Latest / Range Podcast / Why Focusing on Small, Risk Free AI Wins Accelerates Large Scale HR Transformation
Transcript
- Giac AI: Hi, I'm Jack and this is the Range Podcast. Every episode is a conversation with someone in comp or total rewards who's actually building with AI. We talk about what it's doing to our work, what's working for them, and what they'd do differently if they start it again. Thanks for listening. If something in this conversation was useful, send it to a colleague who'd get something from it too. Next up is Ivan Nosov, head of People AI and HR Technology at Campari Group. His episode drops June 21st. We get into what AI adoption actually looks like inside a large global function when nobody on your team writes code. If you've been wondering whether that's even possible without an engineering background, ⁓ has done it. Giac: Hello, hello everyone, and welcome to another episode of The Range Podcast. I'm here with Joshua Lemon, who is a senior director of Total Rewards as Presidio, plus he's co founder at Slash Benefits. He's been in comp and benefits for over 20 years. He is been located in Minnesota. Is that correct, Josh? Joshua: That's right. Yep. Giac: And he's here with us today to talk about AI, specifically AI adoption and rewards. What a surprise. Josh, why don't you take a second to tell us a little bit more about yourself and your journey? Joshua: Yeah. So thank you for that introduction. ⁓ I guess a couple of things that I would just say about my journey. I am ⁓ very much an AI enthusiast, I would say. One of the earliest adopters, I think the first, you know, one tenth of a percent of Chat GPT. And you ⁓ I use AI in a lot of my creative ⁓ work ⁓ and ⁓ know oftentimes beyond the total rewards work that I do. But it was not long after I started playing around with these applications that I learned that there was going to be very significant impacts in the work that we do in HR. And so I embraced it early and and here we are. Yeah. Giac: An early adopter indeed. I came across Josh on LinkedIn actually, yes, because ⁓ he's posting about his creations and his AI builds from simple task automations to more fully flesh tools, and that's how we met. Now, before we get into the whole ha AI thing, I wanted to ask you something more personal, Josh. Now give we know what your job is day to day, right? And I I'm sure you you're a man full of secrets. Now Joshua: ⁓ for sure. Giac: I wanna know what's something you're surprisingly good at given what you do for a living? Joshua: You know, ⁓ I will say if if we're talking about little secrets or things that sometimes people don't always know about me, I love to make music. I have since I was you know, a teenager. And one of the great things that I found with AI is that it's a great enabler to be able to make music. I've got three kids, a very busy family life and everything like that. But with the tools that are out there today, I can get that full band sound. ⁓ and you know, express my creative interests in that way. And so I do ⁓ quite a lot. In fact, if you find me on YouTube under Lemon Sour Music, you will ⁓ actually find a number of songs out there that I've created. Giac: Will I see you with the same microphone you have on the screen? For those that are watching, there's a quite professional microphone that Josh has. But also will will I see you with long hair like the seventies rock and roll style hair? Joshua: If you see my edited videos, you might see me with some long hair. ⁓ I think I've got a a ⁓ video ⁓ Instagram or something like that where I look like a rock star with long hair and leather and everything like that. Yeah. Giac: Okay. You can you can tell that's a wig, Josh. That's okay. We're all ⁓ well in the same boat. great, great. So Josh, like you've been experimenting with AI for some time, and been thinking about how to adopt this ⁓ new how to leverage it. Joshua: Right. Giac: in our domain which is full of surprises, right? Confidential data, personal and identifiable data, regulations that change in every in every jurisdiction. My first question to you, which is probably in many of our listeners' mind, is how did you get started? How do you like lighten up the fire? Joshua: Yeah. Well, I will say the very first thing that I've done in order to kind of get involved with these different AI applications is I play around. I mean, of course I just told you about how I use it in my music, but I've done certain things that are definitely not work related to be able to see how can I use these tools in a in a work related way. And honestly playing around first in a safe space where, you know, I can't really mess something up when I'm making a recipe application, right? Giac: Mm-hmm. Yeah. Joshua: ⁓ it helps when I decide that I wanna then turn around and build an HR application that has, you know, a lot of those restrictions that you're talking about. Giac: So your your advice pre you would be to start small with something that is risk free, where yeah, I mean worst case scenario your partner will complain about bad recipe that you come up with. ⁓ ⁓ and how did like from there's one point where you went from okay, I'm I'm using AI for personal matters, but at some point Joshua: Ha ha ha. Giac: I start using it for work. Can you recall what the first use case was? Because a lot of people ask, what are the realistic use cases, right? Joshua: Right. You know, I probably started using AI very similar to how a lot of people start using AI in their work. You know, I