Hidden Mistakes that Companies Make in their AI Journey | CIO Skip to content
As more companies deploy artificial intelligence (AI) initiatives to help transform their businesses, key areas where projects can go off the rails are becoming clear. Many problems can be avoided with some advanced planning, but several hidden obstacles exist that companies don’t often see until it’s too late.
With a need for speed, organizations must also recognize the fact that almost half of AI projects never make it beyond the proof of concept stage. Blame can go in many directions — such as teams lacking necessary skill sets or little-to-no collaboration among data scientists, IT and business stakeholders. However, there are other reasons projects end up in the AI failure pile.
#1 Watching costs spiral due to data gravity
Many AI teams automatically assume that choosing a cloud-based infrastructure for their models is the best choice in terms of cost and speed. While this may be the case for experiments or initial prototypes, problems can arise when companies attempt to expand AI training to develop a production-ready model or when they see dataset sizes grow exponentially to fuel the AI algorithms.
With growing and more complex data sets, the issue of data gravity can sink an AI project with unmanageable costs if the infrastructure where data is generated is not proximal to the infrastructure where the AI models are to be trained. Data that gets