How Shapes is Building for the HR Role of the Future with Shani Brisilovsky, AI Product Lead at Shapes

[ on-hold music] Welcome to the Foundation Series, your deep dive into startup compensation with industry leaders from across the startup world. Join me, Matt McFarlane, a people operations leader turned compensation specialist, as we uncover the strategies and practices driving success in tech startups around the world. Hear insights from heads of people, founders, and experts as we explore how to build robust compensation frameworks that not only fuel growth and retention, but do so in the dynamic startup environment that we all know and love.

Let's get into it. [on-hold music] Welcome to the Foundation Series. Today, I'm joined by Shani Brizilovsky, who is the AI product lead at Shape and a behavioral economics researcher. We're getting into how HR is leading organizations into the future when it comes to AI, and how that's helping us throw out the old HR routine.

Plus, we talk about how your HR tech stack will enable all of it. Let's go. Shani, welcome to the Foundation Series. I'm excited to have you here.

I wanna dive f- straight in with the most interesting question from... One of the questions I've been most keen to ask you. So you're the AI lead on the product side at Shape. I'm - I've gotta ask, what are the things you're doing with the current pace of change to just keep up to date with the constant, uh, stream of updates and new tools and just the general change in, uh, you know, the, the - I feel like we're all facing at, at work when it comes to AI at the moment?

Yeah. Uh, the pace is, is really, is really, um, as fast as I, as I can remember. Uh, it's really remind me of my early days, uh, as a professional when, uh, it was, like, so much thing to learn. Um, and for me, it's...

First of all, it's to be really conscious about the fact that we need to be up to date and that things are changing- Absolutely... and to really put the effort and time into it. It's not something that is gonna happen, um, if you're not really looking for, uh, sources and trying to learn, uh, proactively, and not waiting for somebody else to, to teach you that. Um, so, um, I'm really...

I like, uh, podcast. It's, like, my favorite, uh, platform and f- my favorite way to, to consume data. For me, it's really interesting to learn how people use AI, um, in the day-to-day, uh, as a productivity tool, um, personal use as well, uh, but also how people that building AI, uh, think about it, uh, think about it and use it. So I love the content that, um, the people from Anthropic, product people from Anthropic, um, have because it's both learning from the source and not, like, secondhand, uh, and also to see how they use it.

So some really creative and interesting ways to, to use a tool that I didn't think about. Yeah. I couldn't agree more. I, I follow the Anthropic team.

Uh, I mean, they put out some incredible resources anyway, but you're right- Mm-hmm... in the sense that it's like, it's kinda nice sometimes not to get it filtered through influencers or something like that. But they do a great job actually of their team, of making sure that they are talking actively about how they're using it. I'm not a big fan of, like, Twitter necessarily, but unfortunately, it is an incredible source of information- [laughs] Yeah...

when it comes to a lot of those sorts of things. Um, and they're super active, which is awesome. Um, something that you mentioned before we started recording is that you're doing a master's. Talk to me a bit about that and how that's also influencing the way that, uh, that AI shows up at work.

Yeah. So I'm doing a master's in behavioral economics. Um, and starting doing a master's and going to academy when AI is, uh, exploding can feel a bit counterintuitive. Uh, but for me, it was really...

It's an amazing experience. Uh, first of all, uh, you hear a lot of people also in these, uh, podcasts I mentioned, uh, talking about going back to, uh, first principles, because technology can change, and it will change, and we don't know even how, like, in so many ways, but human behavior, what motivates us, the decision-making processes will stay the same. Um, and this is something I think that is really important to, to really dive into and to learn and not just be on the chase after the new technology and new solution, um, because these are the things that are in - really in the core, um, of the way we work.

Um, and also, um, in the past couple weeks, uh, doing my... couple years, um, doing my master's, um, when I started, there was not as much research, uh, about these topics, and in the past year or so, there's more and more and really interesting stuff. Uh, especially in MIT, they have the, um, AHA, Advanced Humans with AI, uh, Research Center, and they have their conferences online, and they have amazing research, um, that they're doing, um, about the tools themselves, how people interact with them, and things that I think is really important to, to learn to think about as people that using those tools, but also as people that are building those tools.

