Since 2019, MIT CISR’s data research team has investigated how organizations scale AI in the pursuit of becoming AI-fueled. We have discovered that scaling AI requires advanced data monetization capabilities, an AI explanation capability, and a rethinking of scale to realize deployment success, model recontextualization, and industrialized operations. This year we will investigate how organizations are navigating distinct traditional AI and generative AI needs so that scaling AI can produce ethical, compliant, and lucrative outcomes. Also, we hope to understand emergent roles that organizations need for scaling AI, and we will continue to explore knowledge management implications.
This study will primarily rely on interviews conducted with the MIT CISR Data Research Advisory Board regarding the current state of scaling AI in CISR organizations. The research team also will draw on insights generated from the MIT CISR 2018 Data Monetization Survey, as well as from case studies and vignettes, in particular a 2024 case study about Cemex’s AI journey.
We will focus on the following research questions:
- How are acceptable data use and AI explanation requirements different for traditional AI and generative AI? Why is this the case?
- What new roles are required for scaling AI?
- How can knowledge associated with AI models contribute to an organization’s competitiveness and firm performance?
This project is a continuation of MIT CISR’s 2023 research project on Competing on Knowledge: The Next Challenge for Scaling AI.
SEEKING: We are seeking participation from executives at MIT CISR member organizations with familiarity of their organization’s AI scaling journey.
CONTACT: Barb Wixom