wanna write an email and I don't want to come across as frustrated as as I feel right now. I wanna be professional in the way that my approach is. So, you know, write out the email, pop it into Copile that or Chat GPT or your large language model of choice and say, Make me sound professional and you know, but still get my point across and it rewrites things so that you do the voice that you want to carry ⁓ with that email that you want to write. And that's probably how I started, just other people might start. I quickly moved though into trying to dive a little bit deeper and see what of the what kinds of applications can I actually bring into my workspace. And the good thing about Residio is we actually have a generative AI review process. That includes cybersecurity and it includes legal review for good reason. We want we have intellectual property that we want to protect. We have, you know, personal information about our employees that we want to protect. And we, know, need to be compliant with the different AI rules that are, you know, applicable, like you said, in different jurisdictions. So I started basically keeping the cybersecurity and the legal team busy by submitting a whole bunch of things for review. Over time, I got a little bit better at figuring out what kinds of things they were looking for. And so now I look for that when I'm looking at an AI tool that we want to review. Specifically, the types of things that I'm looking for are what are their safeguards around data privacy? Do they train on the data that I'm using and inputting into the models, do they have a way to turn that off? those are those are some of the things that I'm looking for. ⁓ also looking for do they have a privacy policy? Do they have a privacy page that ⁓ talks about how they treat data? Because ⁓ that's just important for my company, but that's important for other companies that are using these tools. Giac: Excellent. seems like you've made quite a lot of friends in the legal and IT department over at your company. A lot of the reward leaders I speak to often ask, How do I make a solid business case for adopting AI and rewards? ⁓ some of the struggles, like we we tend to be small teams. We manage ⁓ and millions in the resources and dollars of employee pay data. ⁓ Joshua: Mm-hmm. Giac: But sometimes there are departments that are bigger than us and get a little bit more attention. So wonder if you had to do anything to get their attention. Like knowing that yes, you're a small department, but and yet we're happy to work with you and maybe to prioritize your use cases. So how do you make ⁓ that counterparts in IT Joshua: Mm-hmm. Giac: in legal and maybe even HR leadership, before they object, understand the power that you ⁓ bring using I and in in comp and benefits. Joshua: Yeah. that's a great question. I think having, ⁓ you regular conversations and demonstrating the value that you can bring to the table is really important. sometimes that takes a little bit of trust. ⁓ but the good thing is, a lot of these applications are not very expensive. So asking somebody to take a small risk on something that you're trying to accomplish is usually a little bit easier when that small risk is a couple of hundred dollars, not something that's, you know, a $30,000 a year, multi-year subscription sort of a tool. And so that's basically kind of the entry point that we've taken on my team is we've said, you know, where can we end up making real gains ⁓ some small investments? ⁓ then we can demonstrate what's possible with these, ⁓ these tools. ⁓ we've done that in communications, making videos, podcasts, you know, other educational content for our employees. We're doing it with applications that we're building, ⁓ specifically for our team, for the broader HR team, and possibly for the broader employee base. but it's those ⁓ small gains and demonstrated sort of capabilities with these tools that show people what's really possible, bringing them along the journey. Giac: Excellent. And I think you've touched on a very important point 'cause when we think about AI adoption in our domain, we often think about how can we be how can AI make us a better version of ourselves, a more productive version of ourselves. A lot of the, you know, use cases I've heard from other compensation total rewards leaders are pretty much focused on You know, enhancing your team's capabilities, upskilling them, and just doing the work either more quickly or better. But to your point, this is an opportunity for us not just to upskill, right? AI is giving us an opportunity to back to why our function exists in the first place, improve the allocation of resources and the employee experience, the manager experience, the employee engagement. So the natural question for someone that like you has started ⁓ in this journey and you know has ⁓ already deployed tools that take care of maybe some bits and bobs that get automated but also you know you've got AI embedded in some of the more like heavy workflows. How do you measure ⁓ And also if you've developed if designed tools for your audience that sits outside of tool rewards, how do you measure adoption by managers and employees? What wha what are the tells? Joshua: Yeah. well, ⁓ of this is actually ⁓ th those great questions. And I can tell you how we are approaching this. ⁓ I don't I don't think we've got it all figured out, but what I will say ⁓ is to answer a couple of those questions, in some cases they're new capabilities. So we're really comparing