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AI agents are taking an increasingly active role within organizations. Where for a long time, AI was mainly used to make analyses and make predictions, we now see systems that independently formulate recommendations, make decisions and interact directly with people. It is precisely in this shift from analysis to action that one fundamental problem becomes visible: many AI agents are designed as if they were operating in a world where people respond more or less the same to the same incentives. That world does not exist.
To understand why this is so problematic, we must first consider the nature of human behavior itself.
Human behavior can be modelled, but it can never be fully recorded in fixed rules. In economics and data science, we try to explain choices by looking at factors such as price, convenience, risk, time pressure or social norms. This provides valuable insights, but it implicitly assumes that people make similar considerations.
In practice, however, people give different weights to the same factors. What is decisive for one person hardly plays a role for the other. Two people with the same information and in the same context can therefore make completely different choices without either of them acting irrationally.
This variation in behavior is not the exception, but the norm. And that is precisely where the need for a deeper understanding of differences between people arises.
In data and models, we only see part of what influences choices. Many individual drivers remain out of the picture, simply because they are difficult to measure or change per situation. It involves personal values, previous experiences, risk attitude, trust or emotions that don't fit neatly into a dataset.
In statistics, we call this unobserved heterogeneity: systematic individual differences that influence behavior but are not directly observable. Modern models explicitly recognize this by not starting from a single decision-making process, but from a distribution of preferences within a population. The model does not know exactly who prefers which, but does know that these differences exist.
That insight is crucial, because as soon as we ignore this heterogeneity, we will misinterpret behavior.
Many AI agents do just that: they abstract away differences. They optimize one strategy, learn from average patterns, and implicitly assume that there is one best action for a given situation. As long as the variation between users is limited, that seems to work.
But once agents work with real people, frictions occur. Recommendations that are valuable to one user are resisted by another. Nudges that help one feel intrusive to the other. Optimizations that seem efficient in the short term undermine trust and acceptance in the longer term.
What is often seen as a noise or an exception is in reality the visibility of underlying differences between people.
For an AI agent, this heterogeneity is difficult because, by definition, she works with incomplete information. An agent never fully knows what someone's preferences are, what consideration someone is currently making, or how stable those preferences are. Nevertheless, the agent must act and make choices.
This means that AI agents must not only optimize, but also continuously learn. Not afterwards, but during interaction. Feedback and deviant behavior are not errors, but signals about preferences that were not explicitly known yet.
This reality requires a different way of thinking about intelligence.
The central question for AI agents is therefore no longer what works best on average, but for whom something works, in what context and why. Successful AI agents not only predict behavior, but actively learn who they are facing and adapt their decisions accordingly.
This means that personalization is not an extra layer that is added later, but a fundamental part of the decision process itself. The agent must be able to use various assumptions about users and be willing to adjust those hypotheses based on new information.
What started in statistics as a correction for invisible differences becomes a strategic design principle in the context of AI agents. Unexpected heterogeneity means recognizing that not everything is measurable, but that systems can learn to deal with uncertainty and variation.
AI agents designed for this purpose make better decisions, are more robust in complex environments, and build more sustainable trust in interactions with people.
The future of AI agents does not lie in more data or more complex models alone. She lies in people-centered design. Not by abstracting away human differences, but by focusing on them. Not by looking for the perfect average action, but by learning how to deal with human diversity. Ultimately, that's where intelligent AI stands out.
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