Transcript
Speaker: Welcome back, everybody, to another episode of the Transform Recruiting Podcast.
Speaker: As always, I'm your host, Brad Owens.
Speaker: And with me today, I really enjoy getting in the weeds for recruiting tech.
Speaker: I really enjoy...
Speaker: I enjoy talking to those that understand how it ticks, how it could be improved.
Speaker: I understand.
Speaker: I enjoy talking with people that truly understand kind of the behind the scenes, the in-depth, how you tweak it, how you make it better, how you can actually use this thing to make some more money.
Speaker: And I've got the perfect person for you all to learn from today.
Speaker: So I want to introduce Mr. Andrew McKay.
Speaker: Andrew, welcome to the show, sir.
Speaker: Well, thank you very much, Brad.
Speaker: And with that auspicious introduction of me, I better actually say some smart things during the next half an hour.
Speaker: Well, now, let's just have a fun conversation here.
Speaker: So before we dig in, I think it's going to be helpful for everyone to kind of know who you are, where you come from, your background.
Speaker: Can you just hit us with the highlights?
Speaker: Sure.
Speaker: I always tell people that I'm a musician who grew up in the software industry.
Speaker: My grandmother always told me that, take your first love and make it your hobby and your second love and make it your career, which is exactly what I did.
Speaker: Otherwise, I'd be the same drunk person playing in the same awful bar that I played in when I was in college.
Speaker: But to be honest with you, I fell in love with software very early in life, and it was the early stages of the industry.
Speaker: And I'm
Speaker: was a predilection towards startups anyway, just because I would overstay my welcome in the larger companies that purchased us.
Speaker: But I got into relational databases and data warehousing and business intelligence.
Speaker: And somewhere along the line, around 2001, I was introduced to the idea of a search engine.
Speaker: When I say search engine, I mean like Google, not like search engines and recruiting.
Speaker: And I realized that this is actually the technology that should be used for the data warehousing world.
Speaker: And it wasn't.
Speaker: So I said, what do we do if we brought the two of them together?
Speaker: Being able to search through structured information, you know, records and databases and unstructured information, which is just files and documents.
Speaker: Great example in our context, being able to search the candidate records with the resumes, the job records with the job description.
Speaker: So that was the impetus for me to join Fast Search and Transfer.
Speaker: to start the idea there.
Speaker: Then a bunch of us started at Tibio in 2007 and sold that to ServiceNow based on that premise.
Speaker: And during that process, I discovered Salesforce, brought it into the company.
Speaker: I said, I like these guys.
Speaker: They're in the cloud and I think they're going to be huge.
Speaker: So I actually want to take my idea and take it to the Salesforce community.
Speaker: Because I said,
Speaker: Salesforce is my trial by fire.
Speaker: It's a traditional relational construct, unfortunately, predominantly populated with text.
Speaker: And that's actually how I built KonaSearch.
Speaker: And during the course of building KonaSearch in various markets that we got sold in, the largest market we picked up from the beginning was recruiting, staffing, and executive search, as all three like to call each other differently.
Speaker: And that's been our main market ever since.
Speaker: That's incredible.
Speaker: So for that industry, the primary use of your tool would be what?
Speaker: So we started out, of course, providing a search engine.
Speaker: You know, I look at a job order.
Speaker: I go in and I say, OK, I'm going to create a query in my search engine and find me the best candidates.
Speaker: And we did well.
Speaker: And then we said, why do you keep doing that?
Speaker: Why don't you just sit in the job order and click a button that says, find me the best candidates?
Speaker: And we introduced our matching engine so we could build that query.
Speaker: automatically based on the job.
Speaker: And then we said, all right, well, we're entering the industry and everybody kind of likes what we're offering, but your competitors have one thing you don't have.
Speaker: That's a parser.
Speaker: So I said, oh, that makes sense.
Speaker: We'll create a parser.
Speaker: Because we were already searching.
Speaker: This is the unique thing about us.
Speaker: We were able to search at a level, a multi-object level above the individual objects.
Speaker: So candidates, job history and education history and everything else goes along with it.
Speaker: But we were also able to search the resumes along with that and cover letters and everything else to provide a better relevancy score.
Speaker: And the matching engine did the same.
Speaker: When we brought the parser out, we said, well, we're going to create one.
Speaker: It just so happens when we created our parser, LLMs appeared, Gen AI appeared.
