The Pulse of Higher Ed

By Vickie S. Cook, Ph.D.

There are many assumptions about AI use across generations. Gen Z readily embraces artificial intelligence. Millennials are integrating it into their work. Gen X is more cautious and pragmatic. Baby Boomers are reluctant adopters.  Is it really that simple?  Perhaps not.  Generational differences in AI use appear to be real. What those differences mean is far less certain.  There is research that appears to support parts of that narrative. Younger adults generally report using generative AI at substantially higher rates than older adults. But dig a little deeper into the research and the story becomes much more complicated and much more important for leaders.

Before leaders use generational differences to inform decisions about AI adoption, training, workforce development, or organizational change, we need to ask a more fundamental question: Are we seeing differences caused by generation, or are we seeing differences in age, career stage, exposure, opportunity, confidence, perceived risk, or even awareness that AI is being used? The answers to these questions are important as leaders consider their own generational biases regarding age and stage interaction with technology.

Generational Thinking Can Be Useful If We Recognize Limitations

I have written and presented about generations for many years, and I continue to believe generational research can provide useful information for leaders. Broad patterns can help us identify questions we should be asking and differences we should consider.  But, to make decisions based on someone’s generation is quite wrong.  Remember that generational research is not a precise science. Generational patterns can inform the questions leaders ask. They should not determine the conclusions leaders reach about individuals.

The familiar generational categories have been widely used to describe groups of people who share birth years and some formative historical experiences. People do not suddenly acquire a set of characteristics because they were born on one side of a generational dividing line rather than another.

Researchers also face a persistent problem when interpreting apparent generational differences. Are we observing a cohort effect associated with the historical experiences of a particular generation? An age or life-stage effect that might have occurred regardless of generation? A period effect caused by something affecting everyone at the same time? Or differences in education, occupation, organizational culture, access, or opportunity?

AI makes those distinctions especially important because we are living through an extraordinary period effect. All generations are encountering a rapidly changing technology at the same moment, but they are encountering it at very different stages of their lives and careers. Generational data can help leaders see where differences appear. It is considerably less capable of telling us why those differences exist.

There Is an AI Adoption Gap

The differences in reported AI use are difficult to ignore. Pew Research Center’s 2026 research found that 66% of adults ages 18–29 had used AI chatbots, compared with 61% of those ages 30–49, 42% of those ages 50–64, and 23% of adults 65 and older.

Other recent studies using generational categories have found similar patterns. Younger generations generally report greater use of generative AI, greater familiarity with it, and more opportunities to incorporate it into their work. It would be easy to stop there and conclude that younger generations are embracing AI while older generations are not.

But that would confuse a description with an explanation. Age is clearly associated with reported AI adoption. That does not establish that age or a specific generation is causing the difference. The bigger problem that must be addressed: What exactly do we mean when we ask whether someone “uses AI”?

You May Be Using AI Without Thinking of It as AI

Ask someone whether they use ChatGPT, Claude, Gemini, or Copilot and they can probably give you a reasonably accurate answer. Ask whether they use AI however, and the question becomes considerably more complicated.

AI Is Already Embedded in Everyday Technology

AI has become embedded in products and services that people use routinely: navigation systems, search engines, email filtering, predictive text, customer service phone routing, streaming recommendations, online shopping, fraud detection, virtual assistants, social media feeds, and many other applications.

Gallup demonstrated the magnitude of this disconnect in research conducted with nearly 4,000 U.S. adults. Only 36% initially reported using an AI-enabled product during the previous week. But after Gallup asked about six specific categories of AI-enabled products, 99% had used at least one and 83% had used four or more. People were using AI. They simply did not necessarily recognize what they were doing as AI use. In many cases, AI was embedded in products they had chosen to use rather than being a technology they had deliberately chosen to adopt.

What Does It Mean to “Use AI”?

This introduces a problem for interpreting generational adoption data. Let’s consider two employees.

A younger employee regularly opens a generative AI application and describes herself as using AI every day.

An older employee uses navigation, online search, predictive text, email filtering, streaming recommendations, and AI-supported workplace software but says, “I don’t use AI.”

Both may be accurately describing their experience as they understand it.

What differs may not simply be their exposure to AI. It may be their recognition of AI and their understanding of what constitutes AI use.

AI Engagement Is a Continuum

We may therefore need to think about AI engagement as a continuum:

1. Embedded exposure occurs when AI operates within a product or service someone uses. Often without that person being aware that AI is enabled in the process or product.
2. Unrecognized use occurs when someone actively uses an AI-enabled feature without recognizing it as AI or deliberately choosing to use AI.
3. Recognized use occurs when someone knows that a feature or system incorporates AI and chooses to use it.
4. Intentional generative AI use occurs when someone deliberately engages an AI system to accomplish a task such as opening an AI platform with an interactive product like ChatGPT and typing in a prompt.

