The Charter Leadership Forum is a roundup of guidance from the greatest minds in talent. For each edition, Charter’s research team asks expert practitioners to share their insights around the biggest questions shaping the changing world of work.
AI is transforming how every job gets done, reshaping what skills matter and how organizations evolve. Yet as adoption accelerates, older workers, who bring deep experience and perspective, risk being left behind. Many are proving, though, to be among the most curious and capable adopters. We asked experts how leaders can help experienced workers thrive in an AI-driven economy, counter bias in tech adoption, and harness generational knowledge to strengthen their organizations.
Where do you see the biggest misconceptions about older workers’ ability to adopt AI?
Katherine Von Jan (KVJ), Co-Founder and CEO, Tough Day: The pervasive assumption is that older workers are less tech-savvy and therefore less able to adopt AI tools. This assumes that technical fluency matters more than business judgment in effective AI use. Older workers have the business experience and judgment to approach AI outputs with informed curiosity — questioning results, refining prompts, and collaborating with the technology to co-create accurate, powerful outcomes.
Older workers have navigated the internet, mobile, and social media revolutions. They’re experienced digital explorers who know how to adapt to emerging technology and opportunities. Leaders shouldn’t underestimate this capability. These workers can learn new tools while bringing decades of pattern recognition and business context that less experienced workers simply haven’t developed yet.
Edna Kane-Williams, Executive Vice President, Chief Diversity Officer, AARP: A major misconception is that older workers are resistant or unable to learn new technologies like AI. AARP research, however, shows many older adults are eager to learn and often approach AI with curiosity and sound judgment. Their experience helps them ask better questions and apply AI thoughtfully. The real barrier isn’t age, but access to differentiated training and supportive environments. When organizations tailor learning and recognize the value of experience, older workers can adopt and drive innovation and ethical use of technology.
Anna Tavis, Department Chair, Human Capital Management, NYU: When we talk about AI adoption, there is often an unspoken bias of “Digital Native” generational difference. It is the assumption that technological fluidity is connected with age. If you didn’t grow up with a tablet in your hand, you will struggle to grasp the complexities of AI.
This is the biggest misconception in the workforce today. The reality is that Generative AI has shifted the computing paradigm from syntax (code) to semantics (language). In this new era, the learning curve doesn’t prioritize speed and technical skills but it favors wisdom.
In summary, the hottest skill in the modern workforce isn’t coding; it’s context.
Because (LLMs) work based on linguistic inputs, the quality of the output is strictly determined by the quality of the prompt.
- The junior employee who knows how to use ChatGPT but lacks the business context to know what “good” looks like?
- The senior leader who has 30 years of domain expertise, a rich professional vocabulary, and the critical thinking skills to spot a hallucination immediately?
In this context, the experienced workers are actually the superior prompt engineers. They possess the discernment to provide the AI with the necessary constraints, context, and tone. Once they get hold of AI, they could be “flying with it.”
As AI reshapes the workforce, how can leaders redesign roles without sidelining experienced talent?
Anna Tavis, Department Chair, Human Capital Management, NYU: Do not redesign jobs; redesign tasks. Use AI to automate the novice-level work, thereby elevating the veteran to a role of “Editor-in-Chief” or “System Architect.” Redesign roles so the experienced employee is the necessary “circuit breaker” or validator of AI output. Their experience is the safety net.
Katherine Von Jan (KVJ), Co-Founder and CEO, Tough Day: Experienced workers know the jobs to be done, the hidden complexities in workflows, and how the work actually gets done. Without their input, leaders risk automating the wrong things or creating AI-augmented roles that don’t work in practice.
Build teams with diverse life and work experiences and diverse working styles to drive the redesign. Experienced workers bring institutional knowledge. Younger workers bring fresh thinking, unburdened by “how we’ve always done it.” The more diverse the team, the more likely they’ll leverage curiosity, experience, and creativity to make smarter decisions about where AI should augment human work, where there should be a “human in the loop,” and where full automation makes sense.
Jordan Taylor, Co-Founder and CEO, Medley: Leaders should recognize the opportunity the current moment presents for experienced talent. Experienced talent have had more years to hone their human-centric skills which are even more essential as AI reshapes the workforce. Additionally, they have more experience in navigating change throughout their careers, and this expertise is critical for an AI-ready mindset.
