I tend to see talks about the future as a great way to trigger better conversations about the present. Rather than trying to predict what rarely can be predicted, these talks help me think about the business architectures and models we are using now.

And this is also true for artificial intelligence, which has undoubtedly brought a wave of change and clear gains in efficiency and productivity. In the present, I keep hearing about how AI will replace this or that profession; about corporates cutting workforces; about consultancies changing pricing models and freezing hiring for junior roles. All this, in the name of cost rationalisation and productivity increases. I have also already read a few papers about the impact of artificial intelligence on innovation management, and books about how to live, work, and cooperate in the age of AI.

Nonetheless, I ask myself, are we sure efficiency should be our north star? And I mean from both an economic and business perspective, or from either.

As humans we tend to love what is inefficient. Think of how many watchmakers Switzerland has, for example. The world probably doesn’t need them all. If efficiency ruled, one would probably be enough, perhaps a single quartz producer. Or even better, a sport watch with all the sensors and data points we can dream of to improve the way we live and sleep. A watch that is more precise than a mechanical one.

But we value the magic, the complexity, and even the scarcity of great craft. And it’s also for this reason, that there are apparently around 700 Swiss watchmaking firms.

In a paper I have recently read, a four-stage view of AI’s role in innovation was proposed. First, AI as a tool. Second, AI as an interactive support agent. Third, AI as a fully equivalent member of the innovation team. Fourth, AI as an independent orchestrator of innovation efforts and teams.

The issue I have with the machine learning technology we now refer to as AI, in this specific case, is that the management of innovation is future oriented. Still, I tend to agree with Ernesto Gismondi’s rule of not looking to markets first when developing new products. He used to say, “we make proposals to people.” If we develop services only by analysing thousands of data points, we may miss the point. Sometimes the opposite works better. Choosing something that looks inefficient can be right, but only once you understand its purpose and meaning.

We all know the positives of using AI as a tool. That use case is normal by now. If a product is advertised only as AI powered, that is no longer a differentiator. The second stage (agentic AI) made me think of The Dispossessed, the ambiguous utopia by Ursula K. Le Guin. There a central computer allocates activities to citizens who, if I recall correctly, can still refuse them. With AI the real question is not what we can do with it. The question is what we want from it.

The paper offered a few examples. First, a project management system that could autonomously allocate resources identify bottlenecks etc. Many organisations would probably welcome this and point to a “logical actor” to justify decisions. Or AI-driven innovation systems that could scout and monitor a company’s external environment, and “autonomously manage and execute innovation projects without significant human input and oversight.”

Is this what we want?

Or maybe we prefer keeping control of strategic capabilities. As per myself, I’d happily give away the tedious chores, but will keep the thinking, the variety, the reading and socializing for myself and myself only.