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Anish Satpute on Rethinking Employee Engagement in an AI-Driven Workplace
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Employee engagement is broken, and traditional surveys aren’t helping.
In this episode of MustardHub Voices: Behind the Build, Curtis Forbes sits down with Anish Satpute, founder of Reventa, to explore how AI is reshaping the way organizations understand their people. Drawing from his global upbringing, early entrepreneurship, and deep curiosity about human connection, Anish shares how personal experiences with loneliness and social dynamics led him into HR tech.
The conversation dives into why legacy engagement surveys produce low-quality insights, how conversational, AI-driven feedback can uncover real root causes, and why discovery — not dashboards — is the most overlooked stage of people strategy. Anish also offers thoughtful perspectives on bias in AI, employee trust, and what HR leaders must do to stay relevant as work, roles, and organizational structures evolve.
This episode is a must-watch for HR leaders, founders, and operators who want to use AI operationally and strategically to build more human workplaces.
About Anish:
Having grown up an introvert, Anish started his first business in an effort to deeply understand human relationships: both for himself, and then to facilitate this for others. Anish’s curiosity about changing social dynamics, including in corporate settings, led to Reventa. Reventa serves as the adoption layer for AI in the workforce, starting by reimagining employee engagement and the legacy survey model.
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Transcript
Introduction to Anish Satpute and Early Life
00:00:06
Speaker
Welcome back, everyone. It's another installment of Mustard Hub Voices Behind the Build. In these episodes, I sit down with the people building, backing, and running better workplaces. I'm your host, Curtis Forbes. My guest today is Anish Satpute.
00:00:20
Speaker
ah Having grown up as an introvert, Anish started his first business in an effort to deeply understand human relationships, both for himself and then to facilitate this for others.
00:00:31
Speaker
Anish's curiosity about changing social dynamics including in the corporate setting, led to Reventa.
Creation of Reventa and Influences
00:00:37
Speaker
Reventa served as at the adoption layer for AI in the workforce, starting by reimagining employee engagement and the legacy survey model. Welcome to Behind the Build. Thanks so much for joining me today, Anish.
00:00:52
Speaker
It's a pleasure to be here, and it's a pleasure to be back on a podcast after a while as well. so um So you've lived all over the world, and...
00:01:02
Speaker
You make hip hop. i let I want to humanize you a little bit because you know you you you have a pretty awesome and interesting story. um i love that you love music. I do too. What are some of the most interesting places in the world that you've been and how does that influence the hip hop that you create?
00:01:22
Speaker
That's a phenomenal question. We're getting straight into it. Yeah. um As far as influencing music I've created, going have to think on that a little bit. But ah we'll start with the first question. we'll go We'll go one by one. So I lived few different places, mostly in Europe.
00:01:40
Speaker
I'm originally from India, which is also where I happen to be calling in from today. But in terms of my favorite places that I've visited, because I've been fortunate enough to visit, I want to say over 50 countries. I don't know what the exact tally is, but it's it's a lot of countries. So i i'm I'm very fortunate that way.
00:01:59
Speaker
And few a few cities come to mind. I actually really, really love Vancouver. I find it to be a great balance between you have the city and sure the city isn't anything out of the ordinary, but the nature around it is just, you know, phenomenal. Extraordinary for sure. Yeah, absolutely. And I'd say that's been also a common theme of what I've really liked about the places I've lived.
00:02:24
Speaker
I lived in Sweden and i always describe the house that I lived in as something out of a fairy tale. It was kind of in the middle of nowhere, but our our backyard led into like a a a hill.
00:02:35
Speaker
And if you walked 100 meters down the road, you would be able to, there was a lake basically. And in the summer, we'd swim in the lake.
00:02:46
Speaker
And in the winter, it would freeze over and we'd ice skate on it. So it was it was really something out of a kid's Disney movie or something. Love that. Very fortunate.
00:02:57
Speaker
Well, um you started your first business at eight. I mean, that by itself is really remarkable. But tell me about the bit. What what was this business?
00:03:10
Speaker
So ah get ready for this. Very, very high stakes, high, yeah you know, super complicated business. um So a buddy of mine and I, you know, as eight-year-olds do, we were just roaming about the playground.
