CIOs’ lack of success metrics dooms many AI projects

“People think that AI is in some way magic, that it’s going to be a point that’s going to solve all the problems in one go,” he adds. “There is a reasonably significant amount of work in dealing with AI, depending on the use case. It isn’t just a case of picking something up off the shelf and running it.”

In some cases, a failed AI experiment may be educational and point organizations to better projects, Curtis says. But many organizations, after seeing a high majority of their AI POCs fail, may stop experimenting.

“A lot of financial services companies that I work with don’t have a risk culture,” he says. “If something fails and they spent millions of dollars on it, they’re likely not to do it again.”