Scaling Smart: Tyler Weeks on the Future of Recruiting Systems

Hey everybody, happy New Year! Oh my God, it is officially 2026 and I am so happy about that because 2025 sucked. It was bad. I mean You know, nothing really terrible happened like in previous years when I got cancer and all that other stuff, but it just was a rough year, man. You know, I kind of don't really want a new year this year. I want like a gently used year, you know, like a— give me like a 2015, 2017 model that's been, you know, previously owned. And I, I just did this— this whole new year smell thing is just not working for me, but it's gonna be a better one.

You want to get your year from CarMax?

So I want to get my year from CarMax where there's guarantees and when I can get it, I can take it back after a week if I didn't like it. Um, you know, stuff like that. So you know what I mean?

That's so funny. Uh, I feel you, man. Like, uh, I feel like it was like every year I'm just like waiting for the year where it's like an easy one, like just like a— it's like It's like, oh man, that was just like great. No big deal.

We used to have those. What happened?

Yeah, I don't know.

It used to be like just— it was just a crazy year. I think, I think AI did it. Yeah, that's probably what happened.

I, I blame AI and I blame, uh, like DoorDash. I feel like DoorDash has like some sort of like culpability in, in this thing. Things are moving.

There you go. Welcome everybody. So, uh, our guest today is Tyler Weeks, and he is currently, uh, Managing Vice President of People Technology Research and Analytics. It's one of those job titles like the one I used to have where there's like so many words in it, it makes you feel like you're really important. But then whenever you get an introduction letter from LinkedIn, you know that it's spam because it's got that gigantic job title. You know what I mean? But you're in charge of like basically tech and analytics for Marriott, which is this tiny little company that nobody's ever heard of. Um, and you know, it's the hospitality industry. And before that, you used to be a head of HR data science at Intel.

Yeah, yeah, yeah.

For that, um, you were a consultant and you actually worked at a national laboratory for a short while there. So quite the, quite the colorful history.

Yeah. Look, here's the, here's the thing about HR is like none of us meant to be here. Like, like there was never like, it's not like there was like a little, little 5-year-old Tyler that was looking up at the stars and thinking, Mommy, Daddy, I want to be in HR. I'm going to be an astronaut, a rock star, or an HR representative, or an HR But maybe I did, and I'm here to tell you that dreams do come true, right? Yeah, look, I, I've had a, I've had a pretty, like, I, I've enjoyed my career and I, I love where I've landed. Uh, I, I, my background is in physics, so I started my career as a physicist and, uh, was doing some really fun R&D work, uh, really hard R&D work at Intel, and a couple years into that, like, my, my curiosity started to shift from, like, the work I was doing to the company.

People do it.

Oh, like, that's— yeah, the people do. Like, it made no sense. Like, that was weird. Like, I could— like, electrons, plasmas, like, all that stuff, I understood that. Uh, people are nuts, but they are just as unpredictable as quantum particles. More unpredictable. That's the thing is they make no sense. So like, you know, I like, that's why I was like, and here's the thing is that like physicists were the most arrogant of all scientists. So I was like, how hard could it be?

They were, or they are? Sorry.

Are you saying? They are the most arrogant.

So I was like, not nuclear physicists, just physicists?

Just in general, as a general, as a rule, as a group. I think we think we can solve anything. And I would issue this challenge to any of my compatriots out there. Come give this a try. This is—

come give TA a try. Yeah. Don't say you can't solve this. This is the quantum entanglements that we have in our industry are, let's just say, far beyond comprehension, for lack of a better word. But isn't it funny, though, how in physics, like, you know, people in physics will say that it is the fundamental science of everything, right? Because everything is made up of particles or energy, and particles are energy, and it's like this, you know, you can trace it all back to— it's all physics. But really, it's all math.

Yeah, yeah, yeah, that's true.

And really, it's all chemistry.

Well, okay, so now those are fighting words. So here's, here's what I'll, here's what I'll tell you.

That's what people say.

And you just stepped into a little rivalry here.

No, I know. This is what I wanted to do.

One day, a bunch of physicists got together and invented chemistry. So just like, that's our little thing.

Didn't a bunch of mathematicians get together and invent physics though?

They did, but we don't talk about it. Actually, here's the thing, is the only reason I got into physics is because I was like, my first major in college was archaeology, and I was like, gonna be like Indiana Jones. And I had a professor that was like, there's no jobs in this. And I was like, I'd been pretty good at math, and I was like listening to my roommate work on a physics problem, and I was like, oh, that's just calculus. Uh, I'll go try that. Turns out—

so you went into physics from math?