Those are very different experiences. Collapsing all four into a simple category of “AI user” or “AI non-user” tells us very little about an individual’s actual relationship with AI. It also means that some apparent generational differences in AI adoption could reflect differences in AI awareness as well as differences in actual use.

Using AI Does Not Mean Trusting AI

Another assumption begins to break down when we look at attitudes rather than adoption. The people using AI most frequently are not necessarily the people who are most comfortable with its consequences.

Pew’s 2026 research found younger adults were considerably more likely to use AI chatbots than older adults. Yet adults ages 18–29 were also more likely than adults 65 and older to expect AI to have a negative impact on society over the next 20 years. Other research has found substantial concern among younger workers about AI eliminating jobs or eventually replacing their own roles.

Adoption, Confidence, Trust, and Acceptance Are Different

Adoption is not the same as confidence. Confidence is not the same as trust. Trust is not the same as optimism. And none of them necessarily equals acceptance.

An employee can use AI every day and simultaneously be deeply concerned about what it means for her profession and for the future of her field. Another employee can use generative AI infrequently while believing it will ultimately improve his work. A third can be highly confident using AI while exercising poor judgment about when its outputs should be trusted.

If leaders combine all of these dimensions into a single concept of “AI readiness,” they risk misunderstanding their workforce.

How Professional Experience May Shape AI Adoption

The middle generations provide another reason for caution. Recent research suggests Millennials are among the most active workplace users of AI. Gen X occupies an interesting middle position: adoption tends to be lower than among Millennials and Gen Z but considerably higher than among Baby Boomers. These patterns are particularly relevant for organizations because many experienced managers and senior leaders fall within these groups.

Caution Can Reflect Professional Judgment

But there is another way to interpret their behavior. Does professional experience make someone less willing to adopt AI? Or does experience provide context that allows a person to make more discriminating decisions about where AI adds value and where it does not?

Someone who has performed a complex professional task for 25 years may approach AI differently from someone who has performed it for two years. That does not necessarily make either person’s approach superior. The experienced professional may understand nuances, risks, exceptions, and consequences that make caution appropriate.

The less experienced professional may be more willing to question an inefficient process simply because ‘we have always done it that way’.  It is clear that organizations need both perspectives.

Lower Adoption Is Not the Same as Resistance

The potential for generational bias becomes particularly apparent when discussing Baby Boomers. Research consistently finds lower reported generative AI adoption among older adults. That is a legitimate finding. It does not establish that older adults are unable to learn AI, oppose its use, lack interest in it, or cannot become highly sophisticated users.

In fact, adoption among older adults has been rising rapidly. Pew’s tracking of ChatGPT use among adults 65 and older increased from 4% in 2023 to 19% in 2026. Among adults ages 50–64, reported use increased from 13% to 37%.  The question becomes how many opportunities Baby Boomers, some of whom are no longer in the workforce, have to use AI regularly, and for what purposes.

The gap remains. But the direction of change matters. Once we convert “lower current adoption” into “resistant to AI,” we have moved from evidence to stereotype. And stereotypes influence organizational decisions.  Generational leadership should never fall into establishing stereotypes as a means to leading human or AI teams.

What If Organizations Are Helping Create the Gap?

This may be the more consequential leadership question. Suppose a manager assumes younger employees will be comfortable with AI. Those employees are encouraged to experiment. They receive AI-intensive assignments. They attend workshops. They are invited to pilot new applications. Their mistakes are interpreted as part of learning.

The same manager assumes older employees will be reluctant. They receive fewer opportunities to experiment and less encouragement to participate in pilots. Training may be directed elsewhere because the organization assumes younger workers will benefit more.

Six months later, the younger employees demonstrate greater AI proficiency. The manager concludes: “I knew the younger employees would be better at AI.” But what has actually been demonstrated? Generational aptitude based on stereotypical decision-making? Or differential opportunity?

When Assumptions Become Self-Reinforcing

The process can become self-reinforcing. We should not assume this explains generational differences in AI adoption. The research does not establish that causal relationship. Leaders should be asking whether their own decisions are contributing to the differences they observe.

Generational assumption → Differential opportunity → Different experience → Different confidence → Different usage → Apparent confirmation of the original assumption

Generational Bias Works in More Than One Direction

The danger is not limited to assumptions about older workers. A leader might assume a Gen Z employee is naturally proficient with AI and therefore needs little training. But frequent use of generative AI does not necessarily indicate an understanding of hallucinations, data privacy, intellectual property, bias, verification, or the appropriate boundaries between automated assistance and human judgment.  Required workplace use of AI also does not mean that Gen Z employees necessarily view its adoption or use as ethical, desirable, or safe. Assuming competence can be as problematic as assuming incompetence.

