Episode 110

Steven Janke on AI enhances but does not replace the human element in hiring

TIM: We are live on the Objective Hiring Show with Steven. Steven, welcome. Thank you so much for joining us.

STEVEN: Thanks for having me.

TIM: It is absolutely our pleasure. And I'd love to start by hearing just a little bit about yourself, just so our audience can start to think about who they're listening to today.

STEVEN: Sure. My name is Steven Janke. I'm a director of analytics over here at Posigen, which is residential solar. They're based in New Orleans, Louisiana. I've been working here for about a year, but my background, I've been in analytics for over a decade.

TIM: Nice. That's great to hear. And I'd love to start our conversation today with. probably the two most used letters in the history of the world now, AI and particularly within the hiring context have you started to see candidates using AI when applying for your roles? And also on your side, if you started to double with any of the tools to screen candidates to interview candidates or any other bit of the hiring process.

STEVEN: Sure. AI is so new. Generally, we consider AI more of an augment to what we typically do. It's not replacing; I think it's doing a lot of initial screening. I think it's finding keywords, finding matches, and saying, Hey, we're looking to hire for this skill. We're looking for this skill specifically. And utilizing that element has actually brought a lot more targeted audience to me. Usually, it was just a crapshoot of, Hey, find people that are relevant to this. And HR is overburdened with scouring the internet for that sort of thing, but it's allowed it to be a lot more targeted as far as the inflow. That's where I've seen AI for the hiring process.

TIM: And what about on the candidate side? Have you noticed, for example, that all geez, this CV doesn't seem like it's written by a human?

STEVEN: typically, no, That's always the risk, right? Whether whether you have whether you have AI that helps to enhance that or not. That's always the risk is this person authentic? Is what I'm seeing on paper actually there? And I think that a lot of the hiring process helps to call that out. I think an example of that's going to be you've got candidates and I can put anything that I want and have AI embellish it any way that I want. But when it gets down to it, if I get tested on it, I'll fail if I don't have the competencies that I've listed. So I think that's part of the hiring process there.

TIM: Yeah, and you could argue this has always been the case. Anyone could put anything on a CV even before I could claim to be a rocket scientist. But yeah, I'd hope in that first interview when someone starts asking me about physics, I would bomb pretty quickly because I've got no idea what I'm talking about. So yes, it does rely on you having a robust hiring process. Candidates might still chance their arm and go, maybe not complete BS, but exaggerate now every, everyone's, I'm a leader in this, and I pioneered this, as opposed to, They just had a small bit of the project. I wonder if there's still going to be almost an incentive to do that, to get past that first gate.

STEVEN: Yeah, I think the challenge that I face is it goes both ways, right? People can oversell themselves, but a lot of people on their resumes can undersell themselves, and that's very difficult because sometimes you have the talent out there that you want, but they're just not quite showcasing it. And this is less of an AI problem and more of a, How do I accurately display my self problem? And I think a lot of that, especially when it comes to what exactly do we look for? What are the prerequisites? So long as that's structured correctly, you'll start to see more and more quality candidates come in.

TIM: I feel like one of the challenges in writing and crafting a good CV actually is, it's almost like it has to. Hit multiple very different types of audiences and is used in very different ways. So it has to get past HR talent recruiter persona, and then it has to be presented to an analytics leader. Let's say like yourself, and it has to hit those two very different perspectives, an expert and a non expert. And so it's almost like it has to be. Detailed enough to sell itself to you. But also includes like the right, almost like buzzwords, the HR team might be looking for as well. And now you could argue maybe as a third audience it has to pass an AI check or it will have to pass an AI check, I wonder. That sounds very difficult.

STEVEN: I would; it seems overwhelming, almost intimidating. We get a lot of modern applicants that feel that they're just a very small fish in a very large pond. And the tricks that I think that I found that catch my eye and that catch HR's eye are almost. An old trick, but delivered in a different way. Typically in the past if I said the words cover letter, you'd be like, Oh man, those are the things that I got taught on in my undergrad on how to, and they're very stiff and they're very formal, and it's a nicety to have, but understand the reason for the cover letter is to personalize. Your application. And I think that's the trick. You want to make sure AI is looking out for these specific things. It's doing what it's being programmed to do. It's doing it in a more consistent way than a human. But what's important is personalizing these, the particular application that you're going for. As an example, I work for a solar company. I'm probably going to look for someone who's had experience in a solar company in analytics. Great. I've seen people who'll apply with that, and their experience might be minor, but they've dealt with the data. That's a hurdle. That's a hurdle that I've already overcome. And I've been talking to someone who's set themselves ahead and aside of some of these other individuals. So I think. Taking that methodology of how I would design a cover letter and just applying those in ways for us to go into the resume themselves, what would be keywords, and just put yourself into the HR's perspective, right? You have hundreds of applicants for a role. How do you pick the right ones? You're looking for specific keywords. So what are those? Look at the industry, look at the position that you're applying for, and go from there.

