Orgs are investing in generative AI, but like your duct-taped Shark Tank idea, many projects aren't quite ready for prime time. One major reason: It's really hard to do AI without data, Jim Rowan, principal and head of AI at pro-services firm Deloitte, told IT Brew. "Do you understand where the data is coming from, how it's being used, but also, are you using any other third-party information? And what are the usage rights associated with that? And are you allowed to do that, and what are the ramifications—all those different components that you don't normally have to think about when you see a great demo," Rowan said. According to an August 2024 quarterly report from Deloitte, 67% of surveyed organizations said they are increasing investments in the intriguing output machines known as generative AI. Seven out of 10 respondents also admitted their organization has moved 30% or fewer of their GenAI experiments into production. Rowan spoke more with IT Brew about why data keeps great demos from seeing full-scale, production-level usage. Responses below have been edited for length and clarity. How is an IT pro's job challenged as a project moves from proof of concept to production? If an organization is starting from the beginning and saying, "We're going to have a strategy around generative AI; we're going to form a cross-functional team, and that team is going to include the right people: IT, legal, risk, compliance, key business owners," it can scale pretty quickly because they've got the right team around the table. What I've seen and where IT struggles is when [IT teams say], "We've tried out a solution in a department, but we didn't really involve all the other stakeholders. We didn't think we needed to tap into the legal team because it was just going to be used within this one department." Keep reading here.—BH |
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