My proposition is simple, yet far-reaching: in the future, the 70% will largely become AI-driven learning.

At its core, this means elevating learning on the job by embedding AI-powered performance support directly into people's workflows.

Think about how employees currently look for help. They search Google. They browse YouTube. They dig through SharePoint pages. They scan internal knowledge bases or LMS catalogues. When you think about it, this process is extraordinarily inefficient — time-consuming, with no guarantee of finding what you actually need. People rarely need an entire 20-minute video. They need the two minutes between 16:40 and 18:20. They don't need a full e-learning module. They need one paragraph, one example, or one visual. Most of the time, far more effort goes into finding and consuming information than into actually applying it.

This is where AI changes everything. AI can dramatically reduce this friction by guiding employees to the right answer immediately, summarizing and contextualizing information, filtering out what is irrelevant, and delivering support at the moment of need.

The real power of AI goes beyond efficiency. AI has the potential to move learning from reactive to proactive.

Imagine an AI system integrated into a sales platform like Salesforce. Instead of waiting for someone to ask for help, it detects patterns: delays in certain process steps, repeated errors, unusual behaviors compared to peers. Based on those signals, the AI might intervene: "It looks like this step is taking longer than usual. Would you like some guidance or examples?"

We are not fully there yet, but this is clearly the direction of travel. Learning becomes embedded, contextual, and almost invisible. It happens because of work, not around work.

What makes this truly different from earlier interpretations of the 70% is that AI will eventually take on the full learning and development cycle — at an individual level. I first made this prediction at the Learning Technologies Conference in London back in 2018.

That includes identifying learning needs person by person, designing the appropriate intervention, generating or curating content, deploying it through the most effective channel — email, chat, voice, virtual assistant — and analyzing whether performance actually improved afterward.

This is a significant shift. In the traditional view of the 70%, L&D still plays a central role: designing performance support systems, creating content, managing structures and processes. In the AI-driven version, much of this becomes fully or largely automated.

That does not mean L&D disappears. But it does mean that this part of the learning model will no longer be the primary space where L&D differentiates itself. And that brings us to the remaining 20% and 10% — because this is where L&D's future relevance, value, and strategic position truly come into play.

In my view, this part of the model remains remarkably close to the original interpretation: social learning. Humans are, by nature, social learners. We learn constantly from others — often without realizing it. In meetings, in informal conversations, through observation and collaboration. I see this very clearly with my own children: they learn new skills while playing video games with friends, which for them is a deeply social activity.

Social learning does not need to be invented. It already exists. What will change is how visible, intentional, and supported it becomes.

Much of today's social interaction at work already happens in digital environments: online meetings, one-on-one conversations in Teams, collaborative work on documents and deliverables. Because these interactions are digital, they generate data. We already see this through automatic transcriptions, summaries, and action lists in tools like Microsoft Teams.

This opens up a fascinating opportunity. Imagine finishing a meeting and receiving personal, private feedback from AI: What did I do well in this conversation? Where could I improve? How effective was my communication? How inclusive was my behavior? What could I try differently next time?

Suddenly, social learning becomes conscious learning. Reflection is no longer dependent on memory or intuition — it is supported by evidence and feedback.

However, this also means that, as with the 70%, much of the work currently performed by L&D in facilitation and coaching will be taken over by AI. My expectation is that only the best facilitators and coaches — those who clearly demonstrate their added value — will remain, but in reduced numbers.

Let me be very clear: this is a sensitive area. AI-enhanced social learning only works if it is built on trust. It requires individuals to explicitly consent to how their data is used. It requires absolute clarity that confidential conversations are not analyzed for performance appraisal, personal discussions are not used to predict attrition or risk, and insights are used to support the individual — not to control them.

If I have a confidential conversation with a manager or colleague, the content of that conversation should never resurface in a way that could harm me. That boundary must be non-negotiable.

At the same time, there is room for aggregated, anonymized insights — just as we already use aggregated people data today. When done properly, those insights can help L&D, HR, and leaders make better decisions without compromising individual privacy.

The key message is simple: if AI-enhanced social learning is part of your strategy, data privacy and security must be one of your explicitly managed risks. Handled poorly, it will fail immediately. Handled transparently and ethically, it can be incredibly powerful.

This is the part I deliberately no longer call formal learning. Instead, I call it Boutique Learning. And for me, this represents a massive upgrade.

