There is no doubt that artificial intelligence dominates the conversation right now. New tools, products, and applications launch almost daily. Research papers, opinion pieces, and headlines flood our feeds. You can hardly open a newspaper without seeing AI mentioned — often with a sense of urgency, excitement, or even fear.

Keeping up is hard. Even if you try.

I consider myself someone who actively follows developments in this space, and still I struggle to stay fully on top of what is happening. The pace is relentless. The volume of opinion articles and sales pitches is enormous. And separating meaningful insights from poorly informed claims and short-lived hype has become a challenge in its own right.

So instead of adding more noise, I want to try something different: step back and focus on what truly matters for L&D.

One thing I want to state clearly upfront. AI continues to surprise me — in a positive way. You will hear many people say they are disappointed in AI, frustrated by unreliable outputs, hallucinations, or inconsistent quality. I understand that frustration. But I strongly believe the core issue is not the technology itself. My position is simple:

AI as a technology will continue to evolve. As we continue to generate data, AI will continue to improve. And it will continue to amaze us in ways we cannot yet fully predict.

If we look beyond the hype and focus on what is actually happening inside organizations right now, I see two short-term trends that are particularly relevant for Learning and Development.

The first trend is that more and more organizations are deploying their own large language models. Not public, generic AI like ChatGPT or Claude, but AI that is trained on data that exists within the company: internal documents like procedures, policies, best practices, and marketing materials; data available in tools like Salesforce, SAP, Workday, and your learning platforms; knowledge repositories like SharePoint; and even emails and online conversations — naturally with consent of the participants.

The result is, in essence, all of the company's knowledge captured and made available through a single AI. This AI can then be embedded into internal tools and platforms, often powering chatbots that support employees in real time. What is the policy on taking leave? What is the procedure for this process? What are the latest product specifications? Questions like these get answered instantly, in context, at the moment of need.

I believe internal LLMs will create a fundamental shift in how employees learn and obtain performance support. Instead of going to a learning management system, browsing a catalogue, enrolling in a course, and completing a program, employees will increasingly ask a question at the moment of need, receive contextual and job-specific guidance, and get support while working — not before or after working.

In many cases, what we have traditionally labeled as "learning," "a course," or "a training" will be replaced by AI operating and interacting in the flow of work.

This will dramatically reduce the demand for traditional learning and development programs — and it will have a direct impact on L&D budgets and staffing. The programs most affected will be knowledge-based ones: compliance training, procedural training, and even topics like leadership and soft skills that are still largely delivered through passive, knowledge-based formats.

If we consider that roughly 70% of corporate L&D effort goes into knowledge-based programs — and that is a conservative estimate — then the implication is stark:

That number is meant to provoke, not to predict with precision. But the direction is clear. Knowledge-based learning is the first category that in-house LLMs will replace, because the alternative — asking a chatbot — is faster, cheaper, and more relevant than searching an LMS catalogue.

The second major trend is the rapid rise of agentic AI — AI systems that do not just respond to questions and prompts, but actively do things.

Across industries, organizations are building AI agents at astonishing pace to automate structured, task-driven processes. And L&D is no exception. Think about what this means for our domain. AI agents can be trained to perform many tasks that are currently run by L&D operations teams: simple reporting, program planning and scheduling, program administration. These are structured processes — and they are exactly the kind of processes agentic AI is well suited to automate.

I fully expect that many of these activities will increasingly be handled by AI — initially with human oversight, but over time with far less human intervention than we are used to today.

And yes, I expect content development and instructional design to be done by AI as well. In fact, a recent survey from Synthesia shows that 87% of respondents are already using AI — mainly for content creation, including voice generation, content and quiz drafting, video creation, and translation.

Now, I always look at the data behind reports like this. Synthesia is an AI content creation platform, and the survey had only 421 respondents, so we should be cautious about the numbers. However, research from Donald Taylor and Eglė Vinauskaitė — their report 2025: The Race for Impact — supports the same conclusion. That report explicitly states that AI is eroding the foundations of traditional L&D work, especially content creation. AI-generated content is good enough, orders of magnitude faster, and far cheaper than traditional approaches.

And this is happening before almost any L&D team has implemented agentic AI at scale

These developments have serious implications for L&D — and for you as an L&D professional. It is no longer a question of whether AI will change our field. It is a question of what the consequences are and how we can start preparing.

A significant portion of what we currently consider L&D work will either be replaced by AI-driven, just-in-time support or be automated through intelligent agents operating behind the scenes. For professionals whose work is heavily centered on operational, administrative, or content-driven activities, this is an uncomfortable reality. And it puts L&D in a tough spot — not because learning becomes less important, but because how learning happens is changing fundamentally.

The technical developments are currently faster than ever before, but it will take time for people and organizations to adapt. And that is where our opportunity sits. The question is not how to resist these changes, but how to reposition — both as an industry and as individual professionals.

Because while AI will take over much of what L&D does today, it will not replace the need for learning itself. The work changes. The need does not. The challenge is to find where L&D creates value that AI cannot — and to move there with intent.

That is exactly what the rest of this series will explore. In the next episode, I will share my view on the reimagined 70-20-10 model — and introduce a concept I call Boutique Learning, which I believe will become one of L&D's most important strategic assets.

You do not need to wait for the rest of the series to start preparing. Here are three things you can do this week.

I always reflect on how I learn and transpose that to how I think employees work and learn. And I have to be honest — I have not taken a formal training in many years. I use AI extensively to support my work and writing. But I have never done a course on AI. For me, AI is already replacing a lot of what we would traditionally call learning.

My first tip is to observe for yourself, over the next week, how and where AI is replacing situations where you would normally search the LMS, ask a colleague, check a document, or watch a tutorial — and instead use an AI tool to get the answer.

Once you see it, you cannot unsee it. You are experiencing the 70% shifting in real time. The question then becomes: which learning moments are quietly disappearing — and which new ones are emerging?

Take one recurring work question you have and improve the prompt, not the tool. Try adding context: "I work in L&D in a large organization." Add constraints: "Keep it practical, no theory." Add examples: "Use compliance learning as an example."

What you are doing here is training AI — even if it does not feel like it yet. The skill of prompting with intent will matter more than trying every new tool on the market. And it is a skill that directly prepares you for the world I will describe in later episodes, where L&D's role includes ensuring AI learns the right things.

Ask yourself one question at the end of the week: What part of my value comes from creating content — and what part comes from understanding how people actually learn and work?

No action is required here. Just reflection. Or an informal conversation with colleagues over coffee.

AI will keep getting better at producing things. The opportunity for L&D professionals is to step up the value chain — from producing programs to making sense of things. This mental shift prepares you for everything that follows in this series.