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From 4 hours of job matching to 10 minutes · Range Podcast

• Giac Soliman: Welcome to Range. I'm Giac Soliman and this is the podcast for comp and reward people building with AI without losing their grip on the pay decision. Every episode, one practitioner, one honest conversation about what AI can actually do in our world and where you still need a human in the room. Be curious, stay a little skeptical, and here we go. Good morning, good afternoon, or good evening, and welcome to a new episode of the Range Podcast. I'm here today with Greg Laney. Greg is a compensation and HR technology expert with over 20 years of experience improving HR processes through systems, analytics, and automation. He has led workday and HRAS strategy. He built practical compensation tools. He taught total rewards. And he regularly shares insights on HR Tech and innovation. Greg, welcome to the podcast. Greg: Thank you. I'm glad to be here. Giac Soliman: What did you have today for breakfast, Greg? you sound very upbeat, Greg: Well, you know, my my dog's actually got me up. So Giac Soliman: Ha ha ha. Greg: it's a fear of what I'll have to deal with if I don't get up is is really what drives me. more so than the breakfast. I did have toast and toast and orange juice though to get me motivated. Giac Soliman: I'm curious, how how did you end up being like a professional in both areas? Greg: Yeah, so I I mean I started out like a lot of people. I did recruiting, was a generalist. I found that I was more interested in like hay point factor analysis, any type of the statistics and analytical components of human resources, which really naturally pulled me towards compensation. I did compensation for till about twenty eleven, twenty twelve. and throughout that time I really got heavily involved with like learning VBA, Visual Basic for Applications using Excel. I had a mentor that really got me into Microsoft Access and I started building access databases. And I found that all my compensation stuff, I was really gravitating towards what's the best technology I can use for it. So when a manager, a former manager approached me back in 2011, 2012 for an HR analytics position, I naturally took the step. from there we implemented a system called Workday. And I I've Giac Soliman: Ha ha ha. Greg: been in the workday kick for a long time and only recently pivoted back to some some compensation roles. Now, throughout this time, because of my natural pension for technology, in twenty twenty four, pretty much as soon as I heard about this concept of AI, I I got involved. I got a plus membership with Chat GPT and I just started going from there. Giac Soliman: when do you use N8N and when do you use ChatGPT? Greg: So what I would recommend to people is start with a you know start with a basic AI model. Like you talked about Claude. I use Chat GPT, whether it's you're in the office and they only have copilot or my MS three sixty-five co-pilot. Learn what you have available and and and start from there. I started my process using using AI for the basic stuff I think everybody does, writing better emails. How do I write an email response to my manager without getting fired? just simple simplify this document, this 20-page document that I don't want to read the full thing. all the basic stuff. And then what I've noticed is all these tools over time, it it's typically like every two to three months, they just get better and better. So I'll I'll put in a prompt, it'll be total nonsense. And then I'll put in a similar prompt two or three months later, and the answers just gets better and better and better. So when I noticed this, I I and I think you know, once the world saw this, I think the idea came to me that wow, I can really start doing things. so what I realized when looking at this was that with the technology, it's not perfect on day one. It's growing as just as you're growing. And so as you're growing, start it, you know, start basic. if you have a concept and you have a a spot you want to get, just start with the basic. Put in help me write this email. Once you understand that, then start looking at help me analyze the spreadsheet. And and you work iteratively up. And that's that's what I've done. so what You start with the very basic components and and then you build. And then as you see that there's new functionality, what's gonna happen is you're eventually gonna hit a ceiling. so there was a point where chat GPT could no longer do the stuff they needed to do. just from a regular Giac Soliman: Mm-hmm. Greg: prompt. I tried all the prompt engineering I could do, but I could not get to where I needed to go. Then agents came Giac Soliman: Yeah. Greg: into play and I started building out agents. I built a lot of agents and I I was able to increase my level of capability within ChatGPT. But I was still getting ceilings to where I could no longer get to a particular point. Sometimes the workflows got too a little too complex. sometimes the information was just too great. And so I started looking for there has to be a better way. And that's where I went to actually Udemy took a a $20 class on what is this N8N or make or Zapier anyways helped me understand how I can use it to create much bigger, more robust business processes. Once I did that, it helped me get to where I needed to go. Giac Soliman: And that's a crucial point, Greg, because in compensation