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[00:00:00] I'm here with Aiden Hodgins.
[00:00:08] Aiden is the principal solutions architect that new entity Hcm.
[00:00:12] New entity is based into Troy, well Aiden is based in Toronto, Canada and he's part of a team
[00:00:18] of certified workforce offer implementation partners that specialize in time and attendance,
[00:00:26] solution architecture, configuration, integration, reporting and user training.
[00:00:32] Met Aiden in person at Workforce Software's Vision 24 conference down in Miami.
[00:00:38] We had a great chat so we figured we could bring it to you guys virtually.
[00:00:42] And we can learn a lot about what he knows. He has a great experience in technology, AI,
[00:00:50] his presentation at visions was really great. Aiden, thanks for joining us and welcome to our latest HRTechChat.
[00:00:58] Thank you for having me, Dylan. I appreciate it.
[00:01:02] Just to get started, how much do you tell people a little bit about yourself and what you got going on?
[00:01:10] My name is Aiden Hodgins. I'm a principal solution architect like Dylan said with new entity Hcm.
[00:01:16] I've been working with Workforce Time and attendance now for just about eight years.
[00:01:20] It's basically been my whole adult career. I started off as a junior consultant
[00:01:26] and I'm now in an architecture position. I have implemented time and attendance,
[00:01:32] just specifically Workforce Time and attendance across all sorts of really interesting industry verticals.
[00:01:36] I've done entertainment, hospitality, new clear right now. I'm working on
[00:01:42] our first Workforce implementation in the cannabis, the retail cannabis space, which is really exciting.
[00:01:50] A lot of new grounds. I'm based out of Toronto, Canada, like Dylan said.
[00:01:58] Yeah, I really enjoy what I do. This is just strictly time and attendance.
[00:02:06] For those of you guys who maybe don't know what Workforce Time and attendance is. Workforce time and attendance, if you know
[00:02:12] of SAP, SAP is the Workforce Time and attendance is the SAP preferred time and attendance solution
[00:02:18] delivered by Workforce Software. We at new entity were a very small team of about
[00:02:24] I want to say 10 or 11 people now. I can kind of lose track of how many people we're growing
[00:02:29] pretty steadily. We were established in 2022 and we strictly focus on Workforce Time and attendance.
[00:02:38] That is our only stick. We position ourselves to partner with larger companies who maybe don't have their own
[00:02:46] Workforce Time and attendance implementation teams so that we can supplement least staff them and work with larger
[00:02:52] organizations to be there. Their Workforce Time and attendance experts.
[00:02:56] Nice. Yeah, you guys seem to give a really broad and great knowledge of technology that's coming out
[00:03:05] there right now. How did that technology kind of get started with new entity? How did
[00:03:13] that kind of the technology kind of perspective that you brought to the table for Workforce Software?
[00:03:19] Kind of cultivate a partnership with the two of you. Yeah, good, good question. So I
[00:03:27] before I was with new entity and before I was in the Workforce Time and attendance space,
[00:03:33] I was working on hardware testing. And so I've always had sort of a passion for software
[00:03:39] and for development and for implementation. And new entity was a great role for me to both explore
[00:03:47] time and attendance implementations, but also be able to branch out more into the software
[00:03:51] development side of things being able to build our own in-house applications. And that's kind of why I'm here.
[00:03:59] It is working with our smaller team. A lot of us have backgrounds, whether it's with the vendor directly
[00:04:07] with Workforce Software or software development background. So we've got a pretty diverse range of skill sets
[00:04:13] that was really attractive to me and brought me into new entity. So we're just in the early stages of some
[00:04:21] development and some cool tools that we're working on right now, but yeah, just really small.
