How Do You Prove AI ROI When The Return Is Only 1%?

David Rice: Only 17% of organizations have deployed AI agents at all. The multi-department number is around 3%. JLL has stopped counting theirs. On today's show, I'm talking with Carlin Power, Global VP of AI and Platform Technology Enablement at JLL, about what the other 97% are about to learn.

Because JLL isn't experimenting with agents. They're running them in production across facilities management, brand communications, and corporate functions company-wide, and the lessons from that aren't mostly technical. The first thing that broke wasn't the technology, it was the ROI conversation. How do you justify the cost of table stakes?

How do you sit in front of your CFO and say, "We might get a 1% return on this"? Power's answer was to put the value conversation back where it belongs, inside the P&L that's making the investment decision. If a business leader is trying to grow, share a wallet with top clients by 20%, AI becomes a part of that conversation instead of a hammer looking for a nail.

The early career question deserves some focus in this topic, too. Stanford Research draws a hard line. When AI automates, entry-level hiring falls. When it augments, employment holds or grows. JLL made a deliberate choice, and the interns they've sourced recently presented a capstone work so sophisticated that without being told, Power wouldn't have guessed that they weren't there for three or four years into their careers.

So today we're covering what separates agents that survive from the 40% Gartner projects will be canceled by 2027, why ROI framing matters more than the technology itself, how to build AI fluency across eighty countries without losing local relevance, and what a chief people officer with zero agents in production should do in months one through three as they roll them out.

I'm David Rice. This is People Managing People, and if your organization is still figuring out where to start with agents, this conversation gives you a practical blueprint from someone who's already well past that question. So let's get into it.

Welcome to the People Managing People podcast. As always, I'm your host, David Rice, and today I'm joined by Carlin Power. She is the Global Vice President of AI and Platform Technology Enablement at JLL. A lot of organizations out there are experimenting with AI agents but you've actually deployed some successfully. Sure. So-

Carlin Power: Yes, we have ...

David Rice: we're gonna talk a little bit about that and some of the lessons learned through that.

And so the first question I wanted to ask you was around sort of, I'd seen a Gartner CIO survey that was saying, you know, only 17% of organizations have deployed agents at all.

Carlin Power: Sure.

David Rice: And I think the multi-department number is 3% or something like that, right? Yeah, yeah. It's really low.

Carlin Power: Yeah.

David Rice: So for a lot of people, they are at the beginning of this. And you've released, I- the last number I saw was, like, 34. I think you're at more than that now.

Carlin Power: Yeah. We're growing. I think we've just stopped counting.

David Rice: You just stopped... Well, so my, my first question to you though is what do you know now that the other 97% are about to learn?

Carlin Power: Sure. Well, I think it actually kind of goes back to some decisions that we made initially, and we had this investment in what we call JLL Falcon, which is a fancy brand name for a collection of AI capabilities and APIs that all of our software developers could utilize cost-effectively to deploy AI capabilities on technology that they were building brand new and existing technology that they already had in the marketplace.

And so because of that and because we were able to decentralize innovation across all of our engineers they started upskilling a lot faster in how to build and deploy things like agents. So our- we have, you know, agents in our facilities management business that are optimizing work orders and, like, how technicians get deployed to, to sites to, you know, do their facilities management work.

We have brand agents that kind of brand our PowerPoints and our tone of voice for communications that are both internal and external at JLL. We ha- we just deployed you know, about a month and a half ago, Ask JLL, which is a collection of agents that all our domain experts across our corporate functions that help employees get support across all of our corporate functions.

So because of that decentralization across all of those engineering teams, we're just building agents for bespoke business problems- Mm ... you know, against very deliberate ROI outcomes.

David Rice: Is that part of why you think it's been success- You mentioned the bespoke nature of it. Is that part of what's made it successful, is that you're not sort of generally trying to solve something, you're being very specific?

Carlin Power: Yeah. I mean, we do have general agents and we do have general purpose AI tools. So we have a general AI assistant that we call Pulse, affectionately- ... that looks across all of our chats, all of our emails, all of our shared documents. It kind of acts like an administrative assistant that really takes a lot of the burden of the administrative, tactical things that you have to do in a knowledge worker job.

And so we did intentionally deploy some general capabilities, but yes, the, the real ROI, 'cause that, that stuff's more s- like ta- what I would call table stakes, right? Right, right. So that's stuff that all of our employees need in their hands just for us to be able to compete, right, in the marketplace.

All of the the agents that are being deployed by our software engineering teams that support a particular business line or a corporate function are doing, are solving bespoke business problems that drive our business outcomes forward, and that's aligned all the way up multiple layers of the organization.

David Rice: So take me back to agent one, you know? What broke that wasn't technical? Was it permissions and ownership or-

Carlin Power: Yeah ...