Edna Kane-Williams, Executive Vice President, Chief Diversity Officer, AARP: Leaders must intentionally redesign roles to leverage the strengths of experienced workers. This means moving beyond stereotypes and recognizing that older employees can bring institutional knowledge, context, and ethical judgment that AI cannot replicate. Flexible work arrangements, continuous learning opportunities, and roles that blend tech skills with experience are key. By fostering multigenerational teams and valuing mentorship, organizations can ensure experienced talent remains central to innovation and resilience.
How are companies helping employees not just learn new tools but use them with confidence in daily work?
Anna Tavis, Department Chair, Human Capital Management, NYU: Confidence comes from psychological safety. Companies fail when they introduce AI as a “test” rather than an “assistant.” Companies should stop doing generic “Intro to AI” courses. Run workshops titled “How to cut your email time in half” or “How to analyze this spreadsheet in 30 seconds.” Help solve a pain point, don’t just demo a feature. This validates that using the tool is smart, not “cheating.”
Jordan Taylor, Co-Founder and CEO, Medley: Investing in an AI-ready mindset in addition to the skillsets and toolsets needed to thrive. More organizations are moving beyond one-off technical training and instead investing in programs that build adaptability, self-awareness, and relational intelligence. This ensures employees can navigate change with clarity rather than fear.
Edna Kane-Williams, Executive Vice President, Chief Diversity Officer, AARP: Companies succeed when they offer differentiated training—meeting employees where they are. AARP research highlights that mentorship; peer learning; and practical, job-relevant training build both skills and confidence. Creating a culture that values curiosity and supports ongoing learning helps employees of all ages use new tools confidently and effectively in their daily work.
Katherine Von Jan (KVJ), Co-Founder and CEO, Tough Day: Training teaches the mechanics of how tools work, but confidence requires ongoing support. Workers need context for how new tools apply to their specific roles, examples of what effective use looks like, and safe spaces to practice using them and ask questions without judgment.
Solutions like our AI Workplace Advisor, Tuffy, provide on-demand, confidential guidance and emotional support in the flow of work, giving employees the agency and confidence to work effectively with new tools and navigate the change and challenges that come with it. It’s about supporting the human side of adoption, not just the technical side.
What have you seen work best to build AI confidence—not just AI skills—among experienced employees?
Jordan Taylor, Co-Founder and CEO, Medley: Medley works with organizations to deliver group coaching where employees can process uncertainty, practice new skills, and build leadership muscles like reflection, curiosity, empathy, and trust-building. When possible to apply new technologies effectively in their work. In these spaces, people don’t just learn a tool; they understand how to integrate it into their workflow, how to stay grounded amidst disruption, and how to use it in ways that strengthen and not replace their human strengths. The most forward-thinking companies are also grounding their enablement efforts in a structured progression; from awareness, to adoption, and to innovation.
Edna Kane-Williams, Executive Vice President, Chief Diversity Officer, AARP: Confidence grows when training is practical, collaborative, and values experience. Bi-directional or two-way mentoring, where younger employees who have experience with AI share tech insights and older workers offer context and judgment, is powerful. Encouraging experimentation, celebrating small wins, and providing ongoing support helps demystify AI. Most importantly, leaders must communicate that learning is a journey and every generation has something vital to contribute.
Katherine Von Jan (KVJ), Co-Founder and CEO, Tough Day: Sharing concrete examples of how peers are effectively using AI helps demystify the technology — not just generic training, but real use cases relevant to their work. Tactics like reverse mentoring programs, internal showcases of AI in action, and even encouraging practice with personal scenarios (like using AI to plan a birthday party) can spark ideas and build confidence in how workers might apply it to their own roles.
But examples alone aren’t enough. Workers need psychological safety to experiment without fear of looking incompetent. Confidential resources like our AI Workplace Advisor, Tuffy, provide a judgment-free space to ask questions, explore how AI applies to their specific role, and practice before using it in visible work contexts. When workers can see what’s possible and try it safely, confidence follows.
Anna Tavis, Department Chair, Human Capital Management, NYU: Skills are technical; confidence is emotional. To build confidence, companies must validate the employee’s existing expertise as the prerequisite for using AI effectively. AI solves the terror of the blank page, but it cannot solve the “final mile.” While AI gets you to 80%, the veteran employee’s experience and judgment provides the final, crucial 20%. Experience is king.