00:03:23
Speaker
And we saw this like crocodile-shaped hedge in in the school that we went to. And to set a bit of context, I went to an international school because I was an expat in the country. And so you had like your expats. And they were you know people from all over. They weren't necessarily like from crazy wealthy families or anything like that because they were kind of going around with the companies, but the local families were wealthy.
00:03:49
Speaker
And so the school had these like manicured gardens and whatnot. And underneath one of the hedges that we found there there were these fancy looking stones and they they were completely hidden. Like they weren't, ah you know, accessible or visible immediately. But we were like, these stones look pretty cool. I wonder if we could sell them.
00:04:09
Speaker
Turns out we could. And we sold so many that they had to keep replacing the stones underneath that hedge. So we got a free supply, a completely free, 100% profit margin, you know, never going to get a business as good as that. i Eventually i had... ah All of my classmates wanting in on it. um But tragically, we got shut down by the authoritarian government. My my home loan teacher, she she shut down our our biggest deal.
Founding Reventa and Post-COVID Employee Engagement
00:04:34
Speaker
um It was really quite a tragedy. that The fact that at eight years old, you are able to discover ah something with. i I don't know, i was such incredible you know supply chain economics, right? Where you're i know you're printing you're printing money at 100% profit margin is pretty good. We should all be lucky. I'm missing that. I could use that now.
00:05:00
Speaker
Yeah, that's definitely an investable business, I would say. yeah the though the only I think the the critical um you know, red flag, right, was that once discovered by others, the business would be easily replicated. yeah And your and your your customer your customer, you know, base might die.
00:05:23
Speaker
um Anyway, so since that venture- But you live and you learn. Yeah, you live and you learn. So you've racked up some pretty unique, ah diverse experiences. what What is the career path that led you to founding Reventa?
00:05:40
Speaker
Yeah, it's a good question. So, i mean, as you can tell, the ah seeds were planted fairly early, but that took the form of various endeavors before I landed on entrepreneurship in the specific form that I'm ah pursuing it now.
00:05:58
Speaker
um and First, it was music. So from a young age, I was always interested in sort of building things, creating things, unique, new, what have you.
00:06:10
Speaker
And so there was that aspect of it. And then there was also the aspect of okay, I've also learned from my father, from the people around me. For context, my my father wasn't um or isn't a businessman, but he did always encourage entrepreneurial spirit.
00:06:28
Speaker
And so I'm very grateful for that. And i got the idea from a fairly young age that if I want to have impact on the world and if I want to have it fast and in a real way, entrepreneurship is the best way for me to do that.
00:06:42
Speaker
And so all of that kind of culminated in my university experience where I kind of faced the big city loneliness paradox head on. You know, I was away from home for the first time. i was in ah I was in London, big city. And I also chose to go to the university that happens to be known for having the most introverted, antisocial student base that you can imagine. What what university is that? Imperial, Imperial. So it's it's a STEM only university. And so
00:07:14
Speaker
the the stereotype is that the person you meet at Imperial is like your archetypal nerd. And I will say it's it's pretty accurate. So i had to I had to look elsewhere to, well, to be fair, i was I was like that myself. But at the same time, I did value social interaction. And I realized how important it was not only for business, but also for my own personal happiness and contentment and all of that good stuff. And so I initially started to explore how I could improve my own social life. And so I started doing these events with a buddy of mine. And we realized we had found a format, we had found ah a type of event that appealed to a lot of people in the city that were also kind of feeling this sense of loneliness and wanted a place where they could connect with people more deeply. And so that was my first business, the final shot.
00:08:06
Speaker
um But the thing is, I was never super, super passionate about running events. I still enjoy doing it. I still do it every now and then, but I knew it wasn't going to be my lifelong endeavor. And so I wanted to explore other ways to address this kind of bigger problem of the way that social dynamics are changing, especially post-COVID, especially with technology.
Reventa's AI in Employee Engagement
00:08:26
Speaker
And then I started exploring that in the workplace. And that led me to this problem of employee engagement. And mind you, I was completely new to the space. I'd never worked in a big company before. I started exploring the idea actually while I was still studying.
00:08:40
Speaker
But I made it a point that I want to really, really deeply understand this domain. And so I spent the last year and a half or so just speaking to... every possible HR leader, corporate leader, CEO, COO that I could get my hands on basically, call in as many favors as possible. And that that's what led to the hypothesis that then turned into the product, which is Reventa today.
