AI has the thrilling ability to transform a range of businesses. But let’s be frank: it’s also a beautiful, massive disappointment for many companies.  

Here’s a common trajectory for many AI and data science projects in an enterprise: A company decides to incorporate AI into their business. They spend one to two years searching for AI experts to build a team of solid data scientists, but not necessarily industry experts. The team works for a year or so on a project, only for the company to discover that the project is irrelevant and they need very different people. So they restructure the team, winding up back at square one, four years later.

If you’ve been in this situation, you know how hard it is to find data scientists who can do all the tasks required, which range from soft communication skills to hard statistics. In fact, many companies look for data scientists who can comprehend and engineer the data, build and tweak models, communicate with the necessary teams to understand the business, and deliver applicable solutions. This isn’t a run-of-the-mill candidate search. This is a grand quest for AI unicorns.

The other path companies take is to buy a vertical-specific AI solution from a third-party vendor. The software platform looks sleek and pretty and promises to deliver exactly the kind of predictive power the company needs. But looks can be deceiving, and few products have proven their worth at this point. While vendors claim to be vertical experts, these one-size-fits-all types of products often require significant investment to match the companies’ needs. Many of them do not deliver at all. We are simply too early in the AI transformation.