and tool rewards, like we have had access to data through pay surveys. More recently, starting five years ago, we started having access to purpose built tools, platforms for that integrate with HRS systems, HRS systems integrate with ATS, application tracking systems, all of that. So we again have access to new forms of data, but in terms of processes, we haven't always had tools that help us build workflows talking about, you know, what needs to happen, what needs to trigger to no what what triggers a new market review. Typically is a business leader that is complaining that they're not able to hire, Greg: Right. Giac Soliman: but There's no like more systematic way to say, hey, for example, our anchor to the market has moved by above our threshold, let's say 10% above market median, and that triggers a Greg: Right. Giac Soliman: new review and initiates a workflow step by step with some tasks that are automated, some that are where the human is monitoring in real time, and some where they're just Greg: Right. Giac Soliman: working in the background, then the the human reviews the output. I'm not a huge N8N user, but what Greg: Right. Giac Soliman: I understand is that N8N helps you mapping these workflows and connecting other tools to automate the bits and bobs that no one wants to do whilst keeping Greg: Right. Giac Soliman: you in control. Is that a fair assessment of what N8N can do in the HRAS and compensation space? Greg: Yeah, so absolutely. One one one of the things I do wanna call out though is while these large language model systems can move at exponential rate, the there is a constraint. The constraint is the end user and the people that that do it. your creativity and your creativity really drives how fast this tool can move. And so that's why I'm definitely trying to encourage more and more people to use tools like N8N and whatnot. I think at the end of the day, it's not going to be everybody using it, but I think a lot of people, much like Excel, I've talked to a lot of HR business partners, surprisingly, that don't use Microsoft Excel. But if you're going to be a compensation person, you need to know how to use Excel. I think we're going to see similar things for systems such as ChatGPT or Claude or Copilot NADN and make and whatnot, a lot of these tools are going to be a natural requirement for development of of compensation careers or HR careers for people that are much more technically savvy. Really the key point I'm trying to get across here is not everybody is going to be an expert in these tools, and that's okay. Excel has the same functionality. Every compensation person believes that everybody knows Excel inside and out. And the reality is certain roles do. your compensation people do, your financing accounting people do. and other people have some general ideas, but not everybody knows how to run a pivot table or use VBA or create formulas. Giac Soliman: Macross. Greg: Macros, exactly. it's it's a it's a select it's a select group of people that have an invested interest in that particular area. the same thing's gonna be with with ChatGPT. I think there's gonna be a large population that uses AI for writing nice memos and creating meme pictures and stuff like that. And that's okay. but I think for those that are really trying to dig deeper into how can I automate my processes? How can I streamline? How can I make things more efficient? How can I make sense of the the the the chaos in my compensation world. That's where I think the AI tools and the workflow automation tools are going to really be of great value to people. Giac Soliman: And speaking about great value, Greg, there's one part of our job that I hardly ever hear someone telling me, I love doing it. I'm referring to job matching and also drafting job descriptions. But it's not just Greg: Yes, yes, yes. Giac Soliman: those things, then though the connecting those things together. Cause what happens next is you gotta benchmark your roles, you gotta find appropriate matches in the survey, you gotta find like You gotta create a conversion table between your external pay sources and pay data and your Greg: Right. Giac Soliman: internal job architecture. I understand that you've built a workflow, presumably with a combination of chat GPT and NADN that Greg: Yes. Giac Soliman: compares once a job description is submitted, compares the external survey benchmarks and internal roles, and then recommends a likely market match. Greg: So this is a great example. This is something that I originally started building an agent for and ChatGPT probably a year and a half ago. It wasn't great at first, but eventually the technology caught up to where it started working well. And what I did was I had the spreadsheet. I had the spreadsheet from the compensation consultant company that had all the survey matches. I used VVA to create a PDF file. So I I created a file and I loaded that file into the agent and then I was able to successfully create something. But I wasn't able to go far enough. But that only took me to a s a certain point. I still needed to I had it in GPT, which was at home. I wanted to use it at work as a tool. And I had the concept tested out in my my home computer. So what I did was I replicated in and copilot. And I think one of the key things to touch on is that these AI tools are are similar enough that once you understand one, you can usually bounce back and forth. So I created