[00:04:27] I like working with a smaller diverse team rather than being sort of in a larger organization. So that's kind of what
[00:04:33] pulled me towards new entity. And how did the company as a whole get started how to new entity kind of get
[00:04:41] off the ground? Yeah, so we have two founding partners, Cam and Lou. They had worked together pretty
[00:04:48] extensively. And a couple of years ago they decided, hey, let's leave our jobs and let's start our own
[00:04:57] independent smaller gig. And sort of sell this as like I said, like a workforce-only shop, a lot of
[00:05:05] I don't want to say boutique consulting firms, but boutique consulting firms try to branch out into
[00:05:09] other software and other implementation areas. And we're just like, hey, let's just be really,
[00:05:15] really, really good at workforce time and attendance. And it's been a couple of years since then.
[00:05:21] I've only been here for, I want to say nine months now. But yeah, so it's been quite the journey
[00:05:29] and we've had quite a lot of success over the past couple of years, which is really exciting.
[00:05:34] Nice. And do you know how that partnership with workforce software kind of got off the ground?
[00:05:40] Do you know, was it how did it kind of come about? Yeah. So actually, both of our founding partners
[00:05:49] are actually X workforce employees or have worked extensively with the workforce software team.
[00:05:57] So it was kind of a natural progression from, you know, having worked there to having knowledge
[00:06:03] of the partner ecosystem. There's a lot of partners in the ecosystem right now for workforce
[00:06:08] time and attendance same with sort of SAP and all these other HRIS technologies. And it was just
[00:06:15] a natural progression for them to understand, you know, the process of becoming partners with
[00:06:20] workforce software and workforce software has a really, really great partner engagement team that
[00:06:25] works with us to, you know, direct us towards customers and work with us on building our company
[00:06:32] and building our organization. So natural experience from there from their part for sure.
[00:06:38] Yeah, it makes a lot of sense. It's been definitely helps when someone really knows the software
[00:06:43] inside out and then can actually cater to those needs. So one thing like a long time, like eight to
[00:06:49] ten years before they started the company. So it was yeah, it was a natural, natural progression there.
[00:06:54] Oh, nice. Perfect. Yeah. So one thing you talked about at the conference that worked for
[00:07:01] software conference was kind of, you did an amazing introduction to how you felt AI was kind
[00:07:09] of got to change the way we work and it was a very, I feel like it was a much more honest
[00:07:16] representation of what the trajectory of this thing probably because you do have that technology
[00:07:22] knowledge and much more grounded. I feel like then some of the more has in the clouds,
[00:07:28] like oh, it's going to change everything like tomorrow kind of attitude people have for you.
[00:07:32] The way you presented it that it's going to be a very natural progression of tasks that they can do
[00:07:39] and then that's going to lead to more tasks kind of, et cetera, et cetera. And yeah, and one of the
[00:07:43] big things was it's going to take a big, take away repeatable work for humans and you kind of
[00:07:48] talked about how that could be like contract writing and other things like that. But for people
[00:07:54] don't really understand how that works, how would you best explain it to them? Like how like
[00:08:00] I think people have a rough idea like of AI because it's been kind of in the zeitgeist forever,
[00:08:05] but for someone who really doesn't understand it, how would you say it starts like how like
[00:08:12] you have it implemented? How is it collecting the knowledge? How is it actually putting it into
[00:08:18] to use sort of these like playing, playing which models that they usually use?
