In June, at DES 2026 in Málaga, we sat in on a talk by Nicolas Randall, professor at IE, that kept us thinking for weeks. The title was simple and the idea powerful: in an environment saturated with dashboards, algorithms and agents, leaders are no longer judged by how much information they handle, but by their ability to create meaning.
Information today is abundant, cheap and almost instant. What is scarce, and therefore valuable, is interpreting it, connecting it to the real context of the business and turning it into a clear narrative people can act on quickly and confidently. That has a name: sensemaking, a concept the organisational psychologist Karl Weick had been arguing for over decades and that has now become urgent.
From knowing more to understanding better
For decades, the high-performing executive was the one with more data, more reports or better access to information. That advantage is gone. Any manager can ask an artificial intelligence system and get an analysis, a projection or a scenario comparison in seconds.
What does not go away are the deeper questions. What does this result actually mean in our context. What are we failing to see. Which trade-offs are we accepting without saying so out loud. And how do we tell it so that the team, the board or the market understands not just the number but the implication.
Storytelling stops being a soft skill
For years narrative was treated as a decorative competence. In the age of AI it becomes a strategic capability. When systems generate huge volumes of insight, whoever can build a clear story reduces analysis paralysis, aligns teams looking at different metrics, speeds up decisions under uncertainty, and protects the organisation from the false sense of certainty models produce.
A good narrative does not simplify reality until it distorts it. It orders it. It marks what is signal and what is noise. It makes the assumptions explicit. And it leaves room to revise when new data arrives.
What practising it involves
This is not magical intuition but a handful of deliberate practices:
- Contextualise before concluding. What a model produces is powerful but blind to organisational, cultural and political context. A person adds that layer.
- Make the assumptions visible. Every dashboard rests on hypotheses; sensemaking brings them into the open.
- Build frames, not just summaries. A good frame lets others reason for themselves rather than depend on the boss's reading.
- Tell the whole story. Including what is not known, the risks and the discarded alternatives. Transparency builds more trust than false certainty.
- Iterate the narrative. When new data arrives, the story is updated. It is not a single act but a process.
Why it becomes critical now
Organisations adopting AI generate more options, more speed and more ambiguity all at once. Without people able to create meaning, the usual result is decisions slowed by too much data, misalignment between areas each watching their own indicators, excessive trust in the models or reactive rejection of anything that comes from them, and a quiet loss of agency: the organisation ends up doing what the system suggests instead of deciding what it wants to achieve.
That last risk has a name in the literature: automation bias, the tendency to accept what a machine proposes even when there are signs it is wrong. It is also why the EU Artificial Intelligence Act insists on human oversight that is effective rather than merely formal.
The question is no longer whether you have the data, but whether you can turn it into a story that lets this organisation decide well, together and in time.
What this means for us
In the training programmes we run, this is the part that costs the most effort and lasts the longest. Learning to write instructions for a model takes an afternoon and expires within a year. Learning to add context, surface assumptions and present a result so that a committee can decide does not expire.
The executive who masters sensemaking does not compete with artificial intelligence: they use it as raw material to produce clarity. And that, for now, remains a job for people.