Where should you start with AI in your enterprise?
It’s not about chasing hype. It’s about focusing on areas where you already measure success, where your teams are stretched thin, and where AI can plug into your workflows without months of prep work. The best AI projects are the ones that leave you with more than a one-off win—they build reusable assets, internal capabilities, and lasting impact.
At the 2025 Gartner Data & Analytics Summit, AnswerRocket CTO Michael Finley and Overproof’s Chief Analytics Officer Dr. Dr. Ed Dobbles, DBA break down what makes an AI use case worth pursuing - and how to spot opportunities that deliver measurable value, fast.
Watch the full conversation to hear how leading enterprises are finding and delivering on their best AI opportunities.
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When you look at the space, there's probably some places you need to think about where this is going to work better than others and the 1st 2 are definitely would say. Just like I said, you train your people, you teach people who have done stuff before. So if you have a system, a process, a template, a way of thinking about things. This is going to be a great tool for you if you want to come up things from scratch, you probably have to do a couple of iterations to get there. If there's areas where you have resource gaps or skills gaps, this is a great tool to expand your capacity radically and you're going to want to use it for that. Yeah. And so then, then the other, the other obstacle lookout for is projects that require a whole lot of early pre work, right? We so many organizations that I walk into say, yeah, we we need to spend, you know, a year and a half working on our microservices, then we'll be ready. Year and a half is an eternity in the Gennai world, right? Or or yeah, if if only my data were, were were perfectly ready to go. We've spent 30 years trying to make our data perfectly ready to go, spending untold amounts of money on that. The language models don't need these things to be perfect. They don't need it to be excellent. They need it to be the same caliber that a human would need for it to start working. And you're, you can, you can use models to drive those results. The same thing applies to the the idea of, well, how do I, how do I drive additional value from the work that I've already done? How do I reuse these? Assets, right? We're used to the idea of writing a report and using it for many different things. Well, in the Jenna I world, you, you can it, it can be very similar. You can create assets that, for example, integrate a language model with different parts of your organization. Maybe it's your HR documentation, maybe it's your customer service records, maybe it's your manufacturing data. When you integrate those for one AI use case, that becomes reusable across a lot of other AI use cases, right? So, so it's, it's important to look for those. Opportunities to create that reuse that allows you to then sort of very quickly get from your first use case to your second, third, 4th, 5th, and then finally situations where your partners are willing to commit to shared goals, right? This is basically looking across the organization saying, alright, what are the goals that IT has? What are the goals of the business has? What are the goals that that finance has? All of those need to collaborate in order to make this thing come true, right? In order to make that make a I ready to go. If you're fighting against the goals of any one of those groups, then the whole thing's going to get delayed and you're never going to get a chance to start. That flywheel of ROI that you're Gennai solution can bring.
