The Working Group

MIT’s Work of the Future Initiative is convening a multidisciplinary working group of industry, policy, and academic leaders to examine how the design and implementation of generative AI tools can contribute to higher-quality jobs and inclusive access to the latest technologies.


MIT scholars are conducting research on employers' uses of generative AI and its impact on their employees. Their findings will be published as a series of case studies identifying best practices, as well as novel academic papers.


The Working Group will host quarterly meetings among AI leaders from participating organizations to facilitate knowledge sharing. Annual summits will feature corporate leaders identifying best practices for investments in generative AI.


The research team will translate our findings from the working group into teaching and training materials for AI practitioners across industries. Educational resources will be available to working group members, as well as the broader public.

Research Agenda

How can new technology tools like generative AI improve productivity and product quality for firms, as well as flexibility and job quality for workers?

The impact of generative AI depends on how the technology is designed, implemented, and regulated. Recent MIT research emphasizes the importance of management and engineering decisions for achieving “positive-sum automation,” or technology change that improves productivity as well as flexibility – for firms and workers. Our multidisciplinary research across three areas aims to generate new knowledge for AI-focused practitioners.

  1. Scaling up: In what business contexts have generative AI applications scaled? Where have they failed? What metrics have firms used to determine the success of the tools? What role has consumer feedback played in a firm’s decision to scale up or discontinue use of a technology tool? Our research on these questions will gather and analyze early evidence on generative AI adoption across key industries including customer service, healthcare, and software engineering.
  2. Skill demand: When workers begin using these tools, how does it change their jobs tasks, and the skills required to do them? Our research in this domain will rely on extensive interviews with and surveys of individuals in jobs considered “exposed to AI.” Although empirical studies have provided a useful summary of jobs with tasks that AI can perform, there is little assessment of how the quality of these jobs could change with the introduction of generative AI. Field research on worker experiences will introduce new data to inform opportunities and challenges associated with generative AI and work.
  3. Dominant design: As these tools are being developed in more contexts (and by more firms) what – if any – are the emerging principles of a dominant design? Our research will be particularly interested in how these designs include or exclude consumers and workers with less resources and technical expertise. Our previous work has emphasized an industrial digital divide, whereby smaller and more rural companies have been slower to adopt new technologies and hire for digital jobs.