Explore the world of HR technology and AI analytics, uncovering the rigorous strategies behind identifying and validating trends that shape the workplace. Zachary Chertok, senior research manager of employee experience at International Data Corporation, draws on his extensive expertise in workforce analysis to share actionable advice for businesses of all sizes — from startups to global enterprises. As Chertok puts it, "The day we stop questioning things is the day society grinds to a standstill."
Explore the world of HR technology and AI analytics, uncovering the rigorous strategies behind identifying and validating trends that shape the workplace. Zachary Chertok, senior research manager of employee experience at International Data Corporation, draws on his extensive expertise in workforce analysis to share actionable advice for businesses of all sizes — from startups to global enterprises. As Chertok puts it, "The day we stop questioning things is the day society grinds to a standstill."
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[00:00:30] Alex: welcome to the AI+HI Project. I'm Alex Alonso SHRM's. Chief Data and Analytics Officer. Thanks for joining us today. This week we're going behind the scenes of how HR and business tech analytics are formulated. Today we'll be unpacking the process behind identifying trends and creating impactful predictions before offering up some AI trends and predictions ourselves. Our guest this week is Zachary Chertok, senior research manager of Employee Experience for Global [00:01:00] Tech Market Intelligence provider IDC. to the AI+HI project Zach.
[00:01:06] Zach: Thanks, Alex. It's great to be here.
[00:01:08] Alex: Thank you so much for spending the time with us. Obviously being the head of research and thought leadership at SHRM, you can imagine how excited I was when I saw this on my calendar and thought to myself, boy, there are 90 different ways I want to take this. But before I do that, I want to give you a chance to kind of introduce yourself and share a little bit about your professional journey and kind of what inspired you to specialize in HR and employee experience strategies.
[00:01:32] Zach: Oh, it's a good question. I often joke with people, I fell backwards into the HR arena. My original background actually is in civil engineering out of McGill University, but I graduated into the oh eight financial crisis, so between project management and transportation for the city of Boston and interning for Kronos, those project management skills drove me into work for Kronos for four years before I got my first in as an industry analyst working for a smaller boutique firm in Boston.[00:02:00]
[00:02:00] That gave me a lot of exposure to the field of vendors and to the transformation that was going on in the HR tech arena to understand how people in technology were coming together. I got a lot of exposure to strategy, to the digital environment and just to the world of how people were being enabled at work, and that led me to ultimately do my MPA and, and on the policy side, looking at the nexus of labor management and industrial development where I've been ever since.
[00:02:29] It's been a 16 year career as an analyst, and I wouldn't trade any of it for a minute.
[00:02:34] Alex: That's awesome. Personally, I'm also impressed by mcg. I, I will share that I have a cousin who's a faculty member there. Uh, so I'm, I'm always, uh, rooting for McGill as, as one of Canada's finest importers of talent. Uh, so that being said, uh. Let's talk a little bit about some of the things that you're seeing, some of the things, the trends that are coming up, right? Uh, many organizations look to analysts to understand emerging trends. Can you [00:03:00] outline the process that you put forth in identifying and validating trends, especially as it relates to HR and AI technologies? 'cause that could be up in the air.
[00:03:09] Zach: Oh yeah. I mean it's a combination of both the digital markers on the buyer side and the behavioral markers on the adoption side. So at IDC, we take a very data oriented approach as our, the middle letter and our name will suggest, um, to understanding the market that leans into both buyer audiences while drawing on a very close triangle of outgoing conversation and ongoing conversations with vendors, providers, invest and investors.
[00:03:34] Before joining IDCI worked for other analyst firms that built similar data backed approaches from different angles, including ROI and survey-based evaluation. And leading up to IDCI learned and trained on several statistical modeling methodologies that have really benefited the analysis at IDC. So the modeling helps us run lean market surveys during the year that include an annual all HCM survey where we really look at a host of specialized [00:04:00] sub-markets, as well as two additional surveys and employee experience and talent acquisition.
