Why do a majority of AI Projects fail ?
4 Key Reasons and what Leaders and Practitioners can do about it
I spoke at WiDS Kerala yesterday addressing one of the conundrum’s we see – why do we have 9 out of 10 agreeing that AI represents a business opportunity but 7 out of 10 companies see minimal impact from AI.
Check out video for the 20 minute session
#WinningwithAI
#reviewswithranjani
Sources referenced -
[1] https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/
[2] https://sloanreview.mit.edu/projects/winning-with-ai/
[3] https://www.cio.com/article/3429177/6-reasons-why-ai-projects-fail.html
[4] https://towardsdatascience.com/dont-do-data-science-solve-business-problems-6b70c4ee0083
[5] https://designingforanalytics.com/resources/failure-rates-for-analytics-bi-iot-and-big-data-projects-85-yikes/
[6] https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/
[7] https://hbr.org/2013/03/a-data-scientists-real-job-sto
[8] https://quanthub.com/advanced-analytics/
[9] https://sloanreview.mit.edu/article/the-building-blocks-of-an-ai-strategy/
[10] https://www.datanami.com/2020/07/06/data-prep-still-dominates-data-scientists-time-survey-finds/
[11] https://stvp-static-prod.s3.amazonaws.com/uploads/sites/2/2014/04/3328.pdf