How to Use AI to Hire Better: AI Recruitment, Screening & the Future of Hiring | James Terry

Hiring is getting faster, noisier, and harder to evaluate. In this episode Chris talks with James Terry, Head of US Revenue at Indeed Flex, about how AI is reshaping recruiting from application screening to workforce planning. James explains why rising application volume is pushing employers toward AI interviews, how human review still fits into the process, and where AI can help recruiters evaluate candidate skills at a scale traditional hiring workflows cannot handle. 

They also explore how AI can move HR beyond administrative work by connecting workforce, operations, and performance data, plus how James uses tools including Gemini and NotebookLM to accelerate proposals and decision support. The conversation ultimately shifts from replacing jobs to redesigning roles, building stronger AI fluency, and giving teams access to the data that makes AI genuinely useful. Listen for a practical look at what AI adoption becomes when leaders move beyond experimentation and apply it to real operating problems.

Chapters

00.00 Introduction
01:57 AI Hiring and the Race to Build AI Fluency
06:42 Putting AI to Work Across Revenue and Operations
10:17 Why Application Overload Is Breaking Traditional Screening
11:51 AI Interviews at Scale With Human Review
15:55 Can AI Identify Better Candidates?
21:30 Turning HR Into a Strategic Business Function
24:57 Using Workforce Data to Reduce Turnover
30:34 From AI Experiments to Real Workflows
34:48 How AI Will Change Jobs and Roles
41:41 Connecting Data, Building a Second Brain, and Shaping Vendor Roadmaps

Resources:

🔎 Find Out More About James Terry

James Terry on LinkedIn: 
https://www.linkedin.com/in/james-terry-33023717

Indeed Flex:
https://indeedflex.com/ (Indeed Flex US)

Indeed Flex contingent labor webinar featuring James Terry: https://indeedflex.com/employers/resources/industry-reports/webinar-contingent-labor-in-manufacturing-competitive-advantage/

🛠 AI Tools and Resources Mentioned:

Indeed Smart Screening:
https://www.indeed.com/employers/smart-screening 

Google Gemini: 
https://gemini.google.com

Gemini Gems:
https://support.google.com/gemini/answer/15235603 

Google NotebookLM: 
https://notebooklm.google.com

Snowflake: 
https://www.snowflake.com/en

Tableau: 
https://www.tableau.com/

ChatGPT: 
https://openai.com/chatgpt/overview/

Chief AI Officer: 
https://chiefaiofficer.com

Using AI at Work: 
https://usingaiatwork.com

Transcript

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SPEAKER_02 0:35

You post a job, 300 people apply. Well, no one's gonna go through 300 resumes, especially when two years ago I was getting 30 resumes.

SPEAKER_01 0:43

How do they handle this application overload environment?

SPEAKER_02 0:46

I mentioned AI interviews. Like now you can apply for the role within Indeed Flex, and you immediately have an opportunity to do an AI interview. Now, our AI interviews are still reviewed by humans, right? So a human is still looking at the interview to validate, because again, we're still early on in this AI evolution. The great part about it is that from an efficiency standpoint, our recruiters have gone from doing 12, 15 interviews a day to now they're able to review 80. What? So it does it for you, it does it on the candidates' time, it stack ranks the candidates and still gives you the ability to go in and actually review the recording before you take any next steps to do an in-person interview.

SPEAKER_01 1:22

Are the right people being hired more often as a result of all of this?

SPEAKER_02 1:26

We find that actually AI can do a better job than a recruiter of actually validating truly their skill set on that.

SPEAKER_00 1:32

Welcome to Using AI at work. I'm your host, Chris Dag. Each week we'll be learning how today's business owners, entrepreneurs, and ambitious professionals are getting more done with smart use of tomorrow's tech. Let's get started.

SPEAKER_01 1:45

Right now, every business leader is asking the same question. What are we going to do about AI? If this is you, ChiefAIOfficer.com has the answer. We give you a simple path forward where we provide executive and team training so your people know exactly how to safely use generative AI in their day-to-day. We also manage the deployment and implementation to make sure tools actually get adopted and deliver results. And we'll also guide company-wide transformation so AI becomes part of your operating system, not just another shiny object. The companies that act now will increase productivity, cut costs, and grow faster than their competitors. Those that wait will get left behind. So if you want to make AI work in your business, visit ChiefAIOfficer.com and see how we're helping companies of all sizes finally get results from AI. Hey everybody, welcome back to another episode of Using AI at Work. My name is Chris Daigle, and I am your host of the show. And today we're going to be talking about a topic that we've recently had a few episodes about because I think it's very important. And that's AI's impact on like the labor market and hiring and making sure that you're getting the right candidates and you're not getting bamboozled by AI when it comes to uh resumes and things like that. And the guest that we have today, James Terry, is a perfect fit for that. He's a leader at Indeed Flex, which is a division of Indeed. If you've ever done any online hiring for even the smallest of businesses, you've run across Indeed, so you know that. And his practical lane is how is AI impacting recruiting and screening and labor scheduling and workforce operations and all of those things. And the reason that he's such a good fit for this is because he's speaking for the vantage point of somebody who's selling and implementing AI-enabled hiring tools, not just as a pure you know, theory or this is what could happen. They're actually doing this stuff at uh Indeed Flex. And the good news, James isn't just doing it in a job from our pre-interviews, he's an active practitioner and user of AI himself, using Gemini and Gems and Notebook LM and all of those things in his day-to-day work. So um, James, before we get started with kind of the meat of the episode, um, anything that you think the listeners need to know about uh kind of your background and and how AI is is changing things for you?

