An op-ed I recently published in a French publication called LA TRIBUNE explored a shift I believe is becoming essential in the age of artificial intelligence: the move from return on investment to return on learning. That article focused on a simple but important idea. As AI takes on more tasks once tied to human productivity, the value of people does not disappear. It moves. It shifts toward judgment, creativity, empathy, sense-making, and the ability to work effectively with intelligent systems. In that world, the real differentiator is no longer just efficiency. It is learning. But I want to take the idea a step further here, because this is the part that matters most to me.

Return on learning is not just about training more people. It is not about adding a few courses to a learning platform or encouraging employees to spend more time on reskilling. It is about something deeper and more urgent. In a period of accelerating change, our future will be shaped by how quickly we can learn, how broadly we can participate in learning, and how effectively we can turn learning into action. That is why I believe accelerated learning is now critical.

For most of the industrial era, learning was organized around stability. We learned early, specialized, and then applied that knowledge over long periods of time. Education fed work. Work rewarded experience. Institutions changed slowly enough for this model to function. That world is fading. AI is compressing the time between discovery and disruption. New tools appear quickly. Capabilities spread quickly. Entire workflows can change before institutions have time to adapt.

In that kind of environment, the problem is not simply that skills expire faster. The deeper problem is that the systems we built for learning are too slow, too fragmented, and too isolated from the environments where change is actually happening. This is why return on learning matters so much. It forces us to ask a different question. Not “What did we gain from this investment?” but “How quickly did this help us become more capable?” Did we improve our ability to adapt? Did we expand our capacity to respond to change? Did we create conditions where people and institutions can keep learning as the world moves?

That is a very different lens. And it leads to a very different set of priorities. The organizations that thrive in the AI era will not be the ones that simply deploy the most technology. They will be the ones that build the greatest adaptive capacity. They will create environments where learning is continuous, embedded in work, connected to real problems, and shared across teams and partners. Learning becomes part of the environment, not an institution.

Just as important, they will recognize that no organization can do this alone. If learning must accelerate, then it must become more ecosystem-based. Companies need to be connected to broader networks that help them sense change early, absorb new knowledge quickly, and translate that knowledge into practice. That means deeper ties across business, education, startups, research institutions, governments, communities, and technology partners. It means moving beyond the boundaries of the enterprise and participating in systems that make learning faster, more relevant, and more inclusive.

This is one of the most important shifts ahead. In the past, competitive advantage often came from what a firm owned or controlled. Increasingly, it will come from how well it participates in ecosystems that expand its ability to learn. That participation matters because AI is not changing one sector at a time. It is reshaping multiple systems at once. Work is changing. Education is changing. Credentials are changing. Governance is changing. Communities are changing. The pace of adjustment in one area increasingly depends on what happens in another. If learning remains trapped inside traditional silos, we will fall behind the speed of change. If learning becomes more connected, more ambient, and more responsive, we have a chance to keep up.

This also has a human dimension that cannot be ignored. When people talk about AI, they often focus on jobs. That is understandable, but incomplete. What is really at stake is agency. Do people have a path to remain useful, valued, and connected in a world where intelligent systems are taking on more cognitive labor? Do they have access to the kinds of learning experiences that help them move forward rather than drift backward? Do they have institutions around them that support reinvention instead of punishing it?

If the answer is no, then AI will widen inequality and deepen social fracture. Those with access to strong networks, modern tools, and continuous learning will surge ahead. Those without that access will face not only economic risk, but also a loss of identity, confidence, and belonging. That is why return on learning must be understood as more than a workforce issue. It is a societal issue. It is tied to resilience, mobility, inclusion, and cohesion. It is one of the clearest indicators of whether a society is preparing people for the future or leaving them exposed to it.

History reminds us that general-purpose technologies do not simply make life better in a straight line. First they disrupt. Then they reorder institutions. Then, over time, societies build new systems around them. AI is following that pattern, but at a much faster pace and with much broader reach. That makes learning the central bridge between disruption and adaptation.

So when I speak about return on learning, I am speaking about something larger than corporate upskilling. I am speaking about a new social and economic imperative. We need learning systems that operate at the speed of change. We need ecosystems that help people and organizations learn together. And we need leaders who understand that the future will not be won by those who automate the most, but by those who build the strongest capacity to learn, adapt, and evolve.

AI may be one of the most powerful technologies we have ever created. But its ultimate impact will depend on whether we use it to narrow human possibility or expand it. That is why return on learning matters. And that is why accelerated learning may become one of the most important measures of our humanity.