Get AI ROI Unstuck: From Productivity to True Business Value

**SPEAKER_1** (0:02)
AI is everywhere, but what does it mean for your business? Gartner is the world authority on AI, with more than 200,000 client conversations, and more than 6,000 written insights on AI in 2025 alone. Leaders across the C-suite, just like you, are partnering with Gartner to turn AI ambition into impact. Go to gartner.com/ai to learn more.

**Alexis Waringa** (0:34)
Welcome to Gartner ThinkCast, I'm Alexis Waringa. Today, we're tackling a challenge facing nearly every organization investing in AI. Why so much of the promised value never actually shows up on the bottom line. Many leaders assume that if employees become more productive with AI tools, financial returns will naturally follow. But in practice, that value often gets stuck, trapped inside legacy processes, outdated operating models, and siloed ways of working. Previewing one of our recent standout Gartner webinars, you'll hear distinguished VP analyst Fran Karamouzis explore what it really takes to unlock AI, ROI. From rethinking how work gets done to shifting the focus of individual productivity to end-to-end process outcomes, she'll outline why organizational change, not just technology adoption, is the real driver of value.

**Fran Karamouzis** (1:21)
I'm going to start with the first three words of this title. Value is trapped.
80% to 95% of our clients have reported back that they have not delivered financial value or value creation from their AI initiatives. Hence, that's what we mean by value is trapped. There's a value gap.
And this session is really all about how do you unlock that AI return on investment through organizational change. So I'm going to talk about that alignment between the business and the IT function and how you're going to get value and what you need to do first, second, third. Let me kind of give you a synopsis of what we think is going on. We think the pressure is unrelenting. So in 2025, AI investment remains strong.
63% of boards of directors still rank AI and investment in technology and innovation as a top strategy to navigate all this global uncertainty.
Second, we think the investment is going to continue unabated, especially in AI. So while for many years, CIOs tell us, well, our budgets are getting cut, we have to do more with less. When it comes to AI, the potential remains high, and 72% of CEOs are betting that AI is going to be a primary driver for goals. So people are going to continue to spend money.
However, you sort of see AI value remains elusive.
Only 11% of CFOs have been able to concretely measure ROI from AI investments, and that's what's causing all this pressure. We don't think it's going to be acceptable for another full year in 2026, where this kind of thing is going to continue. It's really up to the AI leaders, CIOs, and the entire organization, who will see the entire C-suite, to deliver on this value. So let me sort of jump in and talk about where and start with what we see. First of all, productivity is unequal. What do we mean by that? Pretend we're talking about a call center here, and we have Alicia as one of the call center reps. She's only six months into the role, and her job experience is pretty low.
And also the job complexity, because she's working off of a script, and she feels a very specific set of calls that are categorized and vetted. Her job complexity is actually quite low.
So if in some of the AI tools that are implemented, she gets a fairly large productivity benefit, the individual hours that she's spending on her tasks. And then if you contrast that to Alvaro, his job experience is quite high. He's been five years in the role, but his job complexity is actually quite low. Both of them are doing essentially the same job, but his productivity gains for the same tool is actually a lot less. So one of the elements that people just assume is when they implement some of this AI that they're going to get a fairly equal productivity across the same role, when in fact that's not true. This is even more stark of a number when you look at application developers. When you employ coding assistants, what actually happens in that scenario is the five to seven year application developers, they actually don't trust the tools, and they spend a lot more time, the time that they saved on using the coding assistant, they spend a lot more time trying to break it or doing more testing. And conversely, those that are very new to the role, maybe a one to two year developer, what's happening with them is they trust it too much, and so they're producing a lot more buggy code. So depending on the role, depending on the function, you get the same kind of result, a very unequal level of productivity. The other component of this is that even when you do save a certain number of hours, so if you look at this particular study that we did in 2025, we polled over 1,600 employees who are using AI for at least one core work responsibility. And so we looked at people that saved one hour, two hours, all the way up to five hours. And then we actually looked at their performance as measured by their team manager. And you see a very flat number there, or a flat set of numbers there. Essentially, there wasn't any precipitous increase in performance, and therefore, you're not getting value creation. I had a selection of over 30 different slides that I could have shown you. They all, over and over again, are telling you the same thing.