Ep 9: Reimagining the Workforce to Drive the Evolution of Work with Richard Rosenow

[00:00:09] [SPEAKER_02]: Hello everyone, this is Bob Pulver. Welcome back to the Elevate Your AIQ podcast. In this episode

[00:00:15] [SPEAKER_02]: I'm joined by my friend Richard Rosenow from OneModel. Richard and I discuss people analytics,

[00:00:21] [SPEAKER_02]: talent intelligence, the integration of AI in HR, emphasizing the importance of upskilling,

[00:00:27] [SPEAKER_02]: responsible AI practices, and the evolving definition of the workforce.

[00:00:32] [SPEAKER_02]: We highlight the need for HR to proactively embrace AI to improve productivity and decision

[00:00:36] [SPEAKER_02]: making while addressing challenges like data hygiene and bias. The conversation underscores

[00:00:42] [SPEAKER_02]: the growing demand for AI talent and the future of HR tech involving AI driven workflows with what we

[00:00:47] [SPEAKER_02]: call agentic workflows, which we anticipate will drive significant value and innovation.

[00:00:53] [SPEAKER_02]: Stick around to hear Richard's insights on how to best elevate one's AIQ,

[00:00:57] [SPEAKER_02]: and I hope you enjoy this discussion. Thanks for listening.

[00:01:01] [SPEAKER_02]: Hi everyone, welcome to another episode of Elevate Your AIQ. I'm your host Bob Pulver,

[00:01:05] [SPEAKER_02]: with me today is my friend Richard Rosenow. Hey Richard.

[00:01:09] [SPEAKER_02]: Good to see you Bob. Hi everybody.

[00:01:11] [SPEAKER_02]: Thanks so much for spending some time with me and for our audience. There's, oh my god, there's

[00:01:17] [SPEAKER_02]: so much to talk about. You and I never have shortage of topics, but I thought we could just

[00:01:22] [SPEAKER_02]: kick off with just you just giving a bit of background about what you've been up to,

[00:01:27] [SPEAKER_02]: how you wound up where you are at OneModel and some of the extracurriculars that I know

[00:01:32] [SPEAKER_02]: you help others with like finding jobs in people operations and people analytics.

[00:01:36] [SPEAKER_01]: Absolutely, no I'd be happy to. It's always good to see you Bob, always good to catch up.

[00:01:41] [SPEAKER_01]: So my background, so way back when sociology got interested in like how do people come together,

[00:01:46] [SPEAKER_01]: why do people come together? And I've really been chasing that my whole career.

[00:01:50] [SPEAKER_01]: Had an early career in non-profit, quickly found my way to people analytics space and

[00:01:53] [SPEAKER_01]: got to see some of the like world's best people analytics teams. So I got to see GE Capital,

[00:01:58] [SPEAKER_01]: got to see Facebook, Uber, Nike, Argo AI and really learned a lot about a lot of different teams.

[00:02:06] [SPEAKER_01]: So got to see kind of like early workforce planning at GE, got to see Facebook's team go from 15 to

[00:02:11] [SPEAKER_01]: 150, got to lead teams at kind of Uber, Nike and Argo and really with a big focus on kind of data

[00:02:17] [SPEAKER_01]: and data platforms. That's always been an excitement for me in a space I really enjoyed.

[00:02:21] [SPEAKER_01]: So this role opened up here over here at OneModel. So I'm our VP of People Analytics

[00:02:25] [SPEAKER_01]: Strategy and that's a bit of a funny title. It's one that I think my MBA program loves because

[00:02:30] [SPEAKER_01]: I've got analytics and strategy, what's not to love but I do a lot of different things over here.

[00:02:34] [SPEAKER_01]: So I work horizontally, I get to talk to everybody about kind of what's going on in

[00:02:38] [SPEAKER_01]: people analytics but bringing that into the inside of the company. And then externally

[00:02:42] [SPEAKER_01]: I do a lot of our research, a lot of our community connections and I get to talk

[00:02:46] [SPEAKER_01]: about people analytics day in, day out and that makes me very happy and thrilled to be

[00:02:50] [SPEAKER_02]: here with you today Bob. Excellent, excellent. Now that's great. I think one of the, as I myself

[00:02:56] [SPEAKER_02]: pivoted into the sort of talent space, certainly part of that impetus was because I saw so much

[00:03:03] [SPEAKER_02]: opportunity in this space and obviously had to quickly learn a lot of different terminology,

[00:03:08] [SPEAKER_02]: talent, intelligence, people analytics, workforce analytics, what are all these

[00:03:12] [SPEAKER_02]: different sort of disciplines and why aren't more companies investing in some of these

[00:03:19] [SPEAKER_02]: areas? Why aren't some of these teams working more collaboratively together and I think today

[00:03:25] [SPEAKER_02]: I still ask those questions. Yeah, I think you've posted about this talent, these definitions and

[00:03:31] [SPEAKER_02]: when out getting into sort of terminology debates necessarily, when you think about that

[00:03:37] [SPEAKER_02]: landscape around talent, intelligence, people analytics, looking outside, looking inside

[00:03:44] [SPEAKER_02]: and how these things sort of crisscross, you give us like a TLDR, you know,

[00:03:48] [SPEAKER_01]: sort of version of where you see all that? Oh, totally. Yeah, I think it's funny because like

[00:03:53] [SPEAKER_01]: anyone coming into the space and knew I think that's the first battle they have to deal with

[00:03:57] [SPEAKER_01]: is just like what is everyone saying and why are they saying different things? And that's,

[00:04:01] [SPEAKER_01]: but that's part of like a decentralized movement is like we don't have like a FINRA or governing

[00:04:06] [SPEAKER_01]: body. We have a lot of people trying to figure this out and doing their best and because of that

[00:04:11] [SPEAKER_01]: we have a lot of different ways that people kind of came to this space. So I think I'm pretty laid

[00:04:15] [SPEAKER_01]: back when it comes to the like people analytics workforce analytics, HR analytics, talent analytics,

[00:04:19] [SPEAKER_01]: like a lot of that terminology. I think a lot of that is either nonsense or has a particular

[00:04:24] [SPEAKER_01]: reason but coalescing around people analytics has been healthy I think and we've seen a lot

[00:04:28] [SPEAKER_01]: of that in the past couple years where it tends to be a little bit more heavy in that direction.

[00:04:32] [SPEAKER_01]: I think though we have seen talent intelligence really start to pull away.

[00:04:36] [SPEAKER_01]: I think in no small part to Toby Kulsch has done a phenomenal job with the talent

[00:04:39] [SPEAKER_01]: intelligence collective. I mean he's got a book on talent intelligence just all the love for Toby,

[00:04:43] [SPEAKER_01]: what he does but I think when we start to think about those, I remember somebody telling me well

[00:04:48] [SPEAKER_01]: they're the same thing aren't they? And I'm like at some point words have to mean something

[00:04:53] [SPEAKER_01]: and I think a lot about analytics and intelligence in particular. When I think about analytics I

[00:04:58] [SPEAKER_01]: think about with the data we have let's make a decision. When I think about intelligence

[00:05:03] [SPEAKER_01]: I think about let's gather the information about the world and bring it back.