Yeah. Uh, it turns out most of the math that we're familiar with, that we've like— was all invented to describe physical process, like calculus and all of that stuff. It's— it— all of that was invented to describe things that were happening. And actually, you know, it's the thing that I actually really love about understanding organizations, understanding how we hire, understanding talent in general, is that it's not like I've put all of those years on the shelf. They actually really inform how I think about— how I think about an organization and really kind of understanding what it— like when you say you have an assessment and that it has a certain amount of like prediction, predictive power. What does that actually mean? Like, what can I actually expect from that? And, uh, yeah, it's been a lot of fun.

You know, I think human resources is the least science of the human sciences.

Uh, yeah, I mean, it, it was—

let me—

I'll put a positive spin on it.

It's more social anthropology.

I think there's a lot of opportunity, and right, right, that's what I, I And I actually, I don't think we need more data scientists. We just—

no, that's what I'm saying is it's not more scientific field.

More— we need more scientists. There are great— there's a whole field of IO psychology.

We need logicians. Yeah, we don't have any of those, right? We need ethicists. We don't have any of those.

We actually do. So here's the thing. Yeah, so like if you go— if you look at in the field of IO psychology, industrial organization—

okay, this is what they do.

They, they are sort of like science behind, behind HR. But my, like, and I, I've got some amazing colleagues. I love working with, with IO psychologists. I learned, I've learned a ton from them over the last decade or so. But what I want to see more of is I want to see more physicists, I want to see more chemists, I want to see more, more, more of like, there's a different set of skills that come with that, that I think if we partner with IO psychologists, could be an incredibly powerful collaboration. I want to see more of that.

Yeah, but it doesn't really— it doesn't really gel, um, that often, you know. It just doesn't happen frequently because they don't speak the same language. It's, you know, if you go back to the fundamentals, the thread that I was following, or I was pulling in disassembling that, that sweater there, is a bunch of mathematicians got together and invented physics. Physics. But you know what? A bunch of philosophers got together and invented math.

That's true.

And, and so, right, so at the base of all of this, you have what's called logos, which is logic. And so logic is really at the basis of all this, and it's also the most, the most, the furthest thing from HR.

I, but I, I don't think, I don't, I don't think it, I don't think it has to be.

Actually, funny, no, it doesn't have to be. Why is it though?

Did you know that like the word physicist is not that old? In fact, Einstein was known as a mathematician. Right. Like, that's actually a bit more of a modern word that we say physicist. But yeah, I think you're absolutely right. I think that that sort of, there's a thoughtful approach that I think we can take to solve problems. The challenges is many of the things that we're trying to do aren't deterministic. It's not that I can, I can turn a knob and then get like me predicting what you're going to do is impossible. You as an individual, but me predicting what 100,000 people are going to do, like groups are very predictable. People are not. And that is kind of the fundamental disconnect. Like a lot of physical scientists come over or will try or have tried to kind of step into this world. And that's where they stumble.

Yeah, that makes a lot of sense. Wasn't it Asimov that said— I think it was Isaac Asimov that said, um, that he's a humanist. He said, I love humanity, it's people I hate.

You know, he— I think he probably did. In his Foundation series, he's got like a science that's called like histobiology or hist—

yeah, I know what you're talking about. It's been a while.

It's basically this idea that you could predict groups and not people.

You can, yeah, because of, um, because of the, um, statistical anomalies that are predictable when you have so many— it's entropy, so much happening. Yeah, yeah, yeah, historology or something. Yeah, I know what you're talking about. So I think that, that, you know, you're on to something there. You've You've scaled recruiting systems in some pretty intense tech environments.

Yep. Yeah.

Um, where, where do teams, you know, put your, your, your intermediary hat between logos and HR, right? And, and think about where do you see teams losing speed, getting stuck in the mud the most? Is it sourcing? The— I like to call it Candidate identification? Is it candidate engagement, screening, scheduling, or is it decision making? What, what is, what is the sacred cow that would make the best hamburger in that, you know, in that pack?

Yeah, so if you're asking me specifically about, about TA, yeah, where do we in TA lose the most speed Yeah, um, macro. I, I think there's a, there's a few, there's a few problems when I, when I sort of like categorize them that I have seen that is true for kind of every team. There is what I call the track and chase problem, which I think is where you lose a ton of inertia or energy to this thing. And it, it— track and chase shows up everywhere from I shared a slate or a shortlist with a hiring manager, and I'm following up to see if they, they reviewed the shortlist. I— we had all these interviews, and now I'm following up to see, like, what— get the feedback or the interview notes or, or whatever. Um, yeah, I, you know, there's an offer out to candidate, I'm chasing that candidate.

This is project management in a way, right? It's, it's—

yeah.

Kanban boards, the Trello, but like, you know, it— and, and sometimes ATSs have a view like that because it sort of makes sense, but the problem is that it's, it's not linear. Things deviate and, and like get moved around a lot because it's human and it's not logical.

