Proactivity

Most programs wait for a command. You type in a question, the program answers, then it falls silent again. Proactivity describes the opposite: a system becomes active on its own, without anyone having prompted it. It observes the situation, recognizes a fitting moment, and then comes forward with a suggestion, a warning, or a completed task. A navigation device that warns of a traffic jam without being asked is behaving proactively in this sense. The opposite term is reactive: input first, then reaction.

From tool to thinking partner

Anyone using a reactive program has to know what they want themselves. This is precisely where many useful features fail. People don’t ask about things they don’t know exist. A system that points something out on its own overcomes this problem.

In the industry, proactivity is therefore seen as the step from chatbot to assistant. A chatbot answers questions. An assistant reminds you of an expiring contract before you forget about it. The economic appeal is considerable: a system that speaks up gets used more often and binds users more closely to a product.

But there is a downside, and it is the real reason why this topic is so tricky. Every unsolicited message is an interruption. Too many of them lead to what is called notification fatigue: users eventually turn off alerts entirely, including the important ones. Proactivity is thus primarily a problem of finding the right measure, not a problem of technology.

Trigger, evaluation, intervention

Technically, a proactive system needs three things. First, ongoing observation: it reads along in the calendar, incoming messages, location, or sensor data. Second, a trigger that recognizes a possible opportunity from this observation. Third, an evaluation of whether interrupting is even worth it.

This evaluation is the interesting part. The system estimates how useful the notice would be and how disruptive the interruption would be. Only if the benefit clearly outweighs the cost does it speak up. Some systems learn this threshold over time: those who often dismiss suggestions receive them less often. Experts refer to this as a cost-benefit trade-off of attention.

A common misconception is that proactivity is simply a smarter language model. That’s not true. A language model on its own has no access to the world whatsoever and no reason to speak up. What’s needed is the surrounding environment: access to data, a schedule that checks regularly, and permissions to trigger actions. This exact combination is what’s meant when news reports talk about AI agents, meaning programs that carry out multi-step tasks independently.

Proactive features in current products

In everyday life, proactivity has been around for a long time, just under different names. The phone suggests when you should leave to catch a train. The banking app warns about an unusual charge. The email service asks whether you wanted to reply to a message that’s been sitting for days. None of these features was explicitly requested.

For AI assistants, proactivity is currently a central selling point. Providers announce assistants that presort the inbox, suggest appointments on their own, or start research in the background. In financial news, the term often appears in this role: as the feature meant to distinguish a product from an ordinary chat window.

Skepticism is nonetheless warranted. Proactive systems need far-reaching access to personal data, otherwise they cannot recognize the right moment. And when they get it wrong, it doesn’t just annoy — it can cause harm, for instance with a message sent prematurely. That’s why most providers rely on an intermediate step: the system suggests, the human confirms.