[00:05:07] [SPEAKER_01]: I think a lot of people have still have their own debates about that but that's how in my mind

[00:05:11] [SPEAKER_01]: those two slot together really well is talent intelligence people and analytics teams need

[00:05:17] [SPEAKER_01]: talent intelligence teams to bring them the information and get them the data and acquire

[00:05:20] [SPEAKER_01]: that and bring that back to make sense of it. I think some other people do internal,

[00:05:24] [SPEAKER_01]: external and there's still some debate on that too but at the end of the day I'd say

[00:05:29] [SPEAKER_01]: give yourself some grace if you're trying to figure out what these mean it's confusing

[00:05:32] [SPEAKER_02]: right now. How those teams even formed at the beginning may have guided how much of each like

[00:05:39] [SPEAKER_02]: you said some of it's really about intelligence gathering and sort of calling through a lot of

[00:05:45] [SPEAKER_02]: that data and making sense of it as opposed to doing interrogating the data yourself getting

[00:05:51] [SPEAKER_02]: your hands dirty so to speak manipulating the data doing scenarios and looking at those

[00:05:55] [SPEAKER_02]: large data sets across especially a big organization. So I think that has guided

[00:06:00] [SPEAKER_02]: you know and maybe bifurcated some of the the pools of where people are focusing.

[00:06:06] [SPEAKER_02]: I guess the way I think about it nowadays and I admit I'm doing a little bit of

[00:06:10] [SPEAKER_02]: sort of Monday morning you know quarterbacking here since I didn't grow up in HR or analytics

[00:06:15] [SPEAKER_02]: but it just seems like when you think about your talent life cycle and your global talent

[00:06:21] [SPEAKER_02]: ecosystem holistically and you think about the need to attract and retain the right people

[00:06:29] [SPEAKER_02]: you've got to look in a 360 degree view and you know however you need to

[00:06:37] [SPEAKER_02]: aggregate those insights and I know a lot of this but how do you pull all that disparate data

[00:06:41] [SPEAKER_02]: together and maybe come up with new insights that one particular person or small team

[00:06:47] [SPEAKER_02]: may not have uncovered and what are those do a collection of weak signals amount to a

[00:06:54] [SPEAKER_02]: stronger you know signal that we actually have to take action upon or I just think there's so much

[00:07:00] [SPEAKER_02]: to pay attention to that you've got to start to pull pull that together even if the teams are

[00:07:05] [SPEAKER_01]: day-to-day operating. Yeah absolutely and it's funny because I think within a single team

[00:07:11] [SPEAKER_01]: you have there's the work you have to do and then there's how you talk about that work

[00:07:15] [SPEAKER_01]: day-to-day a lot of times you just have to do the work like you just have to get it done

[00:07:18] [SPEAKER_01]: whatever it is like whether it's labor market internal external analytics like at some

[00:07:23] [SPEAKER_01]: point you're just an HR person doing work and it's a lot more clear I think it's when we try to

[00:07:27] [SPEAKER_01]: like look across companies and we try to benchmark and we try to talk about this publicly that's where

[00:07:32] [SPEAKER_01]: people get into these like holy wars over the names of things or but it is it's helpful to have

[00:07:36] [SPEAKER_01]: common names because you can find each other and I think that's been one of the most difficult

[00:07:41] [SPEAKER_01]: things about the the job market for this space is for people that are trying to break in and

[00:07:46] [SPEAKER_01]: say hey I want to do data and I want to do HR and I know what my company might have called it

[00:07:50] [SPEAKER_01]: before but how do I find those jobs at other companies I think that's been the most difficult

[00:07:55] [SPEAKER_01]: thing is that it creates a real barrier in the labor market but again I keep coming back to like

[00:08:00] [SPEAKER_01]: giving people some grace on this I think about HR teams that are hiring their first people analytics

[00:08:04] [SPEAKER_01]: person and they don't have the experience of being part of a people analytics team and now

[00:08:09] [SPEAKER_01]: they've got to figure out the name for this thing and so I think half of that too comes out

[00:08:12] [SPEAKER_01]: of that just like HR teams are trying their best they're creating a role that they hope will

[00:08:16] [SPEAKER_01]: work to kind of drive things forward and then again you kind of figure it out on the ground

[00:08:20] [SPEAKER_02]: once you're actually in the company right right for people who haven't spent a lot of time in

[00:08:26] [SPEAKER_02]: either analytics or AI or automation it all just sounds like you know magic maybe or

[00:08:35] [SPEAKER_02]: you know non-human you know things doing stuff like they don't they're still getting their

[00:08:41] [SPEAKER_02]: arms around you know what this all means and you know maybe they missed the whole

[00:08:45] [SPEAKER_02]: you know RPA process automation kind of wave where we did try to simplify you know processes

[00:08:52] [SPEAKER_02]: and automate you know steps of a workflow and things like that I do think when we think about

[00:08:58] [SPEAKER_02]: how or what kinds of solutions to deploy against a specific you know business opportunity

[00:09:04] [SPEAKER_02]: business challenge or when we think about how to sort of upscale and reskill people

[00:09:10] [SPEAKER_02]: with you know these days a lot of it around generative AI I do think terminology does

[00:09:16] [SPEAKER_02]: matter because you could wind up spending spinning your wheels and learning things that

[00:09:21] [SPEAKER_02]: you don't necessarily need to know and I think there's just too much out there to not have some

[00:09:26] [SPEAKER_02]: guidance of sort of what to focus on that's relevant to your you know career trajectory

[00:09:34] [SPEAKER_02]: your professional development and the work that you need to get done today and that

[00:09:39] [SPEAKER_01]: your manager expects you to get done yeah no it makes a ton of sense and it's funny because the

[00:09:43] [SPEAKER_01]: the generative AI that's come out in the past kind of two years here has such a gravity to it that

[00:09:48] [SPEAKER_01]: it's almost like pulled all of the AI conversation into that and it's funny because like we we've

[00:09:55] [SPEAKER_01]: had a tool as part of the one model toolkit for a long time called one AI which is this

[00:09:59] [SPEAKER_01]: we've had to start calling it traditional AI sometimes where the end which is almost

[00:10:03] [SPEAKER_01]: like a funny thing too there's like there's old school AI then there's generative AI

[00:10:06] [SPEAKER_01]: whatever it might be but it's this like the predictive algorithms the classification algorithms

[00:10:10] [SPEAKER_01]: these different pieces that didn't quite get into the public imagination in the same way

[00:10:15] [SPEAKER_01]: and that's almost that there's like a more obscure predictive AI that's a little difficult

[00:10:20] [SPEAKER_01]: and once you understand it there's really powerful things that you can do there

[00:10:23] [SPEAKER_01]: and then the interesting thing I think about generative AI is it's one of the first tools

[00:10:27] [SPEAKER_01]: that kind of came with a help manual because like you can ask chat gpt what is chat gpt and

[00:10:32] [SPEAKER_01]: answers you it's a tremendous tool to kind of learn about the space and drive that

[00:10:37] [SPEAKER_01]: what's funny though is a lot of the legislation is going after both

[00:10:41] [SPEAKER_01]: and I think this has been an interesting thing too is like because a lot of people have been