That, that's right. And so your, your recruiters or just your TA teams end up spending an inordinate amount of time chasing people. They have a thing out and they need to know that somebody read it or did a thing and did the action. And like, and so like that one's huge.

It's about 28% of their workload or 28% of their bandwidth.

Yeah, yeah. I think that's, I think that's about right. I think the other problem that's closely related, it's the CYA problem where—

It's about 22% of their bandwidth.

Where they have— when I've in the past—

justification, tracking, auditing, what have you been doing, covering your— yeah, your rear. That's right. And some of that, you know what, we unfortunately created that ourselves.

Yeah, we created ourselves. But also like some of it, I don't think that like, like if I'm a hiring— like we talk a lot about like hiring at a large scale when we talk about statistics in hiring. We're usually in a large company, you're talking about thousands of hires.

Yeah.

If you, if you come at this from the other angle, and let's say I'm a hiring manager and I individually am going to hire one or two people a year, every single one of those hires that I do get, especially in a constrained environment, like, they really matter to me. I really need to make sure, because like, I don't care what the company's doing, I need my team to work to, to to be good and have somebody that meshes with my team and is going to produce and that I want to work with. And so like hiring somebody is a lot less like buying a tube of toothpaste than it is like buying a house. Like it doesn't matter. Like it doesn't matter how many details or how many videos Zillow has. The chances you're going to buy a house based on data is almost zero. You want to walk through, you want to smell it because it's a craft, not a factory, right?

It's not an assembly line. It's a craft, right?

You're going to have to live in that house and live in that neighborhood, and you're going to want to know all sorts of things about how it feels to be there. I think hiring managers are looking at somebody that they're going to have to work with for a long time. They want to know that they can work with this person, that they can— their team can work with this person. And so they want that, that contact. And so, yeah, they're going to ask you for a lot of information, especially if they only think they're going to hire once or twice a year. And so I think like in that sense, a recruiter ends up being a lot more like a realtor and they're like, and so if you— when I've interviewed recruiters in the past and I'm not speaking for Marriott, this is, this is previous work. And I've asked them where they do their work and like what they're doing. What I have found is that like almost all of them do most of their work in a combination of like Outlook. They have a very complex set of Outlook like folders that they may live in their inbox. Like that is like everything they ever sent to every hiring manager they support. So when the hiring manager gets mad and says, oh, the recruiter never sent me candidates, they go, no, on the 13th I sent you, here's the email. The other place that they work is in Excel. Because they keep all these like note, like lists.

And then both of those, Tyler, are so completely unnecessary, right?

And they're in— so they're in Outlook, LinkedIn, and Excel. That's all Microsoft. So if I— if you ask me, like, you— if you ask me who owns TA, it is not Workday, it is not Oracle, it is not SAP. It's Microsoft. And like, I think one of the big one of the big gaps is sort of assuming that this ATS is the center of their world. Like if you let go of that assumption and you go, okay, like let's look at how people and where they work, I think you get closer to some of that like churn that's wasting their energy.

The ATS, ATS is the biggest lie in the history of the world. It is the biggest fallacy in the name of a product or a technology or a technology suite or whatever you wanna call it, a category. Application tracking systems do not track applicants.

Say more, I'm curious. Okay, now you've got my, you've got my, my, my interest here. Say more.

Applicant tracking systems track requisitions, the life cycle of a requisition. Open rec, work rec, fill rec. It's not open candidate, work candidate, close candidate. It's requisition-based. There are multiple candidates in a rec. There are multiple candidates in multiple recs. There are multiple recs, but it's all about the rec. Once the rec closes, that project is complete. So if you look at a project management board of any kind, any project management solution, they all have very similar, you know, RACI-type charts. It's who's responsible, who does what, what are the steps, when these are completed, what's the next step, and then when this— so what is the process? The process is a rec is open and a rec is closed. Candidates do not matter. Yeah, you know, yeah, it's interesting.

I mean, like, what's interesting for me when I realized that the reason it's called a requisition is because these were modeled after old invoicing systems.

Yeah, right, it's a request. Yep, purchase.

It's a, it's an invoice. It's a purchase.

It comes before the PO, I think.

Yeah, yeah, or something like that.

Yeah, exactly. Yeah, so like, I need to requisition 10 pounds of steel. I need to requisition 10 candidates. Yeah, exactly.

Exactly. So I'm defining the requirements of what I need. I'm putting it out to bid and I'm sort of like collecting bids and then I'm going to select the winner and then issue the PO.

Which is why when I invented sourcing, I called it sourcing because from procurement, right?

Right.