[00:08:23] Yeah. So certainly it all kind of comes back to training data, right? Like how much information
[00:08:30] can we give to these natural language models or these large language models who have a really
[00:08:36] intimate understanding of human language and interaction? And also we're trained on a very broad
[00:08:43] data set of publicly available information on the internet, right? So it's for our use cases,
[00:08:49] it's really about how can we integrate our data and what our expected outcomes are with some
[00:08:57] of those AI-based tools and how can we explain to a large language model through whether it's
[00:09:03] prompting or data integrations or how can we explain to a large language model what our expected
[00:09:09] outputs are and what we really want from that? So it kind of starts there with your initial
[00:09:17] training data or as well as you know, as well as what data you can provide
[00:09:23] to a large language model. There's been a couple of really interesting exercises that I use
[00:09:28] on a daily basis where people are like you know you'll give somebody a snapshot of
[00:09:33] something that's written by an AI and they'll go oh I know that's AI written but then you can
[00:09:37] take that and expand that and give it your for example your email history and you can show it how
[00:09:42] you write emails on a day-to-day basis, your cadence, your language, your you know how you sign
[00:09:47] off an email as as context for how it should write emails in the future and then it becomes
[00:09:53] almost indistinguishable for people, right? So it's all about how you use the tool and and
[00:09:58] that sort of that sort of context and it's just getting better at a alarming alarming rate
[00:10:04] all have to say. So a lot of that I guess comes out through just recognizing how patterns and
[00:10:10] like how we talk. Yeah, yeah how we write emails or how we just kind of produce like a human
[00:10:17] data that it gets fed into it? Yeah essentially yeah I would say that. And when it comes to
[00:10:25] time and attendance and you know more work force management type of data what are for a company
[00:10:34] that's you know looking to implement more AI into their time and attendance or course management
[00:10:40] kind of strategy. What would you say are the fundamentals that they need to kind of lock in because
[00:10:45] I'm assuming they need good data to begin with? One of the things you said at the conference was
[00:10:51] garbage garbage in garbage out is kind of how it works and that makes a lot of sense. You can't just
[00:10:56] kind of give anything just a bunch of data expecting it to kind of figure it out because it needs
[00:11:03] patterns to kind of kind of be an AI, I guess essentially. So what are the fundamentals that people need
[00:11:10] to kind of get together if they're thinking about that as the next step for their business?
[00:11:15] Yeah I would say good like you said it starts with good data but also good testing procedures right
[00:11:23] and good controls in place for your data and in your information and your security policies.
[00:11:30] One of the things that we talked about at the conference was over the past year how we've seen
[00:11:36] the organizations that we partner with modify their services agreements to say hey you can use
[00:11:41] AI on our projects or you can't use AI on our projects or you know implementing control gates
[00:11:47] for how we would potentially leverage AI based tools within an individual project. So it starts with
[00:11:54] good data and it ends with good testing and support and if you're looking to deploy this in a
[00:11:59] production environment and a good degree of caution I would say right like I know a lot of
[00:12:06] stakeholders without with limited understanding of the technology or trying to
[00:12:11] you know push it into sort of every possible nook and cranny of of HRIS technology and some areas
[00:12:19] really are just not you know not well suited you know you can't replace the human element of HR
[00:12:25] unfortunately so yeah that's where it kind of starts and finishes good data and good testing.
[00:12:32] You envision that's based off that do you envision that certain hearts of HRS
[00:12:40] you know software or procedures or whatever maybe are going to we're gonna see a lot more advancements
[00:12:45] a lot quicker than others. I would think so yeah and when it comes to AI based tools absolutely
[00:12:54] and I would I would hope so I think a lot of companies like SAP is introduced
[00:12:58] some some AI based chat bots and AI based tools like really really rapidly
[00:13:04] over the past over the past couple of months so I think that that's one of the
[00:13:10] really exciting things for me about AI is at the end of the day it's you know it's a tool right
[00:13:15] and the people who are going to make the best use of that tool are going to be the most
[00:13:21] successful right like it's a tool in the hands of you know our workforce that
[00:13:28] when it comes to for example like you know building something that you know I'm not asking an
[00:13:35] AI to necessarily perform a task I'm asking it to write scripts that help me perform a task right
[00:13:44] so I think over the next I'd be willing to bet over the next 12 months we'll see significant
