The more data we have, the harder decisions get

Contrary to what you might expect, AI complicates strategy rather than simplifying it. The reason is what the author calls "analytical abundance": AI makes it possible to justify several possible futures at once. For decision-makers, the challenge is no longer finding information but choosing one of those paths and committing to it.

In a meeting room, a group of executives reads dashboards showing multiple scenarios, generated and backed by generative AI models. The implicit promise: if not definitive answers, at least more options that offer a glimpse of possible certainties. The ultimate goal is one that is always present, in business and in everyday life too: reducing uncertainty.

An article by Marc Baaij published in California Management Review argues that what actually happens is an overload of options that paralyzes decision-making. To pinpoint where the decision stalls, the author splits building a strategy into three moments: gathering and analyzing information, choosing a path, and putting it in motion. AI handles the first one with room to spare. Everything gets stuck at the second.

According to the article, the analytical abundance that comes from using AI, far from simplifying things, creates a condition of "multiple futures" that coexist as plausible options. Producing analysis is no longer the bottleneck. The challenge becomes setting a clear course and holding it against the temptation to keep exploring alternatives ad infinitum. The paradox is that more data expands the sense of control but erodes the ability to close a decision. This isn't new. Years ago, in the "paradox of choice", Barry Schwartz argued that endless choice also produces paralysis and unhappiness.

The author's solution is to treat those three moments as separate stages, each with a concrete, clear end. At some point, the organization stops analyzing, chooses a scenario, and assigns people and budget to it. From then on, monitoring serves to sustain that choice, not to reopen it every time a model produces a new analysis.

Conclusions

• By generating more analysis, AI makes strategic decision-making more complex.
• The main obstacle for companies is no longer a lack of data but the difficulty of choosing among multiple valid options.
• Strategic success requires more than understanding the situation; it also requires closing off options and securing the organization's commitment.
• It pays to separate analysis, choice, and execution, and to give each stage a clear end point.