When, when I was first doing this in 1996, there wasn't a word for this because we were a recruit— you were a recruiter. That's what you did. In fact, we actually used to call ourselves headhunters back then, but we became, you know, recruiters. And then, uh, well, no, we, we became employment, employment professionals or whatever. The point is that job didn't— sourcing was a component of it, but it wasn't called sourcing. It was just called recruiting. So when I was trying to figure out what to call this thing, because I was traveling all over the world talking about why you need to specialize in this. The first, the first name that I came up with was awful. It didn't work and it didn't stick, and it was called Cyber Sleuthing, which before the age of the internet, I had no idea there was already a cyber sleuthing, and it wasn't exactly what I thought it was. So that one went, you know, but by the time 1999 came around, that one went out the, the window. My first newsletter where I had— I was sending out, you know, articles and, and emails to recruiters in the late '90s was called cyber sleuthing. But then we realized, yeah, it was called— Julius Wright, she says it was called researching. Researching was the job that the recruiter did to find candidates, right? But then I thought, what is this like? And I thought, you know what, this is a lot like procurement. It's supply chain. It's, you know, a requisition for materials. What is the raw material of hiring? It's candidates. So we're sourcing the raw material that we utilize in the manufacturing of hires. So that's where that came from. Little did I know that that was going to become a debate 15 years later, but the point is that it is exactly that. It is a process that should be going back to the ATS about the function of that requisition. So that's really what the ATS does. It doesn't track a candidate. In fact, I would say it's more like an applicant trapping system because it traps them into this workflow. I mean, how often do you see somebody apply for a job, they're a perfect fit, but they never even get looked at, and then they get dispositioned, dispositioned, which by the way, if you look that up in the dictionary, it means throwing it in the garbage, but they get dispositioned and nobody ever looks at them again. And now because they've been dispositioned, the next time they apply for a job, a recruiter looks at them and goes, oh, they've already been rejected in the past, so they reject them again.

Yeah, yeah, that's a trap. I mean, yeah, like, I, I honestly, I think this is one of the— this is, I think, one of the big opportunities, not just in TA but generally when you're talking about talent within a company, is like the supply chain model really doesn't work when you talk about people. And the reason—

no, it doesn't.

Unlike aluminum, people have opinions.

There's a shelf life.

You, you Like the example I like to use is you could go run a skills assessment for delivery driver at 100,000-person company. You probably have 90,000 people that could be delivery drivers, right? That doesn't mean that's your supply because it's the people that can do it and the people that want to and are available to do it now and are available. That's so like, you're right in the sense that like if you want to really do, really do talent, you need a map. You need to map 3 things. You need to map the available, like the opportunity, like where do I need that work, the ability, like who can do it, and the interest, like who actually wants to do it, and the overlap of those three things. But that's not like, you don't track that in a—

No, you don't.

When you're talking about raw materials, like what does the aluminum think?

You know, the aluminum doesn't get to the factory and go, oh, uh-uh, no, I do not want to be here, I'm out.

This is not what I was planning.

This is not what I— yeah, the aluminum doesn't like walk out, you know, like, yeah, that's right. So yeah, absolutely. So I think the answer to the question then is it, you know, where is, where is the point where we lose speed? I think you're saying, um, those two things that you pointed out were the, the, the, you know, tracking and covering CYA and um, the justification and all that, which is about 40%, 45% if you put those two together. So it really isn't about speed, it really isn't about quality, it's about this, I guess, overhead of, of workflow junk that we've artificially created. Some because we need, some because it's government regulatory. I mean, we, you know, we have— well, you probably knew this, but there are 13 different agencies that have oversight and can legislate over recruiting. There's only one that can send you to jail. The EEOC is the only one that you can do prison time for, but there are 12 others that have audit and oversight, you know, and are responsible for this function. But most recruiters can't even tell you about one.

Yeah, look, you know, I, I actually think— here's, here's why.

I—

one of the things that makes me excited about AI has nothing to do with the technology. It's— I think what it is doing is that it's breaking a lot of things that we in the last 20 years have come to sort of expect to be the norm.

Yeah, that aren't— that it's always been that way, that were never true before.

So like, one of my favorite ones is that the highest likelihood— and this, everyone knows this, right? This isn't a surprise. The best way to get a job is through someone you know. The best way to find a candidate is through someone you know. It's referrals. Has always been that way.

It is.

Never— it has never changed, except somewhere around 2012, there was a brief couple of years where if you go look at some of the data out there, it was about 50/50. Our, our ability to apply online, to blind— like, just blind applications like you could— you had a couple years where you actually had a decent chance of getting a job. Um, about half of jobs were probably filled by, by online applications, whereas historically it was like, you know, it's like more like 30% now. But like, and now everybody's freaking out going, hiring's broken, I can't just apply to a bunch of jobs online and get a job. Well, before 2000, it was never that way.

It was never that way either.

