One Size Doesn’t Fit All – Tailoring AI Adoption to Generations and Roles

Article 4 of 4

In our previous articles, we explored the skills, behaviours, and learning cultures needed to thrive in an AI-enabled automotive business. But there’s one more critical factor to consider: not everyone starts from the same place.

AI adoption isn’t just a technical rollout, it’s a human transition. And like any meaningful change, it must be tailored to the people it affects. That means recognising the different needs, expectations, and motivations of employees across generations and experience levels.

AI is like a new team member and your people need to be introduced to it as individuals.

Why Personalisation Matters in AI Adoption

AI is often introduced as a universal solution, but its impact is anything but uniform. A senior leader evaluating AI’s strategic potential will engage with it very differently than a Gen Z technician just entering the industry. And a mid-career manager balancing legacy systems with new tools will have different concerns again.

If we treat AI adoption as a one-size-fits-all initiative, we risk disengagement, resistance, or worse, missed opportunities for growth.

Instead, we need to empathetic of where people are starting their personal journeys. That means understanding both their generational context and their professional experience.

Generational Perspectives: What Shapes Engagement with AI

• Gen Z (1997–2012)
  Digital natives, but often new to the industry.
  Needs: Foundational industry knowledge, critical thinking development, and mentorship to avoid over-reliance on AI tools.
  Opportunity: Pairing their tech fluency with structured learning builds confidence and capability.

• Millennials (1981–1996)
  Tech-savvy and ambitious but navigating disruption.
  Needs: Support in balancing career growth with evolving tools and expectations.
  Opportunity: Integrating AI literacy into development plans helps them lead change, not just adapt to it.

• Gen X (1965–1980)
  Experienced, more likely in an operational or leadership role.
  Needs: Help integrating AI with legacy systems and workflows.
  Opportunity: Position AI as a tool to enhance, not replace, their expertise.

• Boomers (1946–1964)
  Deep institutional knowledge but may prefer traditional methods.
  Needs: Clear value propositions and simplified tools.
  Opportunity: Personalised training and peer support can build confidence and trust.

Experience-Based Needs: Business Context Shapes Readiness

Alongside potential generational approaches it is also useful to consider people’s experience within the automotive business itself.

• Entering the Workforce for the First Time
  New starters with little or no professional experience face a unique challenge: many entry-level tasks, such as admin, scheduling, or basic data handling, are potentially being automated by AI.
  Support: Organisations must rethink onboarding and development strategies, offering structured learning, shadowing opportunities, and project-based experiences that build real-world capability.
  Implication: This shift affects talent pipelines and requires intentional design of roles that still allow new entrants to learn, contribute, and grow.

• New to the Automotive Business
  Whether early in their career or transitioning from another industry, these individuals are still building confidence and contextual understanding.
  Support: Structured onboarding, clear guidance on how AI fits into the business model and mentoring to help them navigate both the technology and the culture.
  Opportunity: Pairing their fresh perspective with foundational learning accelerates integration and innovation.

• Established in Role, Adapting to AI
  These individuals know the business but are adjusting to how AI is changing their workflows, tools, or customer interactions.
  Support: Role-specific learning paths, hands-on training, and time to experiment with AI in real scenarios.
  Benefit: AI can reduce admin burden, freeing them to focus on higher-value tasks like coaching, customer engagement, or strategic planning.

• Senior Leaders with Deep Industry Experience
  These individuals are focused on long-term strategy, ethical oversight, and leading transformation.
  Support: Executive coaching on AI strategy, scenario planning, and responsible innovation.
  Benefit: AI can enhance decision-making and foresight—if leaders are equipped to interpret insights and guide their teams through change.

The Human Advantage: Making AI Work for Everyone

AI has the potential to elevate every role in the business, but only if we implement it with empathy and intention. That means:

• Designing learning and adoption strategies that reflect people’s starting points.

• Creating space for dialogue, experimentation, and feedback.

• Recognising that AI is not just a tool, it’s a shift in how people think, work, and grow.

When we personalise AI adoption, we don’t just improve uptake, we build trust, unlock potential, and create a culture where everyone can thrive.

Wrapping Up the Series

Across this series, we’ve explored the four pillars of AI readiness:

1. Skills – What people need to know and be able to do.

2. Behaviours – How people need to act and adapt.

3. Learning – How people grow and stay relevant.

4. Personalisation – How we meet people where they are.

Together, these pillars form the foundation of a people-first, AI-enabled automotive business, one where technology supports human potential, not the other way around.