Prove the ROI of Your AI Investment in 90 Days 

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Nichol: Hello, everyone. Welcome to the AI + HI Project podcast. I am your host, Nichol Bradford. We are coming to you live from SHRM 26 in Orlando, Florida, and we're joined by a live studio audience. Say hello, everyone. Hello. It's so great to see all of your faces. Before we dive in, I'm gonna ask a quick question from the audience. 

By a show of hands, how many of you are currently responsible for an AI initiative in your organization? [00:01:00] Okay, great. That's, like, 90% of everyone here. And now, a, a slightly harder question: How many of you feel confident that you could clearly explain the business ROI of that initiative to your CFO in less than two minutes?

Yeah, not yet. Not yet. But that's why we're here and why this conversation matters. So We may have all heard about an AI initiative that improves retention and engagement, and makes our complex lives in HR easier. And we can also really see the big picture for how that improved not only our HR functions, but support the organization as a whole. 

But then we have to convince the C-suite that it's worth the investment. Today, we're gonna unpack what HR leaders need to know to prove AI's value in 90 days. 90 days. To walk us through that, we have Paul Kearney. He is the [00:02:00] founder and president of ISHTOT, and he's gonna tell you what that means. As a part of this episode, you're going to hear some questions about SHRM HRx, and by SHRM HRx, I mean the first standard for measuring how HR drives business performance in everything from talent optimization to organizational impact.

SHRM HRx can help you prove the ROI of HR to your C-suite, your board, truly anyone who needs to understand the bottom-line value of the HR function. Uh, Paul, we're really excited to have you here. Thank you for coming.  

Paul Carney: Thank you, Nichol. Very glad to be part of this, uh, effort and to describe it. Yes, so ISHTOT is an acronym for I Should Have Thought Of That.

Uh, it was my first company. We started it 1997. We turn 30 next year, uh, and built three technology companies, web-based, and that's why I'm excited now to work focus on helping organizations figure out this AI initiative. [00:03:00]  

Nichol: Well, I mean, you've seen such a broad scope of technologies over that time, so that leads into my first question.

So historically, technology initiatives are often led by IT. Why is HR increasingly becoming more central to AI adoption conversations?  

Paul Carney: And, and you know, the biggest reason there is because previous technologies were just that, a spreadsheet, a Word document, those types of things. This is a technology that we're asking people to build a relationship with.

We've never done that before. We're asking people to get to know it, it to get to know them, and that requires a human change management. So where does that fall? That's in the human rela- in human resources area. So while there is a tech component to it, it's much more about the people and the human relations part. 

Nichol: Mm-hmm. Well, one of the things that we've seen in our SHRM research, and especially in our most recent 2026 report, is that 55, 56, excuse me, [00:04:00] percent of HR professionals aren't formally measuring the success of their AI investments, and only 16%, that's a one six, are using a true ROI metric. Why do you think there's such a gap between AI adoption and AI measurement?

Paul Carney: I think the challenge, uh, what I've seen over the past couple of years is working with organizations on this, is- They don't have, human resources folks don't have the language to explain the return on investment that AI gives you. And we're gonna talk about that a little bit more here, but, you know, I flipped it last October into a return on intelligence type concept, and that's the piece that when you develop a language that you can speak with the C-suite, the board, even other leaders, about what you're getting when you implement AI, that return. 

We'll just call it a return, um, because we'll explain the difference in the investment and the intelligence part. But it really is getting that language to describe it because AI is just so different than a normal technology. 

Nichol: Yeah. One of the things [00:05:00] that I, I often say when people ask me about it is that the, um, one, it's not Windows 95. 

Like it's not that at all. It is a, a, um, it's a human- Personal human transformation change that is embedded in a technology. Like, the technology's a part of it, you can't separate it, but the amount of change that happen- needs to happen on the human side to actually get to the ROI. Like, I, I think that's what makes it so interesting.

And then also, the first 90 days. Like, I remember, you know, in my very first job out of business school, one of my mentors was like, "The first 90 days is the part that matters the most." You know? And, and he gave me really great, um, you know, guidance about it, uh, in, in the way that I thought of it. But, you know, that seems like it's also true for AI initiatives. 

And so can you describe why you think, [00:06:00] or, you know, the, the thesis around n- the first 90 days about how they become so critical for proving AI investments? 

Paul Carney: I, it, it really is... A- and AI is just one reason. And most things are the same way, but AI, really in particular, has to meet this 90-day requirement because those 90 days are really just the bridge to really getting things done. 

