Sep 23, 2026 Hannah Kuck
ShareArtificial Intelligence (AI) I can analyze data, identify patterns, and support routine decisions. But when the standard case ends, human experience, contextual knowledge, and judgment become especially important. Human-AI Collaboration brings these capabilities together and turns decisions into opportunities for continuous learning.
The production plan looks perfect. Capacities, material availability, and delivery dates have all been taken into account, and the sequence is mathematically optimal. Yet an experienced employee says, “I wouldn’t do it this way today.”
Perhaps he knows from the shift handover that a machine has been behaving unusually. Maybe he is aware of a particularly critical customer requirement or has seen this exact combination lead to problems before. Based on the information available to it, the AI may have calculated correctly. It simply did not have all the information.
Good collaboration between humans and AI requires more than powerful technology. In our “Human in the Loop” series, we have already explored organizational integration, explainable recommendations, how to deal with errors and hallucinations, the right AI mindset, and clear boundaries for AI autonomy. To conclude the series, we turn to another side of this collaboration: what humans themselves contribute, namely experience, contextual knowledge, and judgment.
When AI systems take over more routine cases, people make certain decisions themselves less often.
This aspect becomes increasingly relevant as automation advances. When AI systems take over more routine cases, people make certain decisions themselves less often. But how do they build the experience they need when a situation falls outside the routine?
AI can analyze large volumes of data, identify patterns, simulate scenarios, and compare different courses of action. But data and operational reality are not the same thing. Not every relevant piece of information is available digitally at the right time, and not every relationship can be fully measured. Some factors only emerge in the moment: a short-term disruption, an unusual customer request, information from a conversation, or a shift in priorities.
There is also knowledge that has never been fully documented. Someone who has worked in a process for years may know that a seemingly minor bottleneck regularly causes larger downstream problems, or that a solution that looks efficient on paper does not work in practice. This practical and often tacit knowledge develops as people encounter situations, make decisions, and observe the consequences. Over time, they learn which signals indicate problems, which rules work in standard situations, and when a case deviates from the norm.
Human experience is therefore not an alternative to AI. It is a distinct decision-making resource. The reverse is also true: people overlook information, fall into routines, or misjudge relationships. AI can reveal these blind spots and identify patterns that no individual could realistically keep track of.
This is where Decision Intelligence brings the different strengths together: data and machine-based analysis on one side, human expertise and judgment on the other. The relevant question is therefore not “human or machine?” but how both can contribute to a better decision-making process.
This tension becomes particularly clear when it comes to building experience. Experience develops primarily by dealing with situations directly. Imagine a young production planner joining a company where an AI system already handles a large share of daily production planning.
At first, this is an advantage. The system supports him from day one and takes routine work off his plate. But what happens after five years? Has he gained five years of planning experience, or has he mainly spent five years reviewing system recommendations?
A 2024 scientific study on the impact of AI assistants on human skills describes the risk that frequent AI support can weaken existing cognitive skills and hinder the development of new ones. The authors frame this as a potential risk, not an inevitable consequence. What matters is how systems are used and how learning processes are designed.
The answer is not to keep routine work with people artificially. Instead, companies should add a second question to “What can we automate?”:
Where will the practical experience and judgment come from that people need when data is incomplete, conditions change, or something unexpected happens?
Human in the Loop primarily describes where people remain involved in AI-supported decisions: When do they need to review a recommendation? When can they intervene? What can be automated?
Human-AI Collaboration goes one step further. It focuses on how human experience and practical knowledge interact with machine-based analysis over time and how both can benefit from that interaction.
Someone who approves hundreds of AI recommendations with a single click every day is technically still “in the loop.” But that does not automatically create meaningful knowledge exchange. The interesting moments are those in which the human and the system reach different conclusions.
Take the production plan again. The AI recommends a particular sequence. An experienced employee changes it because of an operational detail, and the order runs more smoothly as a result. That could be the end of the story. Or the company can ask why the employee decided differently and what can be learned from it.
After a human correction, the question should therefore not only be whether the adjustment was right, but also why the person knew something the system did not. Perhaps a data source was missing, an important criterion had not been considered, or the relevant knowledge existed only in the heads of individual employees.
This creates a learning loop: The AI makes a recommendation, the human identifies an exception or missing context, the decision is adjusted, the outcome is observed, and the insight flows back into the system and AI-supported processes. This may mean improving data or rules, adjusting a model, or transferring knowledge into processes, training, and teams. In this way, an exception can become a source of learning rather than merely a disruption.
A recent systematic literature review on reciprocal human-machine learning in production and logistics addresses this very idea. It examines how humans and intelligent systems can learn from one another and further develop their complementary strengths. At the same time, it shows that such bidirectional learning processes need to be deliberately designed.
This also changes the role of humans. People contribute context, identify new relationships, and help develop shared decision knowledge. At the same time, AI broadens the human perspective by making patterns visible and challenging assumptions. Human-AI Collaboration thus becomes a process of reciprocal learning.
For companies, it is not enough to define which decisions AI may take over. They also need to determine which human capabilities will remain important and how those capabilities can be developed and maintained.
Especially in rare, complex, or high-impact situations, employees need enough understanding of processes and consequences to evaluate AI recommendations and intervene effectively when necessary. The way expertise develops is changing as well.
A recent McKinsey article on building expertise in the age of AI describes this tension: Routine tasks have traditionally also served as learning opportunities where early-career employees developed judgment and experience. If AI takes over these tasks, companies need to create learning opportunities more deliberately, for example through coaching, simulations, or workflows in which employees first make their own assessment and then compare it with the AI-generated result.
This allows companies to deliberately strengthen human judgment and apply it where experience, contextual knowledge, and the ability to weigh competing factors improve the quality of operational decisions.
And when experienced employees repeatedly identify the same exceptions, that knowledge should not remain with individuals. It can become organizational knowledge that improves people, processes, and AI-supported systems alike.
AI will take over more routine decisions. That makes it increasingly important to understand where human experience and judgment develop, how they are maintained, and how insights from exceptions flow back into AI-supported processes.
Human-AI Collaboration therefore means more than dividing work between people and machines. It creates learning loops in which people continue to develop their judgment while new insights are used to systematically improve machine-based decision support.
The quality of this collaboration is not defined only by how well humans and AI handle the standard case together. It also depends on how much they learn from the exception.
About our Expert
Hannah Kuck
Corporate Communications Manager
Hannah Kuck has been working as Corporate Communications Manager in Corporate Marketing at INFORM since August 2024. With a passion for creative and effective communication, she helps shape various areas of corporate communications - from press relations to content creation and storytelling.
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