AI Fluency vs AI Skills: What Udemy’s Global Trends Report Reveals About the Future of Work

Submitted by Kerry Summers on 5 December 2025 - 11:39am

In this conversation, Gráinne Wafer, Global Head of Field Enablement at Udemy Business, unpacks the key insights from Udemy’s Global Learning & Skills Trends Report, revealing why AI fluency and not just technical skill is rapidly becoming the new operating system for modern enterprises.

Drawing on data from thousands of organisations, she highlights:

The urgent need for immersive, in-workflow learning
The pivotal role of leadership clarity and trust
The strategic reinvention of L&D as a driver of performance and culture

For senior leaders, the message is clear: AI capability must be built deliberately, cross-functionally, and continuously if organisations are to keep pace with accelerating change.

AI Fluency and AI Literacy: Why are They Important?

One of the clearest insights Gráinne shares is the fundamental distinction between AI skills and AI fluency. Many organisations still treat AI as a technical training challenge, teaching code, tools, and data analysis in the hope that competence will follow. But as she explains, this is a narrow approach.

Teaching people how to use an AI tool is not enough. What organisations actually need, she says, is the ability to:

Adapt
Experiment
Collaborate
Make decisions

with AI embedded into the fabric of daily work. AI fluency and AI literacy is about cultivating a shared language, a level of comfort, and an organisational reflex that allows teams to operate with AI as naturally as they use email or search engines.

‘The core challenge isn’t teaching people how to use the technology. It’s really much bigger. It’s about rewiring the enterprise to play, to experiment, and to find ways to incorporate AI into workflows.’
-Gráinne Wafer, Global Head of Field Enablement, Udemy Business

This is why Gráinne describes AI fluency as ‘a new way of operating, not a technical goal.’ It demands organisational rewiring, not just training hours.

The pace of change makes this even more urgent. Udemy’s platform data shows staggering growth in AI learning consumption:

GitHub Copilot use is up 13,000%
Microsoft Copilot use is up 3,500%
Overall enrolments in AI-related courses are multiplying tenfold

These aren’t just indicators of interest, Gráinne tells us that they are signals that the way work is delivered is evolving at unprecedented speed.

For the C-suite, the strategic takeaway is simple:

‘If AI is your finish line, you’ve already lost the race.’
- Gráinne Wafer, Global Head of Field Enablement, Udemy Business

How Can Immersive Learning Optimise AI Skills Development?

A second major theme in Udemy’s report challenges traditional approaches to learning, suggesting that skills development, especially for AI, cannot be built through instruction alone. It requires immersion.

Gráinne draws a compelling parallel with Udemy’s partnership with McLaren; Formula 1 drivers do not become race-ready by sitting in classrooms; they do it in simulators, on the track, and under real-time feedback.

Gráinne tells us that Carnegie Mellon research reinforces this: learners who practice in context with instant feedback learn three times faster than those who follow traditional lecture-based methods.

‘Employees build their AI skills the same way, by experimenting in real workflows, getting immediate feedback and adjusting quickly.’
- Gráinne Wafer, Global Head of Field Enablement, Udemy Business

For senior leaders, this requires rethinking how learning environments are designed. Organisations, she highlights, must build cultures where:

Experimentation is encouraged:

Trying and failing is not penalised
Learning is measured through improved performance

AI capability will not come from courses alone; it will come from continuous, iterative application. As Gráinne says:

‘Failing fast is a quick way to learn.’
- Gráinne Wafer, Global Head of Field Enablement, Udemy Business

Leadership, Trust and Ethics: Enabling AI Transformation

Perhaps the most critical insight from Gráinne’s interview is the role of leadership in enabling AI transformation; if AI fluency is the new operating system, leaders are the ones who configure, maintain, and model it.

And the data highlights a clear concern:

While 88% of employees believe effective leadership is essential for successful AI initiatives, fewer than half believe their leaders are ready for the era of AI.

Gráinne again turns to Formula 1 for a helpful analogy; during a pit stop, engineers and crew members operate within split-second windows. They do not wait for step-by-step instructions; they act with confidence because priorities are clear, parameters are defined, and trust is high. Teams can move at speed because leadership has already done the work of creating clarity.

Translating this to corporate life:

“Leaders set the clear priorities; safety, performance, strategy; and then empower the team to act fast within those boundaries.”
- Gráinne Wafer, Global Head of Field Enablement, Udemy Business

Leaders, Gráinne says, must ensure that teams understand where AI can be used, where it must not be used, and how to escalate issues. They must also recognise and champion internal experts, those early adopters who naturally think in AI-first terms and can help accelerate adoption across the organisation.