Like, we've, we've, we've sort of like acclimated to this norm where for some, like, we've treated, we've turned recruiting into like inbound marketing where it's just like we're going to fill and we talk about the funnel and we talk about maximizing leads. All you're doing is maximizing disappointment, right? Like you're, you're driving thousands of people to apply for a single role. And this is— it has always been a matter of connecting people to people, like, through your network. I— so here's, here's the punchline. I actually think you can put whatever words you want around it, like talent advisor. There's all these sort of words that people want to change the name of recruiter to, like, eventually. But, like, I think the, the recruiter with their little black book of, of candidates that they have vetted, that they know, that they've got relationships with, are going to become incredibly valuable. Especially as, as it gets harder and harder for me to tell who a real person, a real applicant is. For me to know, like, of the thousands of people that just applied to this job, who's really looking, who's really available, whose resume is real, who's not a bot, like the person that's got that curated, like, set of relationships is going to be incredibly valuable to me. So I actually think we're like on the verge of a time when the value to the org of a true, like, that researcher, sourcer, like that function, the relationship. I think that whoever owns those relationships is going to be incredibly valuable.

Well, it's always been that way. It hasn't gone away. and even for that brief period of time where the applicants volume maybe exceeded the, the employee referrals volume, you have to go back and, and recognize that was not actually, that was not actually originated as an online applicant. And there, there was a lot of conversation back in those days. Uh, yeah, I've been doing this for 30 years, so, right, you know, There was a lot of conversations around source of hire. You might— this may even precede you. I don't even know if it's even— no, I know, I'm very, very familiar with a lot of debate about source of hire. And my biggest contention with that entire conversation was that we were not tracking source of hire, we were tracking last point of entry. Yeah, that's it. So we didn't know where people were coming from. We just knew when they got there what parking spot they used. Yeah, but how they got there is a million different ways. And we then started giving credence to, you know, these sources because we had to justify the spend. We just dropped $100,000 on a LinkedIn license. We got to show that we're making hires from that. So we essentially created LinkedIn because we were driving candidates to there. In order to apply, you needed to go to LinkedIn. We drove that. Yeah, no, we did. Let me, let me sort of fill in the blank that I think you just— the blank that just kind of popped in, the question mark in your head. So my friend Julia told me about this awesome company And so I went on LinkedIn to find them and apply for the job, but I'm really an employee referral because Julia told me about it. She referred me.

Yeah, that's right. No, so like, I, I think you're 100% right. There's this, this problem with attribution models.

It's attribution modeling.

Exactly. And, and putting too much stake in, like, it's not like Like Coca-Cola is probably— they probably do care where you bought a can of soda from, but they're not, they're not spending as much money on making sure they have Coca-Cola in Walmart as they are in running ads during Christmas, right? Like, it's— there's a, there's a motivation thing there that happens of like, you know, OpenAI right now has no problem hiring, hiring talent, and it has nothing to do with whether or not they came through LinkedIn or like they came through any other source. They're like, um, they're— it has everything to do with just like the brand recognition, like the prestige that comes with working with— for OpenAI, right?

Right. Right, yeah, that's right. So, hmm, well, I think it's a, it's, it's a, it's a conundrum because it all ties back to— you were talking about AI and I was gonna actually go into that a little bit. It all ties back to the ability to make decisions. I think that's right. Judgment, I guess, is probably a better way of describing that, you know, decision-making being— I mean, you can— there's a decision science, literally, it's, it's called decision science. So maybe it's not that because now I'm going to— you're getting too clear. It's really judgment. It's human judgment. And what you're saying is AI has the potential to, um, aid, enhance, um, you know, magnify the power of that judgment because it can look at, first of all, it can look at a bigger scale than humans can look. AI can absorb big, it's big data. AI can absorb hundreds of thousands of points of data, much more than a human could absorb, and multiply that times, you know, hundreds of thousands of instances at the same time. It just, it's literally a matter of scale. So I think that I'm following where you're going, but where do you draw the line personally, not I'm not speaking on behalf of any organization, but where you personally draw the hard line of what actually makes things worse, where AI becomes a— I hear the positive, right? It becomes a way to help us apply better judgment because it has better data, more data, can process more of it, can process it more fairly if it's done right, et cetera. But where is that hard line that you don't want to cross, where AI actually becomes a magnifying glass for making things worse?

Look, so, so let me just go back to one thing. So what I, what I'm, what I'm saying is that I think, I think AI is actually making it easy, so easy for candidates to apply to so many jobs.

Yeah.

That the value of an application has gone to essentially zero.

Right.

It tells you almost nothing.

The fact that that's valid.