The... It's easy for stuff to happen in 90 days. It's shiny, it's new, you play with it, you dabble with it. But really, when you build that engagement to the next level, after 90 days, if it's got value, people will continue to use it. But if it doesn't, they're just gonna drop it. It's like a kid with a new toy.

They'll play with it for a while, but then if it doesn't really have any value, it's gone. And AI is exactly the same way. If you don't prepare the teams to really understand how it works, what it is, and it's, it's so very different, that they can't really then bridge to the next level saying, "Okay, this is gonna be a part of my day now." 

So I think that 90-day marker is still very [00:07:00] important for us to get past. 

Nichol: Yeah. A- and something that you said that's interesting is it's not just that it shows value in the first 90 days, because things can be neutral on value, and some things can be negative value. Yes. Like, it actually just causes problems. 

So from what you've seen when leaders launch an AI initiative, what do they most often get wrong? in those first 90 days- And that is- That lead to neutral or negative ... 

Paul Carney: yeah, and that's exactly where I, I... in my speeches, I have an onboarding thing. Here's the thing. When we onboard a person in our teams, we know there's a cost. 

We have those numbers. We know exactly what it's gonna take to onboard that person, develop them, the time it takes to ramp them up, whether they're new to industry, new to our organization, there's still that, and all of our time spent with them, right? There's an onboarding cost. The issue is with AI, we thought it's so smart and all this stuff that it was at the top of the pyramid as a tenured veteran of our organization, but it's not.

It's at the bottom of that thing, and there's a required time for us to get to [00:08:00] know it, build context with it, teach it, learn how it works. And that onboarding time most people are missing, so they get frustrated when they're like, "Wait a minute. This thing should be giving us all this," and you're like, "No, we're still onboarding it." 

And I'll say the onboarding time's a little longer than it is a human being, um, because it involves a lot of people not just, you know, one person on a department or team involves maybe a small handset of people. This is everyone. So it's gonna be you have to build that onboarding time into your calculations, or you're gonna get fr- fr- pretty frustrated because you're gonna be like, "We're not getting anything out of this."

Nichol: Yeah, and, you know, in one of the, um, previous podcasts that we recorded, um, earlier, one of the things that people talked about, it was a, a legal podcast. Uh, one of the things that they were talking about is it, one of the things that people get wrong is they just overestimate what AI can do. And, and so I think it's really interesting. 

You know, you're right. I... as I think about it, um, [00:09:00] people don't really calculate, um, the cost, the ti- the cost of time and, and, um, you know, in order to really onboard a- AI as you would a person. 

Paul Carney: Yeah. We tend to, humans do this anyway, we tend to overestimate in the short term its value and underestimate the long-term value, and AI is no different. 

Nichol: So how do we design this 90-day- pilot to prove AI's value to leadership? Like, what are the steps, you know, if any of these, when, when these people, they're all working on one. Uh, for anyone who is watching or listening to the podcast, what do they do? How do they design a 90-day pilot? 

Paul Carney: So what it should, the way it should happen is I'll break it into three, three pieces. 

It should involve data, f- AI fluency for the staff, and then the tools. Unfortunately, a lot of people are jumping right into the tools because, well, vendors might be selling them, or they're getting and they say, "Hey, we gotta have AI tools," [00:10:00] but they're skipping those first two steps. Your data is really important.

If you don't have really good combinations, if you don't know where your data is and how your data is, 'cause AI loves data. It can take unstructured data and stuff, but if it's not organized and you don't have the security accesses set up to it very well, it's hard because AI's gonna be stuck without data. 

So data's really important. That next thing, though, is the AI fluency. Everybody in the organization has to be able to be comfortable, confident, and competent with what AI is. And y- at least, you know, it's, it's kind of like I, I say this in my, uh, speeches. I talk about if you're gonna learn to swim, I don't care how much I train you.

If you're never comfortable with the fact you're gonna jump in water and keep your head above it, you're never gonna learn. But with AI, it's the same way. If we don't get everyone comfortable that this thing is gonna help us, then build their confidence and build their competence. So those steps have to happen, and then you should be selecting tools. 

And the cool part about it is you should be, uh, making sure if everyone is at [00:11:00] least somewhat comfortable and confident with AI, they'll help you choose the tools. They'll call the BS on the vendors when they come in and say, "Oh, yeah, it's gonna do this." And they're like, "Wait a minute, that's not gonna work that way in my job."