TIM: Now, I don't know if my memory's playing tricks on me, but I swear to God the cover letter had been dead for the past 10 years. And now all I've heard in the past few months is about cover letters. Has it been resurrected? Is it coming back? Because now people are trying to do what you're recommending using, let's say, an LLM to say, Hey, help me craft this cover letter based on this job ad in my CV. Has it come back from the dead?

STEVEN: I think, I don't think the medium has come back from the dead. I think that cover letters are very stiff. They're very nice. The whole point of a cover letter is to set yourself apart. That's it. You're setting yourself apart. You're controlling the narrative of who you are and how you're displayed to an individual. So cover letters have always been that, but I think the trick here is how do I set myself apart by personalizing myself for the role that I'm hiring or that I'm trying to be hired for? And I think again, coming back from the dead, the tricks are still there, whether it's the old dog or the new dog that's doing them. That's the differentiator, right? How are you personalizing this, and how are you getting it to where you are capturing or having AI notice these particular things for the role?

TIM: Yes. And from what I can piece together, a lot of candidates are now doing this, probably with the help of a Chachapiti or Claude or something. And I wonder whether it's done. Maybe doing them a disservice or having the polar opposite impact of what they're going for, which is to stand out. Because if you use the same large language model as everyone on earth to write your CV or resume and cover letter, it's going to come up with a certain linguistic style that kind of sounds the same. And so if your job is to stand out and use the same model as everyone, are you just going to end up in the sea of noise with everyone else? Is it almost worth going against that and going, No, I'm going to write it aggressively human"? I'm going to make it sure that people know it's exactly me; it's not an AI.

STEVEN: Sure. No, that's a great, that is an, that's an excellent question. It can, because it's the same thing, right? Everybody wants to be different, and in everyone's attempt to be different. Now they're all the same. Everybody wants to be different. And so I think that the way that you balance this is. You use AI, as an example; you'd use an LLM, like ChatGPT or Gemini, to help you generate it. But don't just copy the thing and paste it. That's lazy. That's lazy. Use these LLMs as a framework for how you, what are the things that you want or need to ensure that you're putting on a resume, that you're putting on a cover letter, and then inherently make it your own. Make it your own. Use it as a framework. And I think that's the pitfall that a lot of these applicants find is, oh, this must be the gold standard of what this LLM has spit out. Correct. Wrong delivery, right? Otherwise you're just another AI posing as a human. Take the framework of that. Take the elements. Put them into the resume. Make it your own. Make sure you cover all the elements that we need to cover, but make it your own.

TIM: Yeah, I think the critical bit is to not brute force use these tools as like a, I'm a hammer, everything's a nail, I'm just going to whack it and spew out the first output that it's given me. Because yeah, there are so many things you could spar with. You could say, Hey, review my CV. What do you think is currently wrong with it? What am I missing? Are there any words that I've used that might indicate something? I don't want to indicate, is it too long? Is it too short? You could go through endless back and forth with various LLMs to improve it, spelling, grammar, et cetera. Rather than just saying, Hey, here's what I want. Give it to me. Bang, copy, paste, apply. That's going to. That's not going to go well, is it?

STEVEN: No, it's not. And I really do want to go back to what I said earlier. AI is an augment. An argument. The fundamental piece still needs to exist. You still need to be human. Humans want and crave to interact with other humans. But please treat it as an augment, not a substitute. It should never be a substitute.

TIM: What then are your views on candidates using AI technology in other bits of the hiring process? So we've just discussed CVs or resumes and cover letters. Yeah, it can certainly be used, but use it in a thoughtful way where it helps you and doesn't just lead to you falling into the noise of everyone else. What about in an interview? If a candidate was using an LLM during an interview itself, what do you think of that? Is that a problem or is that fair game now, if we're going to use these technologies at work?

