The Changing State of Talent Acquisition

#70: Signal vs. Noise: Navigating the Crowded TA Tech Landscape

• Graham Thornton • Season 6 • Episode 70

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Fresh off conference season, Mark Tomasino and I break down what we actually saw on the show floor. The net out: If your differentiator is "AI-powered," you've already lost.

We dig into the feedback loop nobody's talking about: job seekers using AI to mass apply to jobs, employers using AI to filter the noise, and the whole apparatus just... creating more work for all. 

Mark walks through his six-question framework for cutting through vendor pitches, why context matters more than the algorithm, and why the most interesting solutions may come from founders who've never worked in traditional TA.

Also: why everyone's fishing from the same data ocean, what skills taxonomy actually means (and why you're probably not doing it), and Mark's contrarian take on why AI won't take our jobs – but it will expose which work shouldn't have been ours in the first place.

If you're a TA leader trying to figure out where to put your AI budget, this one's for you.

Talivity helps employers see what others miss – brand reputation risks, workforce shifts, and the real value of AI – so you can make smarter decisions and achieve measurable hiring outcomes. Learn more at Talivity.com.

SPEAKER_00:

Welcome to the Changing State of Talent Acquisition podcast with your host, Grant Thorta. Each episode brings you unfiltered conversations about the tools, trends, and technologies impacting the future of talent acquisition. Our guests share their stories on what's working, what's hype, and what's actually helping companies hire better and grow faster. Have feedback or want to join the show? Head on over to Telivity.com to learn more. But now we're on to this week's episode.

SPEAKER_02:

All right, and we're back with another episode of the Changing State of Town Acquisition podcast. Slightly different format with some exciting news over the last couple weeks with Change Day getting acquired and joining the Recruitics family. Our next guest is Mark Tomasino. Mark, I've known for the better part of boy, uh two decades at this point. And Mark is now in his role as director of partnerships. Uh we'll call it special projects still over at Telivity. Mark, we'd love for you to share a little bit more about your career journey, maybe some of the pivotal moments that you know brought you into the Telivity or changed a team across the board.

SPEAKER_01:

Sure. Thanks for having me, Graham. Honored to be a guest finally on this pod. And um, yeah, a little bit about me is I've been in the industry since 2006, first job out of college. I made 125 cold calls a day at Career Builder, and I managed to not quit or get fired, which is more than most people could say at the time. And uh, you know, so I ended up doing all levels of sales, you know, from SB up to the largest global employers. When career builders started buying a bunch of different software and technology to bring to market, I was one of the early solution architects, uh, which is Graham, how you and I know each other, of working closely, of uh bringing that to market and working with different employers, tech stacks, uh, which really piqued my interest into the broader world of HR Tech and integrations. We were acquired by private equity and 2017. I then was kind of an internal sales consultant between the different portfolio companies that are owners owned. And finally in 2020, I became vice president of partnerships, which I oversaw our reseller integrations and agency partner programs amongst some other things, kind of building on all the things that came before that. I at the time I had a two-year-old son and I had a planned sort of career break and uh to spend more time with my growing family. So took a break from the workforce for a few years, but uh Graham, you know, you at Change State uh brought me over to help on a consulting basis with some HR Tech partnerships. And no surprise we got along so well and enjoyed working that we flipped that to full time. And now here we are, having grown change state and now joined the recruits family and over at Telivity.

SPEAKER_02:

Yeah, well, you know, I think you're underselling, and we're gonna get into it, you know, your background and knowledge of you know the HR Tech space, you know, in particular in the pre-hire world. And you know, I I always you know talk about what gives me energy, and I think there's a lot of problems you know to solve standing in front of a whiteboard. And you know, I think that's kind of where we cut our teeth in general. But you know, we're gonna dive deep into HR Tech. So I guess a great starting point, Mark, maybe is we just got back from HR Tech, and I think it's fair to say we probably couldn't walk uh you know uh 10 feet without seeing another uh AI-powered booth. And you know, you were kind of my co-pilot through all of this. So, you know, what was your biggest takeaway from HR Tech from the conference? What kind of stood out as maybe you know, genuinely, you know, innovative versus just uh well, we'll call it well-marketed, you know, noise?