Leadership readiness, she argues, is not an optional extra. It is the foundation for responsible scaling:

‘Scaling AI isn’t about training individuals in isolation. It’s about scaling leadership with clarity and trust so people can act responsibly at speed.’
- Gráinne Wafer, Global Head of Field Enablement, Udemy Business

The Strategic Future of L&D with AI

While much discussion around AI has focused on its potential to replace L&D roles, Gráinne sees the opposite. Udemy’s report shows that L&D is not being disrupted out of existence; it is being elevated into a far more strategic function.

The future tasks of L&D, Gráinne says, are deeply analytical and business-critical:

Diagnosing skills gaps
Embedding learning into workflows
Driving behaviour change
Shaping culture
Integrating new technologies
Supporting transformation

AI will automate content generation, but it will not automate organisational change. That is where L&D becomes indispensable. As Gráinne puts it, L&D must shift ‘from learning to performance.’

Gráinne argues that training hours and completion rates aren’t compelling measures, and they never have been. The question Gráinne says organisations need to be asking is no longer ‘did people learn?’ but ‘did their learning move the business forward?’

This is where Gráinne offers her most provocative insight. She argues that while the classic Kirkpatrick Model of learning evaluation is still relevant, it has inadvertently damaged L&D by focusing attention on metrics that matter least. Arguably this damage comes, not from the Kirkpatrick Model itself, but from an outdated, limited interpretation that anchors L&D to easy metrics instead of meaningful outcomes. Today, L&D must demonstrate:

Behaviour change
Measurable performance shifts
Alignment with strategic priorities
Contributions to growth, efficiency, or risk reduction

Reaction scores and learning assessments are easy to track, yet they rarely connect to the organisation’s true strategic priorities. For Gráinne,

‘We need to stop measuring what’s easy and start measuring what truly matters to the strategy of the organisation.’
- Gráinne Wafer, Global Head of Field Enablement, Udemy Business

She argues that executives should expect L&D to measure what truly matters: growth, cost reduction, and risk mitigation. That is the level at which L&D earns and keeps its seat at the table.

Your AI Fluency Journey: Where Should You Start?

Gráinne offers a practical blueprint for organisations at the beginning of their AI fluency journey, and it starts with clarity.

Clarity

Leaders, she said, must define what AI fluency and AI literacy means for their business, where their workforce stands today, and where they need to be tomorrow. Without this, efforts default to generic training that rarely delivers sustained impact. For Gráinne,

‘Learning should always be fun, rewarding, and innovative; it’s how the behaviour and the culture really stick.’
- Gráinne Wafer, Global Head of Field Enablement, Udemy Business

AI in context

The next step Gráinne outlines, is to ensure that AI is learned in the context of real roles. Teams must understand how AI affects the specific work they do, not the abstract work they might do someday:

‘A driver always learns faster in one lap of live feedback than in hours of theory.’
- Gráinne Wafer, Global Head of Field Enablement, Udemy Business

Cross-Functional Learning

This is followed by cross-functional learning, encouraging marketing to learn from engineering, or HR from product, because the future of AI is interdisciplinary.
Critically, Gráinne also says that leaders must invest in their own readiness. They must become ‘architects of transformation’ who set the vision, partner cross-functionally, champion internal experts, and create psychological safety so teams can experiment without fear.

AI fluency is a whole-organisation capability. It is cultural, operational, strategic, ethical, and deeply human. The companies moving fastest today, Gráinne says, are those where leaders recognise that AI transformation is not a task for technologists, but rather, she says, it is a responsibility of the entire executive team.

Your AI Strategy for 2026

The Udemy Global Learning & Skills Trends Report reveals a clear inflection point: AI is accelerating; learning consumption is exploding; employees are experimenting faster than their leaders; and the organisations that treat AI as a one-off challenge are already being left behind.

The competitive advantage now belongs to those who recognise that AI fluency requires:

Cultural transformation
Leadership clarity
Hands-on application
Cross-functional collaboration
Continuous adaptation

Both Gráinne and Udemy’s report highlight that leaders must model AI behaviours, invest in immersive learning environments, focus on performance over activity, and ensure their organisations become fluent, not merely trained.

Gráinne’s final message is clear: those who treat AI as the operating system of the future will build organisations capable of continuous reinvention. Those who don’t will find themselves struggling to catch up in a world that has already moved on.

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