Scaling AI to Generate Better and Different Outcomes
Name
2021_1201_ScalingAI_WixomSomehGregory.pdf
Size
2.2 MB
Format
Adobe PDF
Checksum (MD5)
9d1e38d674d41ecd2a137fabccb7f53f
Author(s)
Wixom, Barbara H.
Someh, Ida A.
Gregory, Robert W.
Date Issued
December 16, 2021
Publisher
Cambridge, Mass.: Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology
Citation
Wixom, Barbara H., Ida A. Someh, and Robert W. Gregory. “Scaling AI to Generate Better and Different Outcomes.” MIT CISR Research Briefing Volume XXI, no. 12 (December 2021). https://dspace.mit.edu/handle/1721.1/174856.
Series/Report no.
MIT CISR Research Briefing, Vol. XXI, No. 12
Abstract
Since 2019, MIT CISR has investigated fifty-two AI solutions to learn about AI scaling: growing the value created by both a core trained model and recontextualized adaptations of the model. This research has identified that scaling up happens when an AI solution moves from core model development to pilot to production, with increasing value creation. Further, scaling out happens when an AI model is trained using new data and new expertise, which are required for a new related application. This briefing describes both AI scaling dimensions within the context of an AI project journey at Pegasystems.
Subjects
Data monetization
Information Technologies
Data, AI, and Analytics
Terms of Use
CC BY-NC 4.0 (Attribution-NonCommercial 4.0 International)
Persistent DSpace Link