SPEAKER_01:

Sure. Uh yeah, I mean, you you see the big players, although I would say the big players have a smaller presence there than maybe historic, you know, and I'm talking like the big HCM systems, but what you did see was a ton of startups. So like they have a whole startup area that was just you know folks that have point solutions trying to solve very specific problems in the space. So that was interesting. So there's a lot of emergent players. I would say like a really common one that I was almost surprised to see how many were you know trying to go about solving the same thing were these AI interview tools. Um, and so you know, we can probably get into the nuance of that a little bit and some things I learned, but you know, doing being able to do quick side-by-sides of companies that are essentially going about solving the same problem in the same way, but trying to peel back that onion and try to figure out who's who and what's what and who who has a novel way of solving this problem and who's really just putting a wrapper around the same back-end stuff.

SPEAKER_02:

Yeah. Well, you know, I I'll also say in the startup pavilion and you know, just in booths in general, maybe you maybe you saw this too, maybe you didn't. You know, I think that you know, we saw a lot of new solutions and you know, coming from founders who maybe haven't worked in traditional TA, right? And so, you know, we talk a lot about, you know, everyone talks about Tesla, you know, coming from outside of the you know, the vehicle industry, Uber, you know, people coming from outside of you know the transportation, like disrupting industries as a whole. And like, you know, I do hope, and I think there's you know, some very good, you know, there's a lot of energy behind you know, people coming from outside TA. And I'm hoping that you know we start to bring more fresh thinking into you know TA instead of some of the recaps that I read where it was, hey, yeah, guess what? Like someone bought someone else. I realize that might be ironic given that we just got acquired, but hey, like some big, massive applicant tracking system um or HRS system just bolted on another tool. So, you know, I'm just wondering, like, fresh thinking, do you feel like there's a little bit of a different energy in HR Tech and you know in the industry than maybe in years past?

SPEAKER_01:

Yeah, I would say that. I mean, you know, it's it's a double-edged sword. You have this emergence of AI, and so everyone's gonna use the same words, and you gotta try to figure out who's actually going about solving the problems. And you'll probably hear me refer to this several times on the pod, but it it all goes back to problems. And so the founders that I'm energized by or the products that I found most interesting are the ones that understand the problem that they're trying to solve, and they can articulate it when asked directly. What problem do you solve? What are the use cases that you're finding the most success with clients? And they know exactly what they're doing. You know, almost like starting from first principles of hey, you know, what's really the problem? What's what's the underlying problem here? So to me, that's how you you kind of cut through the noise. And the inverse of that would be if you talk to somebody and it sounds like they just keep adding features so they can keep up on an RFP checklist. Oh, yeah, we do this and we do that, and we do this over here too. And it's almost like you look lose sight of like, yeah, but what what do you stand for? Like, what's the ethos here? What what's the what's the fundamental problem that that you're trying to address? Because sometimes it can feel like our fundamental problem is we need to win more RFPs, or we need to we need to attract investors. So we need to use these words or say that we can do these big things because this other company said they could do those and they got a billion dollar valuation. So yeah, I always be rooted in into the problems, and that's that's what differentiates the solutions for me, personally.

SPEAKER_02:

All right. Well, you know, let's let's stick on this problems path because you know I want to get into our marketplace a little bit, but you know, I think that you know you've kind of you know framed this in a unique way for me, right? And that's as we're walking through these booths, we're talking to a lot of vendors talking about how we're gonna use AI to solve problems. And I think you know, there's probably a little bit of irony in here in that you know, job seekers are you know using AI to apply to a lot more jobs. And you know, I think you know, we've created a lot of volume problems for companies in the sense that we see a large, a much larger volume of candidates or applicant flow than we have historically. And so, you know, on some level, I think you know, we're using AI to solve problems that have been created by AI. Yeah, but what's your perspective of you know the bigger problems that you're hearing companies solving coming out of HR Tech or trying to solve maybe?