STEVEN: we can tell. We can tell. I highly encourage it. I encourage it, but please remember that there's base competency that we expect, right? This is not apply for everything and then just learn it on the job as you go. There's a certain level of competency that we expect. When you're going in I would be a hypocrite if I were to say, don't use it. I use it all the time. I use it for everything, literally everything to enhance my current base competency, but guaranteed as you're going through these, your base competency will show. You will show your true colors. There, there will be the moments where you have to think on the fly and you don't have time to interface with an LLM that don't take that for granted. Again, not a substitute, just a dog augment. I highly encourage it for literally everything. I encourage a lot of the analysts that are under me. I encourage the other team members that I work with to use AI on a day-to-day basis throughout just to enhance. Productivity, speed to delivery, being comprehensive, all of that. Again, your base competency will always be there.

TIM: Yeah, some ones described to me as a leverage tool. So if you have a skill of 10 and it's multiplied by 10, it's different to having a skill of 3 and multiplying it by 10. So the stronger your base competency is the, maybe the more you get out of the tools. If a candidate was in an interview, is it the case that You would say, Hey by the way, feel free to open up Chachapiti Google. Like we're gonna ask you some questions and you can spar with it. Or are they doing it surreptitiously? And then you obviously notice if they're glancing between different monitors and being a little bit slow to answer. Like how is it actually being used and what do you think is the right way?

STEVEN: I think the right way is to prep for the interview with it. Don't use it during the interview. That's incorrect. It's you with the other person will notice if you're distracted, if the answers are slow, just basically what you had said, prep for the interview. Hey, I'm interviewing for a company in this industry. Give me talking points. Give me good questions. These are things that fundamentally a lot of people will prep for there are books out there books and books On how to properly conduct an interview go 15 minutes 30 minutes before Add the prompt into an llm say hey i'm interviewing for this What are things that are covering and give me a compelling question that I can ask them Research this company i'll tell you right now if somebody has The initiative to research the company through an LLM and to ask me very poignant questions. You're a differentiator. I regardless of how you're getting that information, the fact that you've been proactive enough to do that's a way to differentiate yourself.

TIM: Yes. And who cares how you've achieved that? It's the end outcome that matters. You reminded me suddenly of. When I was first going for graduate positions in investment banking way back the start of my career and The expectation when you get into one of these interviews is that you've done quite a lot of thorough research around the bank's recent M& A Transactions in the industries they play and you would have researched each of the deals and have maybe a few opinions on them But I remember getting an interview for the next day at 9 a. m. And it was 5 p. m And I had to go and study for four hours that night. So I had basically no time left to really do much research. I remember hiring a freelancer on the other side of the world overnight to put together some notes for me while I was asleep. That worked pretty well. If I now had that challenge, that's a no brainer for AI because it can go and do the research. You can do the summarization. It can do the. The preparation to help me for that interview. So yeah, it's all about just using it in the smart way that helps you to get the outcome that we should all be doing anyway, which is thorough preparation, really caring about the role, really thinking about it. So yeah, that's a great shout.

STEVEN: Yeah, and I'd say notes on that. I've met individuals that kind of gatekeep the, Here's the way that I've always done it, and obtain information, and you put in your work, and you can't gatekeep that stuff. It's counterintuitive. It's counterintuitive. It's counterintuitive. It's counter-innovative, right? You want to be able to utilize those. Don't feel like a facade or a fake because you're utilizing these, and you didn't put in the hard work for that. That's going to come with experience, and again, you do need to have the base competency, period. You need to have that. You need to be able to do that. Capitalizing on those tools and being unafraid to use them, especially in this sort of thing, that's not cheating. That's just being efficient. Silence.

TIM: They'll say, I am an X, and X is their job. And so we have this: our jobs are so closely tied up with who we are, and particularly for some roles, like software engineer, let's say, you'd say, I am a coder, like literally the task of the job is inherent in who you are. But now, maybe now or even in the next year, coding is done. It's going to be done by an LLM, probably. You prompt it and then write the Python or the SQL or whatever. Do we ever have to rethink what exactly we're doing and who we are, and is it more about all that matters being the business value I deliver, not how I deliver it?

STEVEN: That's a great point. I think that the FP&A side of me would probably say, What's the ROI on how you're doing what you're doing? But I think that's always being evaluated. I think it's good for us to. To evaluate where we're at and how we're doing what we're doing rather than being it. I've never liked being pigeonholed into a role because I do many things within that role. So I always like to say, Here's what I'm doing, but on the opposite side of that, I don't want to be like every other person being like, We're changing the world over here. There's some balance, right? I think it's becoming more objective-based, and then the how You start to evaluate for me efficiency, right? I'm the laziest person I know because I'm trying to get to these objectives as quickly and with the least amount of effort as possible, which keeps me valuable. So I think it's going to shift to more like objective-based. Hey, I'm a director, and these are the things that I do within the company, not how I do them, right?