Speaker: So rather than generating haikus written by 16-year-olds about the inside of a ping pong ball, which is what everybody likes to do with ChatGPT,
Speaker: My 16 year old son, I don't think he starts with a blank piece of paper anymore with any of his homework, but we said, we're going to use a specialized LLM.
Speaker: We're going to stick it entirely behind the firewall because security is a very important thing with PII, which is essentially what you have with resumes, client data.
Speaker: And we're going to use that in order to do fuzzy matching between job descriptions and resumes, match them up and see what matches the best.
Speaker: And that's what we started to do when we introduced the LLM.
Speaker: And we said, you know what?
Speaker: This is smart enough to be part of a parser.
Speaker: So we founded our parser based on this LLM technology.
Speaker: And then we realized that our IP is building edge cases all around that model, which is what we've been doing ever since we started developing the parser, which in fact, we released the first version of this week.
Speaker: That's so amazing.
Speaker: Yeah.
Speaker: Congratulations on that one.
Speaker: I'm really excited for it for sure.
Speaker: So you are seeing how the recruiting industry essentially matches candidates, at least how they try to match candidates.
Speaker: So in your opinion, what do you feel like is really a big challenge in the industry currently?
Speaker: How is it being negatively affected by those types of tools?
Speaker: Well, one of the things I noticed from the grid report, I think everybody, I think your listeners know the grid report from Bullhorn.
Speaker: Yeah, it was interesting.
Speaker: They said that firms that were winning business in 2023 were three times likely to use self-service technology tools, which meant that they still believe that I think that technology is a way for them to differentiate themselves from the competitors and do better business.
Speaker: But the other two graphs that were intriguing was one that said the top challenge
Speaker: for firms today is that they have tight talent pools and they have skill shortages and gaps.
Speaker: And that makes sense post-COVID.
Speaker: We understand that in the industry.
Speaker: But the other graph said that of the revenue, of the firms that had positive revenue and the firms that had negative revenue, they were equal in their implementation and investment in search and match, which says to me, they believe search and match is the technology they need, but it's not really giving them
Speaker: the response that they need to fix their top problems.
Speaker: And I think they're right.
Speaker: I think search and match and in technology in many ways suffers in this industry from a little bit of legacy.
Speaker: It's been around for a while.
Speaker: There's a lot of technical debt and we just happen to be young enough to be able to, you know, not carry that around and build on what the latest technologies offer in terms of what we've learned from the other industries that we've implemented our product in.
Speaker: So the challenge today, I think is,
Speaker: What is the next generation?
Speaker: And why is that a challenge today?
Speaker: It isn't because of that gap or the tightening pool so much as your candidate database is no longer your IP.
Speaker: It just isn't.
Speaker: It's democratized now.
Speaker: Everybody has access to everybody all the time.
Speaker: Yeah, there are clever ways to reach out and create new contacts.
Speaker: You can monitor GitHub and you can find out all the engineers and the languages they use and you can
Speaker: extract from the summaries that they write their areas of expertise, and you can categorize them by topics and all sorts of things.
Speaker: But in the end, everybody has access to that, right?
Speaker: So what is it that's your IP going to be?
Speaker: And our argument is that, and I look at it as Salesforce, I think start off with the idea that Salesforce is an excellent foundational platform for any applicant tracking system moving forward in this world.
Speaker: Agreed.
Speaker: You know, what is it?
Speaker: 70% of it is already there, but it's not even so much about the tech.
Speaker: It's about the delivery and the maintenance and the flexibility and the operations.
Speaker: You have ATSs now that aren't just hard packaged apps.
Speaker: Oh, I'd like feature number 46.
Speaker: Well, you'll get it in six months or a year.
Speaker: It's very definitely a template oriented, more malleable type of solution that I think people are looking for.
Speaker: But having said that, Salesforce is still predominantly
Speaker: what we would call a clerically centric data entry form based type of report based system.
Speaker: But an ATS is now, these next generation of ATSs are more about flow, about actions, putting together processes that are malleable and optimized for your business, how you see matching or making money in terms of putting candidates in jobs or jobs to candidates.
Speaker: And I think that flow based process, the next best action, if you will, I guess, has to be integrated with the knowledge that is distilled from your data and all the data you connect to.
Speaker: And I think that's where the IP happens.
Speaker: It's the interplay of the knowledge and the actions.
Speaker: Actions are context for distilling the knowledge and the distillation of that knowledge determines what the next action is.
Speaker: That interplay
Speaker: It's your IP.
Speaker: Could not agree more.
Speaker: Could not agree more.
Speaker: And that I actually did a speaking engagement on this that I'll at least bring up here and we can dig more into your ideas.
