Thinking about the future has always involved a certain “logical trick”, as Adolfo Castilla rightly points out. For decades, foresight was content with imagining desirable scenarios and charting routes towards them. It was the direct legacy of Seneca: seeking a favourable wind whilst assuming we knew which port we were heading for.
Then came quantum physics. Relativity and the mechanics of the infinitely small shattered our classical conception of space and time. If time is no longer strictly linear, planning for tomorrow as a mere extension of today ceases to make sense. Complexity science now forces us to incorporate the unpredictable, bifurcations, and non-linear systems into the playing field.
Extrapolating trends is no longer enough. The contemporary challenge lies in interpreting the minuscule: the anomalies of our present and the immense analytical capacity that Artificial Intelligence provides us.
Igor Ansoff spoke as early as 1975 of weak signals: those fragmented clues that peek timidly through the data long before they become evident trends. Michel Godet went a step further, warning that ignoring these "small signs" invalidates any attempt at strategic foresight. A true peripheral signal does not usually appear in standard citation metrics or at the centre of academic debate. It hides in the margins. Exactly where early scientific innovation is often mistaken for mere statistical "noise".
The algorithmic telescope
Evidently, having at our disposal algorithms capable of combing through millions of academic publications in milliseconds alters the rules of the game. It allows us to scrutinise that peripheral noise on a previously inconceivable scale.
Thomas Frey, from the DaVinci Institute, relies on a revealing metaphor in a recent reflection: AI acts as an ultra-high-resolution space telescope. It is a phenomenal amplifier. But a telescope discovers nothing on its own. It lacks purpose without an astronomer on the other side to direct the lens, decide which constellation to look at, and, above all, give meaning to the lights captured by the glass.
The Human-in-the-Loop (HITL) approach is indispensable in foresight because it defines exactly what our brain brings to uncertainty: the ability to give meaning to the anomalous. For an algorithm, a "weak signal" and simple statistical noise are mathematically identical. The machine can only point out oddities, but it is blind to their impact.
The Human Factor
What the human factor injects into the loop is abductive reasoning, historical memory, and sociological imagination. Whilst AI merely identifies that "something does not fit" within the data, it is human intuition that connects that anomaly with a shift in values, an impending crisis, or a scientific revolution. The human does not merely supervise; they provide the intentionality, deciding which futures matter to us and why that invisible piece of data has the power to alter them.
Therefore, the true strategic advantage of the 21st century does not lie in possessing the best predictive algorithm, but in cultivating the right mindset. The future, as Frey reminds us, is not an empty destination towards which we blindly head; it is a horizon already emitting its own frequencies. The challenge is to stop staring mesmerised at the centre of the graph and have the audacity to tune into the periphery, paying attention to what the majority prefers to ignore as mere noise.
This article is republished from Futuribles. Here's the original article in Spanish and English.
