From our research, we find evidence of AI impacting processes of individual organizations to varying degrees, with common themes regarding implications for workers, necessary cultural components of AI implementations, productivity gains, and the broader complexities under which organizations make these decisions.
All three of our case study organizations — Tata Steel Europe, Axis Bank, and Zymergen — provide insights into how organizations can best practice cultural and change management components of a successful AI implementation. For instance, the momentum of “organizational buy-in” proved a common theme, whether via support from senior executives or a mission centered around the value of analytics. Similarly, in cases of new AI implementations, helping workers to understand the impetus for the AI implementation early-on, ensuring that workers understand how the new programs work through intelligible and explainable AI, and informing these programs with the perspectives of existing employees proved especially valuable.
Additionally, all three organizations reported varying degrees of productivity improvements from their deployments of AI. In tandem, all three instances of AI implementations entailed changes for the way labor operates: sometimes a shift in the roles humans take on, compared with technology; sometimes a shift in the skills humans must possess to thrive in their roles; and sometimes a shift in the number of humans required to complete a process or deliver a service.
Though labor displacements generally happened outside these implementing organizations, if at all, there may eventually be reductions within the implementing organizations. As skilled workers retire, and as companies face increased industrial pressure to operate leanly, companies may decide to cut jobs, rather than shift their workers into other areas. Yet even in these instances, is is possible this job loss may be offset by internal or external job gains elsewhere, which may be more highly-skilled or -compensated than the jobs they replace.
Though each case subject experienced certain common themes, their particular experiences implementing AI were also informed and constrained by the relevant contexts in which the organizations exist. For instance, we note a greater flexibility for Zymergen – an “AI-native” company – in pursuing its programs, than for longer-established companies, particularly those that may have greater local regulatory complexity or may have unionized workforces. Managers, scholars, and others should not overlook these contextual factors when trying to gauge AI’s impact on the broader economy and on individual organizations. No AI implementation is one-size-fits-all, and the details matter when attempting to understand a topic that is too often discussed in broad strokes. For this, we offer the Partnership on AI’s series of extended case studies.