[00:10:45] [SPEAKER_01]: doing predictive or classification especially around things like resumes or ats and they've

[00:10:50] [SPEAKER_01]: been getting away with things maybe they shouldn't be getting away with from an algorithm perspective

[00:10:54] [SPEAKER_01]: and suddenly generative comes out and every legislator is moving we're going to see some

[00:10:59] [SPEAKER_01]: actual legislation come through regulation come through and it's going to capture everything

[00:11:02] [SPEAKER_01]: because that we know it's not going to be specific about that so I think there's been some really nice

[00:11:06] [SPEAKER_01]: education in the past couple years about this whole space and as much as I would like more

[00:11:11] [SPEAKER_01]: name definition I'm glad that in general people are like okay this is a thing it's real it's happening

[00:11:18] [SPEAKER_01]: let's take care of people in the right way as we're working with these tools yeah no I've

[00:11:21] [SPEAKER_02]: noticed the same thing and when I started learning and getting deeper into like the

[00:11:27] [SPEAKER_02]: responsible AI space especially when it came to like things that are auditable within you know talent

[00:11:33] [SPEAKER_02]: acquisition for example you know it was interesting sort of balance because yes I was trying to keep

[00:11:39] [SPEAKER_02]: up with everything going on with generative AI but generative AI at least at the time

[00:11:44] [SPEAKER_02]: was not the type of AI to your point that was actually subject to an audit it was something

[00:11:49] [SPEAKER_02]: that was doing up the stack ranking and it was like probabilistic and it was

[00:11:54] [SPEAKER_02]: predictive AI I don't know if that's you know synonymous with traditional AI but certainly

[00:11:59] [SPEAKER_02]: there was this whole generation at least one generation of AI and you know of course my

[00:12:04] [SPEAKER_02]: my brain immediately goes to Watson because I saw I was at IBM and Watson came out of the labs and

[00:12:11] [SPEAKER_02]: I saw it being developed and I saw all the different capabilities that it had and

[00:12:17] [SPEAKER_02]: the APIs that were made available that leveraged that technology and so

[00:12:22] [SPEAKER_02]: but you're right nowadays it's and responsibly AI is a lot more than just you know legal

[00:12:29] [SPEAKER_02]: you know protections and what's auditable it's it's the whole you know life cycle and being

[00:12:34] [SPEAKER_02]: responsible by design so everyone that touches it and now everyone that uses it is also potentially

[00:12:40] [SPEAKER_02]: a builder you know building your own custom GPT or you're going into Amazon and using

[00:12:45] [SPEAKER_02]: you know party rock to create your own agent or Microsoft create your own co-pilot whatever

[00:12:50] [SPEAKER_02]: and so the concept of responsible AI takes on more meaning for more people as more people touch it

[00:12:57] [SPEAKER_02]: in different ways but I think one of the things that is important you know going back to if this

[00:13:03] [SPEAKER_02]: is affecting everyone how do you sort of upskill yourself and just the things that you need to

[00:13:08] [SPEAKER_02]: know you know all the terminology kind of aside what's the best way to get this this work

[00:13:14] [SPEAKER_02]: done and maybe it's an actual AI solution maybe it's basic you know automation and maybe it's

[00:13:20] [SPEAKER_02]: something else I mean don't just assume that AI is here and therefore that's my new hammer and

[00:13:25] [SPEAKER_02]: everything's a nail right so I think just think we've got a temper some expectations and

[00:13:31] [SPEAKER_02]: that's part of making sure people know what to what to do with it and when and how to use it

[00:13:36] [SPEAKER_01]: yeah upskilling is a really good conversation too because I think there's a lot of people

[00:13:40] [SPEAKER_01]: that are feeling worried that they're getting left out and we're at an interesting moment though

[00:13:45] [SPEAKER_01]: that enough is changing fast enough that if you upskilled six months ago you might be out of date

[00:13:51] [SPEAKER_01]: which is also very tough without fastest moving but I think we're at a point where

[00:13:56] [SPEAKER_01]: folk in HR if you haven't been looking at this yet you really should and especially when it

[00:14:00] [SPEAKER_01]: comes to the kind of like agent-based workflows and how some of the applications are finally

[00:14:05] [SPEAKER_01]: coming out that you could start to use because at the end of the day like you don't

[00:14:08] [SPEAKER_01]: have to learn how to build an LLM you don't have to learn how to build one of these tools from scratch

[00:14:12] [SPEAKER_01]: you have to learn how to use them and I don't think we were ready to use them before before

[00:14:17] [SPEAKER_01]: pretty recently here so HR if you were on the fence are kind of worried about it say like hey

[00:14:22] [SPEAKER_01]: it's a good time to start dipping your toe in start trying to make use of this start trying

[00:14:25] [SPEAKER_01]: to bring it into your day-to-day usage because the the tools are getting strong enough and

[00:14:29] [SPEAKER_01]: they're starting to become more available and yeah it's a good time to start to up skill right

[00:14:34] [SPEAKER_02]: now as you talk to companies are they doing enough to up skill their own workforce or are they

[00:14:42] [SPEAKER_02]: are they waiting for a strategy or they embracing this or do they still are they still not sure

[00:14:49] [SPEAKER_01]: how to up skill workforce it's a really good question I think part of my answer is going

[00:14:55] [SPEAKER_01]: to be definitely guided by who I talked to so I talked to a lot of people in analytics teams

[00:15:00] [SPEAKER_01]: and I talked pretty specifically to people analytics teams it's rare that I actually catch

[00:15:04] [SPEAKER_01]: up with an hrvp and maybe that's something I should reflect on but I'm thinking about

[00:15:08] [SPEAKER_01]: people in analytics I think it's been harder to start to use for direct application because of

[00:15:14] [SPEAKER_01]: some of these kind of like hallucinations or guardrails and ethics that need to be put in

[00:15:18] [SPEAKER_01]: place I think some of the the systems are just getting there like we've got our our chatbot

[00:15:23] [SPEAKER_01]: and beta we've got some tools that are rolling out to our users kind of across the one

[00:15:26] [SPEAKER_01]: model side but I think about uh people in analytics we still have to do our jobs

[00:15:30] [SPEAKER_01]: which is a lot of this like understand assess be thoughtful about and create hypothesis and

[00:15:35] [SPEAKER_01]: track what's going on across the workforce as much as AI is making a difference there I think

[00:15:40] [SPEAKER_01]: we're seeing a little bit more applications in uh call centers scaled operations L and D

[00:15:47] [SPEAKER_01]: recruiting where you see a lot of this like text that was being generated before is now

[00:15:52] [SPEAKER_01]: we can move a little bit faster through text or whether that's ingesting and thinking

[00:15:55] [SPEAKER_01]: about text or creating text so I think HR teams uh it's a good question about how they're doing I've

[00:16:01] [SPEAKER_01]: heard a couple trainers that are making pay on this that they're they're doing a lot of trainings

[00:16:05] [SPEAKER_01]: on how to do generalized AI I'm uh I'm a little nervous for some of those because there's a

[00:16:10] [SPEAKER_01]: there's a bit of a cottage industry that's really sprung up and it's hard to tell fact from

