For example, technology that can help scan documents, with filters to ensure patient information isn’t accidentally exposed, might be deployed in the future. But today, when it comes to clinical trial site visits, the company is focusing on non-sensitive types of information first, such as the physical equipment being used.
“We can take a picture of the refrigerator and scan for when the maintenance was done, the temperature it’s set at,” he says. “We want to make sure all the right conditions in the facility are in place.”
Taking the time for groundwork
Besides public embarrassment, loss of customers or employees, or legal and compliance liabilities, there are also other, more technical risks of moving too fast with gen AI.
For example, companies that don’t do proper groundwork before rolling AI out might not have the right data foundation or proper guardrails in place, or they might move too quickly to put all their faith in a single vendor.
“There’s a lot of risk that organizations will lock themselves into with a multi-year spend or commitment, and it’ll turn out in a year or two that there’s a cheaper and better way to do things,” says David Guarrera, generative AI lead at EY Americas. And there are organizations that jump into AI without thinking about their enterprise-wide technology strategy.
“What’s happening in many places is that organizations are spinning up tens or hundreds of prototypes,” he says. “They might have a contract analyzer made by the tech shop, and a separate contract analyzer made by the CFO’s office, and they might not even know about each other. We might have a plethora of prototypes being spun up with nowhere to go and so they die.”
Then there’s the issue of wasted money. “Say an organization has FOMO and buys a bunch of GPUs without asking if they’re really needed,” he says. “There’s a risk that investing here might take away from what you actually need in the data space. Maybe what you actually need is more data governance or data cleaning.”
The rush to launch pilots and make hasty spending decisions is driven by everyone panicking and wanting to get on top of gen AI as quickly as possible. “But there are ways to approach this technology to minimize the regrets going forward,” he adds.
Move fast and break things might be a fine slogan for a tiny startup, but it doesn’t work for larger organizations. “You don’t want to put billions of dollars and your markets at risk,” Guarrera says.
