“Last September, a data scientist named Peter Skomoroch tweeted: “As a rule of thumb, you can expect the transition of your enterprise company to machine learning will be about 100x harder than your transition to mobile.” It had the ring of a joke, but Skomoroch wasn’t kidding.
Among the biggest obstacles is getting disparate record-keeping systems to talk to each other. That’s a problem Richard Zane has encountered as the chief innovation officer at UC Health, a network of hospitals and medical clinics in Colorado, Wyoming, and Nebraska
It took a year and a half to deploy Livi, largely because of the IT headaches involved with linking the software to patient medical records, insurance-billing data, and other hospital systems.
When Genpact, an IT services company, helps businesses launch what they consider AI projects, “10% of the work is AI,” says Sanjay Srivastava, the chief digital officer. “Ninety percent of the work is actually data extraction, cleansing, normalizing, wrangling.”
To develop a system like this, “you have to bring your domain experts from the business—I mean your best people,” she says. “That means you have to pull them off other things.” Using top people was essential, she adds, because building the AI engine was “too important, too long, and too expensive” for them to do otherwise.”