against zero, right? The team never did this before and now the team does this, right? Giac: Mm-hmm. Yeah. Joshua: making, you know, videos to talk about things like the secure two dot ⁓ rules for, you know, the four one K plan that we're trying to educate our employees about, we probably would have put a newsletter together and sent it out over email. Now we've got a more engaging piece of content that people can watch and they can understand. we never did that before or if we did, it took resources that went beyond our team, right? We had to engage with our communications team to be able to try to partner with them and get on the schedule. So, yes, we're doing some things that are ⁓ but we're also doing some things that are just completely new. ⁓ when it comes to the applications that we build, ⁓ lot of the ability to see adoption ⁓ inherent in how we're building the tools. Most of the applications that we're building have like a back end administrator dashboard. ⁓ as part of that, we also do user management. ⁓ we're seeing Giac: Mm. Joshua: specifically who has accessed the application ⁓ can end up telling, you know, when they last use it or, you know, we can end up getting information on how frequently they're using it and that sort of thing. Now that is a little bit of a tricky scenario ⁓ in some cases the applications that we build for certain purposes ⁓ might with that sort of ⁓ a sort of audit log, might need a works council review, like for example, in Europe. ⁓ just because of, you know, the possibility that that might be seen as something that might be performance based tracking somebody's usage of something over time, right? And so there are hurdles with that, but there are also capabilities that you can build directly into the applications that will help you gauge that utilization. Giac: Mm-hmm. of curiosity, what are the typical questions you're getting from the trade unions, the work unions of of this world, like specifically in Europe? I ask because there's a lot of I'm I'm based in Europe, right? There's a lot of I wouldn't call it necessarily h hesitancy or anxiety, but you know, there are there are companies that are deliberately playing the waiting game. They're saying right, we're gonna wait until everything gets sorted out first from a regulatory standpoint. But also we don't want the trade unions to think that we're gonna use this as an excuse to cut cost Joshua: Mm-hmm. Giac: ⁓ because we've s we've seen it in like a lot of the narratives coming from big corporates, especially in the US, is you know, we're investing in AI and look now we don't need ten of you, we just need five of you. Right now, whether that's narrat that whether that's just clever narrative of the reality, we don't know. ⁓ but I'm curious to know more about like if I was a reward leader, then I'm adopting AI and I've got a huge presence in Joshua: Right. Giac: ⁓ European countries that are heavily unionized. Think about Spain, France, Germany. Like what are the typical questions I might get from a representative of the workers? Joshua: So ⁓ I can tell you that the types of things that we know are concerns that we just try to avoid right now are ⁓ sort of AI decision making that might, you know decide or not somebody should be offered a job or something like that. We're we're not even playing in that space, right? If we ⁓ ⁓ something like one of the things that we've built is a job matching tool that will basically ⁓ a job description and find out how it fits best into our job architecture. There's no personal information. There's no personal you know ⁓ some no point around a particular individual's job that is associated Associated with that exercise. And so that's something that you know usually is free and clear because it's not impacting somebody's work. And that's primarily where we've focused our applications. We've built applications that will look at a holiday calendar and understand the statutory holidays in a given country to pre-populate it and then give it over to somebody for further review to review it. And we have a workflow and a dashboard and things like that. pretty safe from, you know, I'd say suspicion or scrutiny from like a works council, for example. Giac: interesting debate for sure. It's many different countries are adopting it differently. I think I read last week that they've banned layoffs due to AI adoption in China, ⁓ which is quite a different move compared to what is happening in different parts of the of the world. But to your point earlier, like you're building tools for other humans. You're not building tools Joshua: Mm. Giac: that replace humans. These tools that you build are meant to be used by the same humans that someone might think you're trying to replace, but this is actually the other way around, right? you mentioned the job matching tool. Tell me a little bit more about that. Joshua: Yeah. Yeah, so Residio we built a tool that will or an application ⁓ that would take a job description ⁓ and it up based on the you know best practices that we use internally ⁓ for our descriptions in terms of a job summary, the preferred qualifications, the or the required and preferred qualifications. skills, competencies, etc. ⁓ we have loaded on the back end information about our job architecture, our job families, the descriptions for those families, our job levels, and the descriptions for those job levels, our different career streams. And then ⁓ it will take something that oftentimes managers have put together the