Original article was published on Artificial Intelligence on Medium
5 Challenges to becoming an AI-Driven Enterprise
Almost every single company is being pushed to innovate, and the favorite flavor of innovation currently is Artificial Intelligence (AI). In this article, we will cover some common challenges a company faces in the journey to becoming an AI-Driven Enterprise. Data scientists are difficult to hire. They are scarce and in hot demand, have many options at their feet, which translates to a bidding war with a high risk of churn. For most companies, they will spend about 1.5 years just hiring the team. The next step usually will take another 1.5 years finding out you hired the wrong people, firing them, and starting over. In addition, another difficulty in hiring data scientists is that most companies are not well equipped to interview skills necessary for their businesses. Now let’s assume it is a few years later and you now have a good data science team. Most likely this would be a centralized team — a central team of excellence that serves the whole organization. Based on my experience, I had always claimed that on average 90% of data science projects never fulfill their promise of bringing real value to a company. I later saw statistics from Gartner saying the rate is around 80%, VentureBeat claiming 87%. This is independent of the quality of data scientists you might have. The cause of this high rate of failure is not technical in nature. About half of those projects fail because of bad problem formulation. Let’s take a look at an example. Imagine you sell t-shirts online. One day, Tom, a logistics manager, notices that every year the company spends $15 million in returns. He thinks that maybe AI can help and goes talk to Sarah, a data scientist. Tom will probably collect some historical data on which orders were returned and ask Sarah to build them a model to predict which orders will be returned. Sarah goes hard at work and after a couple of months, comes back with a very accurate model that she is proud of. She presents the results to the whole company, and now you ask how you can use this to impact your business. You see it in use. When someone makes an order on your website and the order is shipped, you now can perfectly predict if that order will be returned or not. Now what? That’s right. Now nothing. There is not much you can do and this model will not be much help to your business. Would you consider to have become an AI-Driven Enterprise? Probably not. This sounds exaggerated and nonsensical, but this happens more than often. The details and use case has been changed but I have seen this same problem a dozen times in companies of all sizes, including Fortune 50 Companies. This stems from the lack of communication between the data science team and the business team.
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