Uh, it's a lot of responsibility. Yeah. Yeah, absolutely. I do...

Wow. There's a couple of incredible resources there. I'll make sure to link them in the show notes. So, all right, we've talked about the pace of change.

We've talked about some of the ways in which you're kinda keeping up with that pace. How are you staying informed, learning about things as they're, as they're evolving. How does that start to translate into the role of the HR professional? So, um, you're speaking to them day in and day out because you're obviously part of the team that's building, you know, ultimately a product and service of them.

I'd love to know, like, how is the role, how is the HR role evolving from this research that you're undertaking? Where is it going to? So we see a big shift in, um, HR teams from trying to use the AI to automate processes, to make - to streamline them, to, to take all the admin and manual work that, uh, they had. But now we see more and more, um, deep team culture-The thoughts and the fact that HR teams are not just using AI to, to make their processes better, but they're also the, the team that in charge of the...

all the changes. Um, and AI is changing cultures, uh, of workspaces, it's changing the roles, it changes how people work, and HR teams are the, the one that are equipped to do that, uh, to support those changes. So they have the, like, parallel challenges, that they need to lead the change in their organization, but also do the change and lead the, the change themselves. We saw a really interesting announcement by Atlassian, uh, that their, all their HR, um, transformation, all their...

sorry, all their AI transformation is under the HR team, under the people team. So something that when you think about it, again, going back to first principle, when you think about the team that needs to lead the change, it's HR. But for some reason, until now, it's not like the, the natural way to do that. It may be the R&D or operation team or something like that, but we see these kind of changes, and it just like the, the start and, and we definitely believe that this is the way to go, um, that the AI is not just about productivity in the day-to-day.

It's changing how, how people work, how teams are working, managers, and how you build organizations. So it's really exciting and challenging to see that. Yeah, I can imagine. I...

that was the first, uh, kind of, uh, example that popped to mind for me as you were, you were saying it, is that Atlassian has now expanded the remit from just people. Well, not just people, 'cause that's a broad, a broad space as it is, but people to now AI transformation. I think Zapier as well, I think is another, like, famous example of a company where, where the CPO roles took on that transformation. Um, so by the sounds of it, they're not alone.

There are obviously some famous cases, but you're seeing... you're actually seeing this transformation happen more broadly. I know... I can think of a few.

I've actually had some guests on this podcast who have, who have also kind of taken ownership of how AI is adopted, uh, across the workforce and, and how it transforms their ways of work. What do you think are some of the, like... what are some of the big challenges that the, the teams with this, like, even broader remit are facing as they're, as they're kind of taking on that mantle? So first of all, change is hard.

Uh, we talked about, uh, behavioral economics. So the default bias is one of the most basic one. People don't like to change. Um, so leading change is hard, and leading change is so transformative, and we're in the middle of the wave.

We don't know yet what is happening. So I think that some teams, some companies and organizations trying to think about exactly the solution, where they need to be, what are the... what is the, like, checklist you need to do in order to be AI ready. But as we see it, like, the, the most successful teams are, are the one that are changing their mindset.

They're in a change mindset. They're opening... they're open to, like, whatever will come. They're, they're, um, flexible.

Um, and I think this is really the key, because things are gonna change so much, uh, in a, in ways we, we don't even know how. Yeah, I can only imagine. Are you... Are there any examples, uh, that can be obviously anonymous, but are there any great examples you can share that you've seen some of this, uh, this transformation start to take shape in the research you've been doing?

Yeah, for sure. So a lot of the, um, people processes, um, that we currently have, you know, having the, uh, performance review twice a year, the comp cycles are related to the talent mapping, uh, all the, uh, survey engage-engagement surveys, they have their, their cadence, and some teams take it for granted, right? It will be every six months, every quarter. But, um, like, those cadence started...

the, the pace started because of the limitation of the, uh, operation, because it's hard to do the performance review and collect the data and analyze it and, and do the other analysis about the comp. It, it takes time in the way companies currently work. So these are the processes that people have, and sometimes, uh, until you get to the, to the actual result, it's too late because your talent already left, um, or didn't see something that you didn't... you had to, to, to pay attention earlier.