a agent within Copilot and was able to do this for work. Pretty successfully. I took work that took four hours a day, basically, and I was able to cut it down to five, ten minutes a day, you know, for Giac Soliman: Wow. Greg: each for each particular instance. And so that saved me a lot of time and allowed me to work on other functions. so when you and I started talking, one of the things I really wanted to share was the functionality of a workflow automation. And so what I did was I I spent about four hours creating this workflow automation. I would love to share with you. and I can share my screen. It's got a it's it's got a process, and what it does is it starts simple. It starts with a job description that a manager has passed, and not even a well-written job description, just a quick paragraph. I load it in and the workflow basically it compares the description provided against the market. It then looks at it looks at the internal structure. And then what it does is it comes back with a great recommendation and even prepares an email for me to send out to the manager. I'd love to show that to you. Giac Soliman: let's have a look at what's on screen. this definitely looks like a workflow to me. I can see some connected nods. Greg: absolutely. So this is the workflow a lot of companies will have various things that I think are common in compensation. They'll have like a salary structure where they may have a grade or band or level or whatnot that have a a range that I've Giac Soliman: Mm-hmm. Greg: created for this example. they'll have salary surveys that they participate in. and here I've just created a sample for this demonstration where you you see I've I've got simple survey description, I've got midpoint, and then I have an internal job catalog for this particular area. Now in this, I have a couple of job descriptions that have been provided to me and lead budget analysts. I'm gonna go ahead and and copy this description that a manager has just sent to me. Giac Soliman: So that's an is that a an internal job description or is a market survey job description? Greg: this is just a paragraph and an email that a manager sent to me. But if if they did send me an internal, like a two-page job description, I can copy and paste that. And then what I do is I have a tool right here and you'll see the workflow. the workflow, so an important concept, I think, especially for someone that's not really sure about N8N or Zapier or Make, any anybody that's considering it, why do I use this versus chat GPT or Claude or whatnot. The reality is those tools work fine for a lot of components. However, if you want to follow the work step by step and you really want to like map out the particular assignment that you're trying to do, these tools are fantastic. So what I've done is every every tool typically starts with a trigger that that launches the product. you know I'm assigning variables. I'm then going out to my spreadsheets. This is fictitious company data that I made up for testing purposes that I just shared with you. And then what it does is a code is created. And then what it does is it uses Chat GPT to do some analysis. I'm analyzing the external market, the internal market, and doing a great system. These are all things that if you think about your role in compensation, you're like, well, that's that's naturally I do that. I I get a job description, I'm matched to a job. And then I I I put a a a dollar value to it and then I compare it to the internal jobs and say, you know what, this job is probably a grade six or a grade five or whatnot. And then what I do is I come back and say, yes, this is where it slots within our organization. And then I say, you know what, it is in fact a grade six. And so this is what the range is, and now I need to send a communication out to the manager so that they can go. So What a what a workflow does basically is once you kick it off, they start off with like a a survey, which which is really you'll see here, I've my survey is very basic. let's say, let's say I create a job and call it lead budget analyst. I I paste the job description in, so nothing special, nothing exciting with it. And now I hit submit. Now, as I hit submit, you'll notice that the workflow is running here. Giac Soliman: It's moving. Greg: So it's like it's doing something. And and so as it does the analysis, it's doing a comparison of all the the particular data that it's grabbed. And what we can do is I want to show you the the brilliance of what the end result is, but I think more importantly, it gives you an audit trail on what's happening, both in the moment and six months from now. And so that's that's where I think the beauty of this this is. So in this particular example, I loaded this particular job. And what I do is I I come into the step here, I've got my download file. And as I as I pull Giac Soliman: that? Is it is that the underlying code? Is it the the tool like good Greg: Yeah, so well yeah, yes and yes it is. However, I didn't type I don't know, I'm not a software engineer. I asked ChatGPT to create the code for that. And and so Giac Soliman: Uh-huh. Greg: and if you use Claude, Claude is fantastic at coding. so really what you do is at the very beginning of design of these, what you do is you say, I have a concept, this is what I want to do. These are the things that I need to have happen. I need to Review a job description. I need to go to