Robot Adoption and Profitability: The Opportunities and Challenges Ahead

We find a U-shaped relationship between the adoption of industrial robots and profit margins. We attribute this finding to firms initially using robots for process innovation before applying the technology to product innovation, corresponding to a strategy of cost leadership and market differentiation, respectively. With recent advancements in artificial intelligence (AI), newer generations of AI-enabled robots are increasingly capable of undertaking unstructured tasks, better at coordinating with humans and with each other. These robots confer greater operational flexibility that also first reduces costs through process innovation before beginning to promote product innovation via more sophisticated customization. However, we anticipate that this trend will result in a more compressed U-shaped relationship. This is because AI-enabled robots’ replacement of tasks with greater cognitive content will free up human efforts to engage in creativity-oriented tasks that might usher in product innovation sooner and thus shorten the transition from cost leadership to market differentiation. Firms can greatly benefit from making adequate preparations for this trend with a renewed sense of urgency, training their workers and managers in digital and business-related skills in order to maintain superior profitability.

Authors

Institutions

Publication Details

Journal
IEEE Engineering Management Review
Published
2024-10-09
DOI
https://doi.org/10.1109/emr.2024.3430053
Citations
2
Primary Topic
Digital Economy and Work Transformation
Type
article
Field-Weighted Citation Impact
2.26

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Robot Adoption and Profitability: The Opportunities and Challenges Ahead

Yifeng Philip Chen, Duncan McFarlane, Chander Velu
2 citations
IEEE Engineering Management Review
Digital Economy and Work Transformation
2.26
article

Robot Adoption and Profitability: The Opportunities and Challenges Ahead

Yifeng Philip Chen, Duncan McFarlane, Chander Velu
article en
2 citations

Abstract

We find a U-shaped relationship between the adoption of industrial robots and profit margins. We attribute this finding to firms initially using robots for process innovation before applying the technology to product innovation, corresponding to a strategy of cost leadership and market differentiation, respectively. With recent advancements in artificial intelligence (AI), newer generations of AI-enabled robots are increasingly capable of undertaking unstructured tasks, better at coordinating with humans and with each other. These robots confer greater operational flexibility that also first reduces costs through process innovation before beginning to promote product innovation via more sophisticated customization. However, we anticipate that this trend will result in a more compressed U-shaped relationship. This is because AI-enabled robots’ replacement of tasks with greater cognitive content will free up human efforts to engage in creativity-oriented tasks that might usher in product innovation sooner and thus shorten the transition from cost leadership to market differentiation. Firms can greatly benefit from making adequate preparations for this trend with a renewed sense of urgency, training their workers and managers in digital and business-related skills in order to maintain superior profitability.

IEEE Engineering Management ReviewVol. 52(6)
University of Cambridge (GB)
Engineering and Physical Sciences Research Council, Economic and Social Research Council
Openalex Percentile: Top 10%
Digital Economy and Work Transformation
2.26
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.