The Changing State of Talent Acquisition

#66: Hiring Humans in the Age of AI

• Graham and Marty from Change State • Season 5 • Episode 66

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In this episode of The Changing State of Talent Acquisition, we sit down with Craig Fisher—CEO of TalentNet Media, author of Hiring Humans, and one of the most respected voices in the HR tech space. From his early days in staffing to launching TalentNet Live, Craig has worn nearly every hat in the talent world.

We explore why friction in the hiring process is actually a good thing, how AI tools can enhance rather than replace human connection, and why most companies are missing the mark on retention and employee advocacy. Craig also shares behind-the-scenes details on rewriting 13,000 job descriptions for one of the world's largest banks—and why SEO and contextual AI optimization are now table stakes for recruiting.

Plus, he gives us a sneak peek at his upcoming book Paint Your Store, designed to help job seekers and solopreneurs level up their personal brands.

Whether you're a recruiter, TA leader, or job seeker navigating today's AI-infused hiring landscape, this is one conversation you won't want to miss.

🛠️ Topics covered:

  • How social media shaped early recruitment marketing
  • The case for human friction in hiring
  • Why AI won't replace recruiters (but bad job descriptions might)
  • Tactical tips for job SEO and optimizing for AI search
  • The overlooked power of onboarding and employee advocacy

🔗 Learn more about Craig's work: hiring-humans.com New episodes every week at changestate.io

Speaker 1:

Welcome to the Changing State of Talent Acquisition, where your hosts, graham Thornton and Martin Kred, share their unfiltered takes on what's happening in the world of talent acquisition today. Each week brings new guests who share their stories on the tools, trends and technologies currently impacting the changing state of talent acquisition. Have feedback or want to join the show? Head on over to changestateio. And now on to this week's episode.

Speaker 2:

All right, and we're back with another episode of the Changing State of Talent Acquisition Podcast, super excited for our next guest with an incredible background, craig Fisher, the CEO of TalentNet Media, author of multiple books, most recently Hiring Humans, host of TalentNet Live conferences and just a general well-known HR tech advisor in our space. So, craig, welcome to the show.

Speaker 3:

Graham, thank you. It is a great pleasure to be here with you today.

Speaker 2:

Would love for you to maybe briefly share your journey, from your early days in recruitment marketing help our audience get to know you a little bit better to your days starting Talent Media, and maybe how have your experiences shaped your perspective on the world of talent acquisition.

Speaker 3:

Yeah. So I came up through staffing like a lot of people in the 90s. Before that I was a drug rep and a medical sales guy, but I went through all the levels recruiter, account manager, leader, owner of a staffing company starting in 2007. And all the while I was doing that, I come from an advertising and marketing college degree background and when I'm doing all this stuff, I'm attempting to differentiate myself by helping both the employers that I'm working with and the candidates that I'm working with to market themselves better. And I developed some strong opinions on it and some nifty hacks and methodologies and I started writing about it in blog posts and on Twitter.

Speaker 3:

And in the early days of Twitter in the late 2000s, it was a hot topic. Is social media good for recruiting? I'm like, yes, it's the best thing ever, come on. And so I got a little Twitter famous for my opinionated outlook on it. But also I started the first hashtag chat for recruiters called TalentNetLive. At the time we went with hashtag TNL.

Speaker 3:

So I own a staffing company, I'm building community. I'm attempting to push my view of the world, which is treat candidates better, explain better about yourself as an employer in a moderated fashion. I'm saying listen, I think I've got a better idea than what's happening right now. That's very forward thinking for a guy that didn't really have any platform for it yet. But my goal was to get on stage and talk about it and get out of the day-to-day of recruiting with my staffing agency and executive search firm. And people did start inviting me to come speak to their teams and talk about it.

Speaker 3:

And then the Twitter chat quickly became a request for IRL in real life meetings and we started the first ever Talent Net Live conference in the fall of 2009.