David Rice: someone's job description, essentially?

Carlin Power: Yeah, right. Agent one, wow. So that's actually Pulse, so, Okay ... also known as the JLL work tools because now we deliver that through you know, an MCP or the, the fancy word for a, a connector right into s- an agent into structured data.

So, so I actually think that it wasn't really a, maybe a technical thing that broke. It was more of how do we justify the cost of this against ROI? And so I was talking to you a little bit about table stakes, right? Yeah. It's a pretty hard conversation to have when you're really only seeing, you know, efficiency numbers in the 1 to 3%, like- Yeah

range, right, where it's kinda hard to sit in front of your CFO and say, "Hey, so I think we should do this thing, but we're, we might get 1% return on it." So it was initially trying to figure out what is the w- how, how do we articulate the value of table stakes? How do we a- articulate the value of a particular agent?

And is that gonna be a standard that we set for building, right? So, how do we make sure that every single builder in our organization can say "I'm gonna deliver this agent to solve this problem that before cost us X amount of labor or you know, delivered X deliverable. But now what it's going to do is we're gonna be able to be at 5X throughput, or we're gonna be able to generate a new line of revenue, or we're gonna be able to increase our market share or our share of wallet for a client."

And once you start getting your teams thinking about it in that way the, the ROI proving out is a lot simpler to justify. But that was the first thing where it's like, where they asked us "Hey," you know, "when should people use this? How should people use this?" "What's, what's the ROI?" And we're like, "Good question."

David Rice: Well, that's interesting you mention sort of, making that, you know, normal normalizing that. Was there a standardization of language that went along with that, or sort of, understanding what qualifies as ROI- yeah ... for, for the people who wanted to do these things? That, that's 'Cause I could imagine people's imaginations might go a little wild.

You go, you gotta go like, "Hey, that's not actually like a real business value."

Carlin Power: Yeah. Thankfully, we have some great finance professionals in our organization. And, and that's actually though it's a centralized function, right, like within finance, it's decentralized in terms of how they sit within our business lines and our functions.

So, our finance team has spent a lot of time thinking about how do we give our teams frameworks for actually coming up with an ROI conversation. And, and I think, or an ROI equation. And I think that's maturing now. I wouldn't, I wouldn't say that, you know, we're I have the answer to that because if I did, I would just quit and start a billion-dollar, you know-

consultancy overnight as would everybody else, but-

David Rice: Yeah ...

Carlin Power: but I do think that it is putting the conversation about value back in the place where it belongs, which is in the P&L that is making the decision about the investment. And so if if a business leader is saying "We need to increase our share of wallet with our top 10 clients by 20%," AI is now part of that conversation instead of it, i- instead of us being a hammer, like, looking for a nail.

David Rice: No, I think it was Gartner projects, like 40% of agentic AI projects will be canceled by the end of 2027. I guess my question to you is like, when you look at the agents that you've built, what makes you confident that they'll be resilient and that they'll be, they'll go on beyond that?

Carlin Power: A- again, it kind of comes back to our our decision to deploy JLL Falcon, right, and then the decentralized decision-making in our engineering functions that are now tied to business line KPIs.

We don't build anything or at least we're trying not to build anything that doesn't have an attributable ROI or a bankable savings now. And so, it, it makes the, the conversation really easy like when, when you're looking at the AI agents that are doing well or doing not. Are they, are they meeting their targets just like an employee would be meeting their business targets, right?

Did we... Di- is, is this technology, is this agent doing what we expected it to do? Is it realizing the benefits that we expected to realize? And if not, why, and should we continue to invest, right? So s- a stop, start, or continue conversation needs to be happening periodically over any of the agents that you're deploying in your, your organization.

David Rice: You mentioned the decentralized decision-making. Was that something that you all built in from the start? Was that a part of your, your processes before you started all this? Or-

Carlin Power: The foundation of our AI strategy is is our enterprise data layer, right? Okay. So we've spent the last five to six years under our CTO Yao Moran's direction in building data fluency and accuracy and health and relevance in all of our data basically in our enterprise data warehouse, right?

And so every single piece of data that we have in JLL goes into our enterprise data warehouse. So then there's a set of governance capabilities that define what that data is, what it's relevant for, who it's relevant for, and how people get access to that information, right? So we have a really robust data layer that sits at the bottom, and then we have JLL Falcon that sits on top of that data layer.

So, you know, when we get into the MCP or the connector conversation, right, that's just going straight into our enterprise data warehouse and pulling data out that's relevant to the user consuming it. So that- that's our you know, that's our- our scaling function, right, from a c- plus a cost effectiveness and just from a- a data relevance perspective.