But if they don't have that confidence, they're not gonna be able to help you answer those questions. So in my mind, those na- 90-day projects, pilots, when you're ready to go, should start with data, with the fluency, and then let's look at the tools that can help us do that.  

Nichol: Does everyone have to have the same level of fluency at the same time?

Paul Carney: No. No, not at all. And again, think about it today. H- How long have spreadsheets been around? And there's still people who are really good at using spreadsheets and others who are really not. So the level of fluency's not that important, but it's that they have some f- that they have some confidence that they understand how it works. 

And that's what's different is with a spreadsheet you can kind of show people, yeah, you put numbers in and hit the sum button and it does that. AI is, is that relationship part. It's like, well, what, how's it thinking? What's it think? We teach people, when you really teach them, to challenge AI. Ask it, "How [00:12:00] did you come up with that?

What did you do?" When you've got someone ready to do that, they're pretty confident now they can direct the AI and not just passively accept what it's telling them, but that's a, that's a learning process. We've gotta get people through that first.  

Nichol: Yeah, and, and I think one of the things is that, um- The, uh, you know, I think one of the things is that people really miss, uh, or they underestimate their own ability to ask those follow-up questions.

'Cause I think, like, any parent of teenagers- When you're like, "What were you doing?" Like, if you, like if you're, if you're a parent of teenagers, you have the ability to question AI, uh, generative AI.  

Paul Carney: You do. Well, I jokingly say it's actually like a very smart seven-year-old. And if you've ever guided and managed a seven-year-old, you'll understand, because it acts like a seven-year-old at times and just will start doing things.

But I turned it into how much do you trust a seven-year-old to do s- different tasks, and we've gotta get people comfortable with that with AI too, is how much do you [00:13:00] trust AI to do certain tasks? Some yes, others no.  

Nichol: Yeah. And so what are the kind of questions that HR leaders can anticipate from their C-suite when they're working on a 90-day plan?

Paul Carney: Yeah. The un- the unfortunate part is the first questions you're gonna get are probably about efficiency and productivity. Show me the numbers, how they get more efficient and productivity. Uh, uh, that's where you've gotta come back with that onboarding story and say, "Well, here's the story. We're still onboarding, so we may not see those things right away." 

But what you can do is when you start talking about that return on intelligence, when you're showing how AI helps multiply intelligence or build capability, that's when you do it. And one of the examples I usually give is when you show people how to challenge AI, instead of analyzing a set of data, that's great, but that's looking backwards.

What you ask is, "All right, that's great. You've analyzed this data I gave you. But tell me what am I not thinking of?" Look forward. It turns from a lagging indicator into a leading indicator. What am I not paying attention to? [00:14:00] How will this project fail in six months? When you start to learn how to challenge AI in that way, you start to build capability and intelligence that you didn't have before, because you got a partner that can process this and come up with things you hadn't thought of. 

And when you start tracking that and telling the story, you start showing AI helped us do this, and that's powerful 

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Nichol: So one of the things that, um, our audience loves is sort of really the how-to.

So we've talked about the three parts that people need. They need data, they need fluency, and they need tools. Can we slow down and dig in a little bit? And on the data side, could you, uh, and we'll go through each one. Like, what, you know, in a little bit more of a detailed, um, you know, take it away on Monday type thing. 

Um, tell us more about in a 90-day plan what people should be doing around data. How do they get their data ready? 

Paul Carney: Okay. So it's, it's depends on the level. If you've got a human capital management [00:16:00] system that d- has the data in the HCM, the question is can you tap into that data with an AI tool you have? 

Not just their AI tools, but is, does your corporate AI tool connect to that? Um, but right down simply to if you've got resumes that are in a directory, uh, if you've got a Windows directory and you've got, uh, resumes that are sitting in there, I show people how they can take that, those resumes, run them through an AI process, and then have it create new files in another directory that you can then review and look at, you know, whether it's directing emails for rejected candidates or something like that.

So it's pretty simple. You just gotta make sure you know where your data is. Mm-hmm. Um, I would say start off with a very small subset. Don't try to boil the oceans on this, and that's another problem is people trying to think big. AI will get there, but really start small. Think about very, very specific things you're doing. 

And the other challenge we've seen over the past couple of years, or la- year in particular, people get the tools, they know they wanna build a workflow to do something, but their tool does not connect to their data system, [00:17:00] so they can't really work on it. So we usually help them generate synthetic data so that you don't stop learning.