SPEAKER_02 3:54

Yeah, Chris, I would say uh I'm probably in a similar boat to a lot of people in that uh I have recently in the last, I don't know, six, twelve, eighteen months, uh started to hear and focus more on using AI. And uh one of the things I've noticed is I've gone to conferences, talked to a lot of peers that are in similar roles to myself, is that everyone is starting to is really trying to figure it out, but no one has the secret sauce. And so if you are out there and you're kind of starting to dabble, and I think where a lot of people start is like, oh, it helps me write emails, it helps me with internal documents or whatever, that's great. But um really focus on seeing how you can level up on those that skill set because uh I would say that like no one, like while you know, you've got this podcast using AI at work and you're probably ahead of where I am. Um and there's a lot of people that are at varying degrees of like their AI usage, but you know, this has only been around for a handful of months or or a year or so in to any large extent. Um and there's no doubt that it's gonna continue to grow and continue to change the world. I'd say the big, the big thing, the big question for me is um are you like are you one of the people that's a laggard or are you at the forefront? Um are you one of the people that's pushing ahead? Because no one has truly 100% figured it out yet, right? No one has actually figured out the secret, the magic bullet. And and everyone, and so even if you haven't really started, you're only six months behind, right? But in a year, in 18 months, in two years, you're gonna be too far behind to be able to catch up. And so, from the standpoint of like growing your own business and growing your capabilities and your productivity, that's one side of it. But another really, really big part that I think is super critical is like, you know, you have an opportunity to really be a front runner within the industry that you're in as a whole, uh, whatever that is, whether you're in finance, sales, operations, whatever that might be. And and and the quicker you take advantage of it and start to figure it out because no one else really has, the better.

SPEAKER_01 5:55

I think that's fantastic advice and quite accurate. Um, are you dialing in from the Indeed Tower in Austin? Is that where you're working from?

SPEAKER_02 6:04

I am dialing in from Indeed in Austin. Yep. The big tower? No, not the big one. We're a little bit north. We're about uh 15 minutes north in an area called the Domain. Uh no, so we're not at the downtown one, although I do go down there sometimes.

SPEAKER_01 6:16

For those of you that aren't familiar, it's to the Austin skyline, like Austin's known as a certainly a tech hub and very entrepreneurial environment and that sort of thing. And the the downtown has transformed over the past decade. And one of the biggest buildings in downtown is the Indeed Tower. I see that as a sign that I know that they're they're renting out other stuff and it's an investment, those sorts of things. We're talking about online recruiting, right? And and and uh like managing the labor force and all those sorts of things. This is a huge topic. So if you're listening to this and you're like, well, I'm a small organization, or somebody else in our organization handles that stuff, I would say with just the fact that we've got somebody from Indeed, which is maybe the largest um of those, sharing their perspectives on how AI is impacting this whole process. Pay attention. So you mentioned that you know you've been going to these industry events and that um your peers and colleagues haven't quite figured it out, quote unquote. W what what is the figuring out that that you think is is missing?

SPEAKER_02 7:18

Yeah, so my sorry, my my team leads uh or my team is responsible for the revenue side of Indeed Flex. So there's really two parts to our business. There is the um client part of it, that's that's my responsibility, and there's also the worker side. And so on the client side, I I would say like that is about, you know, there's there's uh mid-market and enterprise sales, there's implementation, there's client success, there's all those different areas. So this isn't specific to the labor market, which I can definitely get to, but um like where I use it, where my team uses it on a day-to-day basis is if you think about hiring and hiring practices, you know, you have a client, let's say they have 35 different warehouses and distribution centers across the US, and you've had a number of conversations with them as you've scoped out the opportunity and the ability for you to work together and and and go into a relationship with this prospective client. Well, when you're hiring, like think about it. This is a this is fundamentally a people job, right? You've got workers that are showing up at a certain place at a certain time for a certain pay rate to do a job. Uh, and there's a lot of tacit knowledge that needs to be shared across that. And so, you know, previously over the course of the last, you know, call it four or five years ago, think about all the documentation that would have to be done or going back and listening to call recordings or whatever it is. And now you can literally go into your AI system that has an MCP into your sales execution program and say, hey, pull the call, use the template that we have that has the playbook that allows me to hand this over to my implementation team and it fills it out with all the pricing and all the details and all the nitty-gritty and kind of how the sausage is made. You're passing it off, and there's no drop balls or missed expectations or anything like that. Um, and then you take that a step forward and like, you know, you're delivering labor across 35, 45 different warehouses or distribution centers, whatever this might be. And, you know, clients care about what's my fulfillment rate, what's my turnover rate, you know, what's my time to hire, all these different really k key components, metrics, KPIs. You know, we would have to go into our, you know, data lake and snowflake and tableau reporting and whatnot to pull this data manually. Now you literally just have a prompt that you put in and it pulls out this really slick PDF that is customized to the client and exactly the type of things that they want and they need and is interesting to them. And you've taken six hours of data pulling and turned it into 30 minutes, and by the way, the quality is better.

