AI is not just for becoming more efficient (cutting back); it is for going further (multiplying)

A significant part of the conversation around artificial intelligence has settled on an idea that is too narrow: doing the same with less. Less time, lower cost, less friction. It is a logical approach, and often a useful one, but also a limited one. If a company looks at AI only through the lens of savings, its upside is finite. It can optimize processes, reduce operational load, and slim down its structure, but there comes a point where that path runs out (eventually, in a maximum-case scenario, the savings could even become total).

That is not where AI’s greatest potential lies. Its value is not only in cutting back, but in raising the ceiling of what a person, a team, or a company can do. It is not simply about doing the same thing a bit faster, but about making possible things that were previously not viable.

This becomes very clear in the way different professionals incorporate AI into their day-to-day work. From my point of view, with AI the best people “have more work,” and we are not talking only about employment—though that too. We are talking about the fact that, when they gain capacity, new fronts open up. They do not use, or will not use, AI only to finish earlier, but to go further. If before they could explore one idea, now they can explore five. If before an improvement stayed in the drawer because it required too much time, now they can try it. If before a new project felt too large, now they can validate it or execute it.

The real value of AI is not doing the same with less, but making possible what until now we could not do.

That is why strong professionals generate more work for themselves. Not because they work worse, but because they turn every efficiency gain into more scope, more depth, and more ambition. More mediocre profiles, on the other hand, tend to read AI in a much narrower way: they see it simply as a way to solve faster what they already had on their plate. The difference is not so much technological as it is mental. With the same tools, some expand the playing field, while others simply move faster within the same perimeter.

That same logic also helps explain what happens at the company level. Some organizations use AI to optimize downward: reduce time, reduce cost, reduce workload. In some cases this makes perfect sense, especially where there are absurd, manual, or repetitive processes. But it is still a form of efficiency with a ceiling. Its value is real, but limited.

The more interesting path is another one: using AI to optimize upward. In other words, turning efficiency into leverage. Using that extra capacity to open new services, test new ideas, invent new products or services, iterate much faster, take on projects that previously felt out of reach, or radically raise the level of service. This is where the category change happens. This is where a company stops thinking only about what it can eliminate and starts thinking about what it can build now that it could not build before.

This is the key point. The small value of AI is savings. The big value is multiplication. Slimming down an organization has limits. Increasing its capacity disproportionately, much less so. In other words, current cost is a fixed number that can trend toward €0. Creating new revenue potential can be open-ended or permanent. That is why the right question is not only “what can we save?” but above all, “what will we be able to do now that was not viable before?”

The companies that will truly benefit from this moment will not be the ones that use AI only to do the same work with less, but the ones that understand it as a tool to go much further. Because the big opportunity is not downward efficiency. It is upward efficiency: the kind that does not only reduce, but multiplies.