Your AI Skills Gap Runs Along Generational Lines. So Does the Fix.

Ask a senior leader how their organization is building AI capability, and the answer usually involves a vendor, a platform or a training budget. Ask where the most fluent AI users in the business are, and the answer is often a few desks away, two or three levels down.

More than 80% of Gen Z professionals use AI at work, compared with 52% of Baby Boomers, according to a 2025 London School of Economics survey of nearly 2,800 employees across 30 countries.¹ At the same time, the experience needed to judge when an AI answer is wrong, risky or simply irrelevant often sits with people who have spent decades in the business.

Most organizations have both kinds of expertise. Very few have a reliable way to move it between generations. That gap is where cross-generational learning earns its place, not as a culture initiative, but as an upskilling strategy.

Your AI skills gap runs along generational lines

The LSE research found that AI use declines across older age groups: from more than 80% of Gen Z, to 73% of Millennials, 60% of Gen X and 52% of Baby Boomers.¹ Yet access to AI tools does not automatically translate into workforce readiness. Without structured upskilling, organizations risk widening the gap between employees who already use AI confidently and those who need support to adopt it.

These are broad generational trends, not fixed rules about individual capability. But they point to a challenge for organizations: the people responsible for setting AI strategy, approving investments and leading adoption often have less hands-on experience with emerging tools than some of the employees they manage.

Formal training helps, but it becomes more valuable when employees can connect what they learn to the specific workflows and challenges within their own function. The colleague who has already used AI to cut a weekly report from three hours to thirty minutes can show them exactly that.

The judgment gap runs the other way

Fluency with AI tools is not the same as knowing when to trust them. As Glassdoor’s research on AI sentiment notes, experienced workers can often draw on their professional judgment to assess whether an AI-generated answer makes sense.²

Younger employees, meanwhile, are missing some of the informal learning moments earlier generations relied on. As AI becomes a convenient source of workplace guidance, younger employees may increasingly turn to these tools for answers instead of seeking advice from experienced colleagues. The tool answers the question. It doesn’t explain how a client relationship really works, why a similar project failed five years ago, or how to read a room before a difficult conversation.

AI fluency and business judgment are both valuable. They may not sit in the same person, but they often sit in the same team. The opportunity is to bring them together through structured, cross-generational learning.

Institutional knowledge is leaving faster than it is captured

The urgency isn’t only about AI. APQC’s 2025 survey of 1,000 professionals found that 92% of organizations do not consistently capture knowledge from soon-to-be retirees, and 58% of C-suite respondents are very worried about the loss.³ A global Kelly survey also found that fewer than half of executives reported having formal programs to transfer knowledge before employees retire.⁴

That knowledge is often the hardest to rebuild: how decisions actually get made, which stakeholders matter, what was tried before and why it didn’t work. When it leaves without being shared, organizations pay for it twice: once in lost expertise and again in the time it takes the next person to relearn it.

Why proximity alone doesn’t transfer knowledge

Putting several generations in the same workplace doesn’t guarantee they will learn from each other. People often s

Putting several generations in the same workplace doesn’t guarantee they will learn from each other. People often spend most of their time with colleagues at similar levels or in similar roles. Knowledge moves within those groups, but not always across them.

Employers recognize the opportunity. In a 2020 AARP survey of nearly 6,000 employers across 36 countries, 83% of executives said a multigenerational workforce was key to growth and long-term success, and approximately two-thirds said they would purposefully design mixed-age teams.⁵ The difference between intent and impact is structure. Three approaches can help organizations turn cross-generational collaboration into meaningful capability development:

1. Reverse mentoring: Pairing junior employees with senior leaders on defined topics such as AI tools, digital channels or emerging customer behavior. A well-designed program creates opportunities for both participants to share expertise and develop new capabilities.
2. Cross-generational project teams: Bringing different kinds of expertise together around live business challenges, so knowledge transfers through application rather than instruction alone.
3. Structured knowledge capture: Turning the tacit expertise of experienced employees into something others can find and use through recorded walkthroughs, decision logs or shadowing, well before retirement or a role change.

But the real opportunity goes beyond transferring knowledge from one generation to another. It lies in designing an exchange where both sides learn.

Make reverse mentoring work in both directions

Picture a pairing in practice. A 26-year-old analyst shows a regional director how to use generative AI to prepare for a client review: drafting a briefing from past meeting notes, stress-testing assumptions and spotting where the output needs a human check.

In return, the director walks the analyst through how to read that client: what they will push back on, who really makes the decision and how to handle a difficult conversation when the numbers disappoint.

One brings emerging capability. The other brings accumulated judgment. Neither replaces the other. Together, they bring the fluency to use AI tools and the judgment to apply them well.

That reciprocity matters in practice, too. Programs often lose momentum when senior participants stop prioritizing them. After a few canceled sessions, the exchange risks becoming another well-intentioned initiative that never changes how people work.⁶ Making the relationship reciprocal gives both participants something valuable to gain and a reason to remain engaged.

Practical plays for L&D and HR leaders

1. Map capability before you buy it.

Before sourcing external AI training, identify who in the organization is already using AI effectively and in which workflows. Existing expertise can complement formal learning investments.

2. Pair by problem, not just by age.

Match employees around a specific business challenge rather than assuming capability based on generation or seniority. A shared problem gives the exchange a practical purpose and creates opportunities to apply learning immediately.

3. Define what flows each way.

Agree upfront on what each participant will teach and learn. A one-page learning plan can clarify expectations, responsibilities and desired outcomes.

4. Capture knowledge before it is at risk.

Identify roles where critical expertise sits with one or two people. Document decision-making processes, relationships and lessons learned before retirement or a role transition makes that knowledge harder to access.

5. Recognize the teachers.

Make mentoring and knowledge-sharing part of performance and development conversations. Employees at every level need to see that developing others is valued.

6. Measure the exchange, not the activity.

Track what changes as a result of the program: AI tools adopted by senior participants, improvements in decision-making, faster onboarding or reduced time to productivity. The number of mentoring sessions completed tells only part of the story.

The takeaway

Organizations don’t need every generation to think alike. They need the knowledge held across generations to move. For L&D and HR leaders, an effective upskilling strategy starts by identifying the AI fluency, business judgment and institutional knowledge already available across the workforce, then finding ways to bring those capabilities together.

The strongest approach isn’t to choose between internal expertise and external learning. It’s to bring them together in ways that help employees apply new capabilities to real business challenges.

At Emeritus Enterprise, we work with organizations to design learning journeys that connect expertise across roles, levels and generations. We combine programs developed with leading global universities and industry experts with the capability already inside the business, so employees can apply what they learn to real business challenges.

If you’re rethinking how your organization builds AI capability and protects institutional knowledge, let’s talk.

Sources

1. London School of Economics / Protiviti survey (2025), as reported by CFO Brew: https://www.cfobrew.com/stories/2025/10/30/ai-s-generation-gap
2. Glassdoor research on AI sentiment by generation, as reported by YourStory: https://yourstory.com/ai-story/gen-z-uses-ai-most-feels-worse-study
3. APQC, Navigating the Great Retirement with KM & AI (2025): https://www.businesswire.com/news/home/20251013139432/en
4. Kelly global survey on retirement and knowledge transfer: https://abc17news.com/?p=1591080
5. AARP, Insights from Global Employers (2020): https://www.aarp.org/press/releases/2020-8-5-aarp-survey-global-executives-agree-multigenerational-workforce-is-key/
6. Jordan & Sorell, Why Reverse Mentoring Works and How to Do It Right: https://www.physicianleaders.org/articles/reverse-mentoring-works