[00:16:14] [SPEAKER_01]: fiction sometimes but um it's definitely in full swing that people are starting to talk about

[00:16:19] [SPEAKER_01]: getting trained whether full HR functions are trained somewhere I think that's a good

[00:16:23] [SPEAKER_02]: question still yeah I mean I think I think back to some of the programs that IBM had put in place

[00:16:29] [SPEAKER_02]: to try to upscale everyone around what does this AI future really really mean for us right and so

[00:16:34] [SPEAKER_02]: we've got to really understand some of the basics but then how could you put this into practice

[00:16:39] [SPEAKER_02]: and I think uh you know so some of that was oh let's let's hear your ideas and if you think

[00:16:43] [SPEAKER_02]: it's something that could turn into something for real it's not just a pie in the sky thing

[00:16:48] [SPEAKER_02]: I mean be creative and don't be too you know conservative in your assessment let's just let's

[00:16:55] [SPEAKER_02]: just hear your ideas and if you think it has legs let's try to put a team around it and build

[00:16:59] [SPEAKER_02]: see if we could build it and then if you do that let's see if we could get some you know we

[00:17:03] [SPEAKER_02]: could crowdfund it and then if we can crowdfund it maybe we can you know put you through a

[00:17:09] [SPEAKER_02]: shark tank kind of exercise and then see what comes out the other side but like you've got a

[00:17:15] [SPEAKER_02]: experiment and it's not just um about experimenting as an individual because I feel like that's been a

[00:17:24] [SPEAKER_02]: big focus of late of conversations like look at our individual you know productivity gains and then

[00:17:31] [SPEAKER_02]: and just extrapolate that to you know if our average consultant is you know 30 percent more

[00:17:37] [SPEAKER_02]: productive and we've got 100 consultants or whatever look all the time we've saved or

[00:17:41] [SPEAKER_02]: whatever but I don't know if everyone has really thought enough or has moved up the sort of value

[00:17:50] [SPEAKER_02]: curve to look at you know team productivity and and even maybe i'm looking past productivity right

[00:17:57] [SPEAKER_02]: productivity I feel like a lot of that ties to automating things as opposed to augmenting

[00:18:05] [SPEAKER_02]: how our brain works either an individual's you know human intelligence or collective

[00:18:12] [SPEAKER_02]: intelligence but how do we do more on the value creation side in terms of the

[00:18:19] [SPEAKER_02]: not just productivity but how do we make better decisions I guess is one way to

[00:18:24] [SPEAKER_02]: think about it how do we inject like how do you really get people to think broader than just

[00:18:32] [SPEAKER_02]: elevating the individual because it's almost like and I want to tie this back to what you said before

[00:18:37] [SPEAKER_02]: about like workflows and stuff like that if you add AI to one task but you leave everything else

[00:18:43] [SPEAKER_02]: alone have you done anything uh have you actually made a significant improvement in that end to end

[00:18:51] [SPEAKER_02]: workflow because now you know if one person one cog is now moving at 10x but the other cogs are

[00:18:59] [SPEAKER_02]: moving at the original speed that doesn't sound it sounds like something's going to break

[00:19:06] [SPEAKER_01]: yeah I think you're onto something with the individual worst team and I

[00:19:10] [SPEAKER_01]: want to think a lot about recently is that uh the apple event so apple announced a lot of new

[00:19:14] [SPEAKER_01]: kind of AI overlays and kind of consumer based AI tools I think that's been a big ripple

[00:19:20] [SPEAKER_01]: effect to the workplace because suddenly every worker that has an apple device has a AI

[00:19:26] [SPEAKER_01]: experience that they've had so whether or not you've gone to chat gpt or gemini or these other

[00:19:30] [SPEAKER_01]: tools that are out there that was kind of up to chance before but this sort of rollout that's

[00:19:34] [SPEAKER_01]: happening across the consumer experience we'll definitely see that bleed into work which says

[00:19:39] [SPEAKER_01]: like well I can do this on my phone why can't I do this with workday or how can I do this

[00:19:43] [SPEAKER_01]: on my phone why can't I do this with Oracle and um I think those companies will start to see

[00:19:46] [SPEAKER_01]: that pressure from the consumer space towards the kind of HR tech space so we could question

[00:19:51] [SPEAKER_01]: about like kind of team productivity and team connection before we move on I need to let you

[00:19:57] [SPEAKER_00]: know about my friend Mark Pfeffer and his show people tech if you're looking for the latest

[00:20:03] [SPEAKER_00]: on product development marketing funding big deals happening in talent acquisition HR HCM

[00:20:11] [SPEAKER_00]: that's the show you need to listen to go to the work to find network search up people tech

[00:20:17] [SPEAKER_00]: Mark Pfeffer you can find them anywhere I think you see a little bit of that with some of like the

[00:20:24] [SPEAKER_01]: sales enablement solutions which is also a funny thing like people analytics a lot of times

[00:20:29] [SPEAKER_01]: sales enablement stays a little bit separate I don't know why that is and that's something

[00:20:32] [SPEAKER_01]: I want to dig in a little bit more because some of the best people analytics projects around kind

[00:20:35] [SPEAKER_01]: of sales and sales understanding but sometimes sales have their own teams that do that with

[00:20:39] [SPEAKER_01]: like I'm thinking about tools like gong or hockey stack or hockey stick for marketing

[00:20:46] [SPEAKER_01]: some different like AI enabled workflow kind of in the flow of work of that tech system

[00:20:51] [SPEAKER_01]: I think it's another kind of motivation for HR which is like hey if you don't move quick

[00:20:56] [SPEAKER_01]: here to understand what's going on and understand your workforce as it relates to

[00:21:00] [SPEAKER_01]: the augmentation that's happening it's going to start happening in pockets outside of your

[00:21:03] [SPEAKER_01]: vision and so as we think about these different teams whether that's developer experience or

[00:21:08] [SPEAKER_01]: sales enablement or call centers or workforce management that may not sit in HR

[00:21:14] [SPEAKER_01]: they are all getting aggressively interested in this space and if HR doesn't step up to that

[00:21:21] [SPEAKER_01]: and say hey I'm going to lead here this is who we want to be this is where we're going

[00:21:24] [SPEAKER_01]: this is how we're going to treat our people I think we've seen historically the business

[00:21:28] [SPEAKER_02]: units will run with that too yeah unlike the workflows stuff you know if you're

[00:21:33] [SPEAKER_02]: going to make a big impact you've got to have these these agents deployed in a logical

[00:21:37] [SPEAKER_02]: fashion maybe even connect to each other and that may crisscross you know different types of

[00:21:43] [SPEAKER_02]: solutions so you know you mentioned Apple and then you know if someone's bringing their Apple device

[00:21:48] [SPEAKER_02]: into you know an organization that's you know a Microsoft shop and you've got a co-pilot I mean

[00:21:55] [SPEAKER_02]: could someone technically build an agent well I know Apple's you know still the ink is still

[00:22:01] [SPEAKER_02]: wet on the Apple intelligence announcements but it just I started thinking about like what

[00:22:07] [SPEAKER_02]: does this mean so you've got a team and maybe some of those team members are Apple people