[00:13:49] integrations into a lot of HR based tools some guarantee you are going to be rushed right
[00:13:56] are going to be missteps are not going to be as well utilized as as they had anticipated but
[00:14:01] I think we're going to see an even more of an explosion over the next 12 months of integration
[00:14:07] and to take the HR landscape as a full. Yeah 12 months is not a really long time so like we're
[00:14:12] looking at like this time next year you think the landscape is going to be
[00:14:16] fundamentally different or do you think it's kind of just going to be a similar trajectory that we're
[00:14:23] on where you know there's going to be a bigger bigger fish on the horizon I guess in terms of
[00:14:30] AI they have more and more promises. I think I think we're right now at risk of over saturation
[00:14:39] in terms of the tools in the market you know it's going to be AI for this and AI for that and
[00:14:43] AI for this and AI for that I think we're on a pretty steady trajectory right now like I think HR is one of
[00:14:49] those stuff one of those sort of areas of technology where we're dealing with personally
[00:14:53] identifiable information we're dealing with like compliance and and socks and all sorts of other
[00:14:57] regulatory frameworks that put a little bit more control around how we share employee data with
[00:15:04] these AI based tools and that's a big concern for a lot of organizations so I think steady trajectory
[00:15:10] but with the potential of of of over saturation I know a lot of people and a lot of tools
[00:15:17] sell AI features that since hey we got AI integrated and then it you know it does nothing it's like
[00:15:22] makes a list for you or something right anything can be AI is can be a very broad classification
[00:15:28] and it's a it's a buzzword right you look at marketing material and you're like oh LinkedIn AI and it's
[00:15:32] like what is it? I don't know um so yeah I think no sorry good no yeah I think like I said over saturation
[00:15:43] but definitely definitely steadily steady trajectory I would say and when it kind of when it comes to
[00:15:50] kind of what you guys are expert in which is time and attendance or in time in general
[00:15:57] where are you seeing the biggest promise right now? Yeah I'm hoping that it comes along with some
[00:16:08] of the stuff that we talked about in demo that at vision talking about sort of natural language
[00:16:17] integration with end user data and what I mean by that is you know empowering
[00:16:24] employees and managers within an organization to instead of relying on you know traditional training
[00:16:33] to find whether it's a report or where to look in the system for particular pieces of data
[00:16:39] being able to just ask and AI and say hey where is this data what is this data where can I find it?
[00:16:45] I think that's the real um key empowering piece within within time and attendance is you know empowering
[00:16:52] a manager or an employee to say hey you know how many times has Bob been late in the past week right?
[00:16:59] Rather than having to go and execute a report and pull out that data the system allowing
[00:17:04] that sort of natural language interaction with your data not just for your your end users from
[00:17:10] a system but also for for key for key stakeholders within the business right for our
[00:17:15] for our payroll administrators for our our our our C Suite being able to give them
[00:17:21] really quick couples and in snapshots of how things are going from an HR and payroll perspective
[00:17:26] and being able to answer their questions like that I think is really really promising.
[00:17:32] Do you think we're anywhere near the point of a river already here at Ilink of someone
[00:17:37] having like a chat GPT level kind of experience where like hey you know AI Bob
[00:17:43] I want a report of these 30 workers how like plus or how much how often they're on time not
[00:17:55] on time and who are in how does that relate to their performance?
[00:18:01] Are we at a point where you can just type that in an ask it and it's going to produce it for you
[00:18:04] and then it'll pick out the keywords enough to be able to produce that every there yet.
[00:18:10] I absolutely absolutely were there yet it's we're they're right now sorry.
[00:18:18] It's just about these organizations integrating that technology into their existing products
[00:18:23] right and that's where you know workforce is partnership with a couple of other larger
[00:18:28] organizations I think is going to drive them towards that sort of availability right like like
[00:18:35] a lot of larger organizations that work for software works with is like I feel like
[00:18:39] going to be pushing them in the direction of of AI based tooling and that's something that you can
[00:18:44] absolutely by essentially giving the AI like a toolbox right you can say hey
[00:18:50] you've got 10 tools in your toolbox if you want to go look up employees time sheets here
[00:18:55] here's how you use that tool and you and you use feedback from the AI to say hey if if it wants
[00:19:00] to go look for these 10 employees then that's given them that 10 employees behind the scenes right
[00:19:06] so I think we're there absolutely it's just about implementation.