[00:04:04] We also run our SaaS path analysis to uncover buyer trends surrounding enterprise applications, as well as our future of enterprise resilience surveys that run up to eight times per year to look at the market at market impact trends for digital and services spend. Co modeling between these allows me to relate a lot of the responses and emerging trends across the different survey findings, to really hone in on what's driving buyer behavior, what's unnerving them, what's exciting them, what's limiting them across different remits.
[00:04:34] Zach: And when it comes to HR, the tricky part is, is that, yeah, the CHRO will own a remit or the different leaders within the submarkets for HR will own their spaces. In the traditional hierarchy of executive leaders, the CHO is often at the bottom of the totem pole. So we have to look at the other impact factors, like pressures on it, spend what the CFO wants to see, what operational resourcing looks like, [00:05:00] to figure out how employees are being enabled and how that connects into the different trends on the HR side.
[00:05:06] That allows us in partnership with our service services and software tracker teams to look at how market share is changing across the vendor landscape, and also to look at our five year forecast that we publish every year.
[00:05:19] Alex: You know, it's fascinating 'cause some of what you're describing in my, in my world is the large unstructured data problem, right? We've got a variety of different data that are pointing to a variety of different trends, and it's really about making heads or tails and putting that into, uh, if I could be my inner Taylor Mason for a moment, the, the algo, right?
[00:05:36] How do I put that into my algorithm? So what types of data and metrics or SI signals, uh, do you find are kind of the, the, the worthwhile ones? The ones that. Tell you something's worth pursuing versus something is likely to just fade or is noise in the system?
[00:05:52] Zach: So after years of looking at survey data and buyer trends while engaging in all of that active listening that we do during the year, um, looking at direct [00:06:00] customer engagements and all the exposure we get at client events and conferences. I'll admit some of the trends just kind of leap out at me out of the data sets when we get them back right away.
[00:06:09] It's kind of that one of those weird savant skills that when you're looking at data for so long, eventually the important stuff just kind of leaps off the page. I kind of joke that the important data points smell purple, but conversations of vendor inquiries drive a lot of the conversations around what the market's seeking to understand.
[00:06:25] But really it's the buyers that lead me a lot to understand gaps within and driven by their interests, their behaviors, and their concerns. So there's no better source of guidance than the actual buyer. But I also read a lot into broader market and investor analysis, while paying very close attention to external institutional data around how economic and labor markets are shifting.
[00:06:46] All of that gives me a big, broad perspective on what to expect. From people movements and from the challenges that businesses will have. Tapping into talent market intelligence, and it's really about leaning into how individual behavior [00:07:00] shifts among consumers and employers and employees rather, who are really one and the same.
[00:07:05] Just starts to help me keep tabs thematically on where I should direct my attention. When it comes to the KPIs. Every survey, every conversation I have leans into a set of business KPIs, not always the HR ones. I lean into how performance has changed in the last assessment cycle. How productivity measures, if a company measures it has changed, what their model is, how their revenue has changed, customer satisfaction, customer retention.
[00:07:31] And we do a and I do a lot of modeling to connect some of all of the different, um, items that we ask about in surveys. All the different things we listen for back to those changes in KPIs to understand their impact. Sometimes they're primary and we get a direct relational factor. Sometimes they're secondary or tertiary, and we have to really dig into how those relationships are oriented.
[00:07:54] Um, but it really always comes back to getting a handle on the [00:08:00] behavior of the market first. Before you know where you're gonna look for the signal and the noise.
[00:08:05] Alex: You know, it's fascinating. I think about that and I, I think to myself, so much of the, uh, the way you describe that is the right way. The big data, the, the data that really stands out, those trends, they do smell purple or, you know, they stand out in some way that you, you immediately wanna do that. I'm gonna, I'm gonna quote you on that in the future, uh, with full attribution.
[00:08:22] Of course. Uh, so in thinking about this, right, we're, part of what we're doing on this, uh, podcast is thinking about AI tools in particular. How have AI tools changed the nature of your work and changed the accuracy or the methods by which you. You know, identify trends, predict, kind of build algorithms.
[00:08:41] How have they done that?
