomes an asset, enabling workers to integrate AI outputs into complex decision making (Brynjolfsson et al., 2025).
Older workers are increasingly knowledgeable about AI, selectively using it at work, actively engaging in learning, and occupying roles where experience and judgment matter.
Implications for Practice and Policy
Taken together, these data suggest a clear message: Older workers are not disengaged from AI, but the systems designed to support learning and adaptation are uneven. For service providers and workforce programs, this points to the importance of targeted, more personalized, and applied AI training. For employers, it highlights the need to align training offerings with workers’ actual tasks and career pathways, transparency in how and why AI is used, and how AI becomes a tool—and not a replacement—for workers.
For policymakers, the implications are structural. With nearly a quarter of the workforce age 55 or older, mid- and late-career upskilling is not a niche concern—it is core for economic well-being, not only for the individual but also for the community. Policies that support lifelong learning, protect against bias, and encourage age-inclusive technology deployment will shape whether AI becomes a force for extended working lives or cause for premature exit.
Conclusion: A Data-Driven Reframing
The dominant narrative around older workers and AI is one of vulnerability. The data tell a different story. Older workers are increasingly knowledgeable about AI, selectively using it at work, actively engaging in learning, and occupying roles where experience and judgment matter. Their concerns about displacement and bias are real—but so is their capacity to adapt.
For service providers, gerontologists, policymakers, and government leaders, this moment matters. The choices made now—about training, workplace design, and AI governance—will shape whether AI deepens inequities or becomes a tool that supports longer, healthier, and more productive working lives. The challenge ahead is not whether older workers can navigate AI; it is whether institutions will recognize and support the ways they already are.
Rebecca Perron, PhD, MA, MPH is a senior research advisor at AARP in Washington, DC.
Microsoft CoPilot was used to help draft this article.
Photo credit: Shutterstock/Haneneko_Studios
References
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Lin, L., & Parker, K. (2025, February 25). U.S. workers’ exposure to artificial intelligence. Pew Research Center. https://www.pewresearch.org/social-trends/2025/02/25/workers-exposure-to-ai/
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Perron, R. (2026b). How U.S. employers are using artificial intelligence: What it means for a multigenerational workforce. AARP Research.
https://www.aarp.org/pri/topics/work-finances-retirement/employers-workforce/employers-artificial-intelligence-multigenerational-workforce/
Perron, R., Baird, M., & Hood, R. (2025). Multigenerational workforce unlocks untapped value: The value older workers bring to multigenerational workplaces. AARP Research. https://www.aarp.org/pri/topics/work-finances-retirement/employers-workforce/multigenerational-workforce-linkedin/
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