I strongly believe that much of what we currently label as formal learning will move into the 70% and be handled by AI. Especially knowledge-heavy, generic programs. Take compliance learning as an example. I do not believe we will have traditional compliance courses in the future. Instead, you might receive a prompt at the moment of risk, be presented with a short scenario and asked to respond, or receive contextual guidance when handling sensitive data. Compliance becomes embedded in daily work — personalized, relevant, and unavoidable. Exactly where AI excels.

And if we are honest, a very large portion of today's learning catalogue — sometimes 70 to 80% — consists of generic knowledge programs. That generic nature is one of the main reasons why learning impact is often disappointingly low. All of that content belongs in the AI-driven 70%.

I deliberately use the word "boutique" because of the image it evokes. Think of the small French bakery competing with large supermarkets. The bread in the supermarket is cheaper, more convenient, and available everywhere. And yet, the boutique bakery continues to exist — and thrive. Why? Because the quality is exceptional. It fulfills something fundamental. It is part of a social and cultural ritual. It has character, history, and craftsmanship. It offers an experience you simply cannot get elsewhere.

Boutique Learning is exactly that. It is high-quality, deeply contextual, highly personalized, focused on strategic and critical skills, and designed as a memorable — often transformational — experience. The closest equivalent today is probably top-tier leadership development: the kind where senior leaders walk away feeling they have had a genuinely life-changing experience.

Boutique Learning represents a unique opportunity for L&D. It brings the human dimension into an increasingly AI-driven world. It addresses skills that are too nuanced, too strategic, or too rare to teach through AI — sometimes simply because there is insufficient data, or because the topic is so classified that the organization does not want to expose it to an internal language model.

Boutique Learning allows L&D to excel at what it does best: articulating value where AI cannot replace human judgment, experience, and interaction. I firmly believe Boutique Learning will become one of the core businesses of L&D going forward.

But there is a condition. Boutique Learning is more expensive. It requires significant investment. And therefore, quality must be exceptional. That means L&D must be very explicit about what participants gain, what the organization gains, and why the investment is justified. And that is exactly where evidence comes in. Learning analytics becomes not a "nice-to-have" but an absolute necessity.

You do not need to wait for perfect AI, new org charts, or a polished strategy deck. There are concrete steps you can take today to prepare for the AI-driven 70-20-10.

If 70 to 80% of your catalogue is generic knowledge, you are already carrying technical debt. And you are spending time, effort, and money on things that most employees might already be asking ChatGPT. So why keep investing?

Start by labeling your catalogue honestly, using three categories: "AI-suitable and generic," "Contextual but reusable," and "Strategic and boutique." Then begin sunsetting or freezing low-impact content instead of updating it endlessly. For every program, ask one uncomfortable question: if AI could answer this better at the moment of need, should this program still exist?

Social learning already happens. AI is already supporting parts of it. The challenge is to surface it, make it visible and explicit, without breaking trust.

Start small and ethical: use AI summaries for personal reflection. Experiment in areas where AI might already be involved — AI-supported meeting reflections, feedback on communication patterns, coaching prompts after collaboration moments. At the same time, define your clear red lines: no performance appraisal use, no hidden monitoring, no individual data reuse without consent. Trust is the operating system of AI-enhanced social learning. Without it, nothing else works.

Stop spreading your budget thin. Boutique Learning only works if it is exceptional.

Begin by identifying skills that are strategic, rare, high-risk, or deeply contextual — these are your candidates for Boutique Learning programs. Reflect on your criteria: should these experiences be fully human-to-human? Immersive? Challenging? Unforgettable? Probably all of these. And define success before you start designing: What should change in behavior? What decisions should improve? What performance must move? Boutique Learning is not "premium content." It is strategic capability building.

If you do not design for data, you limit what AI and analytics can ever do to support your work. This is why we always strongly advocate designing for data. If you want to show evidence that your Boutique Learning programs deliver, you must decide beforehand: "What should improve if this works?" and "How will we see and prove it?"

This allows you to design the experience so that it not only delivers impact but also generates the data you need for the analytics you want. Good analytics starts at the design stage, not in dashboards.

The AI-driven 70-20-10 is not a prediction about some distant future. The technology is here. The question is whether L&D will shape how it is used — or be shaped by it. The organizations that move first will not just have better learning. They will have better performance, faster adaptation, and a Learning and Development function that has earned its seat at the strategic table.