[00:08:42] Zach: I can tell you that AI can be a blessing and a curse, and I think that whether you're on the buyer side, the investor side, the analyst side, practitioner, wherever you are on it, I think that statement is more universal than we might think On the one hand. When we talk to buyers and vendors and investors, it's helped all three of them [00:09:00] access information more quickly and improve some of the accuracy of how they engage with us when we ask them for information.
[00:09:06] So there's the intake side on that front. While I never really go as far as to demand or openly question, voluntary participation in our research, it, has made all three of those groups, the buyers, the vendors, and the investors a lot more confident. in Their ability to answer and engage, and that's made it easier to put them at ease when they work with us.
[00:09:27] On the other hand, AI models also need to be examined and assessed almost as much as raw data. So I still like to see if I can outrun the computer on the analysis side, while I will put the, insights and trends into any, internal gated systems that we might use for analysis and model generation, I'm still running those models.
[00:09:47] To the best of my ability, both to keep my skills sharp and to, test what I'm seeing against what the computer's putting out. Because we're still in an era of AI modeling that you need to test it, you need to know how the bread [00:10:00] is baked. Um, gotta be able to do the math manually before you do it in the calculator kind of thing. because we also have to make sure that we're looking at not just how on point a trend is, but also whether or not that trend is causal or can actually be truly correlative. Um, we have to be careful about that when we're reporting on it. So I would say it's definitely improved accuracy and output.
[00:10:22] It's definitely led us to, to check on things faster. It's definitely enabled me to validate whether or not the signals I'm seeing are the ones that I should be paying attention to. But I still do a lot of the math, you know, either by hand or, you know, aided by some of the old fashioned tools, whether it's Excel or or other modeling languages, just to make sure that everything is coming out with the right level of assessment.
[00:10:43] Alex: So that begs the question though, has the AI ever seen something that Zach didn't see?
[00:10:49] Zach: Um, I've experimented with building my own internal, um, assessment tools off of some of the, the public engines that are out there that I can just kind of gate it to [00:11:00] my own research. Um, sometimes I prove the computer wrong and mind you, every time I do it, I go back and and check 3, 4, 5 times to make sure.
[00:11:10] 'cause. You know, you're only as good as what you know, and you don't know everything. Um, and the computer has access to a lot more information and, and modeling techniques than, you know, I do in my brain as an individual. Um, but yeah, but I'm going back through it all always to make sure I've caught the computer.
[00:11:26] Sometimes, sometimes the computer's caught me and sometimes the computer gets it right, leads me to the right signal, and I take the model the rest of the way.
[00:11:35] Alex: Very good. Very good. So what advice would you give a business and think of every business, right? The small business to the really large. Businesses, the startup, all the way to the, the big enterprises. Right. What, what advice do you give them when it comes to kind of leveraging HR and AI analytics for predictions, uh, and doing it effectively?
[00:11:55] What, what are the golden rules that you, you kinda laid out, you started with, you know, always questioning and [00:12:00] always doing the math yourself, but what, what else would you throw out there?
[00:12:03] Zach: I think the first thing is a golden rule of life is question everything.
[00:12:06] The day we stop questioning things as the day society grinds to a standstill and we all become the same or start achieving to the same. top, of the diminishing returns curve, and it's the day everything just grinds to a halt.
[00:12:20] So question everything. Nothing is given just because a computer spit it out. You have to know the data that went into it. You have to know the process and methodology it used, and you have to test the outcome because computers are only as good as what we feed them. so no matter how sophisticated the modeling capabilities are, you just, you have to question everything.
[00:12:40] The second part is go at your own pace. For smaller organizations, you know, good news, you're, you're in a prime spot to be able to go from some manual processes. You're in a position, at least on the HR side, where potentially your HR teams can know and interact and [00:13:00] see most of the workforce on a daily basis, even if it's over a remote environment.
[00:13:05] So your HR teams probably know, you know, at least half of your workforce by name. That means that some of those human insights, you can rely on them for a, for a certain time and you can dip your toe in ai data enablement, performance enablement, you know, org structure modeling, whatever tool it is you wanna use, you can really do it at an experimental level and not worry about going all in, all at once.