TIM: What about opportunities? Hiring for AI to improve things. Is there any step of the hiring process where you, on the hiring side, think, Oh wow, I would love AI to come in and, I don't know, completely change the way we do x or improve this step, or, yeah"? Any big upside that you see coming?

STEVEN: That's a good point. To me, I think the largest challenge is going to be, I have a bunch of candidates that I'm hiring and filtering those down. Great. I think that we're deliberately moving towards how do we get quality candidates. But then on top of that, how do we stick within a schedule? Within these candidates, who are we interviewing? I think that's probably the next step. That's a large challenge from a legit logistical standpoint for our entire HR team. They have hundreds of candidates that they have to deal with and organize and schedule. And sometimes there are a lot of times people will be left in the dust because of that whole thing. And I think that AI is an opportunity. There is rigidity as far as great. We've interviewed the following people we've got, who are the candidates that you'd be able to select to sort of interface with us with a machine or something, rather than talking to a human to schedule in the future; we've got their contact info. We've scheduled something in the past, I think, enabling the logistics of being able to go from interview to hire. And being able to whittle those down and jot down your feedback and centralize all of that. I think that's a huge opportunity.

TIM: Yeah, a hundred percent agree. There's a lot of upside there. One thing I'd love to ask you about is. The interview itself. Someone mentioned to me the other week around having an AI interview partner, who's maybe helping you summarize your thoughts or take those notes or what have you. Would you try a tool like that in, in your interview?

STEVEN: we have note taking note taking's okay, it, it adheres to a at the end I have an interview I have an interview with a, another individual, sometimes it's one on one with me, sometimes it's with another a business dev to test the dev side of things, or on the project management side so that they see, we can see what is their knowledge base in project management. When that gets brought in, once we're done with the interview, we'll have a deep debrief. Hey, what are your thoughts on What are your thoughts on all of that? And I think AI is great for collecting maybe answers, but where AI completely will miss is culture. How they responded. Do we feel that they're competent? And I think that some of the answers there will be able to flesh that out. So there's always going to be that human element that needs to be able to be perceptive enough to understand if it's just competencies, I can hire machines all day. But there's a whole human side of things as well. And you need a human to be able to interface with those. So I think that note taking great distilling that down into overall summaries. It's good. It gets very limited and I've done note taking after meetings, just meetings in general. It's like, how often do you actually read those? How often do you ingest those to use those? Sometimes it's valuable. Sometimes it isn't.

TIM: And so it sounds like if I were to say to you, I reckon hiring is going to be completely automated by, I don't know, 2027, you'd probably be saying, No chance.

STEVEN: No, absolutely no chance. There's still the human element. I need to see these individuals. I need to see culture fit. I need to see how they respond. I need to see if there are any initial concerns for me. You still have that side of sentiment and delivery that AI just quite hasn't gotten the grasp of yet.

TIM: Yeah, we'll see how it plays out. I'm really interested. On the one hand, the technology is improving so alarmingly rapidly, which is exciting. So there's a lot of upside. But then at the end of the day, it is only AI. And maybe there's some of these more fundamental things that might take another 100 years to solve. I feel like some of the discussion around AI and the kind of risks are reasonable, but maybe within the hiring context, slightly unjustified because I feel like the way hiring is currently done has so many flaws in it that the upside of AI to improve it is huge. I'll give you one interesting example. So there's been quite a lot of studies in different markets around looking for potential discrimination at that screening step. And so the experiments. Get thousands of different resumes with the only difference among the resumes being the names, which indicate the gender and ethnic background. And then they apply to thousands of jobs, measure the rate at which the CVs or resumes get a callback, and then from that can figure out, Oh this particular subgroup gets a lower rate of callbacks than this one. So in Australia, the experiment they did a few years ago was around people with Chinese names and basically found that if you applied to a role in Australia with a Chinese name versus a Anglo Saxon name. You have only one third the chance of a callback, which is shocking. I feel like AI, in theory, could help with that in having some more objective screening way to just focus purely on, whatever they can deduce from a resume in terms of actual facts, as opposed to focusing on things that don't really matter that are irrelevant. Maybe that's going to be one of the unlocks and the upsides. What do you reckon?

