Human feedback guides AI approach that automates about 84% of decisions

A four-step "targeted automation" model developed by Kai Hoberg of Kühne Logistics University, Christina Imdahl of Eindhoven University of Technology and William Schmidt of Emory University automated about 84% of decisions in an empirical application while retaining human input where it improves the algorithm, according to the paper "Targeted Automation and Sustaining Human-AI Learning" published in Production and Operations Management. The study analyzed more than 475,000 order-related decisions over a four-year period at a major commercial vehicle manufacturer with annual revenues exceeding 4 billion euros. The researchers argue companies should predict which decisions benefit from human expertise, automate the rest, and feed human corrections back into the system to keep algorithms adapting.

Human in the loop 4.0: Human feedback guides AI approach that automates about 84% of decisions Lisa Lock Scientific Editor Andrew Zinin Chief Editor How can we harness the potential of automation while retaining human oversight? And what does successful automation look like in practice? In their paper "Targeted Automation and Sustaining Human-AI Learning," Kai Hoberg, professor of supply chain and operations strategy at Kühne Logistics University KLU , Christina Imdahl, professor of machine learning in operations management at Eindhoven University of Technology, and William Schmidt, professor at Emory University, make the case for targeted automation. Their approach is to automate decisions where human intervention adds little value, while keeping people involved where their input can lead to better outcomes. The work is published https://journals.sagepub.com/doi/10.1177/10591478251381899 in the journal Production and Operations Management . Striking a balance between efficiency and AI improvement "Our research shows that companies should take a selective approach to automation. Rather than automating all decisions, they should deliberately retain human involvement in those decisions where people can help improve the algorithm. "At the same time, sufficient human decision-making must be maintained to ensure that the algorithm can adapt to changing conditions," says Hoberg, professor of supply chain and operations strategy at Kühne Logistics University KLU . This allows companies to strike a balance between short-term efficiency and continuous AI improvement, helping to ensure long-term success. A four-step approach to targeted automation To put this into practice, the researchers developed a four-step approach to targeted decision automation. 1. Identify where human input adds value: First, they analyze when human corrections to algorithmic recommendations lead to better decisions. 2. Predict the value of human expertise: Based on these insights, the model developed by the researchers learns to predict when human expertise is likely to improve decision outcomes. 3. Automate decisions selectively: Decisions where human intervention is expected to offer little additional benefit are automated, while those where human expertise can add value remain in human hands. 4. Continuously improve the algorithms: Finally, human corrections are fed back into the system to refine and improve the algorithms over time. How human expertise improves algorithms "The crucial point is that it is not simply about leaving difficult decisions to humans, but about predicting which decisions actually allow humans to contribute to improving the system," emphasizes Imdahl, who earned her doctorate at Kühne Logistics University and is now a professor at Eindhoven University of Technology. This ensures that valuable human expertise continues to feed into algorithm development while still allowing for a high level of automation. "In our empirical application, we were able to automate around 84% of decisions," she says. Hoberg advises business leaders to take a gradual approach to automation, starting by establishing how they can measure the added value of human intervention. "This provides a basis for making targeted decisions about which tasks to automate and where human expertise is still needed." Humans and AI as partners in a dynamic system For their study, the researchers analyzed an extensive data set covering more than 475,000 order-related decisions processed over a four-year period at a major commercial vehicle manufacturer with annual revenues exceeding 4 billion euros. Their aim was to identify the circumstances in which humans outperformed the algorithm. However, Hoberg, Imdahl and Schmidt were not primarily concerned with the traditional question of "Who performs better: humans or AI?" Instead, they sought to develop a model that views humans and AI as partners within a dynamic system. The goal is to relieve employees of repetitive tasks that add little value while ensuring that AI continues to evolve and improve through ongoing human feedback. More information Christina Imdahl et al., Targeted Automation and Sustaining Human-AI Learning, Production and Operations Management 2025 . DOI: 10.1177/10591478251381899 https://dx.doi.org/10.1177/10591478251381899 Kühne Logistics University https://techxplore.com/partners/k--hne-logistics-university/ Citation : Human in the loop 4.0: Human feedback guides AI approach that automates about 84% of decisions 2026, October 28 retrieved 9 November 2026 from https://techxplore.com/news/2026-10-human-loop-feedback-ai-approach.html