“Our older workers just won’t adapt to AI.”

I hear this statement constantly—in strategy meetings, hallway conversations, even from well-meaning L&D professionals. It’s become conventional wisdom in many organisations.

It’s also largely wrong.

Having worked with thousands of workers across age groups on AI adoption, I’ve found that age predicts AI capability development far less than most assume. What matters more is how we design and deliver the learning experience.

The Myth of Generational Digital Incompetence

Let’s address the assumption directly. Yes, younger workers often have more familiarity with consumer technology. Yes, they may pick up certain digital tools faster initially. But these differences don’t translate into insurmountable barriers for AI adoption.

Here’s what the research actually shows. AHRI workforce data challenges common assumptions about age and technology adoption.

Older workers often bring advantages:

• Deeper domain expertise to guide AI application
• Better judgment about when AI outputs are trustworthy
• Stronger foundational skills AI enhances
• More professional context for appropriate use
• Greater patience with learning curves

And the challenges attributed to age are often actually about:

• Poor training design, not learning inability
• Anxiety-inducing environments, not inherent limitations
• Insufficient time and support, not capability deficits
• Technology that isn’t designed for diverse users

When I see organisations where older workers are thriving with AI, the difference is almost always in how the organisation approached their development.

What Actually Creates Barriers

Before designing solutions, understand what creates barriers:

Anxiety and Threat

For workers who’ve built successful careers on current skills, AI can feel threatening. This creates anxiety that impedes learning. The brain doesn’t learn well in threat states.

This isn’t an age-specific phenomenon, but it may be more intense for workers who’ve invested more years in current approaches.

Jargon and Assumed Knowledge

Much AI training assumes baseline digital literacy and familiarity with tech terminology. Workers without this baseline feel lost from the beginning and disengage.

Pace That Doesn’t Allow Processing

Training designed for quick absorption doesn’t serve all learners. Some need more time to process, practice, and integrate new concepts.

One-Size-Fits-All Approaches

Training designed for digital natives fails workers with different learning preferences. The same program doesn’t work for everyone.

Lack of Relevance

Generic AI training doesn’t connect to specific job tasks. Without clear relevance, motivation suffers.

Insufficient Practice and Support

Brief training without follow-up leaves people unable to apply learning. This affects all ages but hits harder when the learning stretch is greater.

Design Principles That Work

When designing AI training for age-diverse workforces, these principles make the difference:

Start With Relevance

Before any tool training, establish why AI matters for this person’s specific work:

• What tedious tasks could be reduced?
• What quality improvements could be achieved?
• What new capabilities could be developed?
• What risks of not adopting exist?

Relevance creates motivation. Motivation drives learning.

Address Anxiety Explicitly

Don’t pretend AI anxiety doesn’t exist. Address it directly:

• Acknowledge the discomfort of learning new approaches
• Provide reassurance about job security where truthful
• Normalise the learning curve
• Create psychologically safe environments

People can’t learn effectively while managing unaddressed fear.

Build on Existing Competence

Older workers have expertise. Use it as the foundation:

• Position AI as enhancing existing skills, not replacing them
• Draw connections to familiar concepts and processes
• Use examples from domains they know well
• Emphasise judgment and expertise AI can’t provide

This framing reduces threat and activates existing knowledge.

Teach in Plain Language

Avoid jargon. Explain concepts simply:

• “Tell the AI what you want” instead of “prompt engineering”
• “Check if the AI’s answer is right” instead of “validate outputs”
• “The AI guessing based on patterns” instead of “probabilistic models”

Technical language creates barriers. Plain language enables learning.

Provide Adequate Pace and Repetition

Allow enough time for concepts to sink in:

• Smaller chunks of content
• More practice time between concepts
• Repetition of key points
• Multiple exposures to important skills

Fast-paced training optimises for coverage. Appropriate pacing optimises for learning.

Offer Multiple Learning Modalities

People learn differently. Offer options:

• Written guides for those who prefer to read
• Video demonstrations for visual learners
• Hands-on workshops for experiential learners
• One-on-one coaching for those who need individual attention

More options serve more learners.

Create Peer Support Structures

Peers can be powerful learning supports:

• Pair less confident learners with more confident ones
• Create learning circles of similar-level learners
• Enable ongoing peer help-seeking
• Build community around shared learning

Peer support often feels safer than formal support channels.

Provide Extended Practice Opportunities

Learning takes longer than a single session:

• Follow-up practice exercises
• Regular check-in sessions
• Ongoing access to support
• Gradual increase in complexity

Extended practice builds actual capability.

Practical Program Approaches

These design principles translate into specific approaches:

Tiered Learning Paths

Offer entry points at different levels:

• Foundation path: For those starting from scratch, moving slowly through basics
• Standard path: For those with some digital comfort, proceeding at moderate pace
• Accelerated path: For confident adopters ready to move quickly

Let learners self-select or be guided to appropriate paths.

Small Group Workshops

Smaller groups allow:

• More individual attention
• Safer question-asking
• Pace adaptation
• Peer relationship building

Eight to twelve people per facilitator is often ideal.

Practice Labs

Dedicated practice time separate from regular work:

• Sandbox environments for experimentation
• Structured exercises with clear objectives
• Facilitator available for help
• Low-stakes atmosphere

Practice labs give people space to develop competence before applying it.

Follow-Up Support

Training isn’t complete when the session ends:

• Scheduled follow-up sessions
• Drop-in office hours
• Peer help networks
• Easy access to experts

Ongoing support converts initial learning into lasting capability.

Mentoring and Coaching

For learners who need more support:

• One-on-one coaching sessions
• Assigned mentors for ongoing help
• Regular check-ins on progress

Individual attention can make the difference for learners who struggle in group settings.

Working With Resistance

Some workers will resist AI adoption regardless of training quality. Approaches that help:

Understand the Resistance

Resistance often has legitimate roots:

• Fear of appearing incompetent
• Concern about job security
• Scepticism about AI hype
• Preference for proven approaches

Understanding the source enables targeted response.

Find Motivating Entry Points

Different motivations work for different people:

• Reducing tedious work
• Improving work quality
• Keeping up with industry
• Helping colleagues
• Personal interest

Find what matters to the individual.

Start With Quick Wins

Early success builds confidence:

• Simple, high-value applications
• Tasks where AI clearly helps
• Visible improvements

Success experiences overcome resistance more than arguments do.

Involve Influential Peers

Peer influence matters:

• Identify respected older workers who adopt successfully
• Make them visible examples
• Have them help support others

When peers succeed, it demonstrates possibility.

Be Patient

Some people take longer. That’s okay. Maintain support and invitation without pressure that triggers defensive resistance.

What Managers Should Do

Direct managers play a crucial role:

• Protect learning time so development actually happens
• Acknowledge the challenge without condescension
• Provide encouragement and recognise effort
• Model their own learning visibly
• Create safety for questions and mistakes
• Connect to relevance for specific job tasks

Manager support often determines individual adoption success.

The Competitive Advantage of Inclusive Upskilling

Organisations that successfully upskill their entire workforce—not just the digitally native portion—gain significant advantages:

• Deeper experience base enhanced by AI capability
• Less disruption from losing experienced workers
• More comprehensive adoption across functions
• Stronger organisational resilience

The investment in inclusive AI upskilling pays returns.

The Real Question

The question isn’t whether older workers can learn AI tools. They can.

The question is whether organisations are willing to design learning experiences that serve diverse learners rather than optimising for the easiest-to-train segment of the workforce.

That’s a choice about investment, design, and inclusion.

Choose well.