And I think that that is what that is forcing is us to go back to the basics of understanding that these decisions are emotional, personal decisions. And so like recruiters are going to play a bigger, bigger role in helping you, especially for like senior or high-value roles, you getting comfortable as the hiring manager that this is a real person who's who's really going to come in and do the job that I can get along with? Like, that's what people want to know. I think that like there's any of this language or talk around like, oh, recruiting is dead, AI is going to be— there's going to be no recruiters because AI is going to do it. Like, I— it doesn't even make sense because like that's like saying there's going to be no such thing as As travel agents, real estate, like that, like we'll just like AI will be so good that you'll never have to do a home tour.

You'll just sort of like, AI will be so good that you'll never need a travel agent. Yeah, there's still, yeah, like AI will be so good that you'll never need a librarian, yet there's still librarians.

Or even more ridiculously, AI was so good you'll never need to travel. Like, no, that's sort of like missing the point is that people like doing that. So.

I know, 100%.

I— so I think that, that, that was my point. I, I think that your question around like, where does it— where's the tipping point?

Where's the line? Yeah, where— so, you know, you can walk a fine line as long as you know where the boundaries are. So what's that boundary where you want to not— you want to not fall over into the abyss?

I think that boundary line is really— I mean, It's in the decision-making, like the judgment, right? Who is responsible for this decision? And I'm hesitating a little on your question, not because I don't have an answer. I'm trying to give you like the most thoughtful answer.

Yeah, of course.

I think a company is a group of people. It's a team. If you did the thought experiment, like, let's just step out of TA for a second. Let's imagine that you had a group of, like, somebody started a group of agents.

A tribe.

A cult. Those agents, a company that was 100% comprised of AI agents. They collaborated, they did all the work, they charged people. Is that a company? Is that, is that like, so like maybe that thing has some, like, has accrued some money that sits in a bank account and then it spends that money to grow more money in the bank account. But like, that isn't that entity. It'd be hard to argue that that is, that's a company, or that there's, that that thing is creating any real value. I think you have to have people. I think you have to have like humanity for it to be something that we would consider like worthwhile. And if you're going to have people, you have to have people like building those relationships. And I think where AI is going to help us, yes, there's going to be some transaction stuff. It's— there's going to be some stuff around creativity that it helps us produce more and produce faster. But ultimately, I think that the most productive teams, the ones that like stand out and the companies that are most competitive, are going to be the most socially healthy companies. I think we're going to see a thing where you, if you don't have, and I think this is the bright line, if you're, if you're moving so heavily into AI that you're actually jeopardizing the social health and connectivity of your teams, like the time they're spending with each other, face-to-face time, all of those things, you're going to hit a ceiling pretty quickly and not be able to outcompete or have new ideas or sort of like be more productive than your competitors. I think that is a really key piece. And so like as much as people hate return to office, there is decades of research that show that there is no better way. I mean, there are ways to try and promote like collaboration and ideas and like productivity. There is nothing more effective than being together to like being with people. And that's the thing that we've got to like we are going to have to.

Yeah, I understand what you're saying and I get that. But I think that that's the being with people part that has to do with collaboration, not the being with people part that has to do with sitting in a cubicle isolated from everybody else because you happen to be the only person in HR in that building. That's not collaboration.

Yeah.

That's not productive. All of your meetings are virtual and you're never going to collaborate with anybody face-to-face. So in that sense, return to office is pointless.

Well, it, yeah, like, look, you could construct a perfectly isolated. Like, imagine you had a building where one person sat on each floor. That would be worse than working from home because not only are you, are you just on the phone all day, but you now have a commute and you're on the phone all day. Like, we're working remotely from the office is the worst.

It's literally what it is, working remotely. Exactly. Yeah, it used to be work from home. Now it's work from chair in your office.

Yeah, now, so like, you're right, it's not like— it's not, it's not by default better. It's not that you send everybody back to the office and because they need to physically be there, right? Right. Because like, people— companies were highly— there was plenty of inefficient companies when we were all in the office all the time. So it's not default better, but it's default worse. Like, if you have— if you really put thought into it and you really design for like having teams that are co-located and have a good dynamic and you really invest in culture and all that stuff, you will always be able to do it better in person than virtually. Like, there's nothing— there's no way around it. But you're right, like, you could—

but you only need to do that sometimes. You don't need to do that 40 hours a week.

And I, I think where it relates to recruiting is having— people want that. Like, as much as we complain about return to office or commutes, like, when people want when you do walk into an office or into a building that's got a great culture, that feels good, there's a good energy there, people do want that. And I think that that's it. That, like, your career site is never going to be as effective at recruiting people as just an amazing experience when they walk in the building, right? Like, it smells good, it feels good, there's friendly people around. Like, go spend your money on that. Right.