SPEAKER_01 9:34

Yeah, yeah. So, you know, I I I don't, I'm not this isn't part of like what I do. I've got people on the team that they do the screening and those sorts of things. But as an executive, it's easy for me to believe that the AI is only really helping the job seekers. No, I can I can get what you're talking about here. It's like helping with just administrative in general, right? But what I'm starting to understand is that the bigger operational issue isn't so much um helping the job uh seeker or helping us, but it's the actual the volume and the quality, uh, those dynamics of hiring and those sorts of things. So I would imagine that now, because I've heard some anecdotal things about how people are using AI to kind of try and gain systems and that people are using AI to screen the resumes kind of stuff. But what is the issue or what is the concern that that uh an HR professional or a people person inside of an organization or an executive who's thinking about like what how are we going to find the right people as we're scaling and we're becoming AI emergent and those sorts of things? How do they handle this application overload environment? Because do you have any data points as far as like what the the velocity of the increase of resumes being submitted for any jobs on Indeed has been since I guess pre-generative AI and where we are today?

SPEAKER_02 10:53

Yeah, I I I know that there is a stat that someone told me about three, four months ago. I do not remember what it was offhand, but it was enough to like have my jaw hit the floor. I mean, it's just crazy how much the volume of applications has increased. Like it's not that there's necessarily more job seekers, but job seekers are now applying to, you know, instead of five or ten jobs, now 100, 150, 200, 300 jobs, and there's these companies you can go to will it automatically like automatically update your resume for every job and try to make it like really customized for it. And um, and so there's no doubt there's been a huge increase. But like that's I'd say like that's kind of phase one, wave one of this, which is just like the volume side of it. Yeah, really what we're doing and how we're trying to kind of flip that on its head is we are actually, for a lot of our workers, we're actually leveraging AI interviews. So a resume only says so much. I mean, Chris, like there's a stat out there, 70 something percent of people lie on their resumes. So, how good, I mean, by the way, AI is now making the resume for a lot of these people. So, you know, there's questions there, but but but more broadly, like the the problem is is not the fact that people are applying for jobs at a larger number, like, hey, that's actually a good thing, right? Get your name out there. The question is what we're doing to be able to filter those job seekers down into who truly is going to be the best fit for the role. And that's where where we've really put a lot of time, effort, and energy. And so, as an example, I mentioned AI interviews. Like, now you can apply for the for a role within Indeed Flex, and you immediately have an opportunity to do an AI interview. Now, our AI interviews are still reviewed by humans, right? So a human is still looking at the interview to validate, because again, we're still early on in this AI evolution. The great part about it is that from an efficiency standpoint, our recruiters have gone from doing 15, 12, 15 interviews a day to now they're able to review 80. And the reason for that is because they're yeah, right. It's because like now rather than having to do like the pleasantries of the beginning or the dead time or verifying the random piece of information that just kind of you have to do, but like you really want to get to the meat of the behavioral questions. Now they're able to really scroll to like, okay, these are the five or six most important questions we want to make sure we're taking a look at and going a little level deeper on. And so it's it's increased recruiter productivity. But then on the other side of it, the job seekers actually, you kind of would wonder like, do job seekers like this AI interview? Well, I mean, if it's like if if anyone's applied for a job recently, the the word that I hear is that everyone you apply for a job, and then three days later you get a uh an email saying, hey, you weren't accepted this one, and there was no conversation, you didn't have an opportunity to sell yourself. Um, and so you're having all these mass applications, but you're having these mass declines as well that are coming through. Well, how are you actually figuring out and filtering through? It's done based on right, it's like kind of AI talking to AI. Like I've got on one side AI reviewing my resume, and then on another side the hiring manager is using AI to review the resume as well. And so by using this AI interview platform, what it does is it allows the workers to be able to have a shot and really be able to sell their skills and their competencies to effectively be able to stand out from the lot and stand out from the group. The other side that it does is, you know, we all work nine to five jobs. Like you can't be taking interviews in the middle of the day. And so what this does is allows you through the AI interview, you go, you submit your resume, and they say, okay, well, hey, you immediately get an email that says, hey, you know what, do you want to do an interview right now? You log in and you can do the interview and you can talk and have behavioral-based questions. It's going to be first, second, third level questions because the AI is smart enough now to be able to understand and do follow-up questions. Um, and and also the other side of this is that you are removing a lot of the noise. And the noise that I mean is you have inconsistencies in recruiters. Not every recruiter is the same, not every recruiter asks the same questions or is as consistent. And what this does is ensures that you've got a consistent set of questions that are being answered or asked, sorry, at least from the first level, and then that depth can be taken from the candidate's knowledge and the response. And then what happens at the back end? So you post a job, you have this job out and you need, you know, to hire someone. 300 people apply. Well, no one's going to go through 300 resumes, especially when two years ago I was getting 30 resumes now, now I'm getting 300. It's increased by 10x. I can then run the AI interview and then I can go in and see, hey, who are the top candidates? And it stack ranks them. And I have the ability to go in as an indeed client and go and look at here are all of the answers. And I can watch every single one of the 300 interviews that have been completed, or I can just scroll to the most important part of those questions of that interview and be able to look at that specific area. So it does it for you, it does it on the candidates' time, it stack ranks the candidates and still gives you the ability to go in and actually review the recording before you take any next steps to do an in-person interview.