outside market. I need to compare it against inside market. I need to create a level, or I need to assign it to an already existing level. And then I need to send an email to a manager. Those five steps I've created. And then what it'll do is it'll say, This is what you need to do. You need to create a node. And a node basically, if I if I take a step back, a node is basically each one of these boxes. It's a step within a flow chart, basically. Giac Soliman: Got it. Greg: that's a fancy node is a fancy way of saying a step within a flowchart. so what I've done is in this particular example, I've created an analysis where it's got the email address email to the business partner. it's done the external analysis where it says this is most similar to a senior two budget analyst. And Which is position finance professional four with a market value of 122. So, and then an alternate position would be, it's giving me a primary match, and then it's giving me a secondary match. And then you'll notice here internal perspective is low. it's like, why couldn't it not match to an internal match? Well, I'm gonna I'll show you in a second, but then what it does is it comes back and it says, we recommend that you use this particular range and it provides additional notes. And then it gives me a summary of of what I want to do. And this is just stuff I put together in four hours. an organization, a company would obviously probably put a little bit more time into it. if we take this analysis, what we'll notice is in this particular job, I was looking at a lead budget analyst. And if I look at my internal, I have senior budget analysts, but I don't have a lead or anything like that. The senior was at 109, which is why it decided Giac Soliman: Mm-hmm. Greg: it decided, okay, let's look at salary grades. Okay, 109 was the one below it, was this one. And This job was a little bit higher than that. And so what it did was it assigned it a salary grade of 1278. And the reason it did that is because looking at the market survey, where where it matched it was this particular job right here, which was 122. So it took the 122 and it made a determination that 122 most closely matches to 120. And Giac Soliman: Right. Greg: so This is all stuff that a compensation person will typically do, but the the GPT did it. Now, a few things you'll notice is you know, I mentioned the internal confidence was not high because there is no internal position that's created for this. the the other beauty of this whole thing is what happens is I've got a step where it writes all the data back into the spreadsheet. So This is this is the run that we just did right here where it provided feedback. It provides a summary of the analysis. It provides additional, it provides what the match recommendation is. It provides everything. And I can even add a field here if I wanted where I can go in and say, you know, HR business partner sign or compensation partner sign-off. So that if I needed to do an audit report down the road, I could do such a thing. and so that's that's the beauty of the particular process. if you go in and look at it, Giac Soliman: Right. Greg: really what you're doing is you're you're you're really just mapping things. And it's it's it's not hard. I think this is where a Udemy or something like that comes in helpful. because where I was afraid, and and the reason why I I heard of these tools for a while and I I was afraid to look at them. I signed up for a $20 class. I took the class until I felt confident enough to move around in it. which that was probably about a I'll say probably like three or four hours worth of training. After I got to that point, if I'm familiar with with using Excel or PowerPoint or any of these tools, it's not really hard. I think some of the hardest stuff is really just understanding the back end stuff. How do I connect my Google Sheets or my Google Drive to my AI. And if you can do Giac Soliman: Yeah. Greg: if you can do that basic stuff, then AI is going to help you with everything. I can sit in there and say, I don't know how to code in JSON, give me this script. And all this script that I shared right here, this none of this is anything I wrote. This is something that the computer wrote. Giac Soliman: Gotcha. I had a few questions. So, well, first you mentioned auditability, and there's a you showed us a sheet where not only you can go back in time and say, hey, six months ago, you know, we we made a record of what the tool did. We can go back and check it, but it Greg: Yeah. Giac Soliman: also explains what it done does. So you've touched on two major pillars of you know w whenever you vet. AI features in a new platform in a new tool. You want to be able to explain the results. You also want to be able to audit them. So those two pillars have been met. Now, my question for you is you also mentioned connectivity to other like to Greg: Yes. Giac Soliman: the tools you use. And calm people, like we're we've been obsessed with this for so long. We have performance data living in one system. We have our HRES in another. We have applicant job, applicant data in another. How does how does NAN connect to all these different tools? Greg: what you do, NA N8N has the capability to basically it's it's using APIs and I I don't want to get too technical for a podcast, but what it does is there are ways to have N8N ping a particular system. So in this particular system, I'm I'm pinging