Speaker 3:

And we had on our first board and at our first conference were notables like William Tencup and Chris Hoyt and Jim Schneider, bill Borman lots of interesting folks from talent acquisition were there and it really kicked off the unconference theme for recruiting conferences. So from there I was able to pivot into full-time consulting, doing recruitment, marketing and employer brand, starting in 2011. So I kind of made TalentNet Media a thing back then and since then I've gotten to work on amazing projects with you know Fortune 50 companies and some hungry startups and occasionally go in-house to do leadership jobs at companies like CA Technologies and Allegis Global Solutions. I was the CMO there for a while and got to work with big customers like Amex, hsbc and GM. So it's been a wonderful wild ride and I think to those starting out now in the field, have an opinion right. It all starts with having a point of view on things and don't be generic and watered down.

Speaker 4:

Well, thanks for sharing, craig. We're so thrilled to have you on the podcast. We've got a lot of guests that join us, but I don't think anyone quite has the depth of experience and different experiences in the industry as you have, and it's also interesting that you kind of wore a lot of the different hats that exist in this space from your early days to now. So I'd love to kind of start by just getting kind of a long view question. You know you spend a lot of time you've spent a lot of time in this space thinking particularly about emerging trends, cultivating communities you just talked about At this particular moment. What are the trends or shifts that you're particularly interested in or paying attention to? You know, obviously AI is a big topic for everybody. It could be about AI, or maybe it's not about AI. You know what's on your mind these days.

Speaker 3:

Well, it's interesting. So there's multiple factors putting pressure on the talent space right now. Currently, we don't have low unemployment. Well, it's kind of slowly growing. The numbers are a little misleading, but what's really the problem is there's not much churn happening within organizations. There are layoffs, but people aren't voluntarily leaving their jobs. They might be quietly looking, but they are afraid of this economy still and not leaving their jobs. So talent acquisition teams have been shrinking, partly because of the economy, partly because of AI being able to help us automate things.

Speaker 3:

But you know, we've seen these trends in the economy happen every 10 or so years. You know for multiple seasons that this too shall pass and we're going to see a turnaround, because the trend right now is to attempt to automate everything in the candidate process, and that's not enough friction. So this frictionless idea is a little overblown. There has to be some human interaction, because if it is too easy, candidates won't believe it's real, and so when hiring comes back, there will be a need for recruiters again. There probably will be a need for sourcers again. We will be able to do a lot of it, though better and easier, because of the AI tools. I mean, think of the things that you feed into chat GPT right now and say, okay, build me a plan for this and it's so easy to do. But you can't just trust it blindly. Somebody still has to say, yeah, this is good, this is not good, this is ridiculous, this is a hallucination, and you know, make it real.

Speaker 4:

Sure Well, craig, earlier you were talking about the importance of having a point of view and you know I think you just expressed it probably an intentionally provocative one, or at least it sounds like it to me this idea that we need friction in the process, and normally, whether you're talking about customer experience or candidate experience, we seem to, as an industry or business, people try to aim for as less friction as possible. Could you unpack that a little bit? You know what do you mean by that exactly. Why is friction a good thing, and how should organizations be thinking about it?

Speaker 3:

Yeah. So my friend Jim D'Amico and I talk about this and he agrees with me that if there is no friction, the process seems like you're going to get ghosted 100% if there's no human intervention at all, and so there has to be some human touch points. A little bit of friction is important. So I feel that we're going to go over our skis with our attempt to automate everything and take recruiters and humans out of the mix, because it leaves you with a process that just feels vacant.

Speaker 2:

So you know you've written a book recently, you know, Hiring Humans, and I think you know, really, one of the points emphasized, you know, in that book is that hiring is really a human endeavor and you know there is some balance between technology and the human touch playing out. So you know, let's dive into Hiring Humans a bit, craig. So you know what drove you to write that book and let's the high-level theme that is most applicable in today's market my philosophy, graham, as you may know, is that people want to work with people and we have automation.