[00:19:10] Yeah that's pretty exciting then because I feel like one of the things that's kind of
[00:19:15] we've been slowly leading up to you know is obviously simplification but also use ability
[00:19:21] with all this software because I you know looking at some of the
[00:19:25] industries that I look at in my research like you know a deskless workers especially you know they're
[00:19:30] not all software engineers you know some of them are doing some of these things at the end
[00:19:35] of a long physical shift or something let's say and it would be a lot easier for them to ask
[00:19:43] asking AI these things to do these basic kind of these repeatable admin tasks or just kind of very
[00:19:49] black and white admin tasks instead of having to sit there and produce it themselves and
[00:19:56] in terms of time savings or you know just general well I guess time savings is the best thing
[00:20:04] because I guess that will ultimately leave to kind of profit like you know return on investment
[00:20:07] for whoever's using it are we seeing leaps and bounds of that occurring yet or or as a kind of
[00:20:17] just a slow drip and we're gonna see it accumulate over time like are we seeing a massive jump
[00:20:23] in employ usability of some of these tools or is there still a bit of a learning curve just
[00:20:28] because it's not quite there yet for some for some aspects of it. I think with any technology
[00:20:36] like that there's going to be a learning curve right you have to you have to understand that
[00:20:40] there's going to be a portion of the workforce that's a little bit older than us that's a
[00:20:46] little bit less tech savvy that's that's going to take some time to get familiar with these type
[00:20:52] of type of new technologies right I don't know if you've ever you know shown GPD or any of these
[00:20:57] tools to your parents right like it's like oh they're like oh great it can tell me jokes and
[00:21:00] answer questions but there's always gonna be a you know a gradual increase in usability and user adoption
[00:21:10] but I think with proper training and engagement with an employee base and the bright sort of
[00:21:14] tool set I think we'll see leaps and bounds in terms of end user engagement and you know productivity
[00:21:21] and profitability obviously you know like it depends on the depends on the vertical right like
[00:21:26] if you're giving this to construction guys it's going to be a little bit less applicable than
[00:21:29] if you're giving it to you know somebody who's doing data analytics or you know at a at a a
[00:21:35] desktop development role or something like that it definitely depends on the it all depends on the
[00:21:40] end user and and the implementation so yeah that makes a lot of sense it's a lot of sense and
[00:21:48] I feel like I've begun to kind of think about it and thinking about it in the simplest terms possible
[00:21:53] I've kind of after we I saw you know we met later that night I was wondering I'm like I wonder
[00:21:59] how you could explain this to someone who doesn't really know his Google but doesn't really know
[00:22:04] how chat GPT works and essentially it is just kind of like in more advanced Google right now
[00:22:11] I think at the it the like at the entry level entry level understanding of it it's like you know
[00:22:17] Google replaced the actual librarian let's say or like the expert that you would have to go see
[00:22:26] in person or the ins like the video I guess yeah and now and now that's just expanded where you
[00:22:31] can ask not just one librarian or one expert you can ask almost the entire
[00:22:40] of a tire do you've all experts that ever existed I feel like for it kind of entering that
[00:22:44] not that we're not there yet with kind of in Google not that Google didn't bring us there but
[00:22:48] it's almost like you're able to convers with them essentially and it can collaborate at version of them
[00:22:54] yeah yeah I don't know if you've seen some of the the four-row model stuff that's where we're
[00:22:59] talking on the 14th here but there was some really interesting stuff that's been published in the
[00:23:03] last 24 hours by by OpenAI with a with a more recent model that came out that has some really
[00:23:11] really really interesting capabilities for being able to ingest visual data as well like live
[00:23:18] visual and video data from like your phone's camera and telling you you know what's going on
[00:23:23] in the room for like accessibility technologies or all sorts of other really interesting use cases
[00:23:29] so yeah do you think that would ever you know use the visual aspect of this you know I can
[00:23:35] see how they I can be used for you know someone clocking in or time and attendance but the