And then we have a citizen-driven AI strategy, and we have a centrally driven AI strategy. And our- our centrally driven AI strategy is where the decentralization that I've been talking about takes place. So there are initiatives for each business line and corporate function technology team against that strategy to deliver the differentiation that we need in the marketplace.

And the centrally or in the- the citizen-driven strategy is how we're enabling people internally as an enterprise to be able to use our our- our tools and to develop an internal AI fluency. You know, that's- that's like our enabling function, right? So you've got the found- the foundation, which is our data.

You have the scaling function, which is our JLL Falcon platform. We have our our differentiation function, which is that centrally driven, and then we have the enablement function, which is our citizen driven. And then that all rolls up to our three business outcomes across like delivering you know, client value and unlocking excellence in our operations and- and innovation.

So So I think that is core to why we've been, been successful. But, you know, to your question, which was, you know, was that decentralization decision deliberate? Yes, it really was. Did we make it right away? I can't remember. But is that where we ended up? Absolutely.

David Rice: Yeah. One of the, one of the questions I wanted to ask you is I've seen some Stanford research that kind of drew a hard line that, you know, when AI automates tasks, entry-level hiring falls.

Sure. And when it augments, employment sort of holds or grows.

Carlin Power: Yeah.

David Rice: So across 34 agents, or, or, you know, more now, which side of that line do, do you think you've landed on? A- and sort of was it a decision or an outcome?

Carlin Power: Sure. So I think that if a company is focused on efficiency and productivity, and thus an outcome is to reduce you know, labor, early career labor, I think that's more of just an immaturity in their overall AI strategy and engagement model.

And so I, I, I don't, I don't know... it was never our deliberate for us to, to go in and say, "Okay, we're gonna have all of this cost out in the organization, and we're gonna go eliminate 40% of jobs," and, and all of that. I think that was a lot of, you know, at the keynote this morning, they were calling it fearmongering, right?

I, I just feel like that's probably too extreme of a position. And I, and again, I think it's short-lived. I think it actually can be really detrimental to an organization's success to completely cut off your early career talent.

David Rice: Yeah.

Carlin Power: And I actually just this last Monday was sitting at, with our sourcing and procurement interns, and they were presenting their capstone projects a lot of which had used our internal tools and some of the advanced labs that we have partnerships with.

And if, if they wouldn't, if, if the, the head of sourcing and procurement wouldn't have told me that they were interns, I would have told you that they were three to four years into their professional careers because of the depth of knowledge they had about very complex sourcing and procurement problems how close they were with the client accounts that they had to work with to improve like our you know, how much assets we have under management, how much of the contracts we have under management.

And I was... I mean, I learned things. I was, I was blown away by both what they were able to do in such a short period of time and our I was even more blown away by the fact that at the very end, our sourcing and procurement chief basically said, "You all have jobs here at JLL if you want them." And, and I, I think that is a really good example of we're not, we shouldn't be cutting these early career jobs.

We should be setting completely different expectations about what we should be getting out of early career talent. And so we've completely re-architected our early career strategy around kind of these project-based capstones that really create value out of these internship programs that we h- hold inside of JLL.

And so again, I just think that it's probably an immaturity thing and that I think pe- that they will eventually get there, and I would caution firms against trying to eliminate that early career ladder because I, I just think that that's actually where a lot of your innovation is gonna come from in the next- Yeah three to five years.

David Rice: Well, I love that you said that 'cause I've long been a pro- proponent of, you know, like the capstone thing reminds me of it's almost more like an apprenticeship.

Carlin Power: Yeah.

David Rice: We think of internships, and it's I mean, I remember when I did one in college- ... and it's it was pretty boring.

Yeah. There wasn't a whole lot of responsibility that went along with it. Yeah. You know, it was sort of like-

Carlin Power: I think I answered phones and- ... and did filing.

David Rice: Exactly. It was like, it was not that in-depth that I, I went out of there going, "That was that important?"

Carlin Power: Yeah.

David Rice: You you know? So-

Carlin Power: Yeah, put it on my resume.

David Rice: Yeah, okay, I did that. But yeah the apprenticeship model, which I think that kind of gets at, is like you actually do. You are learning. You are doing the work.

Carlin Power: Right.

David Rice: And, and doing it at a depth that you can, like you said they sounded like they were three, four years in.

Carlin Power: Yeah. It wa- it blew me away, and it, and it helped me set a new standard and, and educate the organization on what we should expect out of our early career talent.

And I hope that also sets a, an organizational level fl- competency around we should probably also be specting- expecting a lot more out of our professional workforces. And we've been educating our leaders on what I would call kind of evolving your value proposition. And we take them through a multi-module training workshop where and you would be surprised or maybe you wouldn't be-

but it was very hard for them to articulate their current value proposition to the firm. And so going through hey, when your entire team is augmented, enhanced, replaced by AI, what are you gonna do with that value, right? What, what, what are you... How are we gonna recapture that value, whether that's h- higher throughput, better quality deliverables, more time with clients more time to, to build future lines of revenue?