Speaker 3:

We have all these great tools and I'm a techie guy myself great tools, and I'm a techie guy myself and I like nothing more than to be alone, put my phone down and crank away on a project, write some code, whatever it is right, edit a video, but at some point I need people. So even the hardcore coder that in theory works in a basement is apt to occasionally have a question about their benefits or their quarterly reviews or arrays or something like that. And you can't automate all of those things. You have to have people in the organization.

Speaker 3:

And if there's going to be internal mobility, if there's going to be culture of any kind, if there's going to be what I think is going to be an attempt to keep people in organizations longer because right now we're in this sort of gig economy where things are very temporary and people don't have any very good reason to stay at a company long term. I think without more of a human touch in that process also, we're not going to be very successful. So, yes, automation is great. Yes, tools are good, but they're just tools. If you remember when the iPhone was introduced right 2008, that was going to change everything. We won't need recruiters anymore. Same thing when the internet happened. I mean all these great inventions are going to do away with the need for humans, but it turns out no, they're just tools and they need people to use them.

Speaker 4:

Yeah, I think that's a really interesting point, craig, and it sounds when you say it out loud, it sounds almost obvious, but it is a subtle point and I don't think it's a point that is. I think it's a point that's lost on a lot of people, whether it's recruiting or any other domain that's going to be impacted by AI. There's a lot of fear. Of course, people are worried that it's going to take all the jobs, but it seems to miss this fundamental point, that why are we doing this in the first place? This is a human creation, first of all, and human beings really cannot be reduced to a machine. Sure, a machine may be able to do a lot of the things that a human can do, and do them more quickly or better or more efficiently, but it doesn't have those intangibles, and that's what makes life and work and careers meaningful. Is that a fair encapsulation of your point of view?

Speaker 3:

It is, and so there's an REM record called Life's Rich Pageant and it always sings to me.

Speaker 3:

In respect to the human experience, yeah, we created these machines for our benefit.

Speaker 3:

Computers are finally doing what we always wanted them or hoped for them to do, but I read and heard on a podcast recently that AI models are already topping out with their consumption of knowledge and that we don't have enough knowledge for them to grow and expand at the rate that they have. Up to now, they've basically consumed everything that there is to know as it stands today, and so, while they are good at summarizing things and creating things quickly and giving you suggestions for things, they're also still relatively not great and kind of dumb in a lot of respects. So I'm glad that I can build a custom GPT to help me write a book that learns my voice and that suggests chapter titles and agendas and speaker lineups for conferences. All these things are fantastic and I have so many ideas If anybody wants to reach out to me on LinkedIn, for business development or candidate sourcing all of these wonderful things you can do with these tools, but they're just tools and people still got to be people.

Speaker 2:

Yeah, they're just tools and people still got to be people. Yeah, well, I think, like all data, all ai models are only as good as the data that it's sort of trained on. I think you kind of you know, if you're using or poking around with chat, gpt or any of these, you can see, you can see the holes, right, and I think that you know that's why you talk to anyone. It's, hey, what are the skills that are going to be most important? As you know, know, ai evolves and it's, you know, critically thinking, critical thinking skills, right, and you know. So you could pop into JetGPT and say, hey, like you know, write me an article, you know, or a framework for a blog post on this and find 10, you know 10 sources that back it up.

Speaker 2:

And I think you know what you do find is you find is a lot of these AI tools are pulling from that same repository of data, and I think that ends up being a challenge too, because it's easy to get articles or quotations from a Harvard Business Review or an MIT Sloan article that go out. It's not easy to get out and make sure that, hey, you're looking at the most relevant sources or pulling the most relevant data, and so I would say, like, arguably, chat, gpt, ai is going to be great, but you know you have to be. That's why you see prompt engineers and all of these roles that are growing that are, you know, really critical thinking type skill sets. You know that help you get the most out of AI. Otherwise, you know you're going from you know zero to the 40 yard line, but like there's a whole lot of real estate left to go across the board. Does that resonate, craig? Or like is that what you're seeing too?