SPEAKER_01:

Yeah, I think you're you're touching on something there. I call it the AI arms race and recruiting. But yeah, let's talk about the employer side. So, you know, pretty quickly after ChatGPT rolled out, people started using it for things like you know, drafting emails and letters and in mails and things of that nature, so candidate outreach. And then they thought, oh, you know, this could probably make my job description a little more robust and you know fleshed out. So now it's it's rewriting the job descriptions. And oh, well, it also can help me filter and match, although those tools have been around for a while, but you know, they've definitely grown their providence and uh and more so. So I don't even have to like go through these resumes per se, because now I can have a tool level screen and float the best ones towards the top. And you know, that's all celebrated in the name of efficiency, but you know, it's not not fun when the other side has it too. Um, which then you have then you have uh you know candidates who are like, well, hey, I can use this, I'll rewrite my resume to perfectly match this job description. Okay, and you can send that out, you know, help help me uh answer these interview questions, right? And those tools are just getting more and more sophisticated. And yeah, send off my resume. Oh, congratulations, I you applied to a thousand jobs while you were sleeping, right? And now now you do have uh not only a volume problem, but you have a noise problem. Because what is an algorithm, a matching algorithm, no matter how sophisticated it might be, what's it supposed to do when it has you know hundred resumes that were tailor-made to the job description if that is the underlying matching criteria, right? It's like you know, congratulations, here's you know, a bunch of hundred percent matches. Well that's no longer useful. And now you have way more to go through. Okay, so what do you do? Well, now you have a whole category of solutions, also AI, that is is going to interview them. Because hey, I I don't have enough information and context to f to know who of this are are actually the best. But you know what, you know what I could do is if I had an ability to have a conversation with them, I can get more information on that candidate. And now you know then I could facilitate a better match. So I'm trying to get more information and context by way of conversation interviewing. But well, shoot, my stack of applicants is a thousand, two thousand tall. I can't interview all those folks. Ah, but AI can. Okay. So now we're gonna feed all of these candidates who may or may not deserve or should be interviewed, but we're gonna funnel them through this AI interview tool. Um, and on the other end, the hope is that now I've I've really sifted through and I've I've truly identified, you know, the best quality and most engaged and the best fit for our organization and for this role, you know, by by way of this. But like I just described a whole apparatus that like just wasn't there what, three years ago? And that's a lot of noise to sift through. And it makes me question, be like, wait a minute, what are we doing here?

SPEAKER_02:

Well, I guess you know, it reminds me of you know, maybe a decade ago, and I think you heard this in our past life. Like, you know, first the problem was uh, hey, a job ads, job ads are bad. You know, so your job ads are best case, that's a lie. You know, resumes are are poor, you know, people put you know, arguably some some fluff in resumes. So, you know, resumes and and job ads are both, you know, two false documents. You know, so you're matching one lie and with another lie. And, you know, so it's not a surprise that our matching algorithms are on, also. And I think, you know, this whole idea of context as we're getting into you know moving people through the process is one that I don't know, I'm probably more keen to follow a little bit too, because I think you're right that like, you know, it is becoming increasingly more difficult to match a generic is the word I use, even though I I hesitate, but a generic profile with a generic job description and try and figure out of a thousand people that come through, you know, who really is going to be the best fit.

SPEAKER_01:

Right. Yeah. You know, another term I I discovered yesterday, and it was a real uh brilliant article, but it it's basically this age of quick hit video and algorithm generate content and in gen AI. It's really compression and it's like a compression of the average. So, which just makes everything look the same. And you get like change blindness. You can't, you know, you're trying to compare two things that are like really, really similar, sounding, looking, etc. And the you know, the high fidelity, the signal, like the true uniqueness, the raw file or material, that that's what doesn't really exist anymore. Which, you know, we can talk about that later. I think that that might be the counter uh example differentiator that that employers and you know really anyone can use to stand out. I would love to dig in on context a little bit, if we can. Yeah, yeah. Well, where where do you want to take it? So this was my this was my big aha doing a lot of side-by-side comparisons. And as you can tell, I'm a little skeptical of the AI interviewing tools. Not that they can't work really well or be, you know, super useful. It's just it's the emergence of the solution seems to be trying to combat a problem of our own making, of you know, AI everything to you know, create that volume problem that you're talking about. But I do see these things you know having real great potential and can help a number of our clients who we interact with every day, you know, get through that tunnel and and find the best quality candidates. However, context is really what matters. And you know, I demoed probably six to eight of these tools at the expo, and some of them it's you know, you upload your job description, and then from there, the AI interview tool will analyze that and come up with interview questions that it can ask to susto if somebody matches the qualifications to the job. And so then they're gonna have some either plain voice or a video avatar. Uh, you can kind of pick your flavor there, and then that's what's gonna have the conversation with the candidate, and they're gonna ask these questions and hopefully without not too much lag time, and they're gonna ask those questions. And now you're basically conducting that first screen interview with the candidate, uh, but uh AI is doing it instead of a recruiter, so you can do thousands of these a day instead of you know dozens, maybe. So that's the solution, but the context that is fed into them is what I think is the differentiator. So some tools is like upload job description and go. Some of them are well, we start with the job description, but look, now you can toggle on this or enter this, you know, you can you can edit, you know, and kind of create your flow after doing it, which okay, that's that's good. Or better at least. But I think the best solutions and the ones that are most interesting to me are the ones that take in the full context of the organization. Meaning, like, what if you could upload all of your you know, recruiting best practices that you've established, and you know, the interview questions that you have said, you know, like that that's what we like to ask, or your um what you consider to be your employer brand differentiators, um your employee handbook, your core values, sample candidates who have worked out really, really great in your organization and you know what what their background is, the conversation that you have with the hiring manager in your intake meeting. Okay, now we've now we've included a lot more context. And whether it's a human or AI, they're gonna do a lot better because now they have more context to conduct uh a better interview and to facilitate a better match. And so I think that's it, is you have to you if you're going to use AI as a layer to try to speed up things that humans you know have been doing or could be doing, the the underlying context that they rely on is super important. But the catch is that is no different. Whether it's a robot doing it or if it's a human doing it, everyone performs better with more context. Yeah, I'll pause there.

SPEAKER_02:

Well, and I have a few different ways I want to kind of crystallize this, Mark. So, first, goals a better match, you know, we're talking about AI interviews in you know, in particular, and like, you know, I would argue that that's you know, by some definition, a new category of solutions, right? And I would also say that like, you know, the way we're thinking about our uh Telity marketplace, and we'll maybe talk about this a little bit, is you know, really shouldn't be focused on what category does something live in, but what problems are we trying to solve? And like, you know, hearing you describe feeding in all these additional contextual points, you know, your brand differentiators, your handbook, you know, your green breast practices and so on, like, you know, it almost feels like AI interviews, you know, kind of blend in with assessments. And, you know, maybe it's uh it's just a new type or a new form of candidate assessments. And like, hey, would you feel different if you're a candidate and you're going through the interview process and like, hey, it's not an AI interview necessarily, but like, hey, like this is our AI assisted uh, you know, candidate assessment. It's part of our process. Like, boy, like, does that make like are we, you know, so are we thinking about you know this in a way that is easy, you know, that is maybe just you know, almost off-putting for candidates or anyone when you hear, hey, it's AI, everyone shudders a little bit. But you know, arguably, like, we're not that far off from having this just be a new way to assess candidates. Is it just assessments under a different sort of cloak?

SPEAKER_01:

Yeah, I think it's probably off-putting if you call it an interview, and it's less off-putting if you call it an assessment, um, is the truth. Um, if I were trying to communicate this to a candidate, if I'm an employer, the way I would describe it is we get a lot of job applications. And while we would want to spend time with every single one, that's just not possible. However, we do believe in allowing all of our candidates to share their full story, you know, to get beyond just the resume, so that way we can have more information to see if you're a good match at our organization or elsewhere. You know, so we're using this tool that, you know, to allow you to put your best foot forward in a way that a uh a sterile application or resume can't, right? So that's you know, that's that's how I would convey it to uh to a candidate. Now, on the employer side, I think you're right. You know, we you you think of it as like the initial phone screen, you know, is really what it's it's kind of doing. But these things do have the capability to essentially spin up assessments, you know, type questions, you know, because they can be behavioral questions and then they, you know, they can be hard skill type questions. So, you know, what we consider to be like, oh, you reach this stage and now this assessment gets triggered. I do wonder if this is kind of a merging of two stages in the workflow, and it just to help speed up the process and get to point B a bit quicker. And I do wonder too, if you have an assessment that you like, you know, how how can you marry those things so that assessment gets completed and more of this conversational AI experience versus, hey, go through and you know, type out the information and check the box and you know, et cetera. Because, you know, if you can get all that done in one fell swoop, then maybe that's the maybe that's the answer uh there. And but then you also want to think about consistency. So do you, you know, you probably don't want AI just making up ad hoc assessments by you know by the position and you know, whatever the hiring manager had to say, you know, per se, you know, you I don't want to necessarily get into it, but you might be opening a can of worms with compliance and yeah, and just following a standardized procedure there. But but yeah, I think you're right. I think I think there could be a convergence of what we're calling AI interviewing with actually we're talking about assessments.

