Data and Analytics 2030: The Future AI-Native Enterprise

**Alexis Hueringa** (0:00)
Welcome to Gartner ThinkCast. I'm Alexis Hueringa.
As AI moves from experimentation to enterprise-wide transformation, many data and analytics leaders are asking the same question. What will actually create competitive advantage when AI models and infrastructure become commodities? In this episode, you'll hear Gartner distinguished Vice President Analyst and Chief of Research Rita Sallam in a preview of a recent standout webinar. She'll share Gartner's vision for the AI native enterprise, including the shifts organizations need to make between now and 2030
Drawing on real world examples, she explores why success with AI depends on far more than adopting new tools. It requires investing in trusted data foundations, rethinking how teams work alongside AI agents, and building the context that turns information into actionable intelligence. Now, here's Rita Sallam.

**Rita Sallam** (0:52)
Welcome to Data and Analytics 2030 Here, we'll discuss three shifts that you need to make to create sustained value as a data and analytics leader in the age of AI.
But I'd like to start this discussion with a topic that's near and dear to my heart, and many of you, mammograms. The word mammograms, I know, it elicits different emotions depending upon your age, your gender, or whether or not someone you love has been saved by one. For most women of a certain age, like myself, it's a dreaded annual pilgrimage. And while traditional AI-assisted mammograms have been around for quite some time, they detect current issues. So, a May 2025 FDA-authorized algorithm actually predicts breast cancer, five years in advance, with 70% accuracy.
Now, radiology-assisted mammograms, they don't predict. So, this is a game changer. And how could they do this? Well, they achieved this by, from the sort of innovative, critical thinkers at Clarity and kungfu.ai, two vendors, they combined a unique data set of decades of global and diverse longitudinal mammogram data, and they also applied federated learning techniques to protect privacy, to solve what is a massive problem that will transform the standard of care and save countless lives.
So, my question for you really is what I hope you think about during the session, and maybe afterwards as you go about your day-to-day work, is through 2030, as a DNA leader in your organization, how will you create unique value?
And so, that's sort of the key. Because really, as models and underlying infrastructure commoditize, and they're commoditizing quickly, you need to deliver on essentially three things, right? You need to create highly discoverable, trusted, high-quality, and importantly reusable data and contacts for that data, just like Clarity did when they created this breakthrough mammogram algorithm. Now, imagine getting rid of all your dashboards by converting that highly discoverable, trusted, reusable data and data products into perceptive and proactive and fluid intelligence that's on tap and ready when anybody needs it. You'll probably need to do that as well, and then you'll need to deliver that always-on intelligence to smaller teams of broadly skilled, AI-augmented, likely exceptional critical thinkers who know how to ask the right questions, who can frame the right problems, and maybe even in small teams of the smallest two people, one tech generalist, one business generalist, with agent specialists assisting their work. And you'll see some examples of that today.
What we do know from all of this experience with AI, particularly generative AI since, let's say, late 2022, delivering value is really hard work. And most executives don't understand what it takes. They think you're just going to buy more tools, more AI tools, more ChatGPT, and voila, transformation. But you've all heard the numbers. Our particular surveys show that approximately four in five AI investments have not resulted in measurable ROI. Now, that's the bad news.
The good news is those numbers don't tell the full story. In fact, one in five are quite happy with their investments. And so what do they do differently? One of their attributes is that they spend four times as much as a percent of revenue on foundations. Foundations that you are responsible for as DNA leaders, on data, on governance, on people, and people transformation and change management. They spend four times as more as a percent of revenue than those that are least satisfied. And so how do we get there? How do we be that one in five? Well, it all starts with your AI ambition.
What are your business objectives with respect to AI? How you invest in those foundations, how you execute to build those foundations, your degree of change, all of that will depend on your organization's AI ambitions. And of course, where you are today, how far do you need to go? That's the question. How far, not only do you need to go, how far will you go? Will you keep the same process, processes, and augment them with AI?