David Rice: You know, like people first, I think it needs an instrument or it's just words, right? It's a slogan, basically. Yeah. I'm curious, you know, what is yours? Like, how did you distinguish adoption from compliance from people who are just sort of routing around the, the thing?

Carlin Power: Yeah.

David Rice: The agent.

Carlin Power: That was another very early intentional decision.

We actually spent a good chunk of our budget on engagement and enablement professionals- Mm ... that were helping with at the very beginning, helping all of our employees establish a level of AI fluency. So it was everything from introducing them to new tools kinda giving them fundamentals around how generative AI works and how we're protecting their data and our clients' data, getting them really comfortable with all the questions that were coming up at the very beginning, which is what happens when I when I, you know, upload a client income statement into, you know, into a GPT?

It's like, well, here, we've built it so it's safe and it's firewalled, but, you know, use your best judgment. And so we were also helping them with the balance between what you can trust the generative AI to do and what you can't, right? A trust but verify methodology, right? And getting that ingrained into people very early helped them with the acceleration of adoption of other more advanced capabilities as they were, as they were coming out.

So we had you know, we were building tools. We were rolling them out. We were training people on it. We were-- We built a champion program that could take all of those trainings that we had built and tailor them for the individual roles and personas within all of our business lines and corporate functions 'cause we're a massive company with, you know, 80 in-- operating in 80 countries, right?

There's a lot of things that have to be considered at the local level that a central function isn't designed to support at scale. So we had to enlist the help of the experts within those domains to help us establish the next level of fluency that our, our teams needed to be able to start innovating on their own.

David Rice: So, we're running up against time, but before I let you go, I want to give you a hypothetical- Sure ... 'cause, you know, I, I love a hypothetical.

Carlin Power: Sure.

David Rice: So you have a chief people officer, let's say they have zero agents in production at this point. But their board is starting to put pressure on them.

Okay. They wanna see some progress. They're asking why that's the case. In there. Right? So they got 12 months, they got a budget.

Carlin Power: Yeah.

David Rice: What do they do in months one through three to get themselves- Sure ... get the ball rolling, and what's the thing that they'll be tempted to do first that maybe you think, "Don't do that, that's a little bit of a waste of time"?

Carlin Power: Yeah. I think the first thing is to establish a group of people who you're very comfortable with experimenting across the organization. I'm not sure if I would go full tilt into FDs, but domain experts in your business line and corporate functions that are technically literate that can start to help you define what are some real use cases that even experimentation is gonna be net ROI positive.

Trying to go really wide and really broad all at once is gonna incur a lot of cost and if you don't have people internally that are educated on your different domains in your business or aren't educated on the AI side of things you're just gonna balloon costs and- Yeah ... and you're, it's basically what you've outlined through-

these questions is gonna start happening to you.

David Rice: Yeah.

Carlin Power: Right? So, building success in a few use cases and then scaling those, and then using that ROI to reinvest in more or the more horizontal, what I would call horizontal or general purpose tools delivered to your organization, I think is key. And the other piece is that enable net function, that fluency.

You have to get your people really comfortable leveraging tools, and you have to incentivize them to use it. I think something that I continuously reference and am just so grateful for is our, our CEO, Christian Ulbrich, who really set the vision and empowered people to use AI, right? And, and set global KPIs around fluency and adoption and usage.

So there was no punishment for using these tools, right? It wasn't like, "Oh, you figured out how to automate your department? Cool, we're just gonna let everybody go." That was, like, never, that was never a narrative that existed inside of JLL. It was we need to be able to leverage AI to best serve our clients and better serve our people.

And so all of our executives, one, had to build KPIs around the achievement of that, and also role model those behaviors. Our CEO was very straightforward around, you know, every single one of you needs to be using- AI tools and I'm gonna measure it," right? Because if your people don't see you using it, they're not gonna feel comfortable or empowered to use those tools.

And he wanted the innovation to come from the bottom up- Right ... just as much as he wanted the role modeling to come from the top down. So in giving a, you know, any C-suite leader advice, I would say you need to role model that behavior as well, and you need to align incentives to the behavior that you wanna see in your organization.

David Rice: Well, thank you for coming on the show today. Carlin Power, ladies and gentlemen. Until next time.

Carlin Power: Thank you so much.

David Rice: Well, listeners, if you haven't done so already, head on over to peoplemanagingpeople.com/subscribe. Get signed up for the newsletter. You'll get this podcast and everything else that we create straight to your inbox.

And until next time.