• Event: Learning Technologies UK 25
• Date: 23 April 2025
• Speaker: Simon Brown, Partner, EY
• Chair: Henriette Kloots, Senior L&D Consultant, Interlocked
• Estimated read time: 9 minutes

 

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Quick read summary

This session explored how organisations can move beyond surface level experimentation with AI to build a coherent, enterprise wide AI learning strategy. It examined how AI is reshaping work, what skills gaps are emerging, and how learning functions must adapt their operating models in response.

The discussion matters now because AI adoption is accelerating faster than most organisations’ ability to build capability. Skills gaps, not technology, are becoming the primary constraint on value creation.

Readers will gain a practical view of how a large, complex organisation is approaching AI skills at scale, what this means for learning design and operating models, and how L&D leaders should rethink their role as AI becomes embedded in everyday work.

 

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Why AI learning strategy must be deliberate, not reactive

AI adoption is often framed as a tooling problem, but the session made clear that the real barrier is skills. Research referenced during the session showed that lack of skills and training is consistently cited as the biggest obstacle to AI adoption across industries.

Simon Brown argued that this shifts responsibility squarely onto learning and development. If organisations invest heavily in AI tools without a corresponding learning strategy, the tools will not deliver meaningful impact.

He also challenged traditional views of risk. In many organisations, maintaining the status quo is treated as low risk, while experimentation is seen as dangerous. In an AI driven environment, this logic no longer holds. Failing to act becomes the greater risk as AI reshapes roles, productivity and client expectations.

 

Understanding how AI changes work, not just learning

A key framework discussed examined how work has evolved across time and location, from synchronous and co located activity to asynchronous, distributed work. AI adds another dimension to this model, introducing machine contribution alongside human effort in every context.

This shift has significant implications for learning strategy. If work itself is changing, learning cannot simply be optimised for efficiency. It must help people adapt to new ways of working where humans and AI systems collaborate continuously.

Survey data shared in the session highlighted uneven adoption and impact across regions, industries, gender and generations. These differences reinforce the need for learning strategies that are inclusive, segmented and grounded in real work contexts rather than generic AI awareness programmes.

 

Building AI capability at scale requires segmentation

A central theme of the session was that not everyone needs the same AI learning. A single curriculum cannot serve an entire workforce effectively.

The approach described segmented learners into distinct groups, including those who need foundational understanding, those who must apply AI tools in their roles, those shaping and selling AI solutions, and those building and engineering them. Each group requires different depth, pacing and learning formats.

This segmentation also shaped learning design decisions. Traditional course development cycles were described as too slow for AI related topics, which risk becoming outdated before launch. Instead, learning teams must prioritise speed, relevance and sufficiency over perfection.

 

Data, accreditation and relevance drive engagement

One of the strongest signals from the session was the role of accreditation and application in driving learning uptake. AI learning initiatives that combined structured learning with practical application and visible recognition achieved significantly higher participation.

Badging was positioned not as a vanity exercise, but as a way to create clear learning pathways, motivate completion and encourage application in real work. Making learning relevant to specific roles, use cases and tools consistently outperformed generic programmes.

The session also emphasised the importance of measurement. Completion data, benchmarking against peers and evidence of skill progression were used to assess whether learning was translating into capability, not just activity.

 

Practical application for L&D leaders

Questions leaders should be asking

• Where are AI skills a constraint on strategy execution today?
• Which roles require deep AI capability, and which need practical fluency?
• How quickly can learning be updated as tools and use cases evolve?

Signals to watch in the organisation

• Uneven adoption of AI tools across roles or demographics
• High interest but low application of AI learning
• Learning content becoming outdated faster than it can be refreshed

Common pitfalls

• Treating AI learning as a one off programme
• Over investing in generic content with limited role relevance
• Optimising for content volume rather than learning impact

What good looks like in practice

• Clear segmentation of AI learning by role and depth
• Fast, iterative learning design with strong links to real work
• Accreditation tied to application, not just completion
• Leaders actively modelling AI use in their own work

 

Key takeaways

• AI learning strategy must be treated as a core enabler of business strategy, not a supporting activity
• Skills gaps, not technology, are the primary barrier to AI adoption
• Learning design must prioritise speed, relevance and application over perfection
• Segmentation is essential, not everyone needs the same AI capability
• The role of L&D will evolve as AI becomes embedded in everyday work

 

Quote of the session

“In many areas, not doing anything is really risky.”

Simon Brown, Partner, EY

 

Final thoughts

The session made clear that AI is not just another technology wave for learning teams to respond to. It challenges fundamental assumptions about how work is done, how skills are built and what the learning function exists to deliver.

As AI increasingly performs tasks alongside humans, learning leaders must shift focus from content production to capability building, from optimisation to reinvention. The organisations that succeed will be those that treat AI learning strategy as a long term operating model change, not a short term response to hype.

 

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Speakers

Simon Brown, Partner, EY. Simon Brown leads global learning and development at EY, supporting workforce capability and skills development at scale across the organisation.

Henriette Kloots, Senior L&D Consultant, Interlocked. Henriette Kloots is an organisational psychologist and senior learning and development consultant, focused on connecting business strategy and learning to drive meaningful organisational change through impactful learning interventions.

 

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