• Event: Learning Technologies UK 25
• Date: 23 April 2025
• Speaker: Egle Vinauskaite, Learning Strategist, Nodes
• Chair: Donald H Taylor, Chair, Learning Technologies
• Estimated read time: 14 minutes

 

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Quick read summary

This session examined how artificial intelligence is actually being used in learning and development today, not how it is marketed or imagined. It explored why most L&D teams remain focused on AI for content creation, why this is increasingly insufficient, and what more impactful uses look like in practice.

The discussion matters now because AI is already reshaping work, performance expectations and organisational speed. L&D risks falling behind if it treats AI as another productivity tool rather than a force that changes what people do, how value is created and how capability should be developed.

Readers will gain a clearer view of where AI creates real business value, how peers are using it beyond content, and what conditions need to be in place to move from efficiency gains to workforce impact.

 

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AI in L&D today, productive but narrow

Research referenced in the session shows that the dominant use of AI in L&D remains content related. Typical applications include drafting learning materials, outlining courses, writing quiz questions and summarising subject matter.

These uses are not inherently wrong. Content still plays an important role in building foundational knowledge and providing guidance. The issue is that content creation has been heavily commoditised by generative AI, while L&D continues to invest significant time and resource in producing it.

This creates a mismatch between effort and value. While AI makes content cheaper and faster to produce, the strategic importance of content as a differentiator continues to decline.

The more pressing question is why L&D has not moved its use of AI further into areas that directly affect performance, capability and organisational outcomes.

 

Why L&D struggles to move beyond content

Several explanations surfaced during the session.

Some are practical. Budget constraints, limited access to data, compliance concerns and a lack of AI skills all play a role. Others are organisational, including resistance to change, weak infrastructure or unclear ownership of AI initiatives.

There are also human factors. Many practitioners feel overwhelmed, unsure where to start, or lack the headspace to explore new approaches alongside day to day delivery.

However, the session argued that a deeper issue underpins all of these barriers. L&D often treats AI as an incremental tool rather than a transformational technology.

When AI is framed as just another system to optimise existing processes, its use naturally stays close to current activity. When it is understood as a force that changes how work is done, how value is created and how people progress, different questions start to emerge.

 

Forming a point of view on the future of work

A central argument of the session was that L&D cannot be proactive about AI without a clear point of view on how work itself is changing.

Examples from education illustrate the scale of disruption. Students already use generative AI routinely, while schools and universities struggle to adapt assessment models built on individual output. These graduates are entering the workplace with different habits, expectations and capabilities.

At the same time, AI is changing early career roles. Tasks that once helped people learn the basics of a job are increasingly automated or augmented. This raises questions about onboarding, skill development and how new entrants add value alongside AI.

Research discussed in the session also suggests that AI narrows performance gaps by raising the baseline. Lower performers benefit more than top performers, which has implications for expertise, progression and organisational hierarchy.

Together, these shifts challenge long held assumptions about learning, mastery and career development.

 

From content problems to people problems

The session made a clear distinction between solving internal L&D efficiency issues and addressing the problems employees actually face.

Many L&D teams use AI to improve their own productivity. While this is valuable, it often optimises processes that are becoming obsolete.

The more significant opportunity lies in supporting people who are navigating uncertainty, rapid change and evolving expectations. Common concerns raised include fear of skills becoming irrelevant, lack of clarity about future roles, and anxiety about performance in an AI enabled workplace.

These concerns matter because they are directly tied to business priorities. Organisations are focused on managing costs, driving growth and adapting quickly to change. L&D adds the most value when it helps the workforce deliver against these goals.

 

Where AI creates business value in learning

The session shared multiple examples of how AI has been used to address real business problems.

In one case, a professional services firm implemented a layered AI capability programme. This included introductory learning, advanced curricula, immersive bootcamps and internal knowledge sharing. The focus was not on awareness, but on enabling consultants to deliver client work more effectively.

Another example involved an enterprise software provider that reworked its certification process. AI enabled participants to practise complex client conversations repeatedly and receive immediate feedback. Human assessors then focused on deeper judgement and nuance. This approach improved pass rates and removed a major operational bottleneck.

In a consulting organisation focused on client retention, AI was used to analyse recorded client conversations and identify behaviours associated with high performance. Coaching was then targeted at these behaviours, allowing the organisation to track behaviour change and link learning activity to business outcomes.

These examples illustrate how AI can support performance, not just content delivery.

 

Enablement, not just upskilling or support

A key conceptual shift introduced in the session was the idea of enablement.

Traditionally, L&D has focused on upskilling or performance support. AI introduces a third option, where the technology takes on parts of the task itself.

In this model, people do not need to master every activity or rely on guidance in the flow of work. Instead, AI removes certain tasks entirely, allowing people to focus on higher value activity.

Understanding when to enable rather than train or support is becoming an important design decision for L&D leaders.

 

Moving from basic use to real impact

The session challenged the assumption that AI adoption progresses naturally from simple to sophisticated use cases.

Research cited showed no evidence of a smooth transition. Organisations that use AI for administrative tasks do not automatically move towards performance support, skills intelligence or strategic decision making.

Three conditions were identified as critical to making that shift.

First is basic AI readiness within L&D. This includes skills, confidence and awareness of where AI could add value.

Second is an open L&D mindset. This means being willing to challenge established practices, focus on performance outcomes and use content as a means rather than an end.

Third is business readiness. Advanced use cases require data, infrastructure, leadership support and a culture that allows experimentation without punishing failure.

Without these conditions, L&D risks becoming stuck in mature but low impact uses of AI.

 

Practical application, turning insight into action

Questions leaders should be asking

• Which workforce problems matter most to our business strategy right now
• Where is AI changing the nature or pace of work fastest
• Are we training people to do tasks that AI is already taking over

Signals to watch in the organisation

• Rising anxiety about relevance, skills or career progression
• Increased performance variance between teams using AI and those that are not
• Bottlenecks in decision making or capability deployment

Common pitfalls

• Treating AI tools as a strategy rather than outcomes driven enablers
• Automating complex learning design without improving quality or impact
• Optimising L&D efficiency while missing workforce transformation needs

What good looks like in practice

• AI initiatives linked directly to cost management, growth or adaptability
• Clear decisions about when to upskill, support or enable
• L&D working alongside the business, not in isolation, to solve performance problems

 

Key takeaways

• Most AI use in L&D remains focused on content, despite diminishing returns
• AI should be treated as a transformational force, not an incremental tool
• The biggest opportunity lies in solving workforce performance problems
• Enablement is emerging as a distinct alternative to training and support
• Progress depends on L&D readiness, business readiness and shared purpose

 

Quote of the session

“AI is not an incremental technology, it changes the way work happens.”

Egle Vinauskaite, Learning Strategist, Nodes

 

Final thoughts

AI is already reshaping work, expectations and organisational speed. L&D cannot afford to respond only by producing content more efficiently.

The choices made now, about what problems to solve and how AI is used, will shape the future relevance of the function. There is no automatic path forward. L&D can do nothing, do a little, or lean in fully.

Those that focus on performance, enablement and business impact are far more likely to meet the moment.

 

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Speakers

Egle Vinauskaite, Learning Strategist, Nodes. Egle works with organisations to understand how AI is actually being used in learning and development and what this means for workforce capability.

Donald H Taylor, Chair, Learning Technologies. Donald is a long standing commentator and researcher on trends in workplace learning and development.

 

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