STEVEN: Yeah, I think that's a fundamental human flaw, going into that the biases are always going to exist as much as we go to combat them or to make us relevant or perceptive of the actual inherent biases that we do have. AIs can potentially be coded in a way that they come with more of a flaw on, Hey, I'm looking for this and this implicitly, when we might be in excluding candidates that are excellent on paper, but they did include this keyword, and so therefore we must throw them out, that sort of thing. So it's just, again, it goes both ways, I think, in general, the way that it is improved is through an iterative process, right? I start with great non-bias. We use AI to initially screen the resumes for keywords, but we want a human review of these, right? We want a human to come in and be able to actually double-check: is this, are we on the right track? And then we eventually get to the point where the human review is almost unnecessary. Then we know that we're doing the right thing. We're still getting quality candidates, and it's all based off of, let's put this in, let's see what the candidates look like, and then modifying and iterating to eventually, hopefully, get to a point where we just have a machine out there that can give us quality candidates.

TIM: Yeah, I suspect that's how. These kind of technologies would be rolled out in some kind of what do they call human in the loop type of scenario and then eventually when maybe not needed anymore to do those checks. Oh, to change topics a little bit. What I'd love to talk to you about is something we haven't really discussed much yet on the show, which is how candidates who come from outside the corporate world from academia. transition into a corporate or business environment. And if you've seen any particular areas that they sometimes need a little bit of help on and how they might navigate that.

STEVEN: No, that's a great, that's a great question. For a lack of a better term, I have interviewed and hired candidates that are very green around the years. They are as new as they come. And the differentiator there is real-world application. You get your, you've gone to college, you've gotten your full-stack certification. Great. Congratulations. Congratulations. How are you applying that into the real world? And I think a lot of the individuals will focus; they will hyper-focus on the technical aspect and completely forget about the human aspect. You're asking, How do I work with other humans? A very classic example for me for new analysts is, How do I generate value as an analyst? and a lot of them will just do what they're told. Nothing wrong with that. There's absolutely nothing wrong with that. But when I'm looking for an analyst and I'm looking for a partner, I'm looking for an individual that doesn't just address the symptoms. They're there to try to find the root source and the root cause, and those are the people that move up. Those are the advocates and partners that I look for under me. The individuals that proactively are able to actually go and try to find business owners that come talk to them and say, I need X, Y, and Z. They, great, why do I need that? Now, I don't need people to just go in and say why. We still need to solve and run a business. There's nothing wrong with that, but that's the differentiator I've seen, especially when it comes to academia. You get individuals that are very smart on paper, absolutely no real world application. So that's the opportunity there.

TIM: Is there also something to be said for over fixation on the theory rather than this theory is a theory and it's only 20 percent right in the real world. And, if I can just knock this out in an Excel spreadsheet in 20 minutes, I may as well do that rather than this over engineered, over complicated kind of system. Do you ever get that type of vibe from those candidates?

STEVEN: I don't know how many people are going to like my answer, but it's a blend. It's both. There's no definitive answer. There's absolutely no definitive answer. Pure and simple. The business still needs to run on a day-to-day basis. Pure and simple. You're not, if you, if I had my head in the clouds all the time, believe that I would be designing this amazing solution for all of these things that would take years and the business would grind to a halt because I have my head in the clouds. It depends. It really depends. It depends on who you're working with. It depends on how they like their information fed to them. How did they digest information? It depends on speed to delivery, right? I think that for now, it needs to be separated out into two things. And the two things are. How do I handle the day-to-day? And how am I being deliberate about being better? To being better. In general. I love individuals that will come in, and they balance both. It isn't just hyperfixation on fixing day-to-day. Then you're just replacing band-aids. That's it. Band-aids after band-aids. I also don't like people that are fixated on how to make it better. Any project requests that need to address the current business day-to-day now just go into a black hole. Because they're so hyper-focused on trying to make it better in the long run. Blend both together.

TIM: That's a really interesting way to lay it out. I haven't; I hadn't heard of it explained that way before. And I'm just even now thinking about myself when I've maybe gotten stuck in either one of those areas a little bit too much; you're either firefighting and just barely surviving or you're planning too much. I feel like in startups, probably more like 80%. Just doing stuff, very action-oriented, slightly less thinking, maybe a little bit less planning. Maybe in other companies, the blend might be different. So maybe it almost depends on the environment that you're in and the time you're in, in terms of how big a picture long-term versus short-term gets shit done. You need to be.

STEVEN: Yeah, no, absolutely, I think the It's funny that you mention that, I have a team for I call them the GSD team. And it's literally we sit there and it's the fire that needs to be put out. Nothing's off the table. How do we address this now? All mediums are on the table. Do we have anything? Do we have anything existing? Do we need to get scrappy and get an Excel? Just be cognizant that the things that you're developing should come with no technical debt. Don't stack yourself with technical debt. Don't do that. You may feel valuable. You won't be. And people will find ways around that and the people that innovate around that, those are the people that we're going to be looking at and not the people who are sitting there just maintaining your technical debt on a day to day business.