my Google drive. And asking it to look at a specific document. In a corporate world, what you might do is if you're using Microsoft Copilot, for example, you might have it ping just right into your your organization's Google Drive for your particular for your particular files. it's it's not hard to set up. Like for example, you have accounts that are set up once you put in the API keys. That you know, the the N8N does the rest. So I do this once Giac Soliman: Right. Greg: with the setup. And I'll tell you, to be honest, getting the API code and learning that, that was probably the hardest part for me. figuring out Giac Soliman: Mm-hmm. Greg: how to do it. But again, using a an AI, it's not that hard. I sit there and Giac Soliman: Yeah. Greg: I'll say, I have no idea what you're saying. Explain to me, like I'm a fifth grader, how do I find this particular API code? And then what I'll do is they'll say, go out to the website. And this is typically where you find it. And you know, strangely enough, even myself, and to be honest, with the full disclosure, I am not a software engineer. I am a human resources person. I have a bachelor's degree in psychology, I did a master's in industrial and labor relations. None of this points to computer science engineer. yet, Giac Soliman: Precisely. Greg: because I do you know compensation and whatnot. I have to have a natural curiosity. And I think if the end user has a curiosity, day one, this for example took me about four hours to create this this this process, which which does a lot of work for me and it can save me yeah, exactly. It it it Giac Soliman: And how much time? How much time it saves you. Like that that's Greg: it will save me three and a half hours on the first run, and then three and a half hours every run after that. So I think that's that's Critical to keep in mind is it's important to invest in these particular tools. had I designed this, had I tried to build something like this day one, I couldn't. It would have been too hard. And so what I did was I started with help me look at job descriptions and look at survey data. that was step one. And then it just became an iterative process to where, okay, I think I can make this a little better. I want to compare against the internal market. Help me look at This survey data and then compare against internal data. Okay. Now Giac Soliman: Gradual progress. Greg: survey data, internal data, and then I want to look at my company's salary grades to see what it is. And then survey data, internal data, salary grades. I want to write a letter to the manager to tell them what my results are. and by the way, I want to record so that if Jack and Greg leave the organization, there is some sort of record that says why we evaluated the job the way we did. So I think it's only gonna help as a tool. there's a lot of upside to this. it just it just takes a little bit of effort to get in there and and and peek under the hood. Giac Soliman: Yeah. And I love how it connects all these different tools. And just to recap, like we mentioned API, it just stands for application programming interface. It's it's think about it for like people that are listening, it's a connection point. It lets one piece of software talk to another without human in the middle. So let's say if you have your Mercer's WW, your Aeon Rat for data, and you know you when someone says they've connected Those systems, so your HRES via API, it simply means that the two systems exchange data automatically without the need for manual exports, without the need to copy and paste. my last question for you, Greg, is I I saw the tool like designing salary bands for a new level, but how does maybe it's connected to the API? I don't know. How does it know? what your compensation philosophy is. How does it know that you anchor your midpoint of the range to the 70th percentile of the market? How does it know that you want your range spread to be 40% or 30%? How does it know that you only allow up to 10% overlap between ranges at different levels? Like how does it know all of that? Greg: That's a great question. you've got these the the two brackets. it's a yellow or orange, depending on the screen. what that is is that's a code in JavaScript. the way you build the workflow automation using AI to help the AI is as you're typing you you you first you list the concept. This is what I'm thinking I want to do. And then what you do is you build this node by node or in common terms box by box. And so you'll say, What do I need to do in this box? And then it will say, in this box, you need to insert code. The code needs to be this. And then what you do is you say, Okay, I need more than what the code is you're suggesting. I need it to consider our compensation philosophy, which might be X, Y, and Z. And you cannot Have any exceptions regarding A, B, and C. it will then go back and it will recreate the code so that it does exactly what you want it to do. And the beauty of this process is that instead of doing a prompt engineering project where you have a 20-page prompt that you're loading into the system and then and then you're asking information, what you're doing is you're breaking this down into bite size chewable pieces of the of the process so that if I want to go back in and I want to look at what did you provide me here. if I just grab any random node right here, you'll see it shows me what is the input that came, you know, that