00:09:05
Speaker
So let's talk about today, right? Revento is ah an HR tech solution. um You know, I kind of understand what you find, you know, so interesting about that human resource space.
00:09:19
Speaker
um But tell us about Revento. You know, what what kind of workforce problems does the platform help uncover? You know, how does it impact productivity and retention for organizations that use it?
00:09:32
Speaker
Yeah, absolutely. So what we've really decided to focus on ah with Reventa, and there's a few reasons for this, which which I can get to in in a moment, is the discovery stage of any kind of employee initiative. So really understanding as deeply as possible what the problem actually is and hearing that directly from the employee.
00:09:56
Speaker
And I think there are a lot of, I guess, recent changes in the workplace, the way work gets done, that has made that a bit more complicated than perhaps it once was, and also has made it necessary to kind of reimagine that in some sense. And so...
00:10:15
Speaker
For example, let's say you're at a small company, you're a startup or you're a local brick and mortar store. You're not too concerned about the fact that you don't know what your employees are thinking because you can always just go and ask them.
00:10:31
Speaker
um But when you're at a global organization and your teams are all over the place and there's now this norm of work being done through, first of all, through online communications, but also more recently with AI and and a lot of people working, not only people, but also teams working kind of independently, um there becomes a matter of, okay, how do we ensure everyone's on the same page when it comes to the bigger picture questions, which I think is especially relevant for companies that are looking to
00:11:02
Speaker
make a change. And even if they're not looking to make a change, it's kind of coming upon them, especially companies and sectors like that that are constantly changing now, which, you know, maybe any sector at this stage. But so lot a lot is changing, essentially, which I know is a very vague statement. But in terms of how that's kind of seeped into the domain of interactions in in the corporate space, so like corporate communications, but a step further corporate engagement. That's what interested me.
00:11:34
Speaker
um And that's what also kind of relate, I could make kind of links to what I'd been seeing also on a social level, because I think at a from a kind of first principles point of view, a lot of what's caused this change, a lot of what's caused this problem is you know the same kind of underlying um events or underlying ah changes in society. And so that's what made me think, hey, okay, this is something that I can really sink my teeth into.
00:12:01
Speaker
And so that's how I kind of thought about building out the survey technology, which is kind of the core product of Raventa. And I say survey technology because i think that is the norm, the status quo of what people think about when they think about employee discovery that isn't done organically. um But I'm also very hesitant to use the word survey because I know what a classic survey looks like. And I know that people really don't like what a classic survey looks like.
00:12:30
Speaker
And so that was where I saw the problem as, okay, you know we have this way of gathering data, gathering sentiment, gathering feedback. but it's really not a pleasant experience for anyone and it also doesn't give us the kind of data that we want or need to create these types of bigger picture strategic initiatives um and make any changes any actions for that matter so you always hear in hr tech of how data needs to be actionable and how you need to be able to have insights that you can act on and you know all these tools that help you analyze the insights and act on the insights etc etc but If the insights themselves are low quality, you know, sort of the garbage in, garbage out principle, then that's your first problem. And that's what we're mainly focused on addressing.
00:13:18
Speaker
So make sure I understand, you know, as ah as a type of survey tool, I'm qualifying it that way because I know, you know, you how you just mentioned, sometimes that word can...
00:13:32
Speaker
already elicit an image in someone's mind. But as ah as a type of tool like this, Reventa really kind of exists to be able to gather feedback ah from employees so that businesses can discover what their problems actually are.
00:13:48
Speaker
Exactly. Yep. Okay. Amazing. So, I mean, and and and we're all about data here at Mustard Hub and Raventa can, it sounds like it can provide clients with ton of valuable metrics and and insights.
00:14:00
Speaker
um Tell me, I mean, what, what kind of data should users expect to see? How do you, um you know, how do you qualify it and deliver that to your clients and how can that, you know, ultimately, how how can that information impact their bottom line?
00:14:16
Speaker
Yeah, 100%. So the way that we initiate a data instance, when we're working with clients is that there is an initial kind of flow that they undergo, which is how the AI that our platform uses, gets their context. So any new events that have happened in the company, let's say the CEO has implemented a big new strategy, new AI strategy, everyone's scared, oh, what's going to happen to my job? um
00:14:50
Speaker
and or Or let's say you know there's data that you have access to via the company context through the HR information system ah that the company is using to see that, oh, ah over the last few months, you've your attrition rate has gone way higher.
