Objectives and Scope Objectives and Scope
The impact of artificial intelligence (AI) on the economy, labor, and society has long been a topic of debate — particularly in the last decade — amongst policymakers, business leaders, and the broader public. Estimates of its current and imminent impact have varied widely, often reaching contradictory conclusions. One major question for the public and policymakers has been AI’s impact on the workforce, both in the changing nature of work and net job loss or creation. Another major question is whether AI can enable an acceleration of productivity growth, which has stagnated in many economies. At the same time, a salient question for managers, strategists, and economic analysts has been whether large investments in artificial intelligence and machine learning are warranted: Can the promises of AI be realized, and if so, what are their potential impacts on the various stakeholders involved?
To help elucidate these various areas of uncertainty, the Partnership on AI (PAI)’s Working Group on “AI, Labor, and the Economy” (AILE)See Acknowledgements for more information conducted a series of case studies across three geographies and industries, using interviews with management as an entry point to investigate the productivity impacts and labor implications of AI implementation. We, PAI’s aforementioned AILE Working Group, publish these case studies detailing varied applications of AI with the following objectives:
- To tell a detailed range of stories about the contexts under which organizations deploy AI implementations, and how they manage these
- How do country and regulatory complexity affect decisions to proceed with AI implementations, and in what forms?
- How do companies build AI into their culture and processes, and reconcile its implementations with existing programs?
- What common themes exist regarding enablers and impediments of AI implementations, and how can other organizations and audiences learn from these themes?
- To consider AI’s impacts at the level of an organization, especially on workers, business results, and processes, in addition to the macroeconomic view so often discussed Researchers have argued for the need for “more systematic collection of the use of these technologies at the firm level.” The case study project intends to provide quantitative and qualitative data at the firm level. For more, see “AI, Labor, Productivity and the Need for Firm-Level Data,”Manav Raj and Robert Seamans, April 2018.
- What specific challenges do organizations face when integrating AI and what steps do they take to address them?
- Where do we see evidence of organizational changes from AI in individual organizations that may foreshadow broader economic impacts?
- To provoke and inform conversation about AI’s productivity and workforce impacts
- Are there examples of AI systems enhancing productivity?
- Whether AI enhances productivity or not, what are the implications for workers?
- What factors can engender worker trust and broader organizational buy-in of AI systems and solutions?
Through this synthesis document and the accompanying case study materials, we aim to ground the conversations around workforce impact and productivity in real-world examples of AI implementations. We recognize the methodological and scope-related limitations of this body of work, which we hope will inform ongoing conversation and provoke further inquiry.
This Key Learnings and Synthesis Document focuses on key observations and broader trends across three distinct case studies. It introduces the three subject organizations along with their applications of AI and presents our key observations across broader themes. For further detail, please see the individual case studies, which present a more thorough perspective into the AI implementations at these organizations and the resulting impacts on processes, business results, and the workforce. Terms and techniques used, as well as methodology for this report are also presented below.