AI Agents in Companies: What They Can Do, What They Change and What Matters Now

Perspective · Artificial intelligence · Future skills

AI agents arrived in German companies in 2025. But what is really behind them? And what does it mean for leadership, responsibility and the work of tomorrow?

For years we have been talking about artificial intelligence. Chatbots that answer standard questions. Systems that analyze data. Practical, yes. But hardly revolutionary.

With AI agents, the picture changes fundamentally. They no longer wait for instructions, they act. They search for information, make preliminary decisions, set processes in motion. Tools become actors.

According to Bitkom, 41 percent of German companies already actively use AI, and another 48 percent are planning or discussing its use. The question is no longer whether, but how.

1. What sets AI agents apart from previous AI tools

The difference is not gradual, it is fundamental. A classic AI tool answers a question. An AI agent solves a problem.

Concretely: while previous systems constantly look out for instructions like apprentices, agents work like experienced employees who know where things are heading. They choose the path themselves, within the guardrails that people set. Autonomy is therefore not a free pass, but a new form of delegation.

Four properties make AI agents something qualitatively new:

• Goal pursuit: They work toward an outcome, not just an answer.
• Planning: They break complex tasks down into sub-steps on their own.
• Tool use: They independently access databases, APIs or calendars.
• Adaptation: They correct their approach when new information appears.

2. Where AI agents are already used today

Anyone who thinks agents are still a thing of the future is mistaken. They are already in use, even if often still in pilot projects.

• Sales: Scoring leads, personalizing offers, automating follow-ups.
• Marketing: Analyzing and optimizing campaigns in real time while they run.
• Production: Checking quality standards, planning maintenance before machines fail.
• HR: Screening applications, supporting onboarding processes, evaluating feedback.
• Customer service: Handling up to 80 percent of standard inquiries independently.

All of this seems unspectacular. And that is exactly why it is so remarkable. Autonomy unfolds its power not through big visions, but through a thousand small decisions in daily life.

3. What AI agents mean for efficiency

The temptation is great: more output, less effort. First studies feed these hopes. According to BCG, teams save an average of five hours per week by using AI. Those who integrate AI more deeply into processes save considerably more. PwC has calculated that industries with high AI use have almost quadrupled their productivity since 2022.

The mechanics behind it are simple: routine tasks disappear, data analyses speed up, decision paths get shorter. What remains are the tasks in which machines don’t shine: creativity, strategy, interpersonal intelligence.

However, a dangerous gap also shows: leaders massively underestimate how intensively their teams already use AI. Employees stated three times as often that they use AI for more than 30 percent of their tasks than their supervisors assumed. This so-called shadow AI carries risks for data protection and security.

4. The underestimated construction site: responsibility

Efficiency is only half the truth. With more autonomy, risk grows too. What if an agent evaluates biased data and draws wrong conclusions? Who is liable when this causes errors in the supply chain or customers are advised wrongly?

It gets even more complicated when decisions are not comprehensible. Agents don’t make gut decisions, but their logic often remains a black box. Governance thus becomes a prerequisite, not an option: clear rules, regular reviews, red lines. Without them, there is a danger that efficiency comes at the expense of responsibility.

Organizations should answer three questions before deploying AI agents:

• Which decisions may the agent make, and which not?
• Who reviews the results, and how often?
• Who bears responsibility if something goes wrong?

5. A new leadership task: leading machines

With this, the role of leadership changes fundamentally as well. Delegation used to be a human business. In the future it must also work with machines. Defining goals, setting the framework, keeping control: this becomes a core competence.

An interesting change of perspective: those who see agents only as tools waste potential. Those who understand them as partners can orchestrate processes anew. It takes courage to hand over tasks, and confidence to set boundaries.

By 2030, AI literacy will become the most important core competence in companies. New role models are emerging: the so-called “Agent Boss” will manage a fleet of digital assistants. Those who build this competence today secure a decisive head start.

Conclusion

AI agents are no longer a fringe topic, but a turning point. They act instead of merely reacting. They relieve instead of merely supporting. And they challenge us to rebalance autonomy and responsibility.

For leaders and organizations, this means: the technological change is long here. The real task now is to shape it deliberately. With clear values, clear rules and people who know how to lead machines.