SPEAKER_01 15:54

I got a couple of questions. Um, one of them is about like the interview, like tactically, like what is that like, the experience as a as an applicant? But let me ask, with all of this optimization that's occurring and me as the applicant being able to optimize my resume specific to what the company's looking for, and on the company side, they're able to not just do a sample of the 300, but actually go through all 300 and really dig in on that. Are people are the right people being hired more often as a result of all of this? Does that make does the question make sense?

SPEAKER_02 16:30

Uh yeah, I I that that's actually it's funny. That's actually something that Indeed has always has been focusing on a lot over the course of the last couple of years. Like if you think about the way that Indeed operates, um, now I'm talking about not Indeed Flex, but Indeed Proper. Um that's historically been difficult because if you think about it, like the job candidate can come through Indeed, but then they go into an ATS system and that signal of whether or not they got hired can be tough. They've actually done a lot of work on that recently and and and been able to start to get those signals back from the ATS. Um, I think it's probably still too early to see like whether or not you're having better success with it. Um but I can tell you like from our standpoint, like what we notice with our candidates that we we use AI on. Two guys hire. We've actually found, yeah, that we hire, we've actually found that the AI does a really good job of selecting out candidates um based on truly their experience. So when it comes to like motivational fit and when it comes to more of those soft areas, the system isn't quite as good. But when it comes to actual experience, we find that like truly, if you're gonna place someone into a role that requires a higher level of skill set, let's say we were using that warehouse example, like a forklift driver, we find that actually AI can do a better job than a uh than a recruiter of actually validating truly uh their skill set on that.

SPEAKER_01 17:48

Okay, that was helpful because it seems like the whole idea about AI is that better, faster, stronger. I can produce more, produce it at a higher quality, and produce it faster, right? But how does that translate to the people side of you know a lot more 30x, 50x applications coming through? But we're balancing that out because we're using AI on the back side to do a level of evaluation that the human just wouldn't have the bandwidth to do. So I, you know, I'm I'm I'm curious, like, are the right people getting hired as a result of that? So um thank you for sharing that.

SPEAKER_02 18:20

Yeah, uh it's it's a great question because if you actually think about it, like like let's play out this this this mind map here. Um previously I would apply for this is anecdotal, by the way, right? But previously I would apply for I would uh post a job and I'd get 30 candidates. That's realistic to have a recruiter go through and at least spend 15, 20 seconds reviewing the resume, right? Hey, I know that people that have worked at XYZ company tend to be a good fit. Hey, you know, like they have they have knowledge up in their head about like what good looks like. Um you know, and and and there's a specialism in that. Uh whereas now if I get 300 resumes, I'm not even gonna I'm not gonna look, I'm not gonna look at one because I just can't get through them all. And so I send it through the bot, and the bot tells me who's best, and then I figure it out from there and I I schedule whoever for an interview. So, you know, it's almost like, you know, are the people that are just really good at mass applying or tailoring their resumes more specifically to an organization or to what the system is looking like or looking at, are those the people that are gonna get roles? Maybe, maybe not. Um, but I would say like that's that's a big thing is like, how can you, you know, knowing that a huge number of people lie on their resumes, knowing that AI is probably helping to build and tailor the resume more now, like kind of makes sense to say, hey, like, why don't we give everyone a shot? And if it takes, if it costs me, I don't know, uh a couple bucks per worker to just like put everyone through an AI interview, see who raises their hand and does it, right? Like, it's it's not like you're not gonna hire someone fully based on just an AI interview if you're hiring for, you know, a uh a finance executive, right? But like that can at least get you closer to the right answer, right? It can help to whittle it down a little bit. Yeah. And I think that's where a lot of organizations need to move is like how can you start to like have these balancing factors to really make sure that you're still getting that like qualitative part of it in addition to kind of that just mass, mass number of applications?

SPEAKER_01 20:14

So this AI interview process makes a lot of sense hearing it, but the execution of it, I'm an applicant. Am I being interviewed by like an avatar? Is it a chat only? Is there voice interaction? Like, what is the mechanical side of that look like?

SPEAKER_02 20:32

Yeah, so so uh we do uh we do not use an avatar. We've done a lot of research on this and found that uh that for some reason can be a little bit off-putting. And so it's just kind of like uh little like voice squiggles on a uh on a on a screen. But yeah, you're chatting, you're talking to the AI agent. And um we've done a great job of reducing the latency in it. Um it's much more conversational. Um, like I said, they're able to answer or ask like second and third level questions. And and we've asked workers, like, you know, what do you think? And most what we found is that most workers actually say, I'm either as happy with the human interview or happier. And I think a lot of the reason for the people that say that they prefer it is because they can A, do it on their own time, and B, it's like not as intimidating if you're just talking to, you know, talking to that as opposed to like, you know, talking to an actual person.

SPEAKER_01 21:21

Like, are you seeing individual companies doing the same thing, or are they pretty much relying on Indeed and Indeed Flex to handle all that for them?

SPEAKER_02 21:28

Yeah, so it indeed flex the the direct labor that we hire, we use AI interview on across the board. And like I said, we do have human interview or human um review of it. There are some roles that are super technical that we don't do it on as much, but the volume of candidates isn't as high there. But uh also it indeed indeed has a product, um, it's called smart screening, where it does exactly this. Like you can go in and you can tell it what are the important questions you want it to ask. It'll send them through to the candidates. They can answer the questions, and then you get the video recording of it and, you know, kind of like a summary of what happened and what the what the system's recommendation is on who stack ranked the best.

