The more projects that fail in the early stages, the more likely it is that when projects do go into full production, they’ll provide positive business value. And the more companies find success with particular projects, the more likely they are to run even more experiments, and find more opportunities to create value.
In fact, if all of a company’s POCs and pilot projects go into production, that could just mean the company isn’t being creative, innovative, or experimental enough with their AI ideas. Looking at the number of projects that go into production is not a good metric of success, no matter how much pressure is coming from the board and other senior executives, business unit leaders, employees, partners, or customers.
So it’s not a contraction that most pilots fail while companies also report positive ROI. This isn’t a bug. It’s a feature. The goal is to have the right AI projects in production, not the most. And the biggest mistake a company can make is to skip straight to full deployment, without a pilot project or adequately vetting the results of the POC, says Rosha Pokharel, chief AI architect at UST Healthproof, a healthcare operations company.