TIM: And what would be an example of technical debt that you've seen accidentally get created in this process?

STEVEN: Sure, love it. Plenty of sources of technical debt. One of my favorite classic examples is, I need something built quick in Excel. Love it. We can do that. Excel is no problem, but then they love it. Okay, that's a good problem to have, right? But now I have a bunch of static and stale data that lives in an Excel model that's compartmentalized. How does it interact with any of the other things? What happens if I have two different business owners that come in from different departments that want to access this and use it for their own side? Then you start to get discrepancies between that now lying on stale data now required to be updated on whatever cadence that the business owners want. So be very careful of that because if people like it and it lives in a medium, that's not automated. One of my mantras. That I always tell all the individuals under me. You want to increase efficiency and accuracy through automation. Pure and simple. You do that, you continue to stay relevant, and you just have accuracy across the board.

TIM: As you mentioned that example, I again thought of the start of my career and one month being asked by someone in the design team of the business I was working for. Hey, like, we've got this spreadsheet that someone had created a while ago. It's broken. Like the formulas aren't working anymore. Can you help us out with this? Because they knew I was good with Excel. I was, whatever, a bit of a nerd. And so I helped them that one month and the next month like, yeah, so where's that report? We need that report again. My boss is like, why are you doing this for them? It's got nothing to do with your job. Oh, they asked me to do it. More experience now. I would probably navigate that in a slightly different way, but it's amazing how quickly you can get into this sort of Excel hell environment of maintaining these reports and these little bits of analysis, which as you say almost impossible to keep consistency across the business.

STEVEN: Yeah, the consistency. And again, it just makes you less valuable. It makes it pure and simple. It makes it you're over here maintaining something. And this goes back to just balancing the addressing now versus being strategic on how do we long term solution this. Very relevant example. I was a budding analyst at the time I had inherited an Excel model. It was run; it was sent to the executive leadership team every single day. It was sent every single day, and they gave it to me, and they said all you have to do is run this macro. And the macro was created; the macro was created not implicitly but by hitting record macro and then going through, and you could tell that people were making mistakes in the macro. And it was like, what, why is this macro taking 30 minutes to run? Why am I being locked down for 30 minutes? And I had to sit down and say, Great, I'm running this for the day. Good. Congratulations. It's done. Why are we doing it this way? And I think that's always the question of, like, why are we, why is this, what is this trying to serve right now? And a lot of that has been. An executive with some hair with their hair on fire saying, I need this now, and then no one's put any other effort into it because they have all these other things that their hair is on fire about. That's all.

TIM: Yeah, you mentioned that, and it made me think of another example, actually, of how slippery a slope this is and how bad this can get, so I can remember being in a business that was an Excel hell kind of environment. This was more than 10 years ago now. So to be fair, I'm sure. They've improved the analytics a lot. Now, there wasn't really a warehouse; there were, yeah, these consistent reports that had to be run, but you had to compile them from all these other Excel spreadsheets manually with a macro that was built two years ago that died half the time, and you had to fix. Da. And I remember hiring an analyst to come in and do this. So I was hospital passing it a little bit to them so I could do something slightly more interesting. And the first one I hired lasted two days because he was like this is crap. I don't want to do this. Next one I had lasted a week. It can really impact even talent retention and things like that. If you're too, if you're too fixated on just. Yeah, keeping your head above water without fixing some of the fundamental flaws that you have in the team.

STEVEN: Yeah you inherit all the accountability for it too. That's something that's just, it's very tricky. And that's where the methodology and mindset needs to change, especially for people coming out of academia. The how is great. I'm glad that we've spent years learning the how, but you have to sit down and assess the why, because you inherit these things, and then now you're responsible for iterations on them. Great. That becomes very complex if you don't understand why the hell are you doing what you're doing. And that's the real world application. That's the addressing less of the symptoms and more understanding the cause of it. That's really what I encourage a lot of the analysts to sit down and do. Do not bog yourself with this. You need to stay free. You need to stay free because as soon as you do that, you lose your value. You lose your perceived value, and frankly, you're like the two individuals that you mentioned. You get burnt out. You don't see your inherent value. You don't see that other than, great, I'm glad I'm an Excel machine over here.