So the teams that, um, that are more successful are not just changing the tools, but changing the mindset, not, not letting the, the ways old tools worked to, uh, to dis- um, decide, like, how their new processes are. So they're more up-to-date. They have weaker feedback loops. Um, when they have something that is, is...

they have an alert, they need to pay attention to something, they do it, like, on the spot. They're not waiting and solving things in hindsight. Um, so these are really exciting, and it's really shifted the way we think also about, um, how we build the system and being more creative with it and thinking about what is really needed and not how w- how is the old way of doing s-stuff, and let's just make it, like, shiny with AI, have, like, AI dust all over it. Yeah.

Gosh, you've got my brain, like, whirring because you're so right. Like, the more you were talking, the more I was thinking about all of these, like, I, I guess just, like, the, the status quo of the way systems and, and operations have run within people teams. I mean, I obviously spend a lot of time, uh, focused on compensation, but I mean, often people teams have a calendar, right? Like, it's a fairly rigid thing.

Like you said, we run the... you know, we might run an engagement survey twice a year, and it's... you know, we might do our compensate- compensation cycle once a year. We've got all of these, like, cadences, and actually the shift, by the sounds of it, is more to, like, real time or in the moment and, and how do we make sure the systems...

or I guess we're, we're seeing now this shift to, to systems be able to support that. You're right. Like, I remember, I don't know how many times I've, I've either spoken to or even been in organizations where they were like, "You knowYou know, and this is, this is something they can obviously do when they're small. They're like- Yeah...

"Oh, we review, um, employee salaries at their anniversary." And it's like, gosh, that sounds horrible. Like, you're just constantly, you know, running this process that's never ending. You know, why not batch it into one thing?

'Cause obviously, operationally it's much easier. Now though, y- you're sort of talking about, yeah, this, this future where actually you can... y- you, you do have the technology to be able to handle these sorts of processes on the fly and still preserve fairness and consistency and efficiency and all of these things that are important to, to the business. Exactly.

And, and it's really... We see really creative ways of doing that. Like, when people don't need to wait to have, uh, an analyst go over the data, and when they can dig into the, the data themself and ask questions and, and suddenly, um, have insights that are much more interesting than the, you know, few things they, they plan to, to measure just because this is what the timeframe allowed to. Yeah.

So, I don't know. I think I, I think... So, for me, I think it's relevant, definitely. So, I mean, for me, the interesting thing about AI, and this is a- Mm-hmm...

a topic that comes up as well, is like how, how are we preserving the human element of, of our work and of our relationships and of how we get things done and not letting AI be the default for that or to take some of these things away? Um, how are you seeing this, this question kind of emerge in the workplaces that you're speaking to, and how are people thinking about, you know, making sure that they're not letting AI take the things that a human should be doing? So I think it's really...

it's a really valid question, and I think we deal about it a lot. Um, I started my career in healthcare startups, um, in, in different areas and, and verticals, um, and working on, on AI features and capabilities almost a decade, decade ago when it was not as common. Uh, we saw different... We saw the same, um, the same challenges and objections in, in the healthcare world and, and...

'Cause clinicians, similar to people teams, to HR teams, um, came to, to do something for people, helping people, um, uh, making them improve their lives, saving lives. Um, and bo- both teams, uh, ended up with work, with a day of work that is full of admin and full of manual work and is full of documentation. And the magic of AI, as I see it, is really in improving the, improving the experience and taking away all the admin, taking away all the manual things that, that there's no real value of a human really doing that.

And you wanna free the time to do the more, um, um, the thing that the computer, the machine cannot replace. Uh, the conversation, the deep dive, the deep thinking, the strategy. Um, and it's, it's really important also, I think, in the way we f- we think about it to, um, and we're building our products starting from the, uh, ground up, s- starting from the day-to-day, from the, uh, from the admin, from the noise, all the questions, and you... [chuckles] Like, the, that you have the, the transparent, transparent work that nobody sees, but it happen, and it take, takes up all your time.