Speaker: The conversation that I was having was around, hey, your spreadsheets, your fancy spreadsheet that you keep track of all of your applicants and your warm applicants and everything else.
Speaker: and all of your amazing candidates that you've gotten in your database and everything that you've built over the past five to 10 years, it's absolute meaningless garbage.
Speaker: Because all of the transactions that are occurring, that are meaningful, that AI can actually learn from, that machine learning can actually learn from, they all occur outside of that system.
Speaker: They are you picking up your phone and calling someone.
Speaker: They are you texting someone off of that list.
Speaker: They are you copying that email address, putting a Gmail and sending it out.
Speaker: All of those transactions that actually show you not just who is a good fuzzy match from your job description to your resume.
Speaker: But it now also shows you, hey, this is a person that was a good enough match that we took action on it and we emailed them or we texted them and here's how fast they responded.
Speaker: And here's how many times you've placed them.
Speaker: All of that data, that's what's going to be someone's IP.
Speaker: So completely agree with you.
Speaker: Yeah, there you go.
Speaker: You can think of it this way.
Speaker: It's not the data that's the IP.
Speaker: It's the recruiters interaction with the data.
Speaker: Yes.
Speaker: The knowledge created and distilled in context and the action that results from it is the IP.
Speaker: And that's what's different between you and your competitor.
Speaker: And see, that's exactly.
Speaker: I love that you said that.
Speaker: See, I bring you all people.
Speaker: I bring you the people that understand the future of recruiting.
Speaker: So thank you so much.
Speaker: Even if it's just to agree with me in my own echo chamber.
Speaker: I love it, Andrew.
Speaker: Thank you.
Speaker: Well, honestly, it's like, you know, could be fools seldom differ, but I think great minds also think alike.
Speaker: So, you know, that's.
Speaker: Here's the thing.
Speaker: We should bring up AI, right?
Speaker: Let's do it.
Speaker: Because AI is the big thing now.
Speaker: And everybody's talking about where is AI going.
Speaker: And I think, especially Gen AI, I mean, somebody says, where does AI fit in recruiting?
Speaker: I'd say you're asking the wrong question because AI is very fundamentally two different things.
Speaker: There is learning, whether it's deep learning or machine learning, and whether that learning is based on LLMs or not.
Speaker: And then there is LLMs.
Speaker: as a, you know, for text analytics or distilling information.
Speaker: So look at it two ways.
Speaker: We're using LLMs ourselves.
Speaker: First of all, they have to be behind your firewall entirely.
Speaker: If they're not, then your resume, somebody's resume just ended up in the public space.
Speaker: And this is going to continue until somebody gets sued.
Speaker: That's going to happen.
Speaker: It's going to be an event horizon for that one.
Speaker: Like there has been previously in other industries.
Speaker: But
Speaker: The learning one is actually the more dangerous one long term.
Speaker: I think you think about how data is trained, right?
Speaker: You start with the success criteria and six criteria is what showed up in the search results, what got on the short list or hot list, what got it, who got interviewed and who won the job, right?
Speaker: You have control of the first two.
Speaker: You can make it so that the, whether you're matching or just straight up searching, the results that came back in the order of relevancy scoring is neutral.
Speaker: It's fair.
Speaker: It's not demographically biased.
Speaker: It's not ageist.
Speaker: It's not sexist, racist, or anything.
Speaker: But the final two, which the data will train on is who got interviewed and who got the job.
Speaker: That isn't you making that decision.
Speaker: That's your client making the decision.
Speaker: And you don't know what inherent biases are in them, but that will train your system.
Speaker: And if your system keeps coming back with, oh, just hire a white male between the ages of 24 and 34 and you're done, that's not the right answer as we all know, but it's not even the best answer.
Speaker: And quite frankly, as we move forward in this world, it's not even the economically the best answer either.
Speaker: So, you know, you've got to watch out for that.
Speaker: So one of the things that we're looking into
Speaker: is what we call AI on AI.
Speaker: This gets back to my old thesis on subjective satisfaction of video games.
Speaker: If you're watching the AI train the system as going down a bias lane, the other AI is watching it to correct it and push it back to the middle.
Speaker: You need both, to be honest with you, to be safe.
Speaker: And I think that's one of the things that we should all be cognitive of as we bring training into the system.
Speaker: Now, if you're using LLMs not for training, but you're using it for a nice way to be able to really pull information, meaning out of blobs of text, you can argue that there's some bias in there, but it's really small compared to the one we just talked about.