00:15:10
Speaker
And so a lot of this initial kind of diagnosis of areas worth digging deeper into is done at the onboarding stage. So that's done when you share your company context with the platform.
00:15:22
Speaker
And so, of course, the more company context you have, the better. But even in the situation that you have a lot of that data kind of in your head or in a bunch of documents, you can always input those into the system as well.
00:15:36
Speaker
And so at that stage, then you might decide that, okay, my focus right now is to dig deeper into this specific strategy and see how that's being received by my employees. And so I want to create a data instance for that.
00:15:49
Speaker
And here's where where I'm using the term data instance, and not survey. And so you you enter any more context that you think is relevant, the AI kind of holds your hand through that to basically tell you what it needs to know so that it can ask questions to your employees about these matters, about these problems in the best way possible, in as tailored a way as possible.
00:16:11
Speaker
And then it generates an initial survey schema. And so this is where it actually is a survey. But the way that it differs from a classical survey and the way that it it is deployed is, first of all, it's deployed in the flow of work. So um one of the integrations that we've been mostly focused on is Slack. So deploying is the survey through Slack. So a Slack bot messaging the employees that you want to query and then interacting with them kind of conversationally. So it asks your fixed schema of questions, which you need in order to get those aggregated insights across you know survey responses. But you also have personalized follow-up.
00:16:46
Speaker
follow-ups for each individual to get much more kind of deeper root cause. Interesting. Yeah. And so that's part of what what makes the data as that much more detailed and actually gives you something that you're able to do about it.
00:17:00
Speaker
Now on the side of, yeah, go ahead. Well, I was just going to say, so so so employees will will respond or they will interact with this this you know particular type of survey, but then the AI will basically have some dialogue with them almost to enrich that data that it's already creating.
00:17:17
Speaker
Yeah, exactly. Exactly. Fascinating. Okay. I didn't mean to interrupt, but, but I just wanted to make sure I understood correctly. That's really cool. Keep going. You put it really well. Um, yeah. So once you have that data, of course, there's a lot you can do with it. And that's also a concern in terms of like, okay, great that we have all this data, but what do we do about it? And so that's also why simultaneously with the discovery, and this is where,
00:17:41
Speaker
when I reached this stage of developing the platform, what where I actually got really excited about the developments that were happening in ai happening in ai the reason for that is this was the time when Agentic was really, really kind of taking stage and everyone was talking about it.
00:17:58
Speaker
And the reason it became that much more relevant and useful for our platform, because let me tell you, we never set out thinking that this is going to be like an AI startup.
00:18:11
Speaker
Even now, I don't refer to it as an AI startup because you know we're not an AI research lab. We're solving a problem, a corporate problem, and AI helps us do it. um But anyway, slight tangent.
00:18:22
Speaker
So when Agentec kind of started taking center stage, and we realized that, okay, first of all, we have these agents that are querying the employees. And so that's already helping improve that process and improve the results of the actual follow ups that they create.
00:18:38
Speaker
But a step further than that, they can then directly use this data that they're gathering from employees to immediately act on it. And so this was the big shift with agentic AI is sort of chat GPT with hands, AI with hands actually be able to you know go into your tools and and make changes.
Organizations Benefiting from Reventa
00:18:57
Speaker
And so...
00:18:58
Speaker
if you think of an example of let's say a engineering head saying that you know my product team is not is not giving me um is not giving me the latest product spec quickly enough and so that's you know causing a lag in my in my ability to push features then the agent can be like oh okay i can go and ask your product head right now or i can go better yet like your product head submitted this in like your ah your company tool or they uploaded it on to this group.
00:19:32
Speaker
Let me just pull that for you and send it to you right now. And so it goes a step forward from just aggregating all of these insights and then being like, okay, now we got to do a review and we got to get all the stakeholders on board and we got to create this initiative where there may be a bunch of quick fixes that just get ignored or end up in a black hole because you have this whole process um that are then fixed by this continuous loop, which is only possible because of Agentec.
00:19:59
Speaker
um I think that that's really neat. It sounds very forward thinking and I kind of like how you're, where you're coming from. um I guess a question I would have is, so tell me a little bit about the kinds of organizations that come to
Challenges in AI Adoption in HR
00:20:12
Speaker
Raventa. We don't have to name any names here, but I'm kind of curious about, you know, what does that, um what does that look like? You know, the the types of companies and organizations and and are there specific challenges that they're experiencing I guess, or or solutions they're looking for when they begin to explore what you have to offer?
