Extra-condensed knowledge
- AI efforts can fail to move out of the lab if organizations don’t carefully manage access to data throughout the development and production life cycle.
- Not infrequently, the devil is in the details of implementation
- A lack of understanding by all stakeholders of the full dimensions of data quality, and the siloing of AI initiatives away from operations, can limit the impact of AI projects or derail them altogether.
Genioux knowledge fact condensed as an image.
The “genioux facts” Knowledge Big Picture (g-f KBP) chart
Condensed knowledge
- We recently studied how organizations move their AI initiatives from R&D, lablike settings into production and the problems they encounter in doing so.
- The research is based on interviews with key AI leaders and informants in six North American companies of different sizes and operating in different industries.
- A key finding is that, although many people focus on the accuracy and completeness of data to determine its quality, the degree to which it is accessible by machines — one of the dimensions of data quality — appears to be a bigger challenge in taking AI out of the lab and into the business.
- More important, we found that data accessibility is too often treated exclusively as an IT problem. In reality, our analysis reveals that it is a management problem aggravated by misconceptions about the nature and the role of data accessibility.
Category 2: The Big Picture of The Digital Age
[genioux fact extracted from MIT SMR]
Authors of the genioux fact
References
The Data Problem Stalling AI, Gregory Vial, Jinglu Jiang, Tanya Giannelia, and Ann-Frances Cameron, December 08, 2020, MIT Sloan Management Review.
ABOUT THE AUTHORS
Gregory Vial is an assistant professor of IT at HEC Montréal. Jinglu Jiang is an assistant professor of management information systems at Binghamton University. Tanya Giannelia is a Ph.D. student in information technologies at HEC Montréal. Ann-Frances Cameron is an associate professor of IT at HEC Montréal.