best job description that they can, right? But they're, you know, doing this Giac: Mm. Joshua: on a one-off basis when they have a need for talent. So it's not like they're doing this every day, right? So they take the job description and hand it over to an HR business partner or somebody in the talent team, ⁓ they can then turn around, feed it into this tool, and will clean up the job description so that it meets our standards. And then it will also evaluate and stack rank kind of where it fits best within our job architecture, both from a job family and from a leveling standpoint. So it's really great in terms of helping a manager and like a talent ⁓ acquisition specialist or an HR business partner have an intelligent conversation about what kind of role do they really need to hire for what they're saying needs to be done. and then if they want to, they can actually level up the job or level it down depending on how things play out. And then they can have a a conversation about Okay, is your need really this or this? it something that, you know, has these problem solving responsibilities and accountabilities, ⁓ is it something that maybe we can end up, you know, saving a little bit of money and giving them a little bit lower accountabilities that are aligned with our job description but properly leveled? I think it's really great in setting, you know, appropriate expectations ⁓ for levels of roles that different managers are hiring. Giac: Sounds like you've made your manager's easier, right? Because this is this comes all on top of their regular day to day work. Doing their job, but they're also managing people, they're also, you know, involved in in in matching jobs and head count planning and all of that. you know how sometimes design job and you agree on ⁓ the job mapping, the job level, but then you go to market, you meet some candidates, ⁓ suddenly the falls in love with one specific candidate. Joshua: Mm-hmm. Giac: And they ask if there's any room of flexibility to adjust the job requirements to fit with this particular candidate they're so desperate to onboard. have thought about a way of like making a tool flexible enough to reopen certain sort of decisions or go back to a few decision points based on how things can evolve? Or is it deliberately is the tool deliberately built in a way to prevent that from happening? Joshua: So I would say the tool is flexible, but the structure of the conversation is what probably would dictate that more than anything. In the case of the application itself, we have the ability to level that job up or down. So if somebody wants to go back and revisit a saved job description and see what would this look like if we were to make it a higher level rule, they can do that. The great thing is it's not just changing like the years of experience. It's aligning the whole job description to the leveling guide that we have. So when we do change the level of the role, we have a clear understanding of how does that change the accountabilities and some of the other aspects of the job, which is really great and makes for a more robust conversation. ⁓ I will say if conversation has been had up front about the type of talent that's needed, based on how the conversation went initially. They probably aren't sourcing that candidate that is, you know, outside of the scope of the role because they've appropriately leveled the role to begin with and described it as such in the posting. But I guess there is a chance, you know, somebody knows somebody in their network or something like that. They get involved in the pool anyway, and that conversation is possible. But like I said, I think that's more of a way the conversation goes, not so much a ⁓ way the application works, sort of thing. Giac: Right. And it also stresses the point that you can implement new technologies, state of the art, you can build a lot of things with AI, you know, whether they work or not. It will expedite things, it will extrapolate insights that you might have missed, but it's likely not gonna fix any behavioral issues. That's that's why, you know the human that has built the tool is still pretty much required in running all these processes. And why having a human in the loop is oftentimes the best way to do this. The job matching tool, Josh, is that something you'd be willing to share like on the screen? Joshua: I was gonna say, do you wanna do you wanna take a look at it? I'll go ahead and and do a little show and tell show and tell. ⁓ all right, so gone ahead and pulled it up. Let me know when you can see. what looking at right now ⁓ is job on Indeed. ⁓ And so what we're gonna do is we're gonna just take a job. This is a software engineer that I found. It's you know in the Twin Cities area. I don't know where Oceola, Wisconsin is, but Giac: Let's do it. Yes. Joshua: Must be close enough that it came up. and I'm gonna grab ⁓ some of the things from the ⁓ description here. we'll ahead and take up through the working conditions, and I'm take it into ⁓ the residio tool. So this is the application that we've built, and I'm gonna go ahead and find a match. So we're gonna see where this random software engineer role that I found on the internet. ⁓ fits in within the residio job architecture. We do have software engineers, so this should be something that we can find. I'm gonna just kick ⁓ it in here. I could either upload it as a file if I had it as a Word document or if you know like I did with just copying pasting I can paste it in here and I'm gonna