TIM: For academic candidates making the transition, is there also something to be said just for speed of execution? My perception, not having worked in academia, is that maybe things are on a little bit of a more we've got six months to do this project, whereas you get into the workforce, and you're like, Yeah, I need this for the board meeting tomorrow, so can you get on it? Is that part of the difficulty?

STEVEN: Yeah, absolutely. The largest difficulty in general that I've faced has been project management. How do you manage all the projects? Is anything a priority if everything's priority number one? No. And I've been the culprit in determining too many project ones. But to me, I'm just the deliverer of information. I'm trying to disseminate and say exactly where I want the analytic resources that are under me. Where do I want them to go from a strategic perspective? But something that I don't demand, but I highly expect from the people under me, is to sit down, talk to me about your top three, and talk to me about the things that I'm feeding you. Are you overwhelmed? Do you feel like the top three is reasonable? Are they doable? All of these things, and sitting down and project management itself, can get into the nitty-gritty. You can do point-based, you can do scrum, you can do so many different things on how you approach the projects. One of the analysts that's under me, Favorite way of approaching it. He just has a simple Excel sheet that shows all the projects, when he received them, and what the latest notes are. And he has simple graphs at the top that represent those. That's it. That's it. I sit down with him. I sync. I say, give me your top three. Here are the top three. Great. I would recommend moving this up. Please. Thank you very much. We have full transparency into it. And there's clear direction from this individual on exactly where they need to go and what they should be working on.

TIM: I'm again struck by an anecdote from one of our colleagues in a business he used to work at talking about prioritization. And yeah, he was finding it difficult getting some guidance from above on what really was the most important priority because everything seemed important. So they came up with just a single column. Okay, we're going to stack rank things. Just we're going to put whatever's first, first, and whatever's second. But inevitably within a week, yeah, they were, they somehow had gone horizontal, and suddenly there were five firsts, which kind of breaks the stack ranking system pretty quickly.

STEVEN: That's right. And I think it's important to note that analysts will get frustrated because they feel obligated to conduct all of these. And that's the self-perception of value, right? My value is how much I deliver and how quickly I deliver. And while there are several aspects of that that are true, it's not an analyst's job to determine prioritization. That's for management to help to do that. It's an analyst's job to communicate where they're currently at and what they're currently working on, because I don't know that, right? You're doing yourself a disservice when you're not communicating. And this takes me back to academia, real-world application. The other thing that I noticed that it just is missing is how to communicate with other humans. Pure and simple. How do you talk to people? I had an analyst I hired. He's the type of guy. You shut the doors. You don't talk to him. You let him work. You let him code. Cool. Nothing wrong with that. So long as you're delivering value and exactly what the business owner needs, don't get lost in I sent them an email. Be proactive, reach out to these people, help them solve the problems, and help them solve the problems. And if you need help with that, reach out. We're relying on these individuals to have strong communication skills on how to reach out. As a matter of fact, that's one of the questions that I asked in an interview. How do you talk to business owners? What's your favorite form of communication? How do you gather feedback? How do you implement feedback? How often do you do it? Those are all the things that I see lacking from academia because they're so focused on the technical aspect that they forget the actual execution aspect of it.

TIM: And in those questions you're asking in the interview, what, if you imagine the spectrum of like really great answers to this is not a good answer, yeah, what would be the kind of main differences you'd see, the kind of, let's say, green and red flags?

STEVEN: Sure, green flags are what's your communication style? If they're open to talking to people, they're not afraid; they're not afraid of that. They enjoy talking to people. That's a very good green flag for me. Red flag: I don't like talking to people. You have to; it's a necessity. It's part of a skill. If I could have socializing with other business owners as a course in undergrad, I would absolutely put it in there. That's a necessity. And we do have project managers, and there's nothing wrong with that. There's a lot of the modern tech stack where you have project managers that manage people who never talk to business owners because business owners love scope creep; they love deviating. They love being highly. biased to their own initiatives that they want, right? There's nothing wrong with that. But to actually where rubber meets the road, if there's something that is missing, reach out to them. Again, that's the green flag for me: being able to communicate with them.

TIM: And is there something to be said for maybe there being an introvert, extrovert difference here in, in the way candidates answer this? And is it then a case that if you're an introvert, you just have to just deal with that and know that maybe, yeah, you your core automated way of thinking is not, I'm going to go and speak to lots of people all the time, but you just have to adapt and learn how to do that.