You actually can generate synthetic data that looks like employee records that isn't real, but it helps you learn how AI could do things for you. Um, so that's a challenge, too, with the data part. But really it's just taking some piece of data that you currently work with and know really well, and then start planning, how can I use this? 

And you're probably gonna come up with in the end, you may come up and say, "Listen, um, this isn't really gonna help us be more productive or more efficient," or, "We may not have the right tools or the right connection to data. We may not have..." But document that and set it aside, and actually put a little follow-up date because it changes.

In the next month or two, you could now have the skills, you could now have data, you could now have stuff, but that documentation, we know about HR, when you don't doc- when you don't don't document it, it didn't happen. Document that you went through that exercise and then come back to it later on. Don't be afraid to say, "We got all the way through this. 

It took us a few weeks, and then suddenly we realized it's not gonna be effective," because you still [00:18:00] learned. You still gotta- Yeah ... that tell that story. 

Nichol: So is that data part, is that... Do- should I think of that as my first 30 days, or is that before my 90 days starts? It, well- Is it in it, or is it-  

Paul Carney: Yeah, you really need to...

Before you start that 90 days, you gotta make sure you know what data you have access to and capability of, right? Have, have we built a good data system that I can... You know, if you've got an HCM, it probably flows, but can I get it out of it in order for me to use AI?  

Nichol: Okay. So it's like the prerequisite for the 90 days.

It really is  

Paul Carney: a pre-req, yeah. 

Nichol: And then the fluency, uh, is just the fluency of the people that I'm gonna start with.  

Paul Carney: Correct. It's more than just showing them how to do prompts. It's showing them how to be comfortable with what this thing is and how it works, and, and that you direct it. That's the other thing.

I've seen so many people that still think that it's smarter than them, that, that, that it's just gonna... And, and it's not true at all. When you learn to direct it, when you learn to challenge it, when you re- learn to tell it, "Do that over again, you missed my point," um, you know, that's the type of thing. One of the biggest things I- I've taught [00:19:00] people, and this is a great question, is after you've done something with AI and you ask it, "What 10 assumptions did you make because I didn't give you enough information?" 

And it comes back and shows you all these things, and of course, it's like, oh, these things, I didn't want you to make assumptions. So you provide more context. Yeah, it 

Nichol: makes a lot of assumptions. You...  

Paul Carney: Right. So you start teaching how to provide context, 'cause that's what's really important today in how you're interacting with AI, is you've gotta give it a lot of context about what you're expecting, what you think.

If it doesn't have that, it starts making assumptions. So- And  

Nichol: so for that fluency, 'cause w- what I'd love to do is for, you know, since we're gonna dig more into the 90 days, but right now we're on the prerequisites of data fluency and tools, I would love to be able to, you know, share with the audience, like, how long should that prerequisite take?

You know, so is it, is it really, is it really 120 days and, and I do 30 days to focus on, okay, I'm gonna get my data together. I'm going to, you know, take an initial group of people. We're gonna get them, [00:20:00] you know, all to the same level of comfort. Um, and it also aligns with where we're starting, and we've picked our tools. 

Yes. Like, how, how should we think about that? 

Paul Carney: So the data depends on what level of data you're working on, and if it's coming from a big system or small system. If it's, uh, local in your own directories, it's pretty obvious you know what you've got and you can deal with. So I would say within 30 days you should be able to identify the data set you wanna work with. 

The fluency, we've seen an eight-week program that actually is probably the best one. Um, and the key with that program is it's gotta be specific to their job, not just a bunch of generic prompts that they learn. It's gotta be very specific. We do meta prompting, where they build a job profile and experience profile, and they each get the same lesson, but they generate a prompt according to their own profile and job, so they're all running different prompts because it's to their world.

Then at the end they realize- That's better learning ... this is, this, this is mine. They own it. They're like, "I now understand what AI can do for me in my job," 'cause that's what they care about. Um, [00:21:00] so that fluency, you can get them to that level within eight weeks. They're... I've seen the light bulb switch off on people very fast. 

And then at that point now, they can tell you, "Hey, here are some ways we might look at how AI can do that," and that's how you get into your pilots. That's how you get into that 90 days. Now you've identified, what can it really do for us? 

Nichol: And so then, then let's move into the, the 90 days. What are the, what are the milestones? 

So I'm planning out my 90 days. What are, what are the main milestones that I'm going to have? 

Paul Carney: The first thing is completing a sort of a four-box thing that says, "Can we answer all these questions? Are we get ... Do we have the ethics and privacy data controls in place, the security in place? Do we see what business we're transac-" That's another thing is most people start to use AI and don't really have a business solution. 