SPEAKER_02:

Oh, and I also think like, you know, when we think about where we're finding budget, you know, sure, AI interviews like it's taken, you know, we're saving headcount in theory, right? But like, boy, there's a lot of dollars in assessments too. And, you know, everyone's always trying to figure out where the money's gonna come from. And I wouldn't gloss over the fact that like, boy, there's gonna be some you know definite overlap opportunities with these AI interview tools, you know, and assessments and where, you know, uh everything's in the spirit of giving the better can the right candidates a chance to you know better align themselves with opportunities that they're applying to, right? Well, you know, I want I do want to talk a little bit about a few things, you know, in the marketplace. I but I before we get there, I'm gonna ask one more question though, Mark. So when we talk about all these tools at HR Tech, you know, I think you you brought up an interesting point. And I'm gonna paraphrase it and I'm gonna let you explain it. But when we think about differentiators, you know, you see, we see a lot of data providers. Talk about the data providers at HR Tech and what you're seeing with overlap or maybe you know, sharing um or you know, where people are getting their data from to build some of these tools. You know, and I know that's kind of a leading question, but um, you know, give me um give me your give me your thoughts, you know, when it talks to where people are getting their data from and how we should be thinking about that as TA practitioners.

SPEAKER_01:

Sure. Okay, so I'm gonna put this in a few different categories. And one I'm gonna call the candidate sourcing slash profile search uh tools. And you know, for the sake of this, I'm gonna avoid naming any specific providers or vendors. Um, but you know, I can describe the category. Like these would be tools that recruiters log into. Uh they're trying to search for candidates. There's there's gonna be you know a certain amount of matching features, and these tools are now adding um agentic AI. So if your recruiters don't want to use it, well, we'll have an AI use it and search for the candidates and message them and get them to apply, et cetera. These are basically a set of tools that are like um we'll call them LinkedIn competitors, or hey, you know, you're spending a lot of money over on LinkedIn, you know, here's a you maybe you could find the same candidates and others, you know, in this tool. I guess the breakdown like for the data question, well, where does that data come from in these tools? And the answer is most of them are LinkedIn profiles. Um like they are. Like that's that's like here's a person that does this job. Now that is supplemented with data behind the scenes. And there's probably about a handful, and really a couple that power the data behind the scenes. And this is where they're gonna bring in the phone number and the personal email address and maybe some other context bits that they've left on you know other websites. This is where you'll get like GitHub also thrown in there for the tech positions. That's why they always demo a Java developer because you got the hard skills and you get stuff you know over uh from GitHub. Now, these tools can be super valuable and they all have their own way and workflows and user interfaces that I think are are really slick, but like they can't they can they can't invent candidates. They don't have proprietor proprietary databases where candidates have uploaded their information. They're scraping. And you know, like it or not, the biggest source of that scrape data is LinkedIn, and then it's enriched with these other data sources. So I guess that's that's one, but like, you know, they can put whatever hundred hundreds of millions, a billion candidates on their on their pitch decks and these sorts of things. It's really the same. It's the same candidate pools, enriched with similar data sets. What's going to make those solutions different is the user interface, the different communication tools, and like you know, doesn't plug in with your existing workflow. So that's one, but I'm wondering if you were thinking of other types of data providers.

SPEAKER_02:

Oh no, I think that's I think that's exactly it. Like, you know, everyone talks about you know the billions of different data sources that they're pulling things from. And I think, you know, if we're being honest, it's you know, everyone's fishing from the same ocean, right? And you know, I think uh I think cunliness is one piece, but I I I think that there's a lot of overlap that we don't really talk about in this space.