Speaker 3:

I'm absolutely seeing that, and you do have to be specific, right, if you want sources from specific authors or specific places that aren't sort of the top of the search results. And you grateful for and we just saw it at the Talent Net live conference in Austin on March 7th was that the recruiting community and the talent acquisition community are embracing the tools, not necessarily fighting the tools and using them in very original and interesting ways, and so I really get inspired when I see my colleagues being creative and that's what it takes. That's also what it takes to write a good Boolean search string or to search any database LinkedIn or ats, or even google. Uh, when you're sourcing for either customers or candidates, and it's not much different, I mean you, you learn the tool, you learn the platform, you learn the language and, right, you get creative with it yeah, well, I think that's great and, like you know, I know channel app live and austin.

Speaker 2:

You know it was earlier this month. You know, I think a lot of conversations are focused on, you know, ai, but also combining AI with a human element, which is really, I think you know, central to part of your thesis and hiring humans. I'm curious, you know, coming out of the last week conference, can you think of any good examples where you know practitioners or companies are, you know, just doing a great job of blending AI, you know, with that human? You know intuition. Any examples of hiring outcomes you know are tied to TA, you know that really do a good job of blending, you know, ai with humans.

Speaker 3:

Yeah. So my friend, jason Roberts, who runs some technology functions at an RPO I guess we won't name names here, but we could he's doing some interesting things with AI and building sort of the. At Allegis, we built some kind of Frankenstein software that pulled data from all your sources software that pulled data from all your sources. So if you were to ask a hiring manager or anyone in a large organization how many people do we currently employ on a full-time basis and a contract basis and all the other ways we might employ people, it's almost impossible to get it exactly right, and so what you really want in a talent intelligence platform is all your talent all in one place, to know all the things that have to happen to get business done, including churn. How many people do we have to hire this month? How many widgets have we promised to prospective customers or actual customers?

Speaker 3:

All these business questions that you know talent acquisition should be asking are now starting to be able to get pulled into easy-to-access platforms, because we can feed the data into these language models and they can really summarize it for us in real time. If you build the web hooks into your databases, you can get real time answers for things, and so, taking this to the next level, you can actually start to do capacity planning, and it's a mythical thing that we've talked about forever, but it's becoming more and more real. And so then humans take that and do the human thing that we do with it and say, okay, so we're going to need to hire this many people. We can plan this far out, we can build these kinds of campaigns with the help of these GPT models and actually have a better experience for employees, candidates, hiring managers, sales leaders, because we're able to track that path much better.

Speaker 4:

Yeah, that's a very helpful real example. I mean, I think some of the challenges we see with these conversations about AI is that they're just so high level and it's really hard to get past AI as automation and actually get specific about ways that can really disrupt, in a very good way, what we're doing in the space. So thanks for that. And I think the other thing is that you about what you just said. The example is, you know, because this is such a new technology, I think people suffer from and I'm included from a lack of imagination about what's possible, and so we think of AI just doing the things that humans are already doing better.

Speaker 4:

But you shared an example here where it's like even the smartest, most genius person is not going to be able to ingest all of these different data sources and come up with a cogent, accurate prediction or suggestion. You know that's right, that's. There's no human that can do that and no human that would probably want to do that. It's not a human activity. So I think that's just a really great marriage of those two ideas.

Speaker 3:

Yeah, you have to look in 30 different places to try to pull that information together and by the time you got done doing it, the information wouldn't be accurate anymore.

Speaker 4:

Right, and I think it's worth just pausing here on this topic a little bit because people are so scared that AI is going to take over.