[00:23:40] physical in visual aspect of it that data for time and attendance do you think they'll ever be
[00:23:47] a use for it you know especially for people who don't work in traditional work environments like
[00:23:51] like in an hour and office it's a that's a that's a good question yeah you know I've thought about
[00:23:57] this quite a bit you know that's something that we like to chat about a lot internally or
[00:24:01] you know whether it's overbears or it's part of work like like what's the like pie in this
[00:24:06] guy is sort of possible scenario of of this sort of technology and you know that you've
[00:24:11] probably seen demos online or people you know tracking worker productivity with you know
[00:24:17] uh... machine learning tools that are like vision based machine learning tools to track
[00:24:21] worker productivity and stuff like that and I think there's definitely some really interesting
[00:24:25] use cases for or time and attendance in you know identifying user presence and stuff like that
[00:24:33] not necessarily open AI based but you know maybe some of the machine learning and and other
[00:24:42] image processing technologies that that will see some prevalence and I think there's some
[00:24:46] really interesting use cases there for just right identifying users but in terms of like a you know
[00:24:51] a time and attendance application for you know visual contextual vision I don't think there's
[00:24:56] I don't think there's going to be much um so yeah okay to get to the point where we you know
[00:25:03] like someone no longer needs to actually clock in it just sees okay Jim is locked in the work
[00:25:09] he's here that's the dream that's the dream you walk into work I mean you get you walk
[00:25:15] into work you get a pop up on your phone that says hey are you at work and you say yeah
[00:25:19] and it's quiet you know it clocks in for you you know you don't want to accidentally show up
[00:25:22] to work to pick something up after hours and have it clock you in right like there's a lot of
[00:25:27] I think little side gotcha cases or you know um where you want to be careful with that sort
[00:25:33] of thing but that would be super cool that would be my my uh my my end goal would be that would be
[00:25:38] awesome to have that sort of stuff implemented and I know a lot of organizations would love to
[00:25:43] have something like that to get rid of physical clocks because you you know you remove the
[00:25:48] possibility for buddy punching or for you know other things like that so I think that would be
[00:25:53] really cool there's a lot of gotchas but I think it would be really cool yeah or just simply
[00:25:58] forgetting to clock out I should like that's a very common one especially for people like
[00:26:03] I was a bartender my 20s and it's like people forgot to clock out all the time at the end of
[00:26:10] 10 hour shift or whatever and I guess for you know I know you talked about the compliance aspect
[00:26:16] of some of this I guess if you're like an union or whatever like a pipe fit or union or
[00:26:20] ever every minute you're on that site visually was inside of this it does become a little big
[00:26:25] brother asking but I guess and this will lead to me to find another question that I have
[00:26:29] later but I guess that could be used to help enforce compliance because it's like okay they were
[00:26:36] on I can they were there's no more need for human accountability it's like the eye in the sky
[00:26:41] it's kind of seen them there yeah and this shows exactly to the second how many minutes they were
[00:26:48] in this geo I guess you could do that with just geo tagging in general but it takes any sort it's just
[00:26:53] a one-way street at that point because it's just something's looking at them yep there's a lot
[00:26:58] of sensitivity around that sort of data especially especially like you say with unions so yeah and
[00:27:04] and one thing you did talk about as well which I want to talk about now is kind of how MSAs or
[00:27:10] MSAs or server security agreements are going to change to protect your personal information from
[00:27:16] AI because obviously it is scraping sometimes indiscriminately you know data and obviously we
[00:27:23] get into facial or fingerprints or whatever that's collected and then what happens with that
[00:27:29] obviously I think everyone has concerns about that right now because science fiction exists and
[00:27:35] we all know the quick and accurate so how is that you could be kind of kept inside the box
[00:27:43] and how are we going to prevent foul play I guess you could say and how is that going to change those MSAs
[00:27:50] that's the that's the million dollar question that I don't have a good answer for right like there's