Um, and leave the time to the more, uh, to more important work or more, uh, the, the, the work that is, um, like, um, management, um, training, leadership training, strategy, um, train this manager on this hard conversation they're going to have. Um, so this is the thing that today a lot of the HR teams don't have time to, uh, because they're dealing with all the rest- 'Cause they're caught up with it... of the thing- Yeah... they have on their plate.

Yeah, yeah. Yeah. Yeah, I still remember really vividly this talk a couple of years ago from... I think it was, like...

I think his title was Futurist, which is just the most interesting- Mm-hmm... title, but someone who kind of like- Yeah... you know, talked about how... And actually it was in a healthcare context, but talking about how AI and robotics is gonna revolutionize that space, and it wasn't about taking away the role of the doctor or the nurse or anything like that.

It was like- Mm-hmm... you know, they're, they're gonna be really good at the diagnostic side of things. They're gonna be really good because- Mm-hmm... they've obviously got all of the information, you know, for all of the, the medical data and, and the information, uh, and then whatever that they need a- around the world.

Their ability to, like, understand the symptoms that you've got and be able to, like, reference all that information in the data and do it really quickly is second to none. Obviously, a human's never going to be able to compete with that. The other thing that was really interesting is that actually from a diagnostic perspective, um, and this probably ties into to your master's, is that people apparently seem much more comfortable telling a robot about their symptoms than they do a human- Mm-hmm...

because they're not likely to be judged by a robot. The robot's just like, "Yeah, whatever. Tell me what you need, and, and, and I'll give it to you." So you've kind of got part of what that doctor does that's going away, but actually it's being done way more efficiently, and what it allows is the doctors, the nurses, those sorts of people to be...

to even, like, overemphasize on the human a- and, and the care aspect of it, and it's like, how do we make sure that you're comfortable? How do you feel, you know, respected in, in whatever circumstance you're in? It's like, it's just a... it's an evolution of the role, but it's not a replacement, and I see, I see that being very much the same within a HR context, right?

Is that you're right, like, so many people teams are bogged down with the manual, the mundane, the admin, and actually they, they would much prefer to be doing things like helping their leaders be more successful or helping their people have a better experience at work and, and I see this very much like you do, I think is that it's gonna, it's gonna create a pathway for that as well. Um, a fun anecdote, uh... Let, let me know if it's relevant. So a fun anecdote.

I worked on a AI product for-Emergency rooms. Um, so you go in and you fill your sins, et cetera. So I did a tour, uh, in different, uh, emergency rooms to learn about the flow and the user journey and, and, and the parts that, um, our product, uh, um, came into, came into place in, in this process. And I, I was working with this head of emergency room, like big em-emergency room, um, in the hospital, and he asked me a-after the tour that we did, "Do you know who's the most important, uh, person in the, in the emergency room?

Who's the first contact that tells us if there's somebody that really needs urgent help?" And the truth is, um, apart like, no, maybe the, you know, the, the front office, they take all the forms and he's like, "No, not, not that as well. It, it's the guard that is the, the security guard that stand in the front door." And because he's part of the team, he's, he's been there for so long, if somebody comes in, they didn't even get to the office, the front office, um, but they see there's - they're not okay, they need help, he's the one that's calling anybo-everybody and telling there's somebody that really needs urgent help and, and let's treat them.

So... And this is was the head of the, the emergency room, uh, this really senior, um, doctor that told that to me. So it's really stayed with me, um, as the importance of looking for these human points, human connection throughout the, throughout the process and not just, you know, being fixated on the formal steps that you have. Yeah, I love it.

That's... It's, it's awesome to hear those sorts of stories. Um, I have another anecdote, but I'll, I'll leave it off for this moment. But, um, all right.