[00:13:30] Um, human human nimbleness is still an advantage that small organizations have for medium size for, for SMBs. You gotta think about which category you're in when it comes to diving into AI transformation. If you are an organization that embarked on digital transformation as needed over the last 10, 12 years, you're gonna have some of your data environment that you gotta clean up first before you can go full [00:14:00] fledged into any kind of AI enablement or system consolidation If you were latent in digital transformation.
[00:14:07] You could actually consider bypassing the whole DX phase altogether and go straight into AI centralized tool enablement because the vendor's gonna be all too happy to take your manual data sets, scrub 'em, put 'em into the system, and consolidate you on day one. So within the s and b market, we're actually seeing folks that we're latent with digital transformation leapfrog ahead into AI transformation.
[00:14:28] It's kind of a very funky phenomenon when you get into the large organization, it gets a whole lot messier. You get a bunch, a couple of companies that are, that have very, very siloed or disparate systems, either within, just within the company, you know, across national operations or even globally that are, you know, that are looking at deploying AI capabilities provided by their vendors for the part of the organization that they're using that vendor.
[00:14:57] My recommendation really is take a [00:15:00] comprehensive look and chart strategy consultative. To look at your entire global organizational data model, see where consolidation can really benefit you, and then get into the vendor conversation around AI insights. Because with the data that the enterprise organization sit on is one of the ones, is one of the data sets that I'm really excited about.
[00:15:20] Um, a lot of the large vendors serving enterprise organizations are finally at a point where we can start to look at performance as an outcome. Which means you have to relate a lot of subjective and qualitative data across a lot of different systems. So take a look at that consolidation, build a strategy out first, and then dip your toe into some of the more sophisticated, um, AI models and, and AI insights enablement.
[00:15:46] Because if you get that data, that data set construction right on day one, it's just gonna be so much more powerful on the backend.
[00:15:52] Alex: You know, it's fascinating 'cause you, you started off with uh, I think probably the best golden rule, which is to question everything. And at the [00:16:00] same time you also kind of caveated that by saying, go at your pace. Um, I. to, not to ignore the other things, but it takes me back to grad school and one of my advisors who always said, think about your analytic plan, and always remember that your eyes should never be bigger than your stomach.
[00:16:15] Zach: Yep.
[00:16:15] Alex: Your appetite shouldn't for, for a really interesting finding should never be bigger than what the data, data you're ingesting is, is old. Kind of analogy. Um, I think about that, especially as it relates to the work that you do or the work that I do. And it, it, it does happen very often. I, it it's frightening how much people really do want to go too fast or think broadly or overemphasize a hypothesis in their brain, right?
[00:16:41] And, and, and so. I, I always think back to those kind of opportunities and then I, I like how you rounded that out with the notion of understand the amount of data that you're generating and what it is that you have and what you sit on. 'cause oftentimes you forget about how all those things can relate, even if they're not [00:17:00] technically related in some way or statistically related.
[00:17:03] Zach: Well, and that's the big thing for, for ai. You know, we at, at IEC, we taxonomizing AI into, you know, core AI modeling that's at the center of the ecosystem and the, the whole unified data play. We also then get into the operational ai, the functional side when you get into assistance advisors and agents and with all three of those, yeah, you've gotta be careful and stay on top of the data that's going into them.
[00:17:28] You've gotta be mind, you know, mindful about the purpose that they're supposed to serve. What is it they're supposed to put out? Assistance are about insights. Advisors are about, you know, human led action and agents are about autonomous action. Um, and in all cases, they are designed to pull together both the related and the unrelated data, which means you gotta be really on top of your governance to be able to get to a sophisticated level of using them.
[00:17:56] Cleanliness, compliance and governance are so paramount for, [00:18:00] for buyers and for acceptable and equitable use cases. Before you can get anywhere with any, any, you know, any AI enablement.