SPEAKER_01:

Yeah. There is another category data provider I do want to highlight. And these this is what I would call going back to the context, one of the foundational layers of data that people talk about but don't seem to execute on a lot, or at least they they understand the concept, but they're not really understand what that means. And it has to do with skills taxonomy. So going through the exercise of really understanding your organization and the roles within it, and this, not not just the job title and the job descriptions, but what are the actual skills needed to complete this work? And where does your organization want to go and what skills are needed for that? Well, you have your big HCMs like a workday or SAP, and there's there's areas there for skill data, uh, but it's not populated like out of the box. Like that's something you got to go do. And you can kind of like brainstorm and think and start typing in skills there, but there are data providers that provide this framework of skills taxonomy, which so essentially like think about like really completing your work, your internal workforce data with skills, and then that informing, i.e., context, of okay, talent mobility, who do we need to go get from outside? You know, what are the skills necessary? And now that's gonna facilitate better matching and queries like on those whatever technology you have layered on top of that. So I would add that in the context bucket, and these skills, skill type data providers can provide that enrichment so everything else performs better.

SPEAKER_02:

Yeah, I think we're gonna see quite a shift in um skills demand over the next you know year to decade. You know, we talk about uh, you know, the example I forever give as a, you know, when I went to IU, it was you got to get an informatics degree. That's what my mom said. No one knew what it was, but like, hey, it was had to do with computers, maybe, you know, and and and obviously I didn't get an informatics degree, and here we are. Um but hey, 10 years later, what was the number one degree people were looking to hire for? People with informatics degrees. You know, then like, hey, last year it was prompt engineers. You know, I think you know, the interesting piece you know that I'm following is you know, I think we saw people putting a microphone in front of a lot of folks over at HR Tech saying, hey, what skills do you think are gonna be most in demand in the next, you know, next five years? And my answer is probably contrarian, and it's uh reading and writing, critical thing, being able to critically think, being able to speak. I that is those are the skills that are gonna be in demand. You know, everyone can you know drop an article into Chat GPT and say, hey, turn this into a LinkedIn post. And like uh, you know, the old adage is hey, I I know what it is when I see it. You know, hey, I I I know what it is. Um, you know, human wrote it, you know, versus if it was written by you know ChatGPT and just popped out. And I think you know, we're gonna see a pretty big increase in demand for critical thinking, you know, reading, or well, maybe reading is not the right one, but writing and speaking. Anyway, that's a that's a rabbit hole we don't need to go down today. Um, probably. All right. So coming out, you know, coming out of HRTAC, you know, a lot of pressure for uh from clients from executive leadership saying, hey, I got this great report from Gardner. It says we need to do something with AI. And I would say it's a pretty overwhelming landscape. You know, how do you think about helping, how do you think about evaluating maybe AI solutions first? And you know, if you're a if you're a buyer, um, you know, what are some of the questions that you know you ask that kind of cuts through the marketing speak, Mark? You know, helping a how how would you help a buyer thinking about you know workflow problems rather than you know technology features or benefits?

SPEAKER_01:

Yeah, I guess first first I would say don't pay attention to the Joneses. I do think there's an element of that is part of the marketing rush is to make everyone feel like they're not participating in some sort of party. And if you don't do this, you're you're gonna get left behind. I you know, like yes, you need to be evaluating at all times how you can um improve your process and be efficient and learn about new tools and leverage points, you know, to apply to your business. So I'm not I'm not saying like just put your head in the sand and pretend like this isn't one of the most amazing times in technology innovation because it is. But what I'm what I am saying is is like, well, I heard so-and-so is using this you know, piece of tech or that piece of tech. You know, we need to do that too, or you get pressure from your executive team, like, we need to use AI. And what's absent of a lot of these conversations, and we get inquiries like this all the time, is it it's like they're vaguely in an area, you know, they've been dropped in this territory, but they don't know where to go. Like they don't know is it is it west, is it north, is it east, you know, is it through you know this door behind that tree? So um and then but the reason is is they haven't they haven't slowed down and just thought you go back to the critical thinking. What is the problem? What problem are you trying to solve? And so I guess now I'll I'll get into like what's the framework that I've been using and uh I I think will be really helpful of of helping TA leaders think through this, but also helping partners, you know, vendor solutions clarify, you know, their pitch and thinking. But um I can't take credit for this, and if we have time, I'll tell you the story where it came from. But there's this is called the five questions. Um I've added a six because I think it's important in this day and age.