What's, what's this really trying to do for you? So when you've got all of that involved, within that first 30 days, you've got the plan laid out, and now you start playing with your tools. You start designing your prompts, building templates, and sharing with each other. Here's what works. Here's what doesn't work.

Building common [00:22:00] libraries of what I call snippets so that you've got a common voice. You know, there's a common tone, a common structure so that you're all working to the same level. That's that next 30 days. So in this case here, now you've got your system, what you're doing. Uh, you've got your ideas. 

You've got your system, and now you start really in that last 30 days saying, "Here's what it can do for us." 

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Nichol: So a- as we talk about proving AI's value in the first 90 days, I would like to connect the [00:23:00] conversation to SHRM HRx, AKA the first standard for measuring how HR drives business performance. One of the dimensions of SHRM HRx is what we call market discernment, or the ability to understand business needs, labor market trends, and workforce expectations in order to make smarter decisions Because what we've seen is that high-performing HR functions don't adopt technology just because it's new.

They're using data, market insights, and business priorities to identify where the investments will have the greatest impact. So with that in mind, I would love to hear your perspective on how organizations can make the strongest case for AI and demonstrate the meaningful value early on. So what workforce trends should HR leaders be paying much closer attention to as they're evaluating the AI investments or also what they're gonna focus on in this 90-day plan? 

'Cause it needs to be relevant to [00:24:00] the organization, so how do they look and decide what the 90-day plan should be on? 

Paul Carney: Right. So one of the things that fits into that, and I love the f- the four dimensions that are coming out in the HRX. I've looked through them. I love the ... I've read the white paper on it and, and love the study of this because it really is helping us put number- put, put arms around what we're trying to do, especially when you fit AI into it. 

So in my mind, what we've got to do is, is that, that fluency where we've got people comfortable, because honestly, I think there's still too many people that keep hearing they're gonna lose their jobs or jobs are gonna go away, and I think there's so many people fearful of that, that it, it's the workforce trend is gonna be we as in leaders have to start paying attention to that trend because at some point, if they continue to resist and possibly even actively engage against AI, then we're in trouble because that's gonna be difficult for us.

So I see those workforce trends, and we've got to pay attention to that, which means we gotta back up and make sure we're bringing them along with us on this. That's really important and, and one of the trends I think the HR needs to pay attention to. [00:25:00] The other side of that is, right, um, great book, uh, in fact, uh, Sangeet Paul Choudary was at the conference in San Francisco. 

Nichol: Love him. 

Paul Carney: Awesome guy. He's got a book called Reshuffle, and that is an amazing book because he talks about it's, uh, the, the future of AI's impact is really not about efficiency and productivity on the individual level. It's gonna create and change entire ecosystems, our entire workforces, but we don't know quite what that's gonna look like. 

But he makes very good cases of how in the world other ways this has happened, and when you start to apply that to AI, it starts to give you a clear picture. And I, I love the fact that he does that, yes, at, um, seeing him at, um, the conference in San Francisco reaffirmed that, that we've got to ... HR people really need to pay attention to the fact that it is gonna change this.

We don't know exactly, but if you watch what he talks about, we start to see the picture evolve.  

Nichol: Mm-hmm So, um, when you're seeking executive buy-in during those first 90 days, [00:26:00] what are the, what is the market data or workforce insights that are most effective in building credibility and securing support?

Paul Carney: Yeah. So a lot of folks will, will turn to market data, and I think some of the places that they turn to the most is they'll go out and grab all the data on what AI is doing, and people have... Gartner has stories. Everyone has stories. We saw last August, you know, only the 5% are getting ROI. So everyone started to use that to say, "What are we doing?" 

Um, I think what we need to do is focus back internally on what we are d- are telling our stories, how AI is doing, I call it that, capability building and intelligence building, a- and stop treating it like a cost center and more like an investment, that we can actually show that when we're investing in this, it's gonna do this capability.

We're gonna be able to do this. Coders will be able to be done faster. We'll be able to release software quicker. Um, as an operations team, we'll be able to get manufacturing done faster. Um, I helped a small manufacturer redo their entire input area just by [00:27:00] taking pictures with their phones and asking AI to identify what was missing, what they weren't thinking of. 

20% gain on what they were... They could store things in there they couldn't store before because AI recommended configuration changes that they had never thought of. 

Nichol: And so in, in the spirit of storytelling, um, for the first 90 days, um, if you had a headline or a, a movie title- ... for each 30 days, like what's the first one? 