We're, we're talking about like, I guess, you know, more human moments. We're talking about, um, you know, automation and AI taking away some of these, like these parts of the jobs that maybe we really struggle with or, or kind of over, you know, overtaking the things that we wanna do. I really wanna talk about how I, I guess, like the current sort of suite of HRISs aren't maybe, you know, enough for us right now in this current environment. I mean, uh, I can reference in the show notes as well, but...

And I'm sure you saw this piece published by a16z, the, the, the big VC over in the US, and they did this piece called Workday's Last Workday, and talked about how, um, we are really, and I guess we're, we're all in many ways led by the consumer, uh, technology experience. But we're now entering an environment where people really can, uh, interface with, you know, an AI chat or something like that, and start to have access to a, a, a broad array of information and be able to do things in a much faster manner than they can, uh, than, than, than they could before.

I'd really love to understand, like how are, how are the current crops of HRISs sort of falling short in that respect and, and maybe with respect to that piece from a16z? How, how do you think they're like maybe not keeping up with this role of the head of people that we're talking about? First of all, it's all about the flexibility, modularity, uh, we've talked about, um, that all systems are very rigid. Uh, they were built years ago, um, in a very structured way.

Um, and first of all, every organization is different and every organization is going through changes. Um, it can be changes in headcount or in structure and departments and going into groups or task forces or changing the leveling. So the organization are, um, this, this, uh, changing, uh, creature, you know, naturally. Um, the current systems are much too rigid, and they don't adapt themselves to the different, uh, changes that, um, organization have and as we said, are going to have.

Um, second of all is all the connectivity. So this day and age, it's all about connecting, um, system. Like we have our connector in cloud, we have our MCP. So it's all about having few system talking to each other and having, uh, a broader look at, at our team.

So not just the performance review, not just the people data, the salary, et cetera, the compensation, but having their tasks in your task manager and having the, having the, the sales data and seeing like a 360, uh, picture of the team, um, and not just like very siloed, um, data sources. Um, and the last one is actually, uh, we call it, uh, moving to doing the work, uh, for you. So like we said, um, looking at the product user journey, the workflows, um, of the people team, it really help them do...

connect the dots. Um, so not just getting the report, it can be a great report, um, but and I, and I got it in seconds instead, instead of days. Um, but what do I do with it now? I need to, uh, share it in a deck to show it to the team, and I wanna send it on Slack, and I wanna create a PDF for report.

So this is really something that we're, um, thinking about and adding to the product itself, connecting the dots. So changing... Working on the data, changing the messages, the announcements, the reminders, um, and taking away the, this like the invisible work, um, and thinking, um, more like wider than just the system and what does it do, and thinking b-more about the day-to-day and the workflows and how can our system, um, be part of that very easily and seamlessly because we don't want...

Nobody wants additional work and addit-additional tasks to their, to their day. Yeah, I hear you. I was, I was just having flashbacks then when you were talking about like siloing data and talk - and thinking about like, you know, I don't know how many times I've been in companies where they change their departments or, you know, even sometimes just reporting lines or they want, you know, some, some weird change. And, and of course, like you mentioned at the start with respect to-You know, the HR calendar and the cadence of events.

It's, like, often because of the limitation that we have from a system perspective, we have to work to the confines of that rather than the way we want our business to run and, and, and, and just be able to support that. So it sounds like this, this modularity, this flexibility is very much something that is kind of front of mind with how Shape's being built, and I, I love and I'm definitely keen to hear more about this idea of, like, how the system is doing the work. So this is sounding like agentic AI, is it?

It's sounding like being able to, to curate workflows and actually have some of this be handled by the system. Tell me a bit more about sort of how that's shaping up. Yeah. So, uh, first of all, building the workflow is building the policies for you, um, taking the, the knowhow that we have, um, from our research, from our advisors, um, and partners.

So we're really, like, infuse, uh, HR and people knowhow into the system. So it's not a generic training deck that, uh, we're creating, um, for s- for our performance review cycle that is just coming up. Um, it will have the, the backbone of the actual, uh, professional knowhow. Um, having the alert, like we mentioned before, about something that is wrong, something that, uh, you wanna pay, pay attention to, um, and really closing, uh, closing the gap, uh, sending the messages in advance, seeing who field the, uh, their time sheets and sending the smart nudges, nudges and v- being, uh, very personalized.