STEVEN: I think that there's an element of that. I don't want to break a human's fundamental, like personality or persona. There are several aspects of that that I do like to challenge, though. Especially if there's an individual who's more introverted, they're able to talk to me. They should, right? I'm their leader. They should be able to talk to me. So it isn't that I don't like talking to people. It's just that I don't like talking to unfamiliar people. So how do we enforce that sense of security and safety? And I think that's less on the individual level and more on the managerial level to ensure that we have that environment that people feel safe in, because I think that breaks a lot of these preconceived notions of people of, Oh, I don't want to talk to people. They're, they scare me. It's, do you feel safe? And if you establish that, you're going to get less of that problem of needing and wanting to reach out to people.

TIM: And how, how do you do that practically? Is that I don't know, giving a candidate an opportunity to meet several people during the hiring process? Is that helping introduce them? Is that a thorough onboarding process? How do you break down those barriers?

STEVEN: How I break it down is to introduce them. Get a nice quick 15. minute call. Hey, here's this individual. Here's what they focus on. This is my new, this is my new analyst here. He's going to be helping me out with X, Y, and Z. Do you have any questions? And being able to initiate that, that it's, that's the biggest hurdle, right? We create mountains out of molehills all the time on how formidable these individuals are, whether they hide behind these glorified titles or not. I think there's, that is the one thing that I can help to mitigate is just, let's sit down, let's show these individuals that we're both human that we're both here to help. We're both here to help. And I think that's something I've seen across the board, regardless of the candidate or not, everyone is here to help. Sometimes they get lost in being inundated with a million different things, and they don't quite know exactly how to help, but if you establish that and you say, here's how we can help, here's where this individual can help you out. Here's where they can specialize. That fundamentally breaks down a lot of those initial barriers and the communication and relationship from there is very good.

TIM: Yeah. I guess if you've opened the door for them, then it's up to them, the analysts to maintain that relationship, but you've at least helped them break the ice.

STEVEN: Yes, exactly.

TIM: if you had the proverbial like magic wands to fix hiring, how would you make that? How would you wave that one? Sorry.

STEVEN: if I could improve hiring the actual hiring process, I think that probably the streamlining once we've identified candidates, how do we streamline that? How do we keep that on track? I think that's a huge opportunity. I really think if I could wave the magic wand, all of that is. Facilitated heavily by technology. I'm relying less on humans to schedule time with these other individuals to provide feedback to have a space to say, did you like this individual? Yes or no. There's technology out there that exists for that. But the complete suite of packages for that, I think that's still an opportunity for AI. In general, a lot of the actual hiring process itself is is quality of candidates as well. It's not perfect. I think it can be more. I've, even then, I've done it once where, or I've done it a few times where I've taken a resume and put it, I put it into an LLM, I put it into ChatGPT and say, What do you think of this candidate? They're strong here. They're not strong here, but it still lacks that. Okay, cool. What about how are they in an actual interview? I think that becomes, that could be an opportunity, but that's way in the future. That's that sort of and real time analysis of sentiment and delivery and that sort of thing, which again, all of that is going to be implicit on bias, all of it, regardless of if you have AI there or not.

TIM: Yes. And I feel like maybe we just need a new data set, something that's a bit richer than just a two page resume, because no matter who's analyzing or what is analyzing that it's still a pretty mediocre very incomplete picture of a human.

STEVEN: Yeah, no. And it's interesting. There's a local company here in based in Utah, the local company here called Qualtrics. Pretty sure everyone has heard of Qualtrics. They do surveys. Their hiring process is insane. They will scour social media. Of the candidate and I think that's probably more along the lines of the future that you're looking at. Like how does this inner you should have a paper. It's just a very, it's a very single dimension. Look at a human highly curated. They've had plenty of time. That's why interviews are still necessary is because you get to actually see the person live. And you're going to see a multi faceted version of themselves. So I think making that more robust rather than here's the one source, LinkedIn's getting there. You could probably go to LinkedIn and say, how does this person engage with the platform? How recent is the profile? Is this something that's established or not? And that's where AI. Respectfully, you start to get into do we draw boundaries into what we feed AI, right? That, that, that's where we pause and say, whoa, like data privacy from, for that sake, for even the sake of an interview, right? Is this appropriate? Is this what I would want to dig into for an individual? Is there a certain level that I can say, great, this is a good foundation. I feel strong about this candidate. Let's see how they actually execute and why I want to hire this individual.

TIM: Yeah, I'll be fascinated to see where things go in the next few years with AI and hiring in general. Steven, it's been a great conversation today. Thank you so much for joining us and sharing all your insights with our audience today.

STEVEN: Of course. Thank you for having me, Tim.