FW-HTF-P: Transforming Small and Medium-Sized Manufacturing Firms Through Participatory AI Adoption and Implementation

This project aims to transform small and medium-sized manufacturing enterprises (SMEs) in the U.S. by analyzing how the adoption of artificial intelligence (AI)-based technologies impacts manufacturing SMEs’ labor force and productivity. AI-based technologies pose a number of concerns around retraining and replacement of workers, but also potential benefits around productivity and the creation of new jobs, including ones that are less physically demanding and may support workers currently excluded from manufacturing jobs.

The project focuses on SMEs rather than larger firms both because SMEs employ a large majority of U.S. manufacturing workers and because SMEs may pose unique challenges for the adoption of AI-based technologies in terms of the resources and skills these firms have available to make these technologies work for them. The goals of the project are to: (1) understand the challenges that manufacturing SMEs and their workers currently face in adopting and implementing new AI technologies, restructuring work and tasks and learning new skills; (2) design a controlled manufacturing environment to support studies of workers collaborating with AI-based technologies; (3) develop a framework that SMEs can use when adopting these technologies; and, (4) develop connections with industry partners and community colleges to identify ways to lower the barriers to the adoption of new AI technologies on the factory floor and to develop a robust workforce training program.

To accomplish these goals, the project team will build a collaboration with manufacturing firms, both SMEs and large corporations holding multiple SMEs, in Indiana’s South Bend-Elkhart region. A team of experts in the areas of economics, engineering, AI, information technology, analysis and operations, and sociology will work with these local SMEs and conduct on-site observations and in-depth interviews to understand companies’ current technology use and needs, as well as opportunities for AI-based technologies to meet those needs.

These findings will inform a survey to collect data on a wider range of companies’ financial situations and production capacity, technological sophistication, management and hiring practices, workforce composition and turnover, and work conditions. Together this information will be used to identify promising candidate AI-based technologies to explore further, then design a manufacturing cell that facilitates controlled studies of workers collaborating with these technologies.

Further, the project team will develop novel approaches to training manufacturing SME employees to allow them to be more directly involved in the adoption and use of these AI-based technologies. This curriculum development work will be done in collaboration with Ivy Tech, which has 40 community college locations in Indiana and is developing a School of Advanced Manufacturing, Engineering and Applied Science in response to the needs of the state’s manufacturing industry.

Based on these activities, the project will develop a framework called “Participatory AI Adoption and Implementation” with guidelines for how workplaces, workers, and AI-based technologies can productively interact in manufacturing SMEs, focusing on ways to involve workers in the firm’s decision process when adopting new technologies from design to deployment.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.