00:20:32
Speaker
Yeah, absolutely. So I'd say there's, of course, the offering in theory could appeal to a lot of different companies. But as a founder yourself, I'm sure you know very well that At some point, especially early on, you have to kind of figure out who you're serving so you can make you know a great product specifically for their needs. And so I have found that the type of company that is most interested in this kind of solution, first of all, they need to have...
00:21:07
Speaker
the technology or it's preferable that they have the technology that helps them make best use of it. And so generally technology companies to begin with, so we're technically industry agnostic, but technology companies tend to be easier for us to kind of latch our technology onto.
00:21:25
Speaker
um And then beyond that, there's obviously a sweet spot in terms of the size. So when what what we found is when this kind of data aggregation and being able to get this one-to-one interview cell data at scale, ah the point at which that becomes relevant is actually maybe a bit earlier than you might think, but it's generally around 100 employees.
00:21:51
Speaker
And so that's also kind of another constraint. And once you get too big, there are very, very complex processes that make it a lot harder for us to immediately you know integrate with all of them. And so while that's also something that we explore on the side, it's not the the clients that we focus on serving right now.
00:22:08
Speaker
And so we get the sweet spot of basically anywhere from 100 to 1,000 employees. And I think the final constraint that makes it most of interest or most relevant at this moment in time for the company is if they are undergoing some kind of major change that they've decided to take on, whether that's a new strategy that they're implementing, or they're deciding to offshore a function, or they're undergoing hyper growth, and they want to make sure that they keep that that touch that they have with their with their employees now organically, that they're slowly losing grasp of through through these these growth stages. So these are the kind of general types of organizations that
00:22:47
Speaker
have much more of a reason to be concerned on a day to day of what their employees are thinking and how that affects the direction of their strategy and the execution of their strategy.
00:22:59
Speaker
I think that, I mean, we both know, right, AI can be contentious in some circles, right? Are are the companies that you work with or or you know from what you've seen organizations in general, I guess, are are they eager to embrace AI, maybe even specifically this ah version of it? Do you see any reluctance here? What's that kind of feedback that you get?
00:23:23
Speaker
I'd say everyone is Everyone may be a bit of an exaggeration, but a lot of people are eager to embrace AI in theory. um i would agree with that statement. But yeah, I think it's especially difficult in a space like this, which is naturally risk averse. And I'm not saying that they shouldn't be.
00:23:45
Speaker
think there's a good reason for hr in general to be risk averse in a lot of ways. So that is obviously a challenge, but it's also a good challenge because it forces us to think about safety concerns, you know, privacy concerns, all of these types of things, which are kind of core to the offering that we're building from a very, very early stage.
00:24:08
Speaker
But it's also a matter of what I've kind of found to be challenging is the gap in what I'm excited about in terms of the product capabilities, not only from a technology point of view, because although you know I have somewhat of a technical background, I'd say I'm more excited about a product's capability in terms of what it can do for a client rather than, oh, this is super cool technology. like yeah you know I can appreciate that too, but
00:24:41
Speaker
The former is what you know really gets me going even more so. But there's a gap between that in terms of, okay, I think it's super cool that you know this specific thing that we've built can solve this problem that people complain about a lot. Like, for example, the survey problem, the reason I even came across it was because it was one of the first things that HR leaders mentioned when I was speaking to them about employee engagement.
Impact of AI on Workforce and Skills
00:25:09
Speaker
But when it comes to actually solving that problem, that's especially you know with these with these new technologies, that's where the hesitance comes in, ah the hesitance to adopt because there's this kind of gap between, okay, yeah, it would be really nice if this existed.
00:25:31
Speaker
But at the same time, right now, I'm checking off a box. And so I'm going to just leave it to the side. And so, yeah, I'd say the main, what I'm getting at with that, and this was a roundabout way of of mentioning it, is there's a lot of focus on the operational benefits of AI, which is great. You know, I i totally understand why that's the case.
00:25:51
Speaker
But there's a lot that's being left on the table in terms of the strategic impact that AI can have. And that's what I'm excited about. And I think this might be more of a a longer term position that I'm holding, but it's a position that I intend to continue to hold. But one that I think is harder to get people on board with, at least in this space at this point in time.