[00:18:08] Alex: And it's that kind of conditional relationship that we don't always think about. We're so focused on that correlational or that causal relationship, but sometimes it's that layer that is the conditional components. you raised an excellent point there. Uh, so it's fascinating. We've been talking about a lot of data and especially the analytics that you do as, as it relates to HR tech and more importantly, what you've seen over the last 16 years as you described. Uh, uh, uh, selfishly I sit on the, uh, SHRM Labs Inve Strategic Investment Committee, and, uh, I was the initial executive sponsor for SHRM Labs. So I selfishly I care about what's going on in the world of HR tech. One of the things that strikes me is I, I, I'd love to get your take on what it is that you've seen over the past five to 10 years, uh, in the world of HR tech and, and what do you think has been the, the top two or three kind of milestones that, that relate [00:19:00] to that?
[00:19:00] Zach: I think overall the thing that I'm most excited about, certainly that got catalyzed by Covid. I mean, don't get me wrong, I'm, I'm not about to celebrate something like Covid, but this was a good silver lining to come out of it, is that the digital environment is finally pivoting to actually listen to employees in the line of business.
[00:19:19] And that's something that we talked about employee engagement. We talked about measuring it before covid, but it was a bit tokenized. It was a bit driven forward by the line of business, not necessarily formalized across the organization by HR. And you know, before, before I got into the ex side, before AI was the thing, I started my career when we were talking about the onset of all in one turnkey HR suites.
[00:19:40] Designed around automating core HR processes. Employee experience wasn't a thing yet. Um, or if it was, it was being very, very human driven. There weren't purpose built tools for it. What voice of the employee did was transform. The organizational landscape, so that employee sentiment, employee feedback, [00:20:00] all of the insights could come back to tell HR where employees were being resourced properly, were being enabled where the learning was working or not.
[00:20:08] So that performance drops weren't totally their fault, or failure to achieve OKRs was not their fault. We haven't had that kind of connection between the line of business and, you know, executive OKRs since the end of the 1970s when we started decimating middle management. In favor of automation. Now we're really challenging the top down management models, and it's for, for a good outcome.
[00:20:34] Alex: You know, it's fascinating you raised that because I think to myself there, there. Are several different, uh, kind of watershed moments that we think back upon. And I always think about the great reset that took place in 2020 and how it made us think about not just the tech, but also the things that we, we do from a management, uh, perspective and, and how we analyze things even differently.
[00:20:57] So, uh, certainly that's, that's [00:21:00] a, a, a powerful moment is. Uh, as I think about it, one of the things that I think about is I think back to 2023 and obviously, or 2022, November of 2022, and I think about the, the big announcement and, and, uh, how five days led us to a million users of chat, GBT thanks to Open AI and their, and their social media game. Uh, I I, I wonder specifically, when you look at HR and ai, where is it that you see the biggest impact so far? Where is it that you see that great rate of adoption and what, what parts are really resistant?
[00:21:31] Zach: Learning experience management continues to be the top area where AI has come into play. It's the earliest place where AI personalization has taken off through, you know, behavioral science, modeling, engagement data, all the rest of it. Um, and it's been a big catalyst for learning and skilling, while also a big proof point for operational streamlining.
[00:21:51] So that the learning team produces, maintains and supports only what employees are engaging in the way they're engaging it. Um, that's had extended, you know, value [00:22:00] cases out to operational resourcing. It's given HR value to, to tell finance and ops teams where the organization is and is not empowering employees correctly.
[00:22:09] Um, and it's led into other avenues like personalized data delivery and pay compensation, accruals performance, et cetera. Um, and. After the LXM environment, talent acquisition has been a big place where, um, where particular AI assistance has come in for Canada Communications, automation nudges and talent CRM to personalize workflows within the talent acquisition process flow.
[00:22:34] And it's, it's been a big, big catalyst and an easy place for adoption. AI ultimately faces opposition, not in any one area of the HR remit, but in places where it gets either rolled out too noticeably or too fast. So there are a handful of vendors that are, that are either acquiring advisors or, you know, human advisors or working with their SI partners to build, uh, change [00:23:00] management or behavior change strategies to phase AI enablement and through insights enablement first.
