AI in L&D: Beyond the Hype and Into Real Impact

I recently had the chance to hear a compelling live discussion about the role of AI in Learning & Development. Four prominent voices: David James, Egle Vinauskaite, Ross Stevenson, and Peter Manniche Riber, brought clarity and honesty that are still rare in our field. Their message was clear: AI is no longer a distant future; it’s already transforming how L&D operates, thinks, and creates value.

However, as the speakers often emphasized, the profession is at a true turning point. AI is omnipresent, on every panel, at every conference, in every team meeting. But beneath this buzz, many organizations remain unsure how to distinguish between novelty and strategic value. This conversation cut through that confusion, refocusing on what is real, actionable, and already achievable today with the systems most teams use.

Where AI Is Actually Delivering Value

Despite the excitement around AI, its most precise and consistent impact remains focused on three areas: administration, content creation, and learning design. These might not be glamorous fields, but they are essential. AI accelerates content drafting, supports research, analyzes learner data, and reduces friction throughout the workflow. This isn’t just about improving efficiency. A qualitative shift is emerging. AI enables content that’s more relevant, targeted, and adaptable. It allows L&D teams to analyze years of learner performance data in seconds. It encourages us to think more deeply about the context and audience instead of defaulting to generic, one-size-fits-all solutions.

From Information to Practice: A Meaningful Shift

One of the most insightful parts of the discussion was the collective agreement that the most exciting AI use cases are not content-related at all. They are practice-related.

Conversational agents, simulations, and coaching bots are creating safe, realistic environments where people can practice difficult situations, get feedback, and develop real-world skills. This represents an essential shift from static knowledge sharing to dynamic skill-building. Many panelists believe this will have the most significant long-term impact.

A Real Inflection Point in Adoption

For the first time, a majority of organisations report not only experimenting with AI but actively using it. This represents more than a statistical milestone. AI is a general-purpose technology: it does not arrive with a user manual. Its value emerges through collective learning, shared experimentation, and cross-team storytelling.

In other words, the more we use it, the more we understand what actually works.

Efficiency vs. Effectiveness: The Strategic Question

A recurring theme was the distinction between efficiency and effectiveness. AI can make us faster. It can produce more content in less time. But speed alone does not solve a business problem.

The strategic question is no longer “How can AI help us create more courses?”

It is:

“What business problem are we trying to solve, and how can AI improve the process that leads to better performance?”

This requires discipline. AI is a powerful tool, but it is not a strategy.

The Five Opportunity Areas That Will Redefine L&D

One panelist articulated five opportunity areas that together form a new architecture for AI in learning:

1. Content: not for volume, but for relevance.

2. Practice: replacing static consumption with meaningful, feedback-rich experiences.

3. Memory: transforming scattered knowledge into an organised, accessible system.

4. Context: supporting people in the flow of work, not outside it.

5. Intelligence: understanding and managing skills as strategic business assets.

These last three: memory, context, and intelligence, represent the more profound transformation as they shift L&D from a content provider to a strategic enabler of organisational capability.

The Reality Inside Organisations

The conversation also acknowledged a truth many prefer to ignore. Most L&D teams do not operate in greenfield environments. They work within legacy ecosystems, strict security policies, and corporate governance. They cannot simply adopt every promising tool that trends on social media.

In these environments, AI adoption depends less on creativity and more on organisational maturity. It depends on understanding the capabilities already available, often underused within platforms such as Microsoft 365 and its copilots.

Leadership, Governance, and the Need for Collective Ownership

AI in L&D cannot be delegated to a single “AI specialist” or a small technical team. It touches strategy, ethics, data, and culture. It demands leadership engagement, cross-functional ownership, and a willingness to rethink how the organisation learns and performs. Without this, AI remains a pilot, a prototype, a possibility that never scales.

Start With Problems, Not Tools

If one message echoed across the entire conversation, it is this:

L&D must begin with performance, not technology.

Define the problem, understand the context, map the workflows. Then select the technology, AI or otherwise, that genuinely improves the process. Sometimes the simplest tool is enough. Sometimes AI is the unlock we needed. But in every case, the craft of L&D remains rooted in understanding people, work, and performance. AI expands our capabilities, but it does not replace the thoughtful, human judgment at the heart of our profession.