From human to machine: high-impact tasks for AI in production management – an expert study to reshape decision-making

Abstract Artificial Intelligence (AI) is a key driver for the future transformation of production management. However, to ensure its effective implementation, it is crucial to systematically identify the most relevant application areas. Despite the growing relevance of AI, there is currently no comprehensive assessment of which production management tasks can derive the greatest benefit from the use of AI at an acceptable effort. While applications where AI supports repetitive tasks are increasingly accepted, AI is met with considerable skepticism when it comes to tasks involving social interaction, experiential knowledge, or autonomous decision-making. Final decisions are deliberately kept under human responsibility, reflecting concerns about loss of control and devaluation of human expertise. This highlights the current barriers of AI in replicating human-centered work. Identifying these production management tasks is not trivial, as there are many interdependencies and multi-criteria dependencies in the decision-making process. The subjective perception of production managers and their trust in AI also play an important role. The results clearly show that the tasks of production controlling, process design, financing and investment, operational production management and order management and fulfillment offer great potential to have these tasks performed by an AI with a good effort-benefit ratio. Our results underscore the need to evaluate AI performance on these tasks in direct comparison with humans. Future research should develop simulative environments to test and evaluate AI-adoption.

Authors

Institutions

Publication Details

Journal
Production Engineering
Published
2026-01-08
DOI
https://doi.org/10.1007/s11740-025-01416-0
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From human to machine: high-impact tasks for AI in production management – an expert study to reshape decision-making

Peter Burggräf, Carl René Sauer, Maximilian Schütz, Luisa Stracke et al.
Production Engineering
Digital Transformation in Industry
article

From human to machine: high-impact tasks for AI in production management – an expert study to reshape decision-making

Peter Burggräf, Carl René Sauer, Maximilian Schütz, Luisa Stracke, Joshua Kaiser
article en

Abstract

Abstract Artificial Intelligence (AI) is a key driver for the future transformation of production management. However, to ensure its effective implementation, it is crucial to systematically identify the most relevant application areas. Despite the growing relevance of AI, there is currently no comprehensive assessment of which production management tasks can derive the greatest benefit from the use of AI at an acceptable effort. While applications where AI supports repetitive tasks are increasingly accepted, AI is met with considerable skepticism when it comes to tasks involving social interaction, experiential knowledge, or autonomous decision-making. Final decisions are deliberately kept under human responsibility, reflecting concerns about loss of control and devaluation of human expertise. This highlights the current barriers of AI in replicating human-centered work. Identifying these production management tasks is not trivial, as there are many interdependencies and multi-criteria dependencies in the decision-making process. The subjective perception of production managers and their trust in AI also play an important role. The results clearly show that the tasks of production controlling, process design, financing and investment, operational production management and order management and fulfillment offer great potential to have these tasks performed by an AI with a good effort-benefit ratio. Our results underscore the need to evaluate AI performance on these tasks in direct comparison with humans. Future research should develop simulative environments to test and evaluate AI-adoption.

Production EngineeringVol. 20(1)
University of Siegen (DE)
Peace, Justice and strong institutions
Openalex Percentile: Top 95%
Digital Transformation in Industry
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.