Subject organizations were recruited from a pool of 100+ candidates that ‘AI, Labor, and the Economy’ Working Group compiled. The final set of organizations prioritized for study reflects a combination of their willingness and ability to participate in the project and the intention to profile organizations representing a variety of sizes, geographies and industries. To this extent, the subjects of these case studies were not chosen randomly and were sourced through existing relationships within PAI’s AILE Working Group. Zymergen has raised $574M from investors such as SoftBank Vision Fund, Data Collective, ICONIQ Capital, and McKinsey & Company (a co-author of the case studies), among others. During the time of writing the case study in fall 2018, the company had raised $174M. On December 13, 2018, the company announced a $400M Series C round from multiple investors. See coverage of the announcement on Bloomberg and the Wall Street Journal. Also, the subject organizations may be subject to selection bias. For instance, companies that have had successful experiences implementing AI at their organizations may be more eager to discuss their results, particularly if they have done so without adverse impacts to labor. Yet sourcing these case studies through existing relationships also allowed for an elevated level of trust and candor in probing more deeply about companies’ experiences.

The three case studies were developed over the course of six months in late 2018 and early 2019. Key sources of insight included:

  • Interview-based research with a set of key decision-maker stakeholders from subject organizations such as business unit heads, functional leads, data scientists, and developers. Interviews were generally limited to decision-makers for AI/ML-related applications and their use cases. Interviews thus represented a managerial perspective and did not involve other workers who may have been directly impacted by AI/ML-related applications such as plant operators, call center employees, or R&D lab technicians (and thus should caution over-extrapolation from case observations). Further details of interviewees include:
    • Tata Steel Europe: Ten employees were interviewed (three senior executives, one plant manager, one technical director, two data scientists, one data engineer, one automation engineer, one analytics project manager). Interviews did not include steel plant operators, who are more implementation-focused.
    • Axis Bank: Five people were interviewed (four senior executives within Axis Bank and one employee from the third-party technology vendor contracted to develop the chatbot). Interviews did not include customer service agents, whose jobs had been outsourced.
    • Zymergen: Ten employees were interviewed (three senior executives, one data scientist, two business development representatives, four scientists at different levels of seniority). Interviews did not include lab technicians or Zymergen’s customers.
  • External supplemental research was conducted by representatives from not-for-profit and for-profit organizations affiliated with the Partnership on AI. External research primarily focused on gathering broad industry and macroeconomic insights in the form of expert consultations, literature scans of third-party publications, and general web research.

Representatives from the Partnership on AI’s non-profit and for-profit organizations supported the case study development by conducting interviews, drafting case documents, and supplementing the case with external research or expert interviews on industry and macroeconomic dynamics. Research was syndicated and reviewed by various stakeholders throughout development, including the “AI, Labor, and the Economy” (AILE) Working Group of the Partnership on AI.

We hope these case studies will be insightful additions to the research landscape, and wish to complement these with follow-on research that may help to further address our objectives.