What are you doing to weigh the risks of GenAI?

CIO.com reported “When it comes to AI, the fear of missing out is real. According to research by Coleman Parkes Research on behalf of Riverbed, 91% of decision-makers at large companies are concerned their competitors will have an advantage if they get ahead with AI. So it’s no surprise that every respondent said that when it comes to gen AI, they’ll either be using it, testing it, or planning projects with it over the next 18 months.” The October 9, 2024 article entitled " Weighing the risks of moving too fast with gen AI” (https://tinyurl.com/2r9aaes2) included these comments about “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.

What do you think?