Speaker 4:

But a counterintuitive point I don't remember which economist I just read a sub-stack about this but a limiting factor of the proliferation of these large language models is the bandwidth of human beings to interact with them. Language models is the bandwidth of human beings to interact with them. So, yes, you can have something that could do a year's worth of work that a human might do in an afternoon, but at the end of that there's a human on the other side of that that has to interact with those outputs, that has to at least understand them on some level, maybe not this super intelligent level, but just make sense of them and then make a decision and apply it within the organization level. But just make sense of them and then make a decision and apply it within the organization. So it's easy to think of AIs as just running amok, unchecked, doing things and, you know, continually making progress, and I think people just don't necessarily pause and think that humans have a. There's a natural limiting function in there, I think, or at least arguably.

Speaker 3:

There is it's taxing, by the way right, if you've ever built a webpage or written a long-form article or done a research project. You can only go so far for so long. So your hands get tired of typing, your eyes get tired of lights and screens and your brain just has fatigue. And trying to get the right equation into these language models to get the desired outcome takes a lot of uh of of practice and a lot of testing, and so it you. You really can only go for so long until you have to take a break, and so you're very right about this. We did a project for the world's largest bank Again, I'm not going to name names, but it's the largest financial institution in the world and we had to rewrite 13,000 job descriptions for them. So they use an ATS that displays a little blurb of each job across the careers page on their website, and so they can't all be exactly the same. They have to have some differentiating language up front in order for them not to look just completely generic across the board. So we had to rewrite the templates for all of these jobs and I created a custom GPT for it to get the syntax just right, and we created a large database of bias language for it to look for and omit, and then a model for it to write some distinct and unique marketing language upfront that speaks to the candidate first.

Speaker 3:

You're this type of person. You like this kind of challenge. We're looking for people like you. Imagine what we can do together. Some variation on that 13,000 times what we can do together. Some variation on that 13,000 times. Now it's all great to say, yeah, the computer can just do it, but it can't. You have to still human eyeball every one of them because there's risk involved. Right? You can't just leave that up to a machine. And so imagine the taxing nature of trying to human eyeball test 13,000 jobs, even if you've got a really great custom GPT for it. Yeah, yeah. So the project was supposed to start with three to six months. It took two years.

Speaker 2:

How did that project come about, craig? I'm curious like what's the impetus for, like you know, large bank companies? Is like sees, hey, we've got 13,000 jobs. Like was it driven by? Hey, we don't like the way this looks on our career site. Or like publicly to job seekers. Or was it, like you know, we're not getting the right people. Like I'm just curious, like what's you know what's the impetus for? Like, hey, we need to do this differently.

Speaker 3:

So these were just the tech jobs, by the way. So there's a lot more jobs in this organization. But there are several things that employers have to be aware of and watch out for, and most job descriptions for companies are very outdated. Their templates get cloned and reused over and over again in the applicant tracking system. That has problems of its own right. Those templates, if not recreated properly, can carry over old JSON metadata that makes your jobs look old in search results on places like Indeed, and that's a big, costly problem. And so we fix things like risk mitigation in jobs because of bias words or pay transparency. If your wages aren't in line with what the market says and you're way off, you could get sued for that now. So there's all kinds of reasons to want to look at and update your job descriptions on a regular basis, and that's one of the actually pretty fun things that we do on a at scale.

Speaker 2:

Yeah, well, you know, I think that's funny, right, so you're talking about a really big organization. Like you know, I think we go into projects, you know, probably for similar asks, right, and like you know, we call it the basic blockading tackling right of of recruitment. And yeah, like, hey, I, you know we were working with a a new client recently and you know, let's just say you know they're recruiting for want to make it up, how can I make up uh, or let's just say they're recruiting for software engineers and you know the job description, recruiting for want to make it up. How can I make up a word? Let's just say they're recruiting for software engineers and you know, in the job description that they've been using for months, you didn't have a software engineer in any of the jobs and like you know, it's not rocket yeah.