SPEAKER_01 22:05

You know, one of the things that that came up in the pre-interview we did before this was this idea that the contribution that the AI or that the HR department can have on an organization is starting to, I guess their scope is expanding a little bit, right? With this AI and better data helps our HR actually influence operations a little bit more, not just administer processes like scheduling preferences or shift design, workforce demand, things like that that may not be applicable to all the listeners, but are certainly applicable to larger. Enterprises where human labor is it's not so it's not exclusively knowledge work where it's it's the humans are are picking, pushing, moving, whatever that thing, driving, whatever that thing might be. So what does it mean to turn HR from essentially a cost center into a profit center because they're now helping the organization make more intelligent decisions with the information that they've got?

SPEAKER_02 23:06

Yeah, so so with with my experience and Indeed Flex being in kind of the the human capital side of the business, like that's we're gonna talk about HR. But actually, in my explanation I'm about to give, it doesn't need to be HR. It can be any support function, quite honestly. Um, because the question, the, the, the paradigm shift is how can you turn yourself from like an administrative function into something that's actually gonna be able to help drive the bottom line of an organization? And AI is really helping to enable that. Now there are a huge number of tasks, boxes that have to be checked, things that have to be done to be able to ensure that like the business is functioning well. And like there's those support functions are there for a reason. But AI is allowing us to be able to take a lot of those manual touch points and automate them more effectively. And so you're starting to see more and more with you know, a lot of HCM providers, places, you know, people like Indeed Flex who who do what we do with running our contingent labor programs, you know, how can you remove the need to like make 10 phone calls and send six emails, right? And like all that, all of those things that previously were kind of just necessary evils, and how can you remove them and free up time to actually be doing more value-producing things? And so the the the impetus of this concept is like using AI to be able to remove a lot of the administrative burden. And then if you can do that and you can free up the one or two or three hours a day, right? Like instead of having to re review the 30 resumes, I now can just go and I can see who are the top-rated candidates, the top five that I want to take a look at their interview. Now I've not only filtered it down, but I've also have, you know, quick shot snapshots of my initial interview. So I can send a potentially right to hiring manager interview. Okay, so now what do I do with this extra time? Well, that's a question. So I think what happens is that in addition to creating all these synergies and efficiencies in your processes, AI also has allowed businesses to bring more data into concert. So previously you would have like a warehouse management system, an HRIS system, a finance system all working in separate areas, and the data would not flow in between. And now what you have is you're able to build MCPs into these systems so you can pull the data out and start doing cross-compairs of like, hey, when I hire a worker during these months of the year, what's their retention rate as opposed to those months of the year? Um, you know, I'll give you an example. We had a client who um, you know, they were their their peak period of the year was um, you know, was kind of around like the holiday time frame. And uh through the data that we got, we were able to show them in January uh after everything died down. Hey, did you know that uh when you hire people um in the two weeks leading up to Thanksgiving, your turnover rate is 30% higher? Oh my gosh. So we need to make sure we're ramping up more earlier. And the only way you have that is by taking their workforce management information, the temp labor information, a lot of the demand information they're getting, and then being able to do analysis on that. And so what happens is businesses are able to take all this data, run analysis on it, and then as HR, now we get back to the HR part. This is where it gets fun for me. HR can then go to the ops team and say, hey, you know what, you've always had these really weird, funky shift patterns and schedules or requirements, and you know, like um you have something called split shifts where it's like you work Monday, Tuesday, Wednesday, Thursday, and then the next week you work Tuesday, Wednesday, Thursday, Friday, and the next week you work Wednesday, Thursday, Friday, Saturday. Like, that makes sense for an operational side of the business because that's what is the easiest for them to schedule. But like human beings like consistency. They don't want to have to work four random days every week. And you can then now show them how their turnover rate is impacted based on doing this type of stuff, and you can start making suggestions to the operational team about changes that they should make to be able to have better outcomes. And so rather than going and saying, hey, you know, when we talk to our candidates, we do these surveys, they really don't like this. Now you're going to them and saying, hey, look, your turnover rate is 20% higher when you do X, Y, and Z. If we can lower your turnover rate even by half of that, right, just lower it by 10%, then all of a sudden that means less new hire orientations, less inductions, and by the way, we're able to ramp people on quicker. And when you ramp people on quicker and you keep them for longer, you're gonna have better productivity in your facility. That's gonna drive this X, you know, bottom line revenue impact to your PL. All of a sudden, like now you think about it, it's like, man, those 300 resumes I didn't want to review. Because I'm not doing that, because I'm leveraging AI across the entirety of the value chain, I'm able to drive organizational business impact that that touches the bottom line.

SPEAKER_01 28:46

So, with that new capacity or capability that exists for uh people professionals, HR professionals, is the HR profession already starting to say, hey, professional, with this extra bandwidth, here's how you can enhance the contribution you're making to the organization by giving them data to help them drive operational decisions. Are you seeing that conversation happening in the Sherm groups or or wherever you you might be presenting or talking?