Yeah. So yeah, I think, I think, yeah, we're, we're definitely on to something with that judgment and face time and relationship building being kind of where you want to draw the line. So not letting the robots take over that part of it, or I don't even know if they can, honestly. I don't— they are not going to, so I, I don't even know why I'm even debating that. Um, but when you're, when you're Talk about the, the resistance inside the house. What's been the biggest source of internal pushback in your experience with these rollouts, implementations, uh, you know, technology systems, analytics, all these big things that you've, that you've, you know, these systems system being any collection of tasks, right? So it doesn't have to— system doesn't equal software necessarily, but you know what I mean. All these systems and processes and software and applications and all that. What's been the biggest resistance internally, um, pushback? Has it been recruiters, hiring managers, or leadership that resists the most?

Um, I, I think There's different kinds of resistance, right? I think, I think recruiters—

What's the most surprising source of resistance?

The surprising source?

Yeah.

Where you're like, what?

I did not expect that.

The most surprising source is actually none of them, right?

Like, oh, okay. That's surprising to me.

You know? Yes. Like, I don't think— I don't see recruiters as being any more or less change resistant than any other group. Like, you're always going to have like There's always the, the recruiter in the group that's like, they have entrenched, they have like every Chrome extension, we're not gonna do that, came out, like they're, they're always trying new stuff. Like there's—

oh, you're going the other way, sorry.

They're like, they, they're like, they've got tools you've never heard of, and they're like, I mean, they're teaching everybody else how to do it. You've always got those. You've always got the ones that have done it forever, and they're like, this is my, my method. I don't see them as particularly challenging or particularly like they're about the same. I think the big challenge is—

where is— yeah, thank you.

The big challenge really comes in for the business case for change. So it's not really specifically leadership. It's that in the, in, in the HR world in general, these systems get sold based on soft dollars. But they have to get justified based on hard dollars. So what that means is that they'll add— they'll say to you, hey, this is going to save every HR person or every recruiter or every manager 15 minutes. And if you add up those 15 minutes and their salaries, this new ATS is going to save you $5 billion next year. It is not saving you $5 billion, right? Like Like, no, you can't point to finance, you can't hand a check back to finance that says, here's the $5 billion we just saved. That doesn't exist. So what you, what you end up having to do is go, okay, where can I save money on software, on even headcount? Like, where's the real savings that justifies the investment in this and this thing that I want to go change? That's so—

it's not the change, it's the how do I pay for it?

How do I pay for it? And here, here's the— here's the where it gets really tricky is when it's got good automation and you can see on a slide, on a PowerPoint slide, how it's going to— maybe I need fewer sourcers, maybe I need to like reconfigure my teams, maybe I need whatever, like whatever it is. The challenge, like The question is, do you reorg before you have the technology so that you're ready to use it, or do you bring in the technology and then look at changing the team? Nobody seems to have figured that out. So like all, like all of the frustration you see around AI right now where people are like, oh, well, there's all these AI programs and nobody's saved a dollar. Um, it's because you kind of have this chicken-egg problem. Where actually it's because they're not really AI, but well, there's some of that, but there's a chicken-egg problem where like there's a, there's a, there's a phenomenon out there called Parkinson's Law where you're— the time you take to do a task will expand to fill the time allotted, which means that like as I take work off your plate, you'll just take longer without even realizing it. You'll just take longer to do the rest of your tasks. So you personally never actually feel less busy. No matter how much I automate, you feel the same amount of busy. So like, if I then come to you and say, hey, uh, yeah, I know your colleague just left, we're not going to backfill them, but we did give you all this automation. You're like panicking because you're like, but I'm still very busy. Like, you didn't take anything off my, my plate. So like, that's the— that's where the thing happens where it's like it, like, even if you're trying to do— you've done everything right, you've got the business case, you've automated some specific tasks, your teams are feeling very scared because, like, they, they still feel busy. And so, like, then what you end up doing is not really getting the savings that you thought you were going to get, and then you feel like it's the technology's fault, but really it's not. Leadership. It's not the recruiters or HR or anybody else. It's this, like, it's this fundamental challenge of how do you, how do you implement automation and then reinvest the, reinvest the time savings? Like, I, like, let's say, you know, like, I wanted to— let's say I want to, like, automate all of the work of my, of a bunch of coordinators and I want to make them sourcers. Well, like, how do I actually do that is, is a thing that every team runs in. It has a challenge with, and it never— it's no one person's fault. That's why it's so hard to solve.

Well, the right answer is if you have a team of coordinators or recruiters that you want to make sourcers, is you pick up the phone and you call Shelly Steckel at dsiuniversity.com. And yeah, no, but I totally get what you're saying. And that's really insightful. You know, I've sort of been building up to this, and you probably already saw the pattern. But I got, I got like a, like an apex question that I wanted to lead up to as we draw kind of close to the, the end of our time here together. And all of this just sort of culminates with, with that. And it's this question. It's, it's highly unpopular. And I'm probably going to get some hate mail, but I'm going to go anyway. A lot of teams and leaders, uh, hiring leadership, organizational leadership, use this metric, and they obsess with this metric called time to fill. I think personally that is the absolute most useless pointless, ridiculous, and worthless measurement that you could take of a process that is about relationships and quality. So I want to literally— if somebody asked me and said, "Shally, if you had a magic wand and you could wave your magic wand and remove one thing from our industry, what would it be?" My instant and immediate answer with zero hesitation would be, make it as if time to fill never existed. Remove it from the face of the earth.