Speaker 2:

Right. And so, you know, a lot of times I think we get so excited to, you know, jump to the next great thing and like, hey, how are we going to automate, how are we going to move people through the process? And like, hey, we got to talk about all of our benefits and you forget the basics. And I think what you're saying, I think, is like, hey, we can trust the machines to do so much, but sometimes you need what machines might be. Lacking is a little bit of common sense, that critical thinking aspect. You don't want to run 13,000 jobs through some great new AI tool that might bolt onto Oracle and have that output be. Yeah, we're hiring for engineers, but we're not going to put that in the job description. That's how people get in the door.

Speaker 3:

It's interesting because you're alluding to basic SEO, which is one of my favorite subjects of all time, and that's accurate, right? How do you come up in search results, search for your own jobs right on Google and Indeed and see what happens? If you're getting clobbered by your competitors, there's a problem. But now there's another problem that involves AI, because job candidates are now using AI to hyper-elevate their profiles and resumes to match all these different kinds of jobs, and if your job description isn't AI ready, it's not going to go through the matching software very well and bring up the right candidates, and so employers all over the place are getting too many of the wrong candidates because their job descriptions aren't up to par.

Speaker 2:

Yeah, well, a hundred percent agree.

Speaker 2:

And I'd also say, before we lean too heavy on AI to do everything, I think I saw a stat yesterday that the percentage of jobs everyone's worried about writing their job description, so AI tools are going to recognize it or it's going to come up in chat GPT searches and all this stuff. And so I think that the stat I saw was last year the percentage of search volume share on Google compared to chat GPT and it's, you know something of, you know some obscene number like you know, 99 point. You know, uh, you know 90, 99% of all searches done in the U S in 2024, I guess, like 93%, I just pulled it up 93.57% of all searches done, you know, across search platforms in 2024, we're done on Google. 0.25% we're done on chat GPT, so it's 373 times more searches are still being done on Google. So like, hey, maybe we shouldn't be optimizing for things to come up in chat GPT just yet. You know, should we pay attention to it? Sure, but like, let's not. You know, let's not gloss over the basics.

Speaker 3:

Well, yes, and I think the key thing right there is chat GPT isn't necessarily the only answer right. So now with Google, because Google is not a great search engine anymore. Let's just face it right. You get, you do a search and you get video, video, video video, sponsored ad. Sponsored ad, sponsored ad and it's not giving you the thing you're looking for. But now you have add-ons like Gemini that do summarize the results and do give you the thing you're looking for. So maybe optimizing for chat GPT isn't quite right, but optimizing for AI in general is still a very good idea. I built a whole part of our website about it and I wrote about it, and you can see it at employerseocom. This is a fascinating topic to me. I'm a huge fan.

Speaker 4:

Yeah Well, I'm sure most folks in the audience have at least had the experience of performing a Google search these days and seeing the AI-powered results at the top. So you know, obviously we're pointing towards a future where that's probably the norm. Really fascinating to think about. I'm sure you've thought a lot more than I have, Craig, but I don't even know how you would think of an optimized name for different large language models, for example. I mean, it's been such an arms race over the last 15 years with SEO, and part of that is Google famously has a secret algorithm and people can sort of triangulate how to game it if you will, or optimize for it, but there's not a clear rule book and it strikes me that if you think about optimizing for a chat, GPT or any other generative AI, there's an even bigger black box there. But I don't know what are your thoughts. Do you have any perspective on that?

Speaker 3:

100%, and also I hate the term 100%. I'm sure I didn't just say that. So if you think about contextual search, which is what large language models do, that means that you have to have some storyline in your job descriptions in order for them to do well. Also, search engines, which LLMs rely on for their results, also really hyper focus on locality. So think about it. If you search for something, the first thing Google's going to try to give you is local results. Can we access your current location? And so very often employers are not doing a good enough job of that and there's some nuance to it. There's too much to go into right now but that could be a whole other podcast if we wanted to do it. But there's certainly some nuance to optimizing for locality, for contextual search, and there are 50 other things that ChatGPT and Gemini and BARD and all these Claude they all look for that job descriptions don't have. So it's a puzzle and it's not easily solved. I mean there's a little bit of some study to it.