SPEAKER_02 29:16

Yeah. Um well, I'll tell you the the clients that I like working with the most are the ones that are doing that, you know. But just like with anything, right? Just like with what we were just talking about, and you know, my my opening thoughts was like some areas everyone should use it, but not everyone is yet. Like that's what's happening. You know, there are some people and and and the people that we find that are like really forward thinking, you're like, yeah, they're doing it and they're talking about it and they're having those conversations. Yeah. And there's a lot of others that say, like, oh, like, you know, HR is fundamentally a human profession, and yeah, AI might be able to help out a little bit, but it's not going to be able to help us, you know, supercharge. Um, you know, and the answer honestly is probably somewhere in the middle. Um, but what I just talked about, like the example I gave you, that was not an HR example. That was like a business efficiency example, right? Like that's really where that's really where the interesting part lies.

SPEAKER_01 30:04

It was driven by some savvy, you know, example person in the HR department because they had additional bandwidth and they were asking the right questions to the models and to the data set, they were able to surface this insight using generative AI, not having to use like the data science side of things, and be able to go to their buddy over at ops and say, Hey, I noticed this trend. Like for the listeners, like to me, that's a takeaway. If you've got people, people in your organization, and they are starting to use AI kind of like in some of the examples that we've talked about here. I would encourage them, maybe they ask the models, hey, I'm an HR professional, I've now got a little extra time in my day because we're using AI. What are some ways that I can contribute more to uh business intelligence across different departments? Like as an operator of a business, and if you're listening and you're an operator, I would be thinking about this new, like I don't know, enhanced job description of what my HR people, what my people people are doing with that added bandwidth. Because it's like, if I'm not hiring, it's not like great, now they can look at more resumes. Well, if I'm you know seasonally filling or whatever, or you know, just like dynamically when I need it, I don't need them to go and look at more resumes, which is what they're already doing. I need them to start thinking about ways that they can now that we have this bandwidth and we have this unique perspective and data set that the operator doesn't have, the finance person doesn't have, the sales team doesn't have. How can I, as a people person contribute to the organization? So that that's where my head's going with this. Now, one of the things, I'm gonna switch topics a little bit. One of the things that our organization does, we've trained a lot of non-technical business professionals. I think over 18,000 at this point. What I know is that AI adoption gets real when people are forced to use it on not examples and not like here's a case that, but like their actual work, right? And one of the things I know is that in our our pre-interview, you were kind of sharing that how you were using Gemini and things like that, and one of the examples was with a bid writing support. I guess it was a uh a bid writer was overloaded, and you're just as a as a professional who's AI literate and AI fluent was able to solve that problem. Do you remember that that scenario you were talking about?

SPEAKER_02 32:36

Yeah, I mean that's that's uh that's an easy one. We right right. We have uh, you know, you know, you have a bid library and you set up, I think in this example we were talking about like notebook LM and um you know, and and having having uh your AI system, I think it was Gemini at the time, pull off of that and you know, put in the requirements that the customer has. You know, you have kind of the template and you say, okay, like, you know, tell me what you got. Uh and that's that's really interesting, but that's like kind of like a little bit of that. Like that's that's sort of like 101 stuff, and I I love it and I think that's great. Um that wasn't really the light bulb moment for me, though. Like the light bulb moment for me has really been um taking, like I said, taking some of these like disparate data sources and putting them together. So, like one example that I think is it just happened a couple of weeks ago for for for me, uh like one one particular example where um we have a client and and we're looking to, you know, m one of the people on my team is looking to expand the footprint with that client. And so they need to put together a presentation and a bid, essentially, a proposal. So they went in to AI and uh essentially said, Hey, I want to go and you know, put together a presentation for this client about X, Y, and Z, blah, blah, blah, you know. And what the system does is it will pull off of our internal data set and say, like, here was what your, you know, here are the KPIs that matter, right? For any business, you have a KPI that's important, like, here are the KPIs that matter to this company, pulling them in and saying, like, hey, here's where we've been really successful, here's where we haven't been successful, right? So now I know like the numbers. And then it goes into Slack and it goes into it pulls MCP from every single call recorder we've had at this client and says, Hey, like, when I take the ones you've done really well, here are all the different use cases of how you've done them really well. And here's some examples, and here's like what the client has said about it specifically. And then if I take the ones you haven't done well, let me like pull Slack, let me put pull Gmail, let me also pull the call recordings of like what did you do to be able to triage those, right? Because, like, in you know, I'm in the service industry, right? At the end of the day, like, yes, we have a technology product, but the product that we actually deliver are the people that are going to do a job. There's always issues that happen with that. And so uh, you know, it pulls in, and so the question is like, how did you solve the problem? Because if you're gonna expand with a client, you need to be able to show that, hey, you're able to mitigate these risks. And so within the course of, you know, 10 minutes, it's pulled out all the data around what we've done in the past. It's pulled out like what's worked well, what hasn't worked well. It's then said, hey, like when you had a problem, here are exactly what you did it and the timestamps around which you did it. For the areas where you've had success, you know, here's the reason you've been successful, and here's the examples and the specific conversations you've had with a client that have explained that. And, you know, by the way, then tie in the methodology that we have and our value proposition to it so you can weave it all into this really beautiful deck that tells an amazing story from beginning to end. And that was done within 15 minutes. And right, like it's not the end product, right? You still need to go in and like double check it and make edits and change it and make it sound a little bit more personal. But like you've taken something that previously would have been probably multiple meetings with multiple people, you know, across you know, a two or three-week time sprain, and you've gotten, you know, you haven't gotten to the end product, but you've gotten 80% of the way there in 20 minutes. Yeah. And by the way, like the insights that the AI is pulling are actually really interesting and something that, like, hey, we might not have actually thought about that other component, right? We we know that three of these four are really important. This fourth one, though, that one kind of came out of left field, and that's a really good connection point that the system was able to make.