Um, creating, you know, onboarding flows that, that are not manual and are not generic because every, every person is different. Um, so running this, um, smart onboarding flows and sending the personal message and making sure the manager is ready on time and to flag the things that need to, need to be taken care of and the things that are urgent. Um, and, yeah, because every piece of data have, uh, so much, um, um, things that is relevant for and so many point of view of... In compensation, just the line, just the field of, of the salary, it can mean different to, uh, the leadership now trying to do, uh, sim- like, simulate, you know, the growth, right?

So we have the simulation tools in the product that, you know, in Vibe, uh, views, you can just build your simulation 'cause this is now what you're planning. So now you can do it really quickly. And the same salary means something personal for the employee, maybe their value. It m- they can be really happy and proud about, they can be very disappointed.

So how you announce this type of, like, a salary change or a bonus. And it could be something else very technical for the finance that need their report on time. Uh, and we have the data validation, for example, so we make sure the data is always accurate and no data is missing. So we do this work for you and they flag this, flag it in time and not, like, you know, when it's already too late and you need to go back and understand what is wrong.

So this is just one example of, like, one data point. Uh, and we have so many in the people world, so this is something that we really try to think about, like, the way all the, you know, the dots connected and every person, every role get the things they needed on time with the relevant context. Yeah. Oh, Sani, you're making me almost wanna go back in-house and, and work in the people team with, uh- Um, well we still- [laughs]...

with this GG you're describing. It sounds... Yeah, it sounds amazing. It sounds like it's...

I mean, in so many ways, it's, like, going to be incredible for the way it enables the people team. But then I just think about as well, like, you know, I, I... Data, the HRAS and then the data it contains has so often been, like, such a sore point for credibility within the business as well. Like- Mm-hmm...

you know, with accuracy, how it gets updated, how it gets, you know, managed, how it gets dealt with, all those sorts of things. And so, um, it just sounds like this is a really big sell for a really core problem that, that often, uh, trips up a lot of people teams. Um, what, what do you think is like... What's, what's beyond that?

What's... You know? I, I know it's impossible to predict, you know, where things are going and the pace change, but where, where do you, where do you start to go? Where does your head go to, you know, even beyond- Mm-hmm...

this and these workflows that you're thinking are, are, are gonna be native to the system soon? Yeah. So first of all, it's really making these other workflows, um, really seamless. So also traditional system have their file, like portal, uh, desktop, web, and they have their, maybe their mobile app.

Uh, so... And we have both, uh, of course, but also being where the people are. So in the Slack, uh, having our agent in the Slack, we have the, in Teams, uh, in Microsoft Teams. So really making sure people don't need to work hard to, to get their data, but we are just there.

Um, and having, you know, all the permission in place also to do that because it's not the... This is, this is something that is really, really helpful for us to do this really quickly. Um, and also going beyond the people team, um, and being really the, the, um, the data source and the data platform of how you manage your, your organization. And as we said at the beginning, uh, now it's, it's the team, but also all the agent and all the AI tool that we have 'cause now when you...

If you wanna measure productivity in a, in traditional workspace, you have the salary, you have, you know, the, the cost of a specific team, you have their, uh, success entries, and there you have it. But now when each team have their own, you know, agent, they're using tokens, uh, the, the, um, investment and the resources are now different, and they're shifting. Um, not ne- necessarily having, you know, our, my agent that is replacing me as, as a, as a team member, but it's another resource, uh, resource that is really important to plan for, um, circling back to the AI transformation.

So it's not just about the people doing the work, but the tool that they have and the agent that's working with themTo do that, um, and it's something that we already see some, uh, some teams, uh, think about, and it's definitely, um, a place where we're going to. Yeah. It's an exciting future. Um, one thing that I know comes up for a lot of this stuff, and I, I certainly don't wanna let it go, um, unaddressed, is, you know, a lot of the concerns people have when it comes to AI, it's things like, you know, oh, there's, there's hallucinations.