SPEAKER_02:

It's not to your framework now, Mark. See, it's six. It's six questions. So look at that. So now it's the Thomas Tino method. There we go.

SPEAKER_01:

Oh, great, perfect. Um, no, I can't take credit for this. Um, but uh or you know, I will say I use it and I have refined it over the years. So the first thing is what is the problem? And then I would add it, I'd add a kind of an addendum to that. And what is the impact of that problem? Because if you're in a business, every business has a lot of problems. But you have to quantify that problem, and and that way you can prioritize which problems you're trying to solve. Number two is why is what I'm doing today not working? What is it about your current situation? Maybe it's your current tech stack, maybe it's your current process, maybe it's the people you have or the roles and responsibilities you've divvied up, but like why is what I'm doing today not working? And that's where you get to the root cause. So really it's like the first one is what's my problem? Like, what are the symptoms and how bad are they? Two is why am I experiencing those symptoms? And if you can't get clear on one and two, do not pass go. Because then you're gonna end up talking with a bunch of solutions and they're gonna put all sorts of ideas in your head of what your problem may or may not be. And if you are, you know, end up buying one that's not actually mapped to the root cause or the problem, you're gonna burn a lot of capital, a lot of time, and a whole year business cycle before you even realize that. So, like that is the critical point is the one and two. What's my problem and why is what I'm doing today not working? Okay. From there, now you can go and look for solutions. And um, you can make sure that if you're talking to a solution, that they match your problem situation, they match your one and two. But three, what will you help me do differently? And this is where you get to differentiators. A lot of solutions. Okay, you say you can solve my problem. What are you gonna help me do differently than I'm doing today? And how does that compare to other solutions that are in the market? Number four, why should I believe you if you're talking to a solution? And you know, that can sound a little crass maybe, but really what you're asking is like, what's the believability here? Are you somebody who just started and I'm customer number one? Have you been in business for a while? Where have you had success? Do you have case studies, testimonials? Do you have reviews that aren't just paid for? Like, can I talk to somebody? Like you have a referral. So that's why, why should I believe you? That's your trust and credibility if you're a solution provider, and that's your BS detector if you are a buyer of a solution. Number five, what are the costs, ROI, and roll-up planning? So, what's the pricing model here? You know, is it per user, per module, is it subscription, annual, you know, et cetera. What's the ROI? This goes back to problem. The question number one what is the impact of this problem having on my business? If you can't connect the ROI of that solution to your original problem statement and the impact, like that that step is often missed, but that's how you get stuff done internally. That's how you're gonna sell it to the board or to your your CHRO or who's ever making the final decision on that. You have to be able to show the ROI as it relates to your problem. And then rollout plan. Is this like going live by the end of the week? Is this the three, four months? Do I need to bring in a project manager? Do you provide a project manager? What happens after I'm live? What sort of ongoing support, training, customer success, and those sorts of things? And those things can mitigate your risk of like, hey, it was the right solution, wasn't stood upright. It was the right solution, but man, that support, you know, it's just not where we needed, or we didn't have the ongoing training. All right. So that leads to number six is what are my risks and how can I mitigate them? And some of that is getting the rollout plan right. But the other ones, this is gonna have to do with data security, uh, compliance. And you know, in the age of AI, like, you know, we need to have a conversation about hey, what are you opening me up to in terms of you know bias, emerging law and compliance and those sorts of things? So those are I would call now the six questions. But that's how I talk to partners to evaluate them, to you know, create this idea of a short list of who we feel like we can trust to introduce customers to. And on the flip side, when we're talking with customers, this is how we can help clarify their thinking about you know what they're actually trying to solve.

SPEAKER_02:

Yeah. Well, I think uh you and I went through those same uh five question trainings uh for a well a number a number of years, I'd say. And I think that you really has just helped frame you know how we approach a lot of our problem solving too, which has been super fun. So um, okay, well, I know that we're past time. I don't think we're gonna get into our marketplace, Mark, but you know, I'm gonna say, you know, I got two questions to close with. One, looking forward, so you know, which what's maybe your most contrarian prediction about talent acquisition technology? You know, what is what's everyone think will happen that something that you might be might might strongly disagree with?