[00:23:06] To make employees more aware of the outcomes enablement of ai rather than, you know, saying, we're gonna take this process away from you. You don't have to do this anymore. So it's more about getting the behavioral shift into using these, these enabled tools rather than necessarily any one tool saying, you know, resulting in an employee or an HR leader say, I don't want that.
[00:23:30] Alex: it's fascinating. Uh, I, I think about this, and I know I say that a lot. I, I might, fascinating may be a crutch word for me, but, uh, one of the things that strikes me is I, I've fielded a transition myself from HR leaders, uh, in, in terms of. they're doing around LXM. Right. To your point around how the learning experience is changing, how they're engaging people and creating the experience. And I had somebody who, uh, a year and a half ago would've told me, there's no way in hell we're ever gonna do anything with deep [00:24:00] fakes. I don't want anything to do with deep fakes. And they came back to me a year later and said, what if we were to consider the positive side of DeepFakes as an example and say, we created a personalized learning experience for onboarding. And it was personalized with a, a communication from your CHRO and a communication from your CEO executives that made it possible for you to really leverage and feel personalization in your own, uh, in your own workforce. Now, I promise me, you've never seen anybody actually apply, DeepFakes in that regard unless it was really in a, in, in a, uh, warranted way.
[00:24:36] But what do you see
[00:24:37] Zach: Well, again, I mean, I think it comes down to governance and consent. Ultimately. I mean, I, I don't think you're gonna see a CHR or a learning leader or a talent leader, you know, have a problem with the, with their voice being used as long as they can be shown where the, the gates and the guardrails are around what that.
[00:24:53] You know, gen AI voice can say, so as long as they're, they're, they consent to the language that's being [00:25:00] used to come out of their, their persona simulation. I don't necessarily see anything terribly out, out of sorts with being able to use it that way. But like I said, that that whole, you know, human digital twin.
[00:25:16] Is a little interesting to me. I think the better direction, you know, for employees is to proverbially create a data shell around them that moves with them even when they're new hires, and attaches the right resources to that shell based on how the behavior's moving. So personalizing the experience. I'd rather have rooted in behavioral science for resourcing first.
[00:25:37] Before you get to those, those augmented automated communications, it may send a nudge to the CRO. That, you know, employee A needs this standard communication blasted to them. Um, and when they see it come in from the CHRO, usually that's good enough. It's not always about what is said, but the fact that it is said.
[00:25:55] Alex: So that's fascinating. I I, I, I really appreciate that. Uh, alright, let's talk about the [00:26:00] future. 'cause I'm, I'm really interested in what you have to say about the future and really. As you think about every bit of deep expertise and analysis that you've put forth, what, what is exciting you the most about the future of HR Tech?
[00:26:13] What is it that that lives out there, that's Zach is pumped about?
[00:26:17] Zach: Well, I think I alluded to it earlier, but the fact that we're finally moving in a direction where performance can really be an outcome versus a process is,
[00:26:25] yeah, I'm, I'm totally nerding out over it. we're moving into an era where AI can relate both directly connected and, and disconnected or, or seemingly unrelated data sets.
[00:26:36] Across multiple remits. So the concept that we could have a data asset manager, you know, like a, that builds a common data platform between the HR systems, the ops systems, the finance systems, everybody working from the same consolidated business performance model that ties in all of the workforce engagement and performance insights.
[00:26:55] Is a huge deal. Huge. And you've got a lot of vendors, particularly the big vendors that are driving [00:27:00] towards that. Um, and it's, what's exciting about it is nobody likes a subjective performance review. We, we've stopped at the halfway mark, kind of like getting to nuclear fission, but not quite at fusion, where, you know, employees and managers are enabled with the same data insights about an individual, about their individual performance so that they're on the same side of the table when they're charting the next steps and goals.
[00:27:24] But we're really poised to go so much further than that, that this, these systems will nudge for when an employee needs to be trained or retrained on something because their skill and performance is dropping a little bit. Maybe they need to be working with a better optimized team relative to their historic performance.