[00:22:12] [SPEAKER_02]: and maybe some are Android and then some are on Windows laptops and some are on MacBooks and

[00:22:18] [SPEAKER_02]: like how do you like will these different agents and co-pilots and GPTs like they be able to

[00:22:26] [SPEAKER_02]: talk to each other and connect with each other I mean I don't even know how that would work

[00:22:31] [SPEAKER_01]: yeah well what's coming to mind is actually building on that it's

[00:22:36] [SPEAKER_01]: what I'm grappling with is how this forces us to redesign our understanding of what a worker is

[00:22:43] [SPEAKER_01]: and what a workforce is because I think workers historically were contained to humans you can get

[00:22:49] [SPEAKER_01]: that kind of like worker to belly button count in the kind of descriptive analytics as much as

[00:22:54] [SPEAKER_01]: like RPA was trying it was it was not quite there in terms of like this sophistication

[00:22:57] [SPEAKER_01]: of the technology to really elevate to that disruptive level to what does it need to have

[00:23:01] [SPEAKER_01]: a workforce but as soon as work starts getting done by a lot of these agents and systems and

[00:23:08] [SPEAKER_01]: humans kind of augment or they augment humans whatever it might be a kind of reckoning that HR

[00:23:14] [SPEAKER_01]: is going to have to deal with is this sort of who supports work and workers and who makes

[00:23:20] [SPEAKER_01]: sure that workers can collaborate and I see that broadly as worker and not humans because

[00:23:25] [SPEAKER_01]: at the end of the day like what you're talking about is like if chat gbt and Gemini aren't playing

[00:23:29] [SPEAKER_01]: nice in the workplace like how do we make them talk to each other and like that we know how to

[00:23:33] [SPEAKER_01]: solve that with people you sit him down you coach him and you say play nice and be be nice to your

[00:23:38] [SPEAKER_01]: coworkers it'll be it'll be kind of funny to see this like um agent-based coaching where

[00:23:44] [SPEAKER_02]: you're actually coaching the agents maybe I could see someone making a really funny comedy about

[00:23:52] [SPEAKER_02]: this oh yeah with our with a bunch of digital sort of digital twins acting like

[00:23:58] [SPEAKER_01]: cut this part out we'll make the screenplay bob that's our next move yeah there's definitely

[00:24:04] [SPEAKER_02]: something there back on the um like the upscaling piece I mean I just feel like HR we talked about

[00:24:12] [SPEAKER_02]: this the other day like when we talk about upscaling you know we're not we're not asking

[00:24:18] [SPEAKER_02]: people to become like data scientists or you know AI software developers or whatever it's

[00:24:25] [SPEAKER_02]: I think it's simpler than that I think we're there's so much going on that I feel like

[00:24:30] [SPEAKER_02]: there's everyone starting to feel overwhelmed and wherever they are and the you know AI is

[00:24:36] [SPEAKER_02]: coming from my job versus yeah I can do all these amazing things or somewhere in between

[00:24:42] [SPEAKER_02]: it just seems like to get started and to learn and to be to get to like an intermediate level

[00:24:49] [SPEAKER_02]: of working with AI and learning how to use it like the learning curve is not as drastic as

[00:24:57] [SPEAKER_02]: some of these other disciplines right and so um I think you made you know comment around

[00:25:02] [SPEAKER_02]: like HR like this is a prime opportunity they're their average users are not necessarily

[00:25:08] [SPEAKER_02]: the most tech savvy group but but you can see so clearly where some of the advantages might be

[00:25:16] [SPEAKER_02]: on top of the fact that you know HR teams you know many in HR already think about you know

[00:25:22] [SPEAKER_02]: compliance and you know being human centric and things like that it just seems like there's a

[00:25:27] [SPEAKER_02]: really really amazing opportunity for them to to start experimenting for it for themselves

[00:25:32] [SPEAKER_02]: and then extrapolating the value uh you know to the rest of the organization yeah I totally agree

[00:25:38] [SPEAKER_01]: I think HR has got a great opportunity to be a leader here especially because like a lot of the

[00:25:44] [SPEAKER_01]: things that happen like let's take prompt engineering for a second if you look at like how to write a

[00:25:48] [SPEAKER_01]: good prompt you say like okay here's here's what you're supposed to do here's some examples of

[00:25:52] [SPEAKER_01]: how what good looks like here's how I want you to respond and please give me this result

[00:25:58] [SPEAKER_01]: or whatever it might be like a lot of these different prompt guides as you start to look

[00:26:01] [SPEAKER_01]: at them you start saying hey that's actually a really good way to write a job description

[00:26:05] [SPEAKER_01]: if you can tell someone exactly what they're supposed to be doing if you give them clear

[00:26:08] [SPEAKER_01]: guidelines if you give them clear goals if you tell them what success looks like they're going to

[00:26:12] [SPEAKER_01]: be really good at their jobs and so what's funny is actually the method of interacting with a

[00:26:16] [SPEAKER_01]: lot of these tools is the one that HR is excellent at HR might be the best in the company in

[00:26:21] [SPEAKER_01]: terms of like articulating jobs to humans and that's really what the LLM needs a lot of the

[00:26:26] [SPEAKER_01]: time is that way to articulate what is it you're supposed to do in human language in natural

[00:26:29] [SPEAKER_01]: language and so this ability for HR with has that kind of like compliance mindset too and that ethics

[00:26:36] [SPEAKER_01]: mindset and the kind of like how do workers kind of get jobs done the upskilling path might be a

[00:26:42] [SPEAKER_01]: lot smaller than it was for people analytics I think this sort of like data mindedness in

[00:26:47] [SPEAKER_01]: this data education we've been trying to do and data literacy we've been pushing has been

[00:26:51] [SPEAKER_01]: helpful and a lot of HR has gotten there now but it was still a break from like hey my my

[00:26:56] [SPEAKER_01]: core job is working with humans in a very human way now I've got to go work with data this move

[00:27:01] [SPEAKER_01]: to AI is going to be very similar interactions almost to what HR has been really good at so I

[00:27:07] [SPEAKER_01]: think what's going to be funny is this sort of shift from HR feeling like they've got a somehow

[00:27:12] [SPEAKER_01]: upskilling something that it's not who they are too maybe something more natural in terms of the

[00:27:16] [SPEAKER_01]: way it interacts so I'm if anyone's listening to this and hasn't really dipped a towing yet

[00:27:20] [SPEAKER_01]: like I'm really cheering y'all I'm like go on to chat you go on Gemini go check one of these

[00:27:24] [SPEAKER_01]: things out start to play with it and I think you're going to be surprised at how quickly you can

[00:27:29] [SPEAKER_02]: enable and apply some of the HR skills that you've hold yeah no that's excellent perspective I think

[00:27:35] [SPEAKER_02]: that you know and I don't think there's any one particular role that's necessarily that needs

[00:27:44] [SPEAKER_02]: to be like the you know the gatekeepers or the people that sort of turn around and learn

[00:27:49] [SPEAKER_02]: it and then turn around and teach the teachers or whatever I mean I think anyone can step up

[00:27:52] [SPEAKER_02]: and and take on that sort of early adopter even you know change engine you know evangelist kind of

