I’ll begin with a small apology.

Reaching back nearly four decades is like flipping through a black-and-white photo album in a world of 4K generative AI. But I believe it’s worth the journey. Some ideas take root over time, and what I observed back then—while working at Price Waterhouse and later at Lotus Development Corporation—sheds light on what’s happening today. It’s the same story, told with more impact. The players are different, the stakes are higher, and the technology is far more advanced. But the core issue remains the same: knowledge, when turned into systems, becomes a multiplier of value.

In the early days of enterprise collaboration, our team helped deploy Lotus Notes across professional services firms. At that time, it was seen as revolutionary—if not controversial. Knowledge was stored in people’s minds, and often, those who had mastered their field kept that expertise closely guarded. Internal status often depended on how much you knew and how carefully you chose to share it. Leadership, especially in consulting and advisory services, often followed a path of selective disclosure.

Then suddenly, there was a platform that allowed everyone to see what was once hidden. Conversations, documents, insights, and playbooks—what used to be exclusive became open. The old unspoken rule that knowledge equals power began to falter. And instead, a new question emerged: What if power wasn’t in owning knowledge but in making it accessible, scalable, and actionable?

That was the true disruption. Lotus Notes wasn’t just a collaboration tool; it represented a cultural shift. Not everyone embraced it, with some resisting the exposure while others saw the opportunity. I was lucky to be part of a small group that embraced it, exploring how this new transparency could be structured, purposeful, and ultimately monetized.

We started working on formal Knowledge Management Systems—an emerging field at the time. The goal was no longer just about sharing insights. It was about capturing recurring strategies, documenting best practices, and transforming tacit knowledge into explicit frameworks that could be reused, adapted, and applied by others. Instead of relying on a single expert, we could give every team access to a consistent method. Instead of reinventing the wheel with each client, we provided refined, evolving processes.

And then something happened that changed how I thought about business value forever. Clients started trusting the consistency. Junior teams could deliver results comparable to seniors. Margins improved. Talent grew. Suddenly, the systems we built—the knowledge itself, separated from the individual—became a key part of how the company was valued. Knowledge, once intangible and informal, was now an asset on the strategic balance sheet.

Fast forward to today. What we’re witnessing with artificial intelligence is a rapid acceleration of that same concept. But this time, the knowledge isn’t just stored—it’s active. It responds, learns, and can mimic the subtlety of human conversation, draw conclusions from data, and even predict questions before they’re asked.

The enterprise is no longer dependent on making knowledge accessible. It’s learning how to make knowledge autonomous.

Instead of central repositories, we now have generative models trained on proprietary data. Instead of internal frameworks, we see AI agents executing multi-step workflows across sales, support, compliance, and strategy. Instead of static documentation, companies are embedding entire bodies of expertise into conversational systems that think and respond with domain-specific precision.

The result is incredible. AI doesn’t just cut the cost of expertise. It amplifies it. A company once dependent on the genius of a hundred experts can now duplicate that brilliance millions of times every second, without getting tired and with perfect memory. Revenue per employee skyrockets. Customer engagement becomes more reliable. Decision-making speeds up. And just like during the Knowledge Management era, the company’s valuation responds accordingly.

To put this evolution into perspective, think about how the fundamental nature of knowledge as an asset has changed.

The Evolution of Value: Knowledge as an Asset

In traditional models, value comes from people. You hired talented individuals, built top teams, and grew slowly through training and retention. But when knowledge becomes part of technology, value depends on usage, not headcount. The more the AI knows, the more it’s used. The more it’s used, the more it learns. And the more it learns, the more it becomes defensible and irreplaceable.

Of course, this new landscape isn’t without its risks. In the Lotus Notes era, we worried about losing the human nuance, the contextual sensitivity of expert judgment. With AI, we face similar, but amplified, concerns. How do we audit what the system knows? What happens when a model drifts or misfires? Can you really claim to “own” the intelligence inside a neural network trained on third-party infrastructure?

These are fair questions. But they don’t diminish the core truth.

Just as we saw 35 years ago, the companies that understand how to transform tacit insight into structured, scalable knowledge will pull ahead. The difference now is scale. What once took months to train and deploy can now be spun up in minutes. What once required human replication now requires only computation and thoughtful design.

Knowledge has always been power. But when systems begin to think, power becomes multiplicative.

Today’s leaders must think differently about how value is created. It’s no longer just about what your people know—it’s about how your systems know. It’s about turning insight into infrastructure, wisdom into code, and experience into executable intelligence.

This is not just the future of valuation. It’s the future of enterprise evolution.

And for those of us who remember when tech-captured knowledge first stepped out of the shadows, it’s a thrilling thing to witness again.

Thierry Hubert