Speaker: Right.
Speaker: It's just taking the thing, like let's a great example of how I've seen people using this in the industry, at least the LLM part.
Speaker: The generative AI part is here is a resume.
Speaker: Here is the job description I'm putting them up against.
Speaker: Tell me what makes them a good fit for that.
Speaker: All that's doing is looking at those two sources and coming up with that generative script, that generative idea of here's the paragraph that makes them a good fit.
Speaker: That I agree.
Speaker: Not so much that you can do to bias that one unless it comes up with fake things in the person's background to show that it's a good fit.
Speaker: There's really not a lot of risk there.
Speaker: You'll find people that will get very particular about this and say, even the language itself is biased.
Speaker: Okay.
Speaker: Existentially, I suppose that's true, but there's nothing we can do about that one.
Speaker: Right.
Speaker: Exactly.
Speaker: So controlling what we can control here.
Speaker: Um,
Speaker: Yeah, I like that.
Speaker: And I agree.
Speaker: I think the having AI watch AI is good.
Speaker: I mean, there's always that level of there's going to be something that we didn't predict that probably will come up.
Speaker: Then at least for now, we can start doing things to notice it, to recognize that any AI tool we use for a matching thing is going to be risky if it's not also being policed by something.
Speaker: Right, right, exactly.
Speaker: But getting back to the original thing that you asked, which was
Speaker: you know, what is the underlying future?
Speaker: What is the underlying problem and the future?
Speaker: I want to get back to this idea of knowledge and action in its interplay.
Speaker: You know, to get, this is a very abstract idea, but to get very pragmatic about it, we can see examples of this.
Speaker: I mean, imagine putting a flow that has if-then-else paths in it in your ATS.
Speaker: A candidate reacts this way,
Speaker: or a conversation between the recruiter and the candidate goes this way, or I get an update for the interview they had over here.
Speaker: Well, where do we put that candidate?
Speaker: What do we do as the next action?
Speaker: All of that really depends on the knowledge we distilled from all of this conversational data.
Speaker: That's the data that counts now, not just the resume.
Speaker: I mean, there are some arguments out there that say we should just be done with resumes altogether, you know?
Speaker: And I think we all know that that's actually probably a good thing.
Speaker: And, you know, to be honest with you, not to be controversial, but I would put job boards in that same category, with one exception that proves the rule, LinkedIn, because nobody knows what LinkedIn is really their strategy.
Speaker: I don't think Microsoft even knows what it is, but it's too good to pass up.
Speaker: There's something there.
Speaker: We haven't quite grabbed onto it yet.
Speaker: But in general, I think you get the same ideas that
Speaker: All this information that's out there to present everybody in the current format is already a little speculative to begin with, but is what we have.
Speaker: If we can get the conversations in there to also bias that, the interactions, as you had mentioned earlier, between the recruiter and the candidate or the company and the recruiter or the company and the candidate, then we have a real system for the next generation.
Speaker: Yeah, I agree with that.
Speaker: I'd like that a lot.
Speaker: I would very much like to solve that one.
Speaker: But that's.
Speaker: I'm about to get on my AI soapbox.
Speaker: I'm going to leave that one for the next conversation.
Speaker: We can always have a part two.
Speaker: So thinking about that future of what the system could be and what it could be doing.
Speaker: If you were talking to a recruiting firm owner right now, that's like, man, I want to make sure that whenever that stuff comes out, I am ready to use it.
Speaker: I am.
Speaker: My system is just revved up.
Speaker: It'll be the perfect location to have some of this AI.
Speaker: What are kind of the first one or two steps that you feel like someone could take to start setting them up for that inevitable being able to use AI on their data?
Speaker: Oh, that's a great question.
Speaker: Well, the first thing that I think they need to do is that they have to understand what it means to have an applicant tracking system.
Speaker: I think they need to move towards the more malleable template based type of systems that are out there.
Speaker: And I'm going to be biased.
Speaker: I'll tell you my business has been centered around the Salesforce community, but I think I can adequately defend the idea that an ATS based on Salesforce is probably overall the best thing for you to have.
Speaker: That's number one.
Speaker: Number two is that bias towards an ATS that is focusing more, it focuses more on the if then else actions than it does on just
Speaker: data and matching.
Speaker: And I say this as a company that provides a matching engine, right?
Speaker: But I think you need to do that because then the matching and the search and all the other parts have far greater value as they interplay and affect the actions.
Speaker: It makes the process more efficient.