[00:28:00] [SPEAKER_02]: role whether you're an HRBP which is probably a great one but if you're involved in talent

[00:28:06] [SPEAKER_02]: acquisition or you talk to hiring managers or whatever like you said I mean you're having

[00:28:12] [SPEAKER_02]: these sort of natural human-like conversations as you as you prompt it and sort of nudge it

[00:28:19] [SPEAKER_02]: to get to what you want not not in a trying to influence it kind of way but just in a more

[00:28:25] [SPEAKER_02]: in a collaborative you know can you help me you know assimilate this this information or can you

[00:28:31] [SPEAKER_02]: help me sort of pivot you know the way that this job description you know reads to something else I

[00:28:37] [SPEAKER_02]: don't know there's a very it's a very natural interaction just the interface itself that

[00:28:43] [SPEAKER_02]: would allow you to you know be be more effective and I think the quicker you get started you know the

[00:28:49] [SPEAKER_01]: better off everyone is yeah and I think we'd be remiss not to say like spend some time upscaling on

[00:28:55] [SPEAKER_01]: the limitations too so like understand what hallucinations mean how they happen uh how to

[00:29:00] [SPEAKER_01]: look out for things I think spending some time to figure out kind of what should be AI and what

[00:29:05] [SPEAKER_01]: should be human like when I think about hallucinations they're one of the funny things

[00:29:09] [SPEAKER_01]: like if you really pressed a human to give you an answer to something they didn't know about

[00:29:13] [SPEAKER_01]: they might make something up and like very similarly if you press one of the little

[00:29:16] [SPEAKER_01]: ones to do something they don't know it might make something up and so even that I think HR is more

[00:29:21] [SPEAKER_01]: accustomed to like our our subject matter like is much more fluid and flexible than a lot of

[00:29:26] [SPEAKER_01]: other functions in terms of what truth is and how to figure out what truth might be within the

[00:29:30] [SPEAKER_01]: business so I think we're primed to kind of look for those kind of like hallucinations and pieces

[00:29:35] [SPEAKER_01]: but um it's definitely a like like educate yourself on some of the risks

[00:29:39] [SPEAKER_01]: I think that's a really big one and then I think where where HR could really stub a toe I think is

[00:29:46] [SPEAKER_01]: where if you used it for things that really shouldn't be used for which is like when like really deep

[00:29:50] [SPEAKER_01]: human connection is needed and I think we have a lot of that in our jobs which will stop us from

[00:29:55] [SPEAKER_01]: being automated uh for a long time which I think I'm grateful for within the HR domain

[00:29:58] [SPEAKER_01]: but that's sort of like what does human connection mean what is meaning what does it

[00:30:03] [SPEAKER_01]: mean to have purpose at work those things that it's really important to have a human behind it

[00:30:08] [SPEAKER_01]: keep that in mind as you guys are kind of rolling that out but I think as much as you can pulling

[00:30:12] [SPEAKER_01]: in and getting involved is a great idea just being cautious is still important yes I agree

[00:30:19] [SPEAKER_02]: hallucinations I mean everybody's got a friend who's like this kind of know it all right so you

[00:30:24] [SPEAKER_02]: asked him the question and you don't think they have they would have the answer but they

[00:30:28] [SPEAKER_02]: gave you something and you're just like yeah okay well you're not exactly you're not a doctor

[00:30:33] [SPEAKER_02]: remember right so there's definitely some of that and and there might be you know bias there just like

[00:30:39] [SPEAKER_02]: there's you know human you know bias in the way that we you know either choose to give an answer or

[00:30:46] [SPEAKER_02]: in the way that we answered a particular question um you know the appearance of knowledge is not

[00:30:50] [SPEAKER_02]: the same as having expertise so I think using it with a with a critical eye you know it's not

[00:30:57] [SPEAKER_02]: a calculator and just really understanding where some of its limitations are I think is

[00:31:02] [SPEAKER_02]: is important and that ties to some of um you know what we always talk about when we talk about

[00:31:08] [SPEAKER_02]: you know upscaling it's not just a matter of writing better prompts or you know learning

[00:31:13] [SPEAKER_02]: some specific aspects of the different you know generative AI tool but understanding that there's

[00:31:19] [SPEAKER_02]: there's a we're all when it comes to responsible I were we're all responsible

[00:31:23] [SPEAKER_02]: and again to your point before this is HR's you know bread and butter is making sure we're

[00:31:29] [SPEAKER_02]: making human-centric decisions making sure we're making you know keeping fairness in mind

[00:31:35] [SPEAKER_02]: and then of course you know transparency and explainability and all those things

[00:31:40] [SPEAKER_02]: when you think about just tying that to the the theme of the the podcast here when you

[00:31:48] [SPEAKER_02]: think about all these things and you think about the concept of an AI queue

[00:31:53] [SPEAKER_01]: what comes to mind when you when you hear that yeah it's a good question right I think about

[00:31:59] [SPEAKER_01]: you have to know what it can do and what it can't do I think that that's really important I think

[00:32:04] [SPEAKER_01]: knowing kind of like use cases and then when to stop we've touched on that a little bit

[00:32:08] [SPEAKER_01]: I think another one we didn't touch on quite as much is what goes into it so I think about

[00:32:13] [SPEAKER_01]: a educational space that uh I think HR might have avoided for a little while it's just like

[00:32:19] [SPEAKER_01]: where did your data come from and what does it mean to create data and what data was fed

[00:32:23] [SPEAKER_01]: into this machine because there's that sort of um I picture like a schoolhouse rock that like

[00:32:28] [SPEAKER_01]: I'm just a bill on Capitol Hill like and then it walks through how a bill is created we've got to do

[00:32:33] [SPEAKER_01]: that at some point for HR data and kind of if you stretch it the whole way back to understand

[00:32:37] [SPEAKER_01]: it's actually like when somebody said they quit the company who did they tell how did that person

[00:32:41] [SPEAKER_01]: enter it where did they enter where did that data go where did it come out do we have it out

[00:32:46] [SPEAKER_01]: and then have we actually extracted it architected it modeled it got ready for that AI

[00:32:50] [SPEAKER_01]: that's an education that I think is still coming but it's something that that um that data engineering

[00:32:55] [SPEAKER_01]: space HR is still relatively new to that but it's important one for AIQ and I think that's

[00:33:01] [SPEAKER_01]: that's a broader piece around educating yourself around where the data comes from that goes into

[00:33:07] [SPEAKER_01]: your AI is important for local decisions around your own company but also broader

[00:33:12] [SPEAKER_01]: ethical decisions around fair use correct use copyright all those things about kind of like

[00:33:17] [SPEAKER_01]: the data that actually was used to train this thing so yeah I think a AIQ also means data awareness

[00:33:24] [SPEAKER_01]: and data intelligence in addition to AIQ no one's put it quite that way before and you're right

[00:33:31] [SPEAKER_02]: it's not to again make everyone that's trying to learn AI be you know data experts but it's part

[00:33:38] [SPEAKER_02]: of taking that critical lens to say well just like if you were going to do some root cause

[00:33:44] [SPEAKER_02]: analysis of where something went wrong you've got to go back to you know sound you know data

