AI Is Ready, Your Workforce Isn't: Why AI ROI Falls Short

**Karen Stokes-Lockhart** (0:02)
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Welcome to Gartner ThinkCast. I'm Karen Stokes-Lockhart. Today, we're tackling one of the biggest barriers to AI value creation, human readiness. While AI capabilities continue to accelerate, most organizations are still struggling to turn that potential into real business outcomes. Not because the tools don't work, but because workforces, roles and leaders aren't ready. The hard truth, technology isn't the bottleneck, people are. In this episode, previewing a Gartner Webinar, VP Analyst Alicia Mullery breaks down what's actually happening. It's not a job apocalypse. It's hiring restraint, role redesign and a fundamental remix of talent. You'll hear why only a small fraction of AI initiatives are delivering true ROI, how IT and business roles are being reshaped by AI, and the essential skills every employee now needs in today's environment. Now, here's Alicia.

**Alicia Mullery** (1:33)
I am excited to take you through some of the big concepts that we've been sharing with our clients all over the globe. So let me dive right in and start off with just kind of the state of where organizations are around AI right now. At the end of last year, 2025, one out of five AI initiatives were achieving ROI. So about 20 percent, and one in 50 were achieving true disruptive transformative value. So for a lot of organizations, they would say to me, it's all right, but we want more. We want more value. And when we look at what does value mean, what's the kind of value that's being seen, from a CFO perspective, 74 percent say that they're seeing productivity gains. So this is like Alicia saved 26 minutes a day, or is making faster decisions. That's good, but it's not a financial number. We can take out the bottom line. And that financial ROI, only about 11 percent of CFOs are saying, yes, we're seeing clear ROI. So obviously, this is not good enough for most CFOs and for most organizations.
And what's happening at the same time is there is this adoption gap. So if you look at the first curve up at the top here, we call it the AI innovation race. This is everything happening out in the industry. This is when you wake up every morning and you look at your phone and there's a million podcasts and news updates about the technology space, about what the vendors are creating and what are the capabilities that are out there. That is growing very rapidly and high. Whereas the second curve at the bottom, this is the AI outcomes race, the ability for organizations to get value from all that technology. You'll notice not only is there a gap, but it is widening and it is the widest gap we have seen between the technology that's available and the ability for companies to capture it and get value out of it. This is the state of what a lot of organizations are feeling right now. If I had to sum this up in one sentence of where we think companies are, what I'd love you to take away from today's session, is this, not all the AI technology is ready, right? Not all of it is 100%, there's still things to be developed to there, but it's a whole lot more ready than human side. And when I say this human side, I mean everything within our organizations, right? AI readiness is helping us understand, is this science fiction or is this real? Like what's the capability that's out there in the marketplace that we can access? Human readiness is everything within our organizations that lets us take that opportunity, capture value and then keep getting it. It's our workforce, it's our organizational structures, it's our processes, it's our change management, it's our data capture, it's everything within our organizations to capture that value. And that is the bigger gap consistently across organizations. It's not working right now that we need to improve. So not to say everything as I said before, not to say that everything with AI isn't ready, but the human readiness side is more important. So I want to take you through a couple of things that I think are important on this human readiness. Number one is first, in our organizations, do we even understand what value is? I think a lot of IT leaders on the line, you have a definition of it. If we interviewed your CFO, your head of HR, your marketing team, your CEO, everybody would have a different definition of value, right? I'll take you through each of the three layers here. We think there are three types of value with AI. The first one we call defend. So this is about value. These are about use cases where we are augmenting our employees. This is about using AI, in many cases, generative AI to save some time, to write better emails, to get decisions done faster. And that's great stuff, but typically it's not going to be a financial return. It's not something you can take off the bottom line. So we call this ROE, return on employee. In the middle is what we call extend business cases. And this is about using AI to create competitive differentiation. So using AI to re-engineer an end to end process that you have, or to use AI to create better pricing for your customers. That is where we see financial gain. In fact, what we see is somewhere between, you need to re-engineer somewhere between 30% to 60% of your end-to-end processes to see this financial ROI. But that's where you'll see it in these kinds of business cases. The third kind of value is what we call an upend business case. So this is about using AI to completely disrupt the marketplace, to discover new products, new services. We call this return on the future or ROF. Not because you won't get a financial return on it, but it's just a longer term bet, right? You're not going to get it immediately. And why I think this is important is because what ends up happening is a lot of times I'll hear an executive or a business leader listen to a podcast, talk to a vendor, and they'll see an extend business case. So they're going to accept ROI, but then they go buy a defend use case and they get ROE. And so there's this disconnect that ends up happening of what kind of value we're expecting with the value we're getting. So one of the first pieces of human readiness is really clear on the value we want. And my recommendation here would be to take a portfolio approach here. If you think about all the 100 pennies and how much do you want to put into AI investment, I would probably only put 20 of those pennies into defend business cases. And maybe more like 50 or 60 percent of those pennies in the extent, right? And depending on how aggressive and risk a risk averse you are, how much you really want to upend. But this is number one, right? Having a language around value and be really clear of what we're looking for. Now, after that, one question that comes up a lot, it's often an elephant in the room. When I talk about human readiness is on the people sides about what we sometimes hear referred to as this job apocalypse. Has that happened, right? We see in the news, in a lot of headlines, all of this talk about AI replacing jobs. So we've done a lot of research into this and continue to look out into it. And what we have seen, the first half of last year, we did a large analysis and we continue to keep track of it. Almost 80 percent of job loss for last year had nothing to do with AI. It had to do with uncertainty, it had to do with tariffs, it had to do with inflation, it had to do with lots of things.