SPEAKER_01 36:14

So I I guess I I want to address maybe the elephant in the room. And 2023, people started using Chad GPT 3.5, 4.0 came out, that sort of thing. They're like, this is amazing. But the news and the narrative was around people are gonna be losing jobs because of AI. Okay. Pretty quickly I realized Chad GPT is not gonna take my job. And then it kind of switched. Well, AI is not gonna take it, but somebody who knows AI is a better candidate than somebody who doesn't know AI. All things being equal, sure. I think you and I would agree that they can produce more, produce it faster, and produce it at a higher quality. But at large, because now, like in 2023, agents were a topic, but they were tough to build if they were tough, like you had to you had to build an agent. Now, off the shelf, I can go and get a $20, $30 a month account, and I mean in Chat GPT, they've got workspace agents, like it's super easy to set up as long as you've got a couple connectors and that sort of thing. So those are lightweight, but I see people building pretty robust agents. An agent is something that to me, if if it performs, yeah, that might take somebody's job. Or it might mean that the company's not hiring as many people for that role because the agent, 24-7, a lot fewer mistakes, um, doesn't fight with a core, like all the things that come with that. What's your what are you hearing? What's your personal take on um generative AI in particular, like the impact that it's going to have on like an individual's economic viability if they're competing now with an agent or uh an automated workflow or something like that.

SPEAKER_02 38:01

I mean I I I I think that you know everyone says like it's not that AI is gonna take your job, someone to use AI, it's gonna take your job. Um you know, uh I mean I I I I I think that your job is gonna change, right? Like fundamentally, like uh your job, like the job of an HR, we were just talking for 15 minutes about HR, like their job is not gonna be reviewing the 30 resumes anymore. Like, yeah, so that doesn't mean that AI is taking their job, it means their job is evolving and shifting into something else. And by the way, like I think that reviewing the resumes was actually maybe something you didn't even really want to do in the first place, but was a necessary evil of the job. So I I I you know, I'm I'm not a, you know, there's a there was a guy, Thomas Malthus, he was a like I think 19th century economist, and he had this, he had this belief that uh once the world population hit a certain level, we wouldn't be able to grow enough food to feed ourselves. Whenever I hear people say like everyone's gonna run out of like there's gonna be no work because of AI, I'm like, that's just it's a it's like a it's the 21st century Malthusian view of like like like let's be real, like the world evolves, people evolve, people change. Um I won't get into the economics of it, but like if there's no one has jobs and there's no money, then like no one can actually buy the AI tokens anyway. So like it doesn't really matter. So I think that people's jobs will evolve. Like the way I think about it is like people will just become more productive. And so all the things that, you know, people become more productive when people become more productive, there's more wealth to spread around, more people come out of poverty. I think it's all in all net sum a great thing, but there's no doubt that people's jobs will change, they will evolve. Um, I think that you can see, you know, you can probably see HR orgs where maybe there's 20 people and maybe in the future there'll only be five people. That doesn't mean there's the other 15 jobs disappear. It means that these companies are so profitable that they can invest elsewhere. And there's gonna be other companies that are found in. So those other 15 people are just gonna be working somewhere else. This is all net growth, net positive. Um, you know, I so I I I really ascribe to, I guess you could call it like the optimistic belief about it, but I think you'd be ignorant to say that there's not gonna be a change. I think there is fundamentally gonna be a change. And I also think that, you know, myself on the re on the revenue side of the business, which is kind of sales, client success, whatnot, when I hear people say, oh yeah, like I use AI to help me write emails, I'm like, dude, like that's all? Seriously? Like, yeah, those people are gonna have a hard time. And so the question is, are they going to be fast about that evolution or are they going to be slower? There is going to be people that are going to be displaced and they're gonna have to find work elsewhere, right? As these as these organizations evolve. But the question is, like, do you want to be the person who not only has the ability to accelerate past everyone, but like you also have an opportunity right now to kind of like make the playbook of what does good look like? Like, no one has really figured this out yet. And to have the opportunity, like, how many times in humanity if you have an opportunity to have like such a groundbreaking opportunity in technology that is starting right now, right? I think Jensen Wong said it, like we are literally alive in the best time to be alive in humanity, and that is true because we have an opportunity to shape what it's gonna look like. Like we do. We have an opportunity to shape truly what it's gonna look like for the next generation, and we can have our thumbprints on that.

SPEAKER_01 41:23

Well, hopefully, episodes like this of the podcast are like part of getting people to say, I did it a certain way. We now have AI, I can have AI help me do it that old way, or I can start to create this hybrid environment where we still do what made sense in the parts of this role that we liked, but now with all this other bandwidth, we're able to create, you know, provide better uh decision-making capabilities for the rest of the organization, or some of these examples that we talked about, which I hadn't really thought about prior to this conversation of, well, great, now that the ops person has more time or the sales team isn't spending as much time on the stuff they don't like, and they're spending more time with like what can we have them do with that additional bandwidth that may not necessarily be symmetric to what they've always done in their role. And I think like that's what you're talking about here. We have the opportunity to ask those questions and start to go, like in that case, hey, wait a minute, HR folks, you guys have insights into some things, start asking different questions and feed that information, like give them a mandate almost to, hey, we need you providing a uh, you know, a weekly summary of some suggestions to the operations team on how they could improve. Like that sort of thing would be like we've talked about, you know, maybe it's not every output that we get is like, oh, this is groundbreaking, but ooh, number four looks good. And enough of those questions being asked with enough of that information being shared across these different departments that didn't have dialogue, especially like intelligent design dialogue between them, are now gonna open up some opportunities. I like the direction that that's going. So are you let me ask, are you putting out go ahead?