Speaker 2:

Yeah, I think that's great and I'm going to selfishly try and get one more question in that. I want to talk about Craig before we wrap, and that's like we've been talking a lot about attraction. Really love is this concept of the nurture wheel outlining stages of candidate attraction? I'm just curious. Well, first let's set the stage. I think the six stages are attraction, engagement, education, conversion, retention and then employee advocacy. I'm just curious with AI tools coming into place, with automation, which of these stages, if any you know, do you kind of see, you know, companies sort of neglecting you know the most, or AI sort of shaking up the most in this candidate life cycle?

Speaker 3:

Yeah. So I think retention and advocacy are the most overlooked. We're starting to get it right. Like you know, I said on the front end of things and possibly automating too much, and we'll circle back to a more human experience at some point. But we could use these tools to really have a fantastic onboarding experience. We're not doing that.

Speaker 3:

You can set up the algorithms to message a manager to say, hey, now would be a good time to tap in with your brand new employee with this kind of message and really make employees feel wanted, comfortable.

Speaker 3:

Summarize the massive amounts of information we're giving them, rather than make them read and watch hours and days of videos them, rather than make them read and watch hours and days of videos. There's plenty of opportunity there. And then regular touch points you know, set alerts, set. You can now there's a chat GPT that will do repetitive tasks and go back and, you know, update things and you can have a much richer experience if you're doing your touch points correctly. And then the advocacy piece right Survey your people as soon as they get hired that's the time they're the happiest and ask them how they felt about the experience and then you know if they have a rich answer for you. Give them a button, ask them if they want to write something about that on LinkedIn and possibly even Glassdoor or Indeed reviews, and you know, you can change the trajectory of your review status very quickly by doing something like this. And all of this could be easily automated with with AI.

Speaker 2:

Yeah, I think that. I think that's great and I'll say, you know, retention, attrition, you know, are two of the not two of the two highest measurements that TA leaders are paying attention to, you know, when we look at our TA trend study that we just published earlier this year. So the good news is I think, hey, if that's a gap that you see or an opportunity you see, I think TA leaders are starting to recognize it too, or at least you know the people that are, we like to think the smartest people are paying attention to our surveys and insights. So nice to hear that resonate, craig.

Speaker 3:

Yeah, can I plug one more thing real quick? Yeah, of course. My next book is called Paint your Store, and a lot of the concepts we talked about today are in Paint your Store. But it's, whereas hiring humans is for employers, hiring managers, recruiters, paint your Store is for the job seeker or the person looking for their next career move, or the salesperson looking to gather more clients, or the organization looking to bring on more customers clients or the organization looking to bring on more customers. If you're a local independent consultant and you want to just attract more gigs, this book's for you. It comes out March 25th and it will be on Amazon. How to Network your Way to your Next Career Move Paint your Store by Craig Fisher.

Speaker 2:

That's awesome. Well, this podcast is going to come out on the 25th, so it's perfect timing. It's like we almost planned it that way. Craig, I'll ask the easiest question of all. We'll certainly link everything in the show notes. Where else can listeners learn a little bit more about you and your work online?

Speaker 3:

Yeah, the easiest place is hiring-humanscom it's kind of a catch-all for my books and my work, and dash humanscom it's kind of a catch all for my books and my work, and it's at the talent net live website, uh, and you can find out all about great ways to engage with me and I'm also easily searchable. So find me on LinkedIn and absolutely connect.

Speaker 2:

Yeah, and I love the uh fish dogs. Uh to you know, marty and I a lot of us, especially over at G's day all big fans of dogs. So, uh, anyone else can check out that story in your free time. On how Craig came up with that website domain too, I love it, thank you. Awesome. Well, thanks, Craig. All right, Thanks for tuning in. As always, head on over to changestateio 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.