came in and then what is the output that I have here. I know with this data, with this code, This is what I get as an output. And so I'm able to test it step by step. And as I go step by step, I say, okay, is it working? Is it working? Is it working? Is it working? so again, it's it's that iterative concept of get the first box to work, then get the second box to work, then get the third box to work. And then eventually you've got a a full process. I'm doing this at four hours. I was doing this at probably about less than five minutes a clip for some of these. some of the coding I I wanted to go in and I wanted to test. I wanted to make I have to go in and you know set up the Google Sheet just to do it right. And and I created some samples you know for demonstration purposes. That that took Giac Soliman: Mm-hmm. Greg: some time too. So it's really not hard when you be use your AI as a basically as a a colleague or a coworker or co-designer. It does all the heavy lifting. All you have to do is conceptually, what do I want it to do? Tell the AI what you want it to do and it will build it for you. Now you've got to go back and test it to make sure did it do what I wanted it to do or did it misunderstand me? I I I think Giac Soliman: Yeah. And Greg: that's the the critical thing. Giac Soliman: The doing is not a problem anymore. Like you have the tool that can do it, do the job better in you. But it's having that like intentional, like imagination, like having that idea of right, this is how I'm gonna leverage technology. Here's how the technology is gonna enable me to get this job done. That's what really that's what you bring now. Like just before we wrap up this episode, Greg, first first and foremost, like thank you so much for sharing all of this. The all the nodes they can frighten you at the beginning, but yeah, but they're all interconnected Greg: Right. Giac Soliman: to your point you start small, you work from one node and then you expand to another and another and another. So but even if it gets big, you've built i every single node, so you know exactly what they do. Greg: It by the by the way, these are things that I think compensation people have done for years, regardless. They just haven't done it in this particular format. So I remember years ago I used to do compensation like a merit process or bonus process. we were introduced to a concept called project management where they would use the project management book of knowledge. Where you would you would say, okay, I need to map this out. I need to have a very sequential process, who's responsible, create racy charts so I know who's responsible, who's accountable, who's consolidated. I did all that stuff anyways. Now what I'm doing is I'm just creating it and I'm I'm I'm putting it on a you know on an app N8N, and and I'm using AI to help me build it. So I don't even have to be an expert in these particular areas. Now, I think where the compensation piece comes into play is you have to know if it makes good sense. AI hasn't caught up to us as far as our professional expertise in particular areas. It will tell you, yes, this is the process, and yes, I followed your process, but it doesn't know, you know, you should. If I should make this job instead of a grade six, I should make it a grade twenty because it's higher than five. You as a professional need to make that decision in that professional call. So I think it's critical to understand that the end user is ultimately responsible for the results. And I think there's still a need for someone that's expert enough to do testing and to verify. But with these types of tools, the the the grunt work, if you will, the the building out of these processes. things that honestly, some of these processes I built out stuff that would take me one to two years, that I've built out in two months. you know, so this this will save people a lot of time. So don't come up with the the penultimate brain you know the you know brainiac idea on day one. Come up with something small and work up to it. The second thing I do too, for a lot of these processes, what I've done is I've started very, very small and said that's too complicated. I don't think I can do that. Let me start with this and and and Then what I'll do is instead of just coding, coding, coding, coding, you know, working on it, what I'll do is I'll do a little bit, I'll get frustrated or whatnot. And then what I'll do is I'll take a day or two off from looking at it and just going for a walk, talking with family, sitting at a sitting at a barbecue or sitting at a beach, even, and then all of a sudden it'll hit me. Wait a second, I didn't think about tying this into it. And that's usually how I come up with my best ideas. So I would encourage anyone to start small. And take a class, take a basic class. You don't have to take the whole class, but listen to it for an hour or two until you get an idea of the concept. And then as soon as you get frustrated with it, walk away for you know for a few hours, a few days, whatever. And then just go back to it. Ideas will pop up. I am not a rocket scientist by any means. Anybody can do this. It just it just takes a little patience and it takes, you know, it takes it takes imagination, basically. Giac Soliman: That's a wrap. If this was useful, follow the show and send it to the one other comperson you know who needs it. Thanks for listening and I'll see you in the next one.