Speaker: It makes it more intelligent.
Speaker: And I think that's where your IP comes in.
Speaker: So we said that before.
Speaker: So I think you need to do is get your ATS
Speaker: ready for that next generation.
Speaker: Get the right ATS for it.
Speaker: That's number one.
Speaker: Number two, I think there's not a whole lot you have to do on the data side.
Speaker: Look, when it comes to AI, there are a lot of systems out there that think that since AI is bright and shiny and new, it should be in everybody's face and everybody won't mind spending two months training and unloading and offloading data and uploading, downloading data and all that sort of onboarding process that frankly makes no sense at all.
Speaker: You should be able to get any system that you pick up
Speaker: Install it, configure it, and build what you need to make it your own and get it up and running on day one or day two or day eight, not day 40.
Speaker: And certainly not because I can't use any of the stuff until my data is trained.
Speaker: This reminds me of plumbing in the late 19th century when it was brought indoors.
Speaker: When it was brought indoors in the city, people would say, I've made it.
Speaker: I've got indoor plumbing.
Speaker: So they would put the plumbing on the outside of the walls so that you can sit there and round your living room and show your guests how successful you are because the plumbing is on the outside of the walls.
Speaker: Of course, who would do that today?
Speaker: Well, the plumbing on the outside of the walls is where AI is in front of the eyeballs of mere mortals, and it shouldn't be, right?
Speaker: So that's the other thing too, is demand that you still have a system that, you know, more or less when you get it,
Speaker: There's time to tailor it to your particular needs, but you know, the technology underneath like the AI and everything else is hidden.
Speaker: It just works.
Speaker: Right.
Speaker: I think that's the second thing they do.
Speaker: But other than that, I would say there's not a whole lot more you can do.
Speaker: Yeah.
Speaker: We can talk about reaching out to, you know, the corners of the earth where you can find new candidate information, like going to GitHub, for example, if you, if you put out engineers, but to be honest with you, you're going to do that anyway.
Speaker: and the system itself still needs to be good.
Speaker: So that's what I would say is that those two things are probably what you need to prepare for what I would call the next generation of where this business is going.
Speaker: Sure.
Speaker: Okay.
Speaker: I will bring up one last thing.
Speaker: I always notice this industry that's a very in-tray, out-tray model.
Speaker: A job comes up, you find somebody to match it, you got the deal, you win, you collect the money and you move on to the next one.
Speaker: But I take a look at the arc of how investment companies, personal investment companies work.
Speaker: And they said, I want to own you for your whole life.
Speaker: So I'm going to put together a plans that are tactical, but they manage you through school.
Speaker: They manage you through your career.
Speaker: They manage your retirement funds.
Speaker: They might manage you how you retire, what your next stage in life is.
Speaker: And they manage all the finances all the way through.
Speaker: What if you could do that to candidates, especially high value ones?
Speaker: What if you could do it?
Speaker: for the job company, a company looking for the job.
Speaker: You're sort of doing that now, but imagine carrying a candidate all the way through their career and filling gaps through educational discoveries.
Speaker: I'll give you an exact example of a client of ours.
Speaker: I won't mention who they are.
Speaker: They were placing candidates for so long into this one huge company in Europe that they had a long history sitting in Kona about all the different places these candidates went.
Speaker: And I wish we had made money on this.
Speaker: We didn't make any money on this part of it.
Speaker: And what they did was they said, the client went to them and said, you know what?
Speaker: We're getting really high scores of satisfaction working for us, but we have high churn.
Speaker: Can you help us with that?
Speaker: So they ran a whole bunch of clever searches about what candidates were doing over time.
Speaker: And they felt that the candidates were leaving.
Speaker: Once they worked for that company, they would not only leave the company, they would leave the entire industry that they were in.
Speaker: And there were other indicators they found as well that gave them the conclusion that they love working there, but you're burning them out.
Speaker: They're working too many hours.
Speaker: They reduced the amount of pressure on the employees and their churn went down, right?
Speaker: This is looking over time.
Speaker: at changes and trends that are going on with that same person from job to job, and then looking at the aggregate of it to see what they can do as a high value service.
Speaker: So that's another one I've noticed.
Speaker: That's the value of the data.
Speaker: That's the value of the actual transactions and interactions happening on that data and how they relate to others.
Speaker: It's not people's candidate profiles and their resume.
Speaker: There's so much more to it than that.
Speaker: Yeah, the hidden data, the data that's in your face, it's hidden in plain view because it's there every time you get on the phone with somebody.
