9 Lessons learned from failed AI PoCs
After several AI PoCs, I realized that it is quite easy to launch AI PoCs with initially positive results, but at the same time, it is difficult to scale up AI to enterprise-wide applications and reach the production stage. In this article, I’ll share some of the reasons why I failed in a couple of projects.
Data
When working with organizations, I noticed that only a fraction of decision-makers fully understand the importance of having a good dataset by that I mean a well-curated collection of data to train an AI system.
This lack of curated data is easily one of the biggest pain points and barriers to moving from a proof of concept to a production system.
When working with large organizations, the issue isn’t so much that there isn’t enough data, but that the data is locked away and hard to access. It takes so long to gather the necessary data since you need to explain/convince so many different managers. Machine learning won’t work if your data is rigidly siloed.
Another issue frequently encountered is data quality…
Data quality is a perception or an assessment of data’s fitness to serve its purpose in a given context. The quality of data is determined by factors such as accuracy, completeness, reliability, relevance and how up to date it is.