[00:27:56] so many offerings in the landscape right now there's so many options
[00:28:02] and what you have to think about at the end of the day is these companies like open AI
[00:28:08] at the end of the day they're data their data their data companies right so
[00:28:13] it breaches our breaches of data are almost in an habitability these days right whether it's
[00:28:20] you know bad foreign actors or whatever right so it's about I think limiting
[00:28:27] data that would be risky if exposed to a exposed to the public in the realm that we work in
[00:28:35] and in time and attendance specifically like a lot of the times you know we don't we don't do stuff
[00:28:40] around pay rates we don't do stuff around employees you know home home address or home phone number
[00:28:44] we don't care about that we you know we care but when you show up to work and when you leave
[00:28:48] so the potential for exposing you know information beyond your first name last name your job
[00:28:55] which is typically publicly available information if you google somebody anyway
[00:28:59] and you know maybe when you're working not so much but not necessarily risky information but yeah
[00:29:04] something that you know you gotta consider is is how that's going to be exposed and
[00:29:10] it's going to be about how organizations engage and set up their policies and evaluate their pipelines
[00:29:15] for those type of integrations and up to them to determine their risk tolerance overall right
[00:29:20] and how how much data they want to be integrating with these sort of tools and going on
[00:29:28] to that compliance part of it you did mention or the at the conference I know I keep referencing
[00:29:33] it but it was a such a great talk the compliance aspect you know these are VAI is going to be
[00:29:43] a game changer in the way it kind of reviews documents and how quickly it can review documents
[00:29:50] yeah can you kind of explain in layman terms how that works and how it is kind of
[00:29:55] sifting through these documents you know compliance documents in general yeah yeah I didn't so the
[00:30:01] primary use case for us I think in terms of compliance documentation was not only like state
[00:30:08] and and specific legislature based compliance documentation but also like these unions and
[00:30:15] these collective bargaining agreement based documents and ensuring we we stay compliant to those so
[00:30:21] how that works is we typically provide a snapshot or whether it's a text extract or a
[00:30:30] dump of information to the AI and we ask you know we ask very specific point of questions or we
[00:30:38] look for very specific point of outputs from these compliance or from these pieces of
[00:30:42] legislature often there you know collective bargaining agreements or these legislative pieces of
[00:30:46] legislature are you know 100 100 pages long and taking that work way from us to be able to just
[00:30:52] pull out the information that is specifically applicable to time and attendance is where that sort of
[00:30:57] where where that sort of power lies as well as you know taking that piece of compliance documentation
[00:31:04] and combining it with some of our documentation and saying okay does any of our documentation
[00:31:08] from a compliance perspective not match what would be expected from a legislative perspective right
[00:31:15] so essentially you know uploading and uploading a PDF is sometimes it can be as simple as uploading a
[00:31:22] PDF well that's yeah same amount of time as well for people who are maybe not as legal savvy I
[00:31:31] imagine oh yeah oh yeah I a lot of contracts are written in very very weird and very complex ways
[00:31:37] so it can be it can be super super helpful and if AI review of these documents
[00:31:46] does become more commonplace do you do you imagine that there's going to be a bit of a flattening
[00:31:54] or a homogeneity that kind of emerges out of this that contracts are going to start all looking the
[00:31:59] same because the same machine tools are looking at them and producing kind of the same results
[00:32:05] so we have a bit of a leveling of the playing field perhaps or I think absolutely
[00:32:11] and that's a big concern with it in this sort of AI space right now is that
[00:32:16] the training data sets you know with some of these models that we have available to us right now
[00:32:21] the training data sets are based on you know publicly available data over the past couple of years
[00:32:26] and over the next four or five six years a lot of the publicly available data like you said
[00:32:32] may have been produced by an AI right so you're training an AI on AI produced data so you
[00:32:39] have this sort of you know at almost self-destructive nature where we're you know an AI can't