Likewise, Millennials should not automatically be treated as enthusiastic AI adopters, Gen X should not be characterized as reluctant to change established professional practices, and Baby Boomers should not be presumed to require persuasion before they will experiment. Population-level patterns can help us formulate questions. They do not provide answers about individuals.

Questions Leaders Should Ask About AI Adoption

Instead of asking, “How do I get each generation to adopt AI?” leaders might begin with a different set of questions:

• What exposure has this individual had to AI?
• Do they recognize where AI is already embedded in their work?
• What opportunities have they had to experiment safely?
• How confident are they in using AI, and is that confidence warranted?
• What do they believe AI could contribute to their work?
• What concerns do they have about its consequences?
• What training or support would improve their capability?
• Does this individual have a reason or need to use AI in their work?
• Where does their professional experience provide context and judgment that AI cannot?

Those questions move the conversation away from generation as destiny and toward the interaction of the person, the work, and the organizational environment.

A Lens, Not a Label

Generational research remains valuable. It can help leaders identify patterns, challenge assumptions, anticipate different reactions to organizational change, and ask better questions. It can assist the leader in helping to see the individuals and plan for professional development. But generational research becomes less advantageous when a population-level observation becomes an expectation about an individual. AI raises the stakes.

Organizations are making decisions now about who receives training, who participates in pilots, who is encouraged to experiment, whose concerns are considered legitimate, and who is selected to lead AI-enabled transformation. We must pay attention to generational patterns as those decisions are made. But we should be equally attentive to the biases we bring to interpreting those patterns. Perhaps the most consequential generational AI bias is not what different generations believe about artificial intelligence. It may be what leaders believe about different generations.

The leadership challenge is not to become blind to generational differences. It is to recognize those differences without allowing them to become assumptions about individual capability, motivation, or willingness.

What does this work require? What does this individual know? What experience do they bring? What development do they need? What opportunity have they been given?

Vickie Cook is a nationally recognized higher education leader specializing in enrollment strategy, online and digital learning, organizational transformation, team development, and leadership growth. She currently serves as a Senior Fellow and Strategic Advisor for UPCEA.  You can follow Vickie on LinkedIn.  To learn more about UPCEA Research and Consulting, please contact [email protected].  

Frequently Asked Questions About Generations and AI

Does Gen Z use AI more than older generations?

Generally, yes. Recent research finds substantially higher reported generative AI use among younger adults. But higher use does not establish greater capability, trust, optimism, or acceptance.

Are Baby Boomers resistant to AI?

The evidence does not support that conclusion. Older adults report lower generative AI adoption, but adoption is increasing. Lower use may also reflect differences in workplace exposure, opportunity, perceived utility, or reason to use AI.

Can someone use AI without realizing it?

Yes. AI is embedded in many everyday technologies, including search, navigation, email filtering, recommendations, fraud detection, and customer-service systems. Gallup found a substantial gap between people’s reported AI use and their use of AI-enabled products.

Does frequent AI use mean someone is AI-literate?

No. Frequency of use does not necessarily indicate proficiency in verification, privacy, intellectual property, bias, ethical use, or knowing when human judgment should override an AI-generated response.

How should leaders use generational research when making AI decisions?

Use generational patterns to identify questions, not to predict an individual’s capability or willingness. Leaders should consider an individual’s experience, opportunity, confidence, professional judgment, concerns, development needs, and reason for using AI.

What is the greatest risk of generational bias in AI adoption?

Leaders may unintentionally create the differences they expect to see. If one age group receives more encouragement, experimentation, training, or AI-intensive assignments, differences in proficiency can emerge that appear to confirm the original generational assumption.

References

Deloitte. (2024). 2024 Gen Z and Millennial survey: Living and working with purpose in a transforming world. Deloitte. https://www.deloitte.com/ie/en/services/consulting/perspectives/gen-z-millennial-survey.html

Gottfried, J., Bishop, W., Anderson, M., Faverio, M., Park, E., & McClain, C. (2026, June 17). How opinions and use of AI differ by age. Pew Research Center. https://www.pewresearch.org/internet/2026/06/17/how-opinions-and-use-of-ai-differ-by-age/

Maese, E. (2025, January 14). Americans use AI in everyday products without realizing it. Gallup. https://news.gallup.com/poll/654905/americans-everyday-products-without-realizing.aspx

Randstad USA. (2024, November 1). The generational divide in AI adoption. https://www.randstadusa.com/business/business-insights/workplace-trends/generational-divide-ai-adoption/