[00:27:41] So they're gonna get slightly moved internally. And all of this is feeding into continuous workforce planning that's agent driven. Um, so that organizations can better adapt to changes in their market without needing to always stay on top of, you know, I'm gonna try to control my market segment and push [00:28:00] risk down to the front lines of business because I just need to control everything.
[00:28:03] I can Keep the company on track. No, we're going into more fluid business planning cycles, and for HR, that's increasing their relevance at the table with finance and operations, because they're the third leg of the stool. If you don't have workforce performance embedded into those models, you just can't get a handle on the entire frame for business performance.
[00:28:23] So HR may finally get the seat at the table. I always joke they were HR when they realized that they're sitting on a lot of operational insights from voice, the employee about resourcing, not working or working. They bring it to the table first. Only to find out that they didn't have a seat at the table.
[00:28:40] Finance and ops move their seats aside, so HR could bring a folding chair as long as they towed the line. And we're starting to, we're at the very precipice of seeing that start to change. And all of that hinges on AI enabled performances and outcome, and I'm very excited about it.
[00:28:56] Alex: I really appreciate that perspective. 'cause I've been [00:29:00] on the road talking specifically about how ai, agentry, and human agency are the key levers to productivity.
[00:29:09] Zach: Absolutely,
[00:29:09] Alex: talking about productivity, you're missing half of the equation.
[00:29:13] Zach: and it has to be performative. You know, that we can no longer measure productivity on just what the standard operational output is. It has to be performative and it has to be behavioral.
[00:29:24] Alex: Agreed. Couldn't agree more. So I'm gonna put on my CHRO hat, and I'm gonna ask you one final question here. What's that one wisdom, that one takeaway that you want to every CHRO, every, every C-suite leader to walk away with and think about and say, if they're talking about H HR and AI analytics, what would it be?
[00:29:44] What's that one? Guidance or wisdom?
[00:29:46] Zach: Well, the first one is, I don't think that this is anything new, but HR leaders fight for your seat at the table. You're smarter than the other. Stakeholders often lead you to believe. Um, and you deserve your seat at the table. The second one is, it is gonna be your [00:30:00] friend. As AI comes into play, HR is one of the first places that vendors are experimenting with AI capabilities.
[00:30:08] Why? Because HR leaders and stakeholders often need their solutions to be more end to end complete and outta the box ready than their counterpart stakeholders. Yep. Your technical literacy is not often as strong as your counterparts. That works to your benefit to be a first experimental house for AI enablement.
[00:30:27] That means that in partnering with it, learn how to speak each other's language. Because when you've gotta carry those behavioral insights into the other remits, it is gonna be the partner that's gonna help you get there. That HR IT partnership is fundamental to success in the future of a digital enabled work environment.
[00:30:44] And then the last piece is carefully monitor and, and. Take the goal that's given to you when it's time to escalate your rise in the hierarchy of stakeholders. Finance is gonna [00:31:00] resist giving up their seem. The, the control they feel they have over the organization. Operations is gonna resist even AI to a certain extent because they don't wanna give up the blank check that is procurement.
[00:31:10] And right now, neither one of them know which end is up because of global compliance disruptions. You've got a chance right now to really take a lead on organizational transformation. Step into that role, embrace it.
[00:31:22] Alex: I really appreciate that. And more than that, I think specifically about the power that is, that reset continuing that we talked about 2020 being a great reset and this continues to be the next layer of our reset for this entire profession. I. That's gonna do it for this week's episode. A big thank you to Zachary for sharing his expertise and deep insights with us.
[00:31:42] Zach, I can't thank you enough. Before we say goodbye, I encourage you to follow the AI+HI project wherever you enjoy your podcast. If you enjoyed today's episode, please take a moment to comment, leave a review, et cetera, anything that you wanna do to let us know how you feel about today's episode. Finally, you can find [00:32:00] all our episodes on our website at SHRM dot org slash AIHI. Thanks for joining the conversation and we'll catch you next time.
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