[00:33:51] [SPEAKER_02]: you know practices good data hygiene um and and trustworthy you know data because that is

[00:33:59] [SPEAKER_02]: you know the the fuel for any of these algorithms and so again I'm not saying that people need

[00:34:05] [SPEAKER_02]: to add a whole suite of data courses but it would definitely be helpful to know you know through

[00:34:15] [SPEAKER_02]: your company or wherever they've sourced data whether it's from another team inside the company

[00:34:20] [SPEAKER_02]: or you've sourced you know external data maybe it's you know social media data whatever what is

[00:34:26] [SPEAKER_02]: the provenance of of that data because if your data is suspect and it was you know biased from

[00:34:33] [SPEAKER_02]: the beginning then obviously the AI is just going to become you know biased based on the historical

[00:34:38] [SPEAKER_01]: data that is collected yeah it's something I'd say leverage your peers leverage your communities and

[00:34:44] [SPEAKER_01]: leverage your vendors make your vendors work on this too make them explain what's going on help

[00:34:48] [SPEAKER_01]: help kind of reach out and find out what they're doing there that's something I feel a lot of

[00:34:52] [SPEAKER_01]: questions about kind of data AI and how to get access to data that's a lot of what we get

[00:34:56] [SPEAKER_01]: up to over here one model is actually unlocking that full power of your HR tech stack and data

[00:35:01] [SPEAKER_01]: and so we're always fielding questions and talking to teams about it and trying to share the

[00:35:06] [SPEAKER_01]: share the good word about what's needed to kind of make this happen at scale because

[00:35:10] [SPEAKER_01]: as a whole I think I think maybe that's something that I'd be really excited to

[00:35:14] [SPEAKER_01]: share is just from talking to everybody about this both from the HR tech as well as the

[00:35:19] [SPEAKER_01]: practitioner side everybody is moving forward together there's a lot of community involvement

[00:35:24] [SPEAKER_01]: on this and excitement around it what it could mean for the world to work

[00:35:27] [SPEAKER_01]: so yeah just to emphasize that reach out to your friends reach out to your community

[00:35:31] [SPEAKER_01]: and start there before you put yourself through a whole bunch of courses

[00:35:35] [SPEAKER_02]: yeah that's awesome do you um I know you've done a lot of work to help the community at large

[00:35:41] [SPEAKER_02]: around um you know just finding you know job opportunities around you know people analytics

[00:35:48] [SPEAKER_02]: and related areas and how how's that going I mean I know I see people expressing their

[00:35:53] [SPEAKER_02]: gratitude constantly myself included I just think it's regardless of what the economic numbers

[00:36:02] [SPEAKER_02]: sound like there's always a lot of you know transition happening in the workforce and

[00:36:10] [SPEAKER_02]: just want to give you an opportunity to sort of you know plug how that's that's going and

[00:36:15] [SPEAKER_01]: what resources people can thank you Bob no really it's um I'm very grateful to one

[00:36:20] [SPEAKER_01]: model that we're able to kind of put that roles page together and it's a way that we give back

[00:36:24] [SPEAKER_01]: to the community to say let's make this a little bit easier to get people connected

[00:36:28] [SPEAKER_01]: I think what's really exciting though and this really dovetails into the rest of our

[00:36:31] [SPEAKER_01]: conversation is this AI boom has led to an AI talent demand and suddenly the

[00:36:37] [SPEAKER_01]: Nvidia's Microsoft Google's apples are fighting over core talent but then actually

[00:36:44] [SPEAKER_01]: everybody in the Fortune 500 is looking for AI talent right now and suddenly if you go back

[00:36:48] [SPEAKER_01]: to like okay how do I actually find good talent I have to have a talented intelligence team that

[00:36:52] [SPEAKER_01]: knows the market I have to have really good recruiters who can actually go find that talent

[00:36:56] [SPEAKER_01]: and make sure it could land I have to have great people analytics team to make sure I

[00:36:59] [SPEAKER_01]: can keep that talent and I need a workforce planning team to actually grow that team over

[00:37:03] [SPEAKER_01]: time and make sure I'm making the right decisions from a strategic perspective

[00:37:06] [SPEAKER_01]: I am I am really hopeful that we're going to see a forecast to kind of wave of an

[00:37:11] [SPEAKER_01]: investment in HR around this space uh the demand for AI talent whether that's upskilling

[00:37:16] [SPEAKER_01]: with your LND team or finding growing keeping AI talent that's actually out there in the market

[00:37:20] [SPEAKER_01]: today I'd be on the lookout for recruiters right now if I was hiring if I wanted to get AI talent

[00:37:25] [SPEAKER_01]: in six months you need a recruiter today so I think there's good news happening across the

[00:37:30] [SPEAKER_01]: board and I'm starting to see little inklings of that and the people analytics board we're seeing

[00:37:33] [SPEAKER_01]: a lot of tech companies start to rehire we're seeing a couple kind of data science and AI

[00:37:37] [SPEAKER_01]: rules start to pop up very specifically and I'm looking forward to seeing more of that from

[00:37:41] [SPEAKER_02]: the job board too nice that's great when you when you say AI skills or AI talent I think

[00:37:49] [SPEAKER_02]: there's some confusing or nebulous kind of thoughts that enter my mind because

[00:37:58] [SPEAKER_02]: if when I think about you know low code no code anybody can build their own GPT or whatever

[00:38:04] [SPEAKER_02]: I mean does does that mean I'm if I can do those things am I AI talent or are you still looking for

[00:38:12] [SPEAKER_02]: a developer who's created from you know cobalt to uh you know working with certain LLMs or whatever

[00:38:20] [SPEAKER_02]: so I guess I just want to understand because everyone you're right everyone's looking for AI

[00:38:25] [SPEAKER_02]: talent there've been some headlines recently where people are saying well you know even

[00:38:29] [SPEAKER_02]: the concept of a knowledge worker is changing because if I have knowledge at my fingertips now

[00:38:36] [SPEAKER_02]: then aren't I better off hiring somebody who just has good AI related prompting skills and knows

[00:38:44] [SPEAKER_02]: you know some of this latest and greatest stuff and can learn everything else or knows how to

[00:38:49] [SPEAKER_02]: access the knowledge from everywhere else uh isn't that just as valuable as you know a gen X

[00:38:56] [SPEAKER_02]: or like me who's seen a lot and knows a lot but you know need to hire me or I think there's

[00:39:02] [SPEAKER_01]: there's a really interesting distinction there when I when I was saying AI talent I think what I meant

[00:39:07] [SPEAKER_01]: was that like generating AI tool kits and tooling so you have a lot of those like again

[00:39:13] [SPEAKER_01]: like the nvideos the googles the facebooks that are all fighting over that kind of like

[00:39:16] [SPEAKER_01]: someone actually to create this stuff and the researchers around it like that that talent

[00:39:20] [SPEAKER_01]: pool is so limited so there's definitely a talent war happening there I think there's another one

[00:39:25] [SPEAKER_01]: that's on the horizon which is this like AI minded or AI literate talent which is like you can use and

[00:39:32] [SPEAKER_01]: deploy and scale operations through the use of AI and so that would be like maybe maybe a metaphor