hit that analyze. It's gonna go ahead and look now ⁓ our job families, our career streams, ⁓ our job levels ⁓ it's gonna give me a stack Ranked list of what it thinks fits best. So it's come up with a senior software engineer in our organization. This is a professional seven role. ⁓ it says it's a 90% match to that. ⁓ some things that it says are key alignments and why it's a good fit, some things that might you know be a little bit different. it's also said that this could be maybe a little bit higher level role, same job family, but a professional eight, senior advanced. Giac: Mm-hmm. Joshua: ⁓ and then it's also said, hey, maybe it's ⁓ maybe it's a slightly lower level rule. ⁓ looking at the job, I will say ⁓ I'm just kind of basing this based on the salary and everything like that. I'm I'm not doing a deep dive because we're doing this live, but I'd probably take a a little bit closer look to see what are the true responsibilities and everything like that. I'm gonna say just based on the pricing and how I know these jobs, it's probably more like ⁓ it's got some experience, but it's probably not. our senior level just given the pricing on it. So I'm gonna go ahead and refine with this professional six. It's an experienced, not an entry level, but not quite at that career level either. ⁓ as you can see here, it takes me into a where now it's populated stuff along the lines of our best practices. So I've got a job summary, I've got an outline of primary duties and responsibilities, required and preferred experience, competencies and skills. And if I want to, I can edit any of these. I can just go into the fields and and type something different, or I can go ahead and add something. Let me just do something that's totally odd and just say this is is the team party planner. ⁓ not typically a responsibility ⁓ a software engineer. Right, right. ⁓ but let's say ⁓ you know, I added that ⁓ and it somehow got through, right? I'm gonna go ahead and validate the alignment. Giac: Ha ha. It's a huge responsibility. Joshua: Now it's going to take another look at this and it's going to say, based on the changes that you made and everything like that, here are some suggestions that I'm going to make for you. It's saying that, you know, there is some good alignment, but as I look over here on the field analysis, it's going to give me some suggestions. And down here at the bottom it says, hey, you know, you should really remove the informal line. This is the team party planner. You know, maybe consider adding a sentence about how it contributes to process improvements, etc. So I'm gonna go ahead and take that suggestion into consideration. I'm gonna delete this. I'm gonna validate the alignment again. I'm gonna have a cleaner ⁓ now. It's pretty similar. I didn't change a whole lot with the job, right? In terms of the percentage match. ⁓ down here, the you know, suggestions you can see that it's you know different than what I had put in previously. And it's giving me different suggestions. Now I can continue to fine-tune this if I want to, but at ⁓ a certain point I might say, well, 92.5% is good enough for me. I can go ahead and finalize this if I want. now I'm not gonna use this because I'm not gonna finalize this right now, but let's say I want to, ⁓ know, get some feedback from the manager, I can actually export it as a Word document, or I can copy and paste it into an email, and I have the ability to, you know, take it somewhere else after I've done that. Giac: I've got so many questions, Just. This is this looks amazing. in the interest of time, I'm gonna aim for those that I think the audience, most of the audience that might have. So, like ⁓ the elephant in the room first, without spilling any secrets. we had a 92.5% match. That is a very nuanced. Joshua: Yeah. Giac: accurate number one might think, why not ninety two? Why not eighty nine? Like what is driving all that? Joshua: Yeah. there is some matching logic in terms of how it comes up with that score. And we've you refined that a little bit. ⁓ I couldn't tell you from the top of my head how we ended up calibrating that, but that was ⁓ a conversation at length with the AI tool as we were building this application on how it would score the match. ⁓ so ⁓ there's you know appropriateness in terms of the career stream, appropriateness in terms of the level, appropriateness in terms of the match to the family. I couldn't tell you about the proprietary blend, probably just from recall, but ⁓ we do have we we had a conversation at length with the AI to be able to refine that the way we wanted it. Giac: Excellent. Second question. needs to map your job architecture. And we all know ⁓ are some very naughty managers out there that right, we cannot go to the salary number the candidate was asking for. Let's beef up their job title. Right. Is that like how do you how's the how does the tool handle requests like that? Can can someone can a manager just go in and Joshua: Sure. Well Giac: Change job titles. Joshua: So you can actually ⁓ modify the business title. There is a job title that's tied to our architecture. So they can they can modify the business title if they want. There's a certain amount of leniency that we allow in our organization. It's not going to change the underlying job title. But let's say we do want to increase the level. So there's a couple of things that I'm going to just call attention to. You know, easiest thing for me to call out is something that's quantifiable that we can identify. ⁓ So years of experience, but you know, certain things like the preferred experience or sorry, the competencies and skills and things like that, intermediate technical knowledge, developing prep professional expertise, things like this, these are characteristics that align to both the role and our leveling guide. Now, if I level this role up, see that it's not just gonna be the years of experience that's modified. It's also going to be some of those competencies and other things. I mean, I'm not going through all the detail here, but now all of a sudden I'm leading the architectural design, I'm driving the full life cycle development. I've got more years of experience, yes. But when I go into competencies, I'm a subject matter expertise. You know, I have the ability to solve complex, ambiguous technical problems. So the great thing about this is if a manager says, you know, in order to get the person I really want, I want to kind of juice up that title. ⁓ yeah, that that might be possible, but by the time they actually change the leveling, in order to have it still align well with our job architecture, it's gonna have also other characteristics that are gonna set different expectations for what the role is and what it does. Giac: Josh, thank you so much for walking us through this tool. We're gonna end our recording with a very simple question that looks forward. Job matching, you've already tackled one of the biggest beasts in compensation. The kind of grunt work that many of us to do and also hate to do at the same time. Now, if we look in like two ⁓ that's too long. One things keep on changing. Let's say six months to a year time. What is that one task, one piece work in the Total space that you're looking forward to either enhancing, automating, augmenting, whatever verb you want to use with AI? Joshua: Yeah. Yeah. Well, I can tell you, you know, we've got in the total rewards team right now, we've got four projects that we're working on. And I sure hope that none of them take six months to a year for us to get across the finish line because we're actively working on these things. A couple of things though that I'll tell you that we are doing. We're building a microsite, the microsite that we are going to manage and use to engage with our employees and constantly maintain and update. Now the there is as a part of that an AI component where we're gonna have a chatbot that will use the microsite as the knowledge base for the responses that it gives. So as we have additional documentation that we want to put in front of our employees and things like that, we can load it into the microsite and they can then engage with that ⁓ you know, chat bot on the site and get answers. That's something we're really excited about. but what the what the other ones really come down to, and I'm just gonna say, you know, before I tell you what they are, we chose them, right? They're the things that have caused different members of our team pain in the work that they're doing. ⁓ ⁓ of the things that you can do as a working in total rewards is you can probably figure out what your pain points are, right? What do you want to complain about with your spouse when you come home at w at you know from work ⁓ to man, this is really terrible today, ⁓ right? Anything that you can do that about is an area where you have space to design something that can make your life better with AI. so one of the things that is that for us or for somebody on my team is a global holiday calendar. We've got 35 different countries, a couple of business units, ⁓ differences and how how different groups ⁓ take time away from work and everything like that. So we built an application that will go through, we can load in the countries. It will pre-populate based on the year that we've chosen, the statutory holidays within the country, and then we can assign that to somebody in country that can then do a review and make adjustments as possible. The great thing about this is it never is ⁓ a spreadsheet that goes out that has to have version control or something like that. It goes out the person who manages that on my team can see on a dashboard when they reviewed it, when they've clicked submit. And once everybody who's gotten an assignment Has clicked submit, they can end up generating the global holiday calendar for our organization. Really great ⁓ application, a limited use of AI, but ⁓ you know we used AI to help build it, and there is a little bit of AI that's you know enhancing the functionality of the tool. Another thing that we built is a compensation offer tool, something that uses all of our ranges, all of our geo differentials across our entire footprint, and can end up helping. ⁓ somebody who's in a talent team or an HR business partner understand what kind of offer do we need to make to an individual that's going through the the the candidate process ⁓ ⁓ sure that we're offering a competitive salary ⁓ that gives that full access. Again, that one's not using a whole lot of AI in the output, but it ⁓ we used AI to build what we needed. ⁓ that's where I would say ⁓ ⁓ about your pain points and you will find solutions in AI. Giac: Very deep and insightful. for those that are struggling to envisage what's ⁓ with AI, Josh had the answer for you right there. Josh, thank you so much for coming to the podcast and sharing your experience and insights for with us. We look forward to catching up soon. Joshua: Thanks, Sean. All right, sounds good. Giac: Cheers. Take care. Joshua: Thanks. Bye.