Paul Carney: A movie title? Ooh, that's a tough one. Hell- 

Nichol: Or just a headline. Just a headline. Like if you were to call it something.  

Paul Carney: Right. 

Nichol: So we can, so we can share with everyone, okay? And you kind of talked about it a little bit before- Yeah ... but the first 30... 'Cause we talked about the prerequisites- Yeah, yeah ... which is like the pre-30 and what people can do across, um, uh, data fluency- Mm-hmm 

and tools. And so now we, and we've decided we're, we're in our 90-day plan. What is the first 30 days? 

Paul Carney: Yep. So I would say a, a headline would be something like Organizational X-ray Identifies Five Broken Assumptions. Wow. We discovered things [00:28:00] with AI that we didn't even know or we, we were assuming. Like I said, this group was assuming they had their infrastructure, their in- intake area set up the way they needed to move all these parts around all the time, but AI showed them you really don't. 

Here's a better way to do it in ways they hadn't thought of. That's an organizational X-ray. It shows you something. It gives you surprises you didn't expect. 

Nichol: Great. Uh, and then the next 30 days, what's that?  

Paul Carney: And this is the, the next 30 days is now where we're into that fluent- or not the fluency, where we're actually building the systems.

Nichol: Yeah.  

Paul Carney: So this is a cool one. I'll give you this one here, is Rejected Candidate Turns into Ambassador for Company. And I have a true story where a rejected candidate, because we changed the way they sent rejection letters, actually recommended two people who got jobs at the company. She's a champion for the organization even though she got rejected.

Nichol: The last 90 days, what, what's that headline?  

Paul Carney: That last 90 days is, here is what we've learned, what we [00:29:00] prevented, and what we discovered You tell a little story about something you learned, something you've prevented and stuff. And the prevention part's another one. The whole risk management, AI is considered a risk to organizations.

But flip it around as a risk management platform and say, "Here's how we collected information across all candidates from the managers so it's consistent rubric of grading, and then now we can defend any decision. If someone comes and challenges us that we discriminated against them, nope, we have all of it right here, defensible." 

Nichol: And then what happens after the first 90 days? 

Paul Carney: Well, at that point, this is where the team looks at it and says, "Is this worth it?" Right? Uh, "Do we keep going? Can we get more value out of this? Do we see a path to ROI?" A- again, in the end, every organization, for-profit organization, has to prove we gotta make more money or save money, and a nonprofit organization has to say, "We've gotta take money we're getting and v- uh, allocate it efficiently." 

And you gotta prove that. So, uh, as much as I c- talk about return on intelligence, I'm a businessperson too, as we all should be, [00:30:00] that you have to show in the end how it's gonna hit the bottom line. So at the end of the 90 days, you've got to plan this is the time it's gonna take. Like we do with onboarding an employee, we know there's that upfront cost.

It's gonna take a while to get an ROI on them. But if you don't have a plan that says, you know, by 9 months, 12 months, 16 months in, if that employee's not producing we got a problem. And the same thing's true with these projects.  

Nichol: So that's the, so that's the ROI. So at the end, return on intelligence. Um, and so at the end of the 90 days then, uh, we have a return on intelligence plan based on what has happened and our lessons learned- Correct

and how it fits on our system. And then the, the return on investment part, how do we tie those two together?  

Paul Carney: So that's right. You still have to tie those two together. So it's great that we built this capability, but does it help us make more money or save money? Is it gonna get us ahead of the competitors before they are able to do something?

You've got to tie it back to that return on investment piece. Yeah. How do you do that? So it, it all depends on what you're tracking. Um, so [00:31:00] again, if a capability for human beings, if you've got people who are now more capable, um, I'll give you a quick example. If you've got someone that's saved, uh, uh, five hours in a week, and I have a story about this where a woman saved five hours in a week, but at $50 an hour she's paid $100,000 a year. 

But if she didn't take those five hours and do something with them, effective, that's not gonna help. This one woman we took from doing menial tasks to doing a succession plan she'd put on the shelf for a while. We increased her per capita value. The bottom line of the company didn't change, but her per capita value went from a $50 task person, pretty expensive, to a $50 strategic person, pretty cheap.

Nichol: Paul, thank you for sharing your insights today. And to our audience, we look so forward to continue to explore the intersection between artificial and hu- intelligence and human intelligence with you. And looking forward to seeing you next time on the AIHI Project Podcast.[00:32:00]  

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Nichol: SHRM