From history. Scrub it. Scrub it from history.

Scrub it.

It's useless.

It does nothing but harm. All it does, there's no benefit to it. So knowing that you agree with me on that completely.

I do.

What is the metric that we should obsess over instead? Because once I disappear time, once I wave my wand and time to fill disappears, we're gonna have to replace it with some other top metric. What is it and why isn't anybody brave enough to make it their North Star?

So mine, mine that I would replace it with is time to respond. And I've been sort of like—

Respond to candidate, hiring manager, just anything.

So it depends. So it depends. So you make it an experience metric and you say, okay, gotcha. In various ways. From the time that a candidate applies.

Respond to the candidate.

How long did it take for somebody to respond? Did that candidate get a meaningful response from me? So not just a thank you for application, but like they either got invited to an interview, they got dispositioned, they got, they had a prescreening call with a recruiter.

They were legitimately moved forward in the process.

Something that said I got contacted. How long? How long is that? So like when I was at Intel, we actually did this. We started looking at time to respond. Here's what was interesting is that our time to fill, which is like everybody's, it was like 45 days or something like that.

That's literally the industrial grade.

It's always like 45 days, right? No matter what you do. When we looked at time to respond, it was like 56 days. So what that means is that most of those like time to fill and all those metrics, What they're measuring is the best case scenario.

No, no, they're not. They're not. They're measuring a cluster. So what happens if you take a cluster of data? You know this. If you take a cluster of data and you average it out, the 45 is a made up number. Nobody gets hired in 45 days.

Yeah, but even then, that cluster is based on the one person that got hired. So the person—

that's right.

So like, that's right. Like on that, the freeway of that survey, it's like you're only measuring traffic in the commuter lane. But what you're not seeing is that it took 55 days or 60 days for your typical candidate on that rec to hear anything from you, which means that most people are getting a much worse experience. And the reason they're—

almost everybody that didn't get hired, it's almost everybody get hired.

And the reason is, is because recruiters are trying to be efficient and they're hanging on to all those applicants until they actually close the offer and like, you know, get to like pre-hire or whatever in the ATS. And then they all get auto-dispositioned. Right.

And that explains the panic. You just nailed it. That explains the panic at the disco because everybody is worried that there's too many applicants. And so what you just described is happening at a scale unlike anywhere else. And so literally everybody's like, oh my God, now I got 50,000 people that I'm going to piss off.

100%. So now you've got 10,000 10,000 people that applied for this job that you used to get 100 applicants for, you just auto-dispositioned 999,000 of them. It had nothing to do with AI, and AI didn't do that. But like, if you go on LinkedIn, everyone's like, AI dispositioned me. No, it didn't.

Somebody pushed the button.

Like, you just used AI to apply to 10,000 jobs just like everybody else. So like, you're just one of the 999,000 that got dispositioned by a candidate. It's just a form letter. We've always had form letters. That's not— we did like— yeah, so I— that, that I would change is to pay— I would replace that time to fill with time to respond, and I'd make that my north star.

Dude, that's awesome. That's awesome. Wow, it's been absolutely fantastic to have you on the show. I feel like we, we just need to do this again. Like, we're, we're like brothers from another mother, man.

I mean Feelings mutual, Shelly. This has been awesome.

Yeah, it's been great.

Thank you for having me on, by the way.

I'm sad that you got to go, but you know, um, everybody's got to move on. However, um, I would like you to stay if you have maybe 5 minutes for an after-hours piece for our members. So folks, if you're watching or listening or reading this or whatever you might happen to be doing, here's what I'm talking about is the uh, after-hours content that is extra for members only is available at tsiuniversity.com, and it's a benefit that we offer our members. So this show doesn't have any sponsorship. There's no vendors that influence my, my conversations, which is why I'm able to bring in people like Tyler to speak their mind, because there's nobody paying for this. However, I do need to make some money to pay the bills. So if you join TSI University, even as a $12 a month member, you get all these extra benefits, like for example some additional analysis in after hours. So if you don't mind, we'll stay on for that. And otherwise, for everybody that is online, thank you for being with us today or listening in. Please do tune in next week where we will actually have, um, a gentleman by the name of Adam Mikoff M-E-E-K-H-O-F. I hope I spelled that correctly. You might want to look them up. It's going to be really fun. Until then, thanks again, Tyler, and everybody, be kind to yourselves. Take care.