There's obviously privacy and security and data concerns. How, how are you thinking about this within the context of all of these things that we're talking about? Yeah. So this is really, really important, especially dealing with people data, which is very, you know, important.

Um, and, and moving from regular dashboards to the world of AI that is less deterministic is something that we really, um, um, take seriously. So first of all, we do... We have the permission, um, in place, and it's something that is not... It's really engraved.

It's really part of our core system, uh, the way our permissions work. Uh, and we have really robust set of permissions, so making sure nobody see an accident and not letting the, you know, the AI, the model decide what's... what types of data you can and cannot see. So this is something we really take care in a much more, um, foundational level of the system.

And also, uh, we do a lot of tests. We have our internal evaluations and alerts, and we... Like you said, having the, um, knowing the HR teams and knowing what people are asking for and what they need and what maybe they don't wanna see 'cause it's too sensitive, um, this is something that we're, um, really defining closely with our partners and from our research to make sure nobody see, see anything that they don't need to see, and the data, the data is accurate. Um, yeah.

And, uh, the vibe, the vibe of users we have is really exciting because then it's a bit, a bit of a different story than the, than the agent because the vibe is building the s- the screen, building the, the tab, the dashboard, but the data is your data. Um, so it's also something that we, that we are really exciting about. Yeah. Well, look, I know, you know, I've, I've, I've been granted access to it, and I've been having a play with it, and some of the things that, uh, that it can do is, is pretty incredible.

I mean, the other day, I recorded myself, and I, I did a post about it, but, um, about generating a set of salary bands, and it was from the data that we already have with our existing employees and using that to build some ranges. And you can, you can put a couple of assumptions in there around, you know, how broad you want them to be, but it was, it was incredible. You know, I put one prompt in, I waited a few minutes, and then all of a sudden, I had something where previously I had nothing.

So the ability to go from zero to, you know, even if it's 50 or 60% of the way there is such a huge headstart compared to what that experience is like in the past. So yeah, it's an exciting feature, and I'm, I'm certainly excited to hear more about, uh, how that evolves and how, how the agentic stuff comes to, uh, comes to play as well. Let's, let's bring this to a close. So, Shani, I guess last question from me.

Again, you've got all of this insight not only from your role, from the research, from the conversations that you're having. If you think ahead 12 months from now, what do you think are some of the things that the people leaders can do to really lean into this future that we're describing and, and kinda not get caught flat-footed? So I think the most important thing is to just start and play and don't be intimidated by the, the fact that it's called AI or you have, like, very tech people, like, uh, using it.

Jump in. Um, there's so many resources online. Um, just try, play with it, see what you get, um, and, and don't be afraid of it. Don't wait till somebody tell you you have to change because then it's gonna be already too late.

Yeah. Yeah. I couldn't agree more. You need to be ahead of the curve.

Yeah. Yeah. There's so many people out there, out there talking about, you know, "Oh, you know, I've made a million dollars with AI." And it's like, I think you can't let that sort of- [laughs]...

stuff distract you, and it's just a case of, like, starting small, one foot in front of the other- Yeah... trying it out, and, and it's just getting familiar with it. I think that's the only thing that's gonna help people be less intimidated by and actually start to see some real, some real progress from it. Exactly.

Exactly. Yeah. I love it. And I think that you don't need to have, like, a whole team of agent running your household, uh, as step one.

You just need to jump, uh, right in, and, uh, as a product person, the, the advantages I see with those tools, you just need to speak with it, uh, and tell it what you're interested in. And it's the same conversation you may have with your analyst that you're waiting for, uh, to, to make a report for you. So just, like, talk to it, play with it, see what's going on, a- and it's very addictive. So when you start, you, you cannot stop.

Start the, the snowball rolling. Yeah. Well, this has been- Yeah, yeah... a super insightful conversation, Shani.

I, I appreciate it so much. Thanks for jumping on and, uh, and, and sharing some of your insights with us. Thank you, Matt, for having me. Thanks for joining us on another edition of the Foundation Series.

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