Speaker: You just don't use it for anything.
Speaker: Yeah, it's what they do all day, every day.
Speaker: Yeah.
Speaker: And that's not what they're putting value on.
Speaker: Yeah, exactly.
Speaker: It absolutely should be.
Speaker: All right.
Speaker: Well, this is incredible.
Speaker: I really appreciate you coming on and having this kind of discussion with us because I feel like there's yes, there's a lot happening currently in the market, but oh, my gosh, it's not even scratching the surface.
Speaker: And I think the one the firms that are going to truly own the future are the ones that can start to think about this now and start to prepare themselves and get their system set up in such a way they could take advantage of that so that they're not trying to catch up to those that really did it.
Speaker: I'm really glad that we at least had this conversation now.
Speaker: Let's put together your dream solution then.
Speaker: Unlimited budget, unlimited resources.
Speaker: What would you create or change for our industry today?
Speaker: Well, you know, when I read, I think you had, there was a question you posted to me, I think earlier about that one.
Speaker: And I looked at that one and I just sort of, half of me salivated and the other half of me got worried.
Speaker: Because what could you actually do to build that perfect system?
Speaker: So there's some obvious things in there.
Speaker: I start with the idea is that your database is the world's database and the world's database is your database.
Speaker: Don't ever think otherwise.
Speaker: You've got to get your company around the idea that your intellectual property is no longer your contact database.
Speaker: And that means because it's all democratized and it's out there, you don't need to start throwing money left, right and center at job boards.
Speaker: You can if you want, if there's good access to get that some of that information as a source.
Speaker: But what it is is a source.
Speaker: That's all it is.
Speaker: Right.
Speaker: The next thing you need to do is to.
Speaker: And I think this is also the other thing I would introduce.
Speaker: Search, match, parse, connect to the heterogeneous environments, including the job boards out there, democratizes the data and gets you that world's database.
Speaker: But using that technology in order to automate, learn, and anti-bias the matching that goes between the two sides and integrating that with the flows and including and using and supporting the data from the conversations
Speaker: that are going on during the process when they're working with your organization.
Speaker: All of that together creates a system that will find not only the best candidates in the jobs together, which is the thing that makes your money, but will also then promote the idea of carrying that person's career and that company's job needs in a much longer trail than the transactional one and done type of model that we tend to see today.
Speaker: And you'll
Speaker: be able to automate that process at a level which doesn't require so much cost per transaction.
Speaker: And that's kind of like Nirvana.
Speaker: We get it, but I think that's where we are.
Speaker: I think that'll transition the industry to what we used to call headhunters, to something that is actually, I would call them career investors.
Speaker: Sure.
Speaker: And they're working both sides and they're there for the long haul.
Speaker: And they make a lot more money doing that.
Speaker: Yeah, they would.
Speaker: Oh, it's amazing.
Speaker: Okay.
Speaker: Well, now you've got everyone super excited and they're going to want to find out a whole lot more about you and your products and things that you're doing.
Speaker: So where can they find you?
Speaker: So I, of course, I have my LinkedIn page and I put out these crazy little caffeine articles every once in a while.
Speaker: If you want to sign up for those.
Speaker: Half the time I'm talking about drinks you can make and one hit wonder.
Speaker: songs from the 1970s, but most of the time it's talking about AI and related to the industry.
Speaker: But I'd like you guys all to come to a webinar that is actually happening March 20th, 11 a.m.
Speaker: Eastern Standard Time.
Speaker: Sorry that I'm plugging this.
Speaker: And I think you guys will find that that webinar really digs a lot more in detail around what we've been discussing here.
Speaker: So if you're interested at all, what we're saying.
Speaker: No, I'll make sure that that webinar is linked below and your LinkedIn page is also linked below so that we get lots of people there because I think this kind of conversation is absolutely needed for the industry.
Speaker: So, Andrew, thank you so much for spending time with us.
Speaker: Thanks for your knowledge.
Speaker: Thanks for your expertise.
Speaker: And I will point people in your direction for more.
Speaker: Thanks, Brad.
Speaker: And I look forward to talking to you again, continuing the conversation.
Speaker: Now, please come back.
Speaker: Please come back.
Speaker: All right, everyone.
Speaker: That's it for another episode of the Transform Recruiting Podcast.
Speaker: If you like this and like to hear some more, it's pretty simple.
Speaker: It's at transformrecruiting.com.
Speaker: So we'll see you there and I hope to catch you on the next one.
Speaker: Talk to you soon.