[00:39:38] [SPEAKER_01]: kind of like someone's got to build computers and someone's got to use computers you know it's

[00:39:42] [SPEAKER_01]: like you had the IBMs and the the intels uh and someone's actually got to use computers but then

[00:39:47] [SPEAKER_01]: someone's actually got to do the work too so I think we're still going to have a there's kind

[00:39:52] [SPEAKER_01]: open debate I think right now is that sort of like will the current companies all kind of get up

[00:39:57] [SPEAKER_01]: skilled or will we see some revolution in some of the companies that kind of get taken over by maybe

[00:40:02] [SPEAKER_01]: AI startups that come out of the blue and I think that's one of the things that's really interesting

[00:40:06] [SPEAKER_01]: about this AI talent war right now is there's a lot of people that still think they can build it

[00:40:10] [SPEAKER_01]: in house and they're going after things that maybe we're out of reach before to be competitive

[00:40:16] [SPEAKER_01]: so I'm hearing about teams that are trying to train their own systems or build their own

[00:40:19] [SPEAKER_01]: tools or create agents and a lot of things that maybe HR tech was doing before they're trying to

[00:40:25] [SPEAKER_01]: like get started on it in house to see how far they can get so I think maybe it kicks off a little

[00:40:30] [SPEAKER_01]: bit of a build versus buy conversation again in a very new way with these new technologies

[00:40:34] [SPEAKER_02]: and tools that we just didn't see before yeah no it'll be interesting to see how that evolves

[00:40:40] [SPEAKER_02]: because I do you know I look across generations I guess I mean I look at folks like myself I

[00:40:46] [SPEAKER_02]: look at you know how my you know my retired parents might be trying to use AI like on their

[00:40:53] [SPEAKER_02]: you know Apple device or just general just looking for you know help or guidance but

[00:40:59] [SPEAKER_02]: going back to your point I mean this this consumerization of AI in a way does affect

[00:41:06] [SPEAKER_02]: organizations but I also think about the younger generations like I you know we both have

[00:41:11] [SPEAKER_02]: have kids and other you know preparedness for for the workforce not just for for college right

[00:41:17] [SPEAKER_02]: but you know mold enough to have nieces and nephews you know graduating from college and

[00:41:22] [SPEAKER_02]: internships and things like that and like that's absolutely giving them a leg up if they know that

[00:41:30] [SPEAKER_02]: how to use that stuff so I think about how you're right I mean there's a lot of analogies

[00:41:35] [SPEAKER_02]: you could you can come up with I mean is it like being a digital native like using knowing how to use

[00:41:42] [SPEAKER_02]: the internet and you know digital tools is it is it like giving them a computer again for the first

[00:41:48] [SPEAKER_02]: time like do I actually know this isn't you know this isn't a typewriter right like it's

[00:41:53] [SPEAKER_02]: it's an actual computer it can do all kinds of things to help you be more efficient so I do

[00:41:59] [SPEAKER_02]: think that whatever your professional pursuits you're right there's always going to be people

[00:42:04] [SPEAKER_02]: that need to build the foundational people to build the car not just learn how to drive the car

[00:42:10] [SPEAKER_01]: but yeah there's a lot there yeah it's a it's definitely a tough time though to be

[00:42:15] [SPEAKER_01]: to be entry level at this moment with or without AI skills there is a tough labor market I think

[00:42:22] [SPEAKER_01]: there's a lot of companies are holding off they're waiting they're seeing they're trying to

[00:42:25] [SPEAKER_01]: see what they could do with the AI and the agents and even in trouble one model we're seeing

[00:42:30] [SPEAKER_01]: some interesting things that kind of agent-based workflows and some some announcements coming soon

[00:42:34] [SPEAKER_01]: around that because there's there's some remarkable things you can do to really accelerate what we used

[00:42:39] [SPEAKER_01]: to do and I think we were just starting to see the tip of that so even with HR tech this year

[00:42:45] [SPEAKER_01]: I'm expecting HR tech will see some like announcements everyone's going to be talking AI

[00:42:48] [SPEAKER_01]: I know skills last year I'd be shocked if we see skills at every booth this year it's going to

[00:42:52] [SPEAKER_01]: be AI the whole way through but I think it's going to be still people talking about it

[00:42:57] [SPEAKER_01]: I'm I've got a feeling that 2025 HR tech we're going to see some really like

[00:43:02] [SPEAKER_01]: very new ways of working that this this AI ground up rebuild that companies are either working on

[00:43:08] [SPEAKER_01]: or they're going to get demolished by these startups that are going after it exciting things

[00:43:11] [SPEAKER_02]: are going on in the market today yeah I would say AI and skills were the top two topics at

[00:43:16] [SPEAKER_02]: Unleash and probably at HR tech last year too but my observation just you know quickly on

[00:43:24] [SPEAKER_02]: on Unleash I would say 80 to 90 percent of the conversations that I heard or was part of

[00:43:32] [SPEAKER_02]: more about AI but very few on the just to tie in the responsible AI piece people were not

[00:43:38] [SPEAKER_02]: generally talking about that unless I personally sort of nudge them in that direction but

[00:43:43] [SPEAKER_02]: that's good Bob that's good someone's gotta do it right and so yeah I mean I know

[00:43:48] [SPEAKER_02]: people say you know what that sort of underpins all of this but like you need to

[00:43:52] [SPEAKER_02]: you really do you need to be more explicit about that how are you where are you getting

[00:43:58] [SPEAKER_02]: your data are you prepared for an audit if you were subject to one are you doing

[00:44:03] [SPEAKER_02]: things the right way from to your point back from when you you know pull the data together

[00:44:08] [SPEAKER_02]: or decided to use a particular algorithm or you know borrowed snippets of open source

[00:44:15] [SPEAKER_02]: you know code or you know you borrowed some well-known methodology well is it

[00:44:20] [SPEAKER_02]: proven to be mitigating you know bias did you transparent in the way you're

[00:44:25] [SPEAKER_02]: you know analyzing that data and whatever because once it goes downstream through these

[00:44:30] [SPEAKER_02]: authentic workflows and the data and the decisions are flowing to all these other places

[00:44:38] [SPEAKER_02]: I mean that that course is way out of the barn at that point and then what are you gonna do

[00:44:45] [SPEAKER_02]: so I guess maybe I am the responsible yeah I

[00:44:49] [SPEAKER_01]: hey it's it's a it's a good banner to have Richard this has been great how can people

[00:44:55] [SPEAKER_02]: get ahold of you and I'll put in the show notes you know your contact info as well

[00:44:59] [SPEAKER_02]: as the link to your the job board that you're curating yeah thank you I yeah LinkedIn's best

[00:45:05] [SPEAKER_01]: Richard Rosenout find me on LinkedIn and then one model.co if you're interested in talking

[00:45:11] [SPEAKER_01]: data orchestration data visualization data science come find us we're always happy to talk

[00:45:16] [SPEAKER_01]: people analytics AI data and I'm always happy to talk about those things too so Bob thanks for having

[00:45:21] [SPEAKER_02]: me on today absolutely thanks so much Richard it's been a pleasure thanks everyone for joining