SPEAKER_02 43:07

No, I was I was just gonna say, um, I I I I think you're right on. And and and I'd say like one of the big areas that I think uh I I work for a company that is like very forward on AI. I talk to a lot of clients who work for companies that are very conservative. And so I feel like I'm speaking to you from a place of privilege, in so much as it's not that my company pays for like the the whatever subscription for the LLM. It's the fact that what we do is we have we have invested in connecting as many different sources to our AI system because that's really where it gets amplified. Like, in my opinion, it's like go find the right model for yourself and for what you're doing. Go and build a second brain, train it with the skills, you know, and you just what's a second brain? Well, ask it what a second brain is. What is a markdown file? Just ask it what a markdown file is. How do I like just ask it, and it will help you actually crack this? You have to be critical for it, but like just if you just ask the questions, it'll do it. And you take like a skill step for like what your specialism is, and then you can start tying in these data sources, and that's really where it's gotten interesting. It's like every single conversation that every single one of my people has is recorded, and I can pull all of that into AI and say, like, hey, what are the best people doing and what are the not great people doing? Hey, create a deck and like include quotes that I can like reference to a client that's gonna help drive the message. Like, that's where the beauty happens. It's not just by like having your own little like shielded AI case, it's like have it talk to the rest of the con data in concert with with in within your organization, and that's really where you start to see like, oh my god, I had no idea that something like this was absolutely possible.

SPEAKER_01 44:50

I love it. So are you putting out um perspective anywhere? Like, are you writing a blog or Substack or LinkedIn or anything like that?

SPEAKER_02 45:00

No, I don't do that. I've people have told me I should, but no, I I I don't. Do you think I should, Chris?

SPEAKER_01 45:06

Where would you I I do? Um because like you said, we have this is the time that that people like you and I and the listeners can start to put our fingerprints on what tomorrow looks like, right? Like, and that's why I ask I do this podcast so I can ask questions of people that are thinking about a different sliver of the big picture than I am. And like, what have you discovered? Like, what like what do you think is gonna happen?

SPEAKER_02 45:30

So you know what I do? You you know what I do is like we uh I'm I'm a I'm a customer for a lot of vendors. And um what I do is like when I find vendors that I'm like really passionate about and I like what they do, and especially and everyone's like if you go to any any SaaS website right now, and every single one of them, like one of the first three words will be AI. Um, and so everyone's trying to push AI on us. And so, like, really where I spend my time is if I see like this is a good vendor, they have a lot of opportunity, I like their dev team. I'll go have conversations with them and just say, look, here's here's my perspective from from a client standpoint. Because a lot of the times, like the product, the RD teams, they kind of sit in a bubble. They don't always have as many client conversations as they should. And that's not a fault of anyone, but like I kind of raise my hand and say, like, hey, I'm willing to help out, like, I'm willing to consult you a little bit on this, and like here's what it looks like as a practitioner. Um, and and hey, like, do you know what? Like, the dirty secret is then the roadmap gets built for what I what I want as well.

SPEAKER_01 46:27

Yes.

SPEAKER_02 46:28

Um, yes, but yeah, that's that's I think how you can start to share a lot of the knowledge.

SPEAKER_01 46:31

I like that idea. So uh for listeners, start talking to your vendors. How are you guys using AI? Um how we're using AI with your tools is this way. Start opening up that dialogue, and like James just suggested, you start to influence like, oh, wait a minute, that thing that I asked for, it showed up. It's in the new release. So I love that idea. So um, James, we're gonna add your LinkedIn profile and that sort of thing, but also any um any parting words, I guess, for uh because we've got all strata. We've got the decision makers, we've got the folks at all tiers of the business that are listening to this. But in general, what would you uh want to leave as kind of parting words of wisdom on this episode?

SPEAKER_02 47:12

Uh I I would I would say, you know, embrace it um and put yourself out there. Like go and go and do something that is a little bit uncomfortable, um, and test the boundaries, test the limits of of what the system can do. Um and I think you'd be surprised by it.

SPEAKER_01 47:29

Agreed. Great advice. All right, everybody. Thank you so much for joining us for this episode. We're gonna be back next week with uh another amazing episode about using AI at work. And um want to thank our sponsor, Airbnb. Not really. We're just we happen to be on the road when we're recording this. But James, thanks so much for taking time. I know that um this is an exciting time for Indeed Flex and for you in particular being uh involved in revenue generation. Anytime that you're not on the phone with clients is uh costless. But I appreciate you making the investment with our audience here today. Thanks so much, Chris. Thanks everybody.

SPEAKER_00 48:01

Thanks for tuning in to Using AI at work. Don't forget to subscribe for more conversations about how to use AI at work. And a special thank you to our sponsor, Chief AI Officer, for empowering businesses with AI education and training.

SPEAKER_01 48:14

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