SPEAKER_01:

Um so I would say there's there's a lot of fear, uncertainty. You know, AI is gonna take all of our jobs. And um particularly it's like the like the idea that there's gonna be this superintelligence or AGI that could become malevolent and want to turn us all into paperclips. And if we don't, you know, if we don't stop this now or get alignment, you know, we're all doomed. I I have strong reasons for believing, you know, that stuff's not true. But really, it it boils down to of just having a fundamental fundamental understanding of what it means to be human and what humans uniquely do, and what AI or any robots or you know computer programs, what they are, and what they're meant to do. And so I'm not scared of that, and that's probably a separate podcast of digging in the exact reasons why. But because I'm not scared of that, I also view that as the the quintessential opportunity here. There will be tasks that AI will do, and they will do them faster, better, cheaper, and at scale that you just can't do as a human. And that's a good thing. There are going to be things that are uniquely human, and that is the only place where employers, candidates, businesses of all shapes and sizes, and just being a person in this world, that's where you differentiate yourself. And I I feel like there's going to be a counter movement, or, you know, all the everything will be AI'd, or you know, there's going to be agents doing all this stuff. And really where you're going to stand out is um doing the things that only humans can do, i.e., critical thinking, like you alluded to before, storytelling, uh, making uh making things that are compelling and providing a human connection. And I think that's where you'll see people win.

SPEAKER_02:

Yeah, well, I guess great. Well, yeah, last question, you know, I think you bring some pretty uh, I think you have a very unique lens, and I know hey, whatever everyone's reading or listening to kind of drives how they learn. So what are you reading or listening to these days, Mark? And you know, maybe like where do you go when you're trying to learn or stay ahead of our our landscape or this AI landscape in general?

SPEAKER_01:

Yeah. Well, given the nature of what I do, I'm plugged into the industry. So I'm always talking with industry peers and you know folks at these different providers. So that's certainly a source of information that you know it's not everyone has time to do that because it's not their job. I would say more broadly is I really, I really love taking ideas from everywhere. And so not specifically, you know, industry or you know, directly, but like, you know, these are you call them your social media follows, but I I pride myself on finding independent thinkers who I admire intellectually, and then I love it when they disagree with each other on whatever topic it is, because that gives me an opportunity to like weigh two really good arguments and then decide between those. And so I guess I don't want to give you a list of people to follow or anything like that, but I I would urge others, you know, like you know, if you find yourself in an echo chamber where everyone seems to be agreeing with them themselves, step outside of that because there's always smart people on both sides of an argument, and getting good arguments from two sides helps you find a better, you know, the more correct answer ultimately. And a book I'm reading, and I recommend it to everybody, but it's you know, it takes a lot to get your brain wrapped around. It's called The Beginning of Infinity by David Deutsch. And you might want to start with some podcast interviews. His last name is spelled uh D-E-U-T-S-C-H, but gives you a real kind of what I was talking before of giving the foundation of understanding what it means to be human, what is knowledge, uh, how do we grow knowledge and you know why we shouldn't be so worried about the robots taking over.

SPEAKER_02:

Yeah, so just light, light reading, you know, for your for your for your afternoon coffee, or maybe the perfect book if uh you know you if you're if you can't sleep. No, David Deutsch is now on my Twitter feed every day, uh since you and I have been having those conversations. So fantastic recommendation. And again, I go back to like you know, the energy at HR Tech is you know, it it is good that we have people coming in from outside of the TA space and building products. And you know, I think sure, adding a new uh widget or feature to you know to an ATS or HRS is something that you know these large vendors want to talk about. But like, you know, I think we're gonna have some really good ideas come from people who are not, you know, in the weeds every day, too. And you know, I love that you know, we are all trying to leave, you know, to learn from outside of our industry, period. All right. Well, I think that's a good place to put a pin in this one, Mark. Well, I think our six questions has to turn into a webinar at some point too, because you know, I I think we both get energized talking about problem solving and and our approach to it. So maybe we'll add that one into the old ticker also. Well, till next time, we'll link everything in the show notes and you'll know where to find us. But yeah, thanks, Mark, as always, for this and the continual journey we're on with now with Tellivity. So onward.

SPEAKER_01:

Thanks for having me and looking forward to it. And I'll see you in the next one. All right, thanks for tuning in.

SPEAKER_02:

As always, head on over to changestate.io or shoot us a note on all the social media. We'd love to hear from you, and we'll check you guys next week.