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Overview

MIT's Work of the Future Initiative is convening a multidisciplinary working group of industry, policy, and academic leaders to understand how generative AI tools can contribute to higher-quality jobs and inclusive access to the latest technologies. Launching in fall 2023, the working group will produce case studies on AI best practices; host summits to facilitate knowledge sharing; and conduct trainings featuring latest data from the field.

Executive Summary

Program Goals

  1. Convene a network of scholars and AI industry leads at biannual summits to exchange data and best practices regarding AI deployment, responsible use, and workforce training.
  2. Train future AI leaders by supporting MIT graduate students as Work of the Future Fellows to study applications of generative AI in practice, developing expertise that they can apply industry, policy, and academia.
  3. Publish case studies identifying the latest and most effective uses of generative AI tools through in-depth research with organizations at the technology frontier.

Engagement Opportunities

RESEARCH PARTNERSHIP: welcome MIT scholars and graduate students to conduct a case study on your organization's uses of generative AI tools. Industry partner receives access to data and insights on how they compare to their peers.

BENCHMARKING STUDY: participate in a three-part survey on how your organization uses generative AI. Receive a customized benchmarking report with survey data that put your organization's technology uses in context.

BIANNUAL SUMMITS: attend conferences including industry and academia featuring working group research and discussion of the biggest obstacles and opportunities in deploying these technologies.

Research Themes

Led by AI researcher Julie Shah and social scientist Ben Armstrong, the working group is interested in how new technology tools like generative AI can improve productivity and product quality for firms, while also enhancing flexibility and job quality for workers. Our research agenda falls into three categories.

Scaling generative AI: 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 expand 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 retail, manufacturing, healthcare, finance, insurance and real estate (FIRE), and software.

Jobs and skills: 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.” Field research on worker experiences will introduce new data to inform opportunities and challenges associated with generative AI and work.

Dominant designs: 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.

Next steps

1) Arrange introductory call.

2) Agree on research scope for case study.

3) Determine company point of contact.

Interested in participating? Contact Ben Armstrong (armst@mit.edu)