[00:32:44] differentiate between you know human and AI generated content so I definitely see us moving towards
[00:32:52] you know if it's a if we can get wider adoption this is probably five to ten years out
[00:32:56] but definitely a flattening in terms of a contract review language at the end of the day
[00:33:01] we're never replacing lawyers so they won't let us so so maybe some flattening but never never
[00:33:09] never too much lawyers need to keep their jobs yeah yeah though yeah it seems like if anything
[00:33:16] their jobs are going to be either greatly made easier or enhanced because I do wonder if it's
[00:33:21] going to come you know when we're going to have that level of comfortability where it's like
[00:33:25] I'm not going to be with the AI did you know with things like this where it is like you've been
[00:33:30] home and a contract millions or billions of dollars can be at stake which leads me to another question
[00:33:38] which is about kind of chat bots but I think you talked about them a bit
[00:33:42] at the conference and you know even from my personal experience maybe because we grew up with
[00:33:47] chat bots that were not that amazing uh there's a bit of like a I don't want to talk to this thing
[00:33:54] kind of mentality I still personally have especially when I see that thing pop up on the right
[00:33:59] hand corner of some websites yes it could be helpful but no I don't want to talk to it
[00:34:04] um how could AI increase just a kind of user enjoyment factor of speaking to a chat bot
[00:34:16] and how how do you think what are the steps of your kind of need to take the kind of make people
[00:34:22] have made that a more enjoyable experience for people so they are actually engaging with them
[00:34:27] and not just clicking the X on the chat bot yeah I think click in the X on the chat bot
[00:34:33] and then the ability for a lot of people right like that's like you said it's just kind of built
[00:34:36] in for me I hear that sound I can see that pop up and I'm like just just now um you know I think
[00:34:43] it's about showcasing the potential for these sort of chat bots right like it's at the end of
[00:34:49] the day it is it is taking work away from other people right like like end user agents if we're talking
[00:34:55] about you know actually chatting with a real person or you know making a phone call um I
[00:35:00] think it's about showcasing the potential for the technology but also you know maybe not moving too
[00:35:05] fast on that sort of thing on these AI based chat bots I know there was a story a couple
[00:35:10] months ago where I got on a I think it was a GM website gotten AI chat bot to promise him a free
[00:35:16] free car or something like that um so it's you know there's there's there's there's caches
[00:35:22] there's caveats with it and I think that um it's strength is going to be an understanding
[00:35:28] and user's questions a little bit better and being able to direct you to a place where you can
[00:35:34] get an answer but not necessarily responding to and resolving your your full query white yet um
[00:35:42] those that sort of AI based chat bot in like a website is one thing but then there is you know
[00:35:49] like that's kind of like an annoyance but something that's built into like a time and attendance
[00:35:53] tool I think would be really powerful um you know having it as an assistant instead of uh instead
[00:36:00] of a you know a prompt or an annoyance to you on any given website so hmm well I guess we can all hope
[00:36:09] for a better chat bot future right now if it's looking a little bleak but yeah this doesn't
[00:36:16] aid and thank you so much for joining me today it's been a great conversation and uh thank you
[00:36:23] I want you to tell people where they can find you if uh if they're able to reach out or what
[00:36:28] you got what you got going on or and anything exciting that new entities up to coming up in the next
[00:36:33] months yeah so not a whole lot that we're uh gonna be pushing out in the next couple of months
[00:36:38] but if you need to find us you can check us out at new entity hcm.com if you want to send us an email
[00:36:44] we're info at new entity hcm.com if you have any questions about anything we've talked about anything
[00:36:51] AI and HRIS related we're happy to have a conversation with you we're working on some cool tools
[00:36:58] internally that we're hoping to publish out to the public in the next six to 12 months
[00:37:03] for the HR landscape we're starting to develop our own tools so you know keep your your
[00:37:07] background and we may have some stuff published on LinkedIn but yeah check us out I love to
[00:37:12] chat with you. Oh well with that thanks everyone for tuning in and see you next time
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