00:26:13
Speaker
You know, I... i think we' I mean, I'm excited about the same things, and I think that it's certainly a long-tail play. um you know I think that it's going to completely transform the way we do business. um you know And I think that one of the biggest selling points of AI is, number one, it it it presents to you at very low costs this concept of infinite knowledge, right? In infinite knowledge and and and wisdom.
00:26:51
Speaker
um Well, maybe that's an an overstatement still since it's still in the ah state of hallucination, you know, periodically. We're not we're not quite over that. but But it presents to you, um you know, this this ability to have at very low cost and very low effort, infinite knowledge.
00:27:11
Speaker
And... With that, right, you, I think, have organizations whose primary intent might be to reduce, you know, reduce their potential.
00:27:32
Speaker
Reduce payroll, right? Which is generally the biggest cost center of any organization, right? yeah um And, you know, with good reason. So, you know, it's not necessarily a bad thing, right? I mean, it's good for business, but not necessarily good for for people. It's probably going to create jobs just like it might take some away.
00:27:55
Speaker
There's an argument that could be made of that it might... reduce jobs more than it might create, but that's, we we we don't know yet. Nobody's got a crystal ball. um I think some fear that AI is going to take their jobs and a portion of of entry level and and less skilled workers worried that they're going to become unemployable.
00:28:12
Speaker
Very possible. um Do you feel like that's a legitimate fear? It's a legitimate fear. But I think a lot of the conversations that happen around it, or rather the reaction to that fear, of like, okay, this is something that people are thinking, people are feeling, how do we address that? I think that can sometimes be a bit misplaced.
00:28:40
Speaker
And of course, there are a lot of, you know, taglines that you you will hear these days, like, oh, AI won't take your job, but somebody using it well. and you know, all ah all of this kind of, you know, it's it's become, you know, almost a ah trope at this point. But ah first of all, I think, for one, there is some truth in that, but there's more truth in understanding that we have to kind of embrace the idea that Once again, another trope, the way that work gets done is going to change.
00:29:15
Speaker
But it's true. And so what we do about that is not kind of run in fear or or say, like, you know, let's just stop the development of this technology altogether. Like, there's, of course, there's totally a place place for guardrails and all of that kind of thing. And that's an entirely different space that, you know, I don't think either of us are directly involved with but there are people involved with it, which is good. But that tangent aside, um rather than you know trying to delay the inevitable or say, like you know um
00:29:50
Speaker
double down on what makes us human, well, that's something that we kind of have to start rethinking from first principles. And so that's why exploring how AI can be used strategically and how it's involved in not only these operational matters, because it's it's obvious of, okay, these more operational...
00:30:07
Speaker
high fidelity workflows that we can replicate with AI, sometimes already with like 90% accuracy, are going to eventually be replaced, or at least there's going to be a significantly lower requirement for it um in terms of just sheer numbers of people that you hire, payroll, etc. um But that said, the people who do decide that, okay, I want to have agency and control in the work that I'm doing. um AI is a great tool for that. And so figuring out how that plays into their workflow and companies also encouraging that, which I think is is the big part, um is going to be kind of the key of figuring out like
00:30:53
Speaker
how can we ensure that a maximum number of people who do get displaced from their jobs are useful in some other way? And I think they absolutely will be. like it's It's an era where high agency work, ah things that people actually like doing, um things that people feel energized by, et cetera, et cetera. And sure, some people are energized by admin and they're going to have to find a new thing to get energized by.
00:31:19
Speaker
But a lot of these things are being made possible because of that.
Ensuring Unbiased AI Data in HR
00:31:24
Speaker
And that's something that I think needs to be more more actively encouraged and explored in companies.
00:31:30
Speaker
You know, just kind of, i' I'll even dive a little deeper into a part of this. You know, I think that there's a lot of fear around AI bringing some buyer bias, you know into into the workplace in different ways. I mean, i think there's,
00:31:48
Speaker
you know, so much, you know, about, um, the hiring process specifically, um, you know, you and I were both at, at HR tech, right. And, uh, 80% of those in the startup area were all based and in yeah some way on the, the pre hire events, right. Whether you're talking about yeah sourcing,
00:32:11
Speaker
or interviewing or ATS or whatever it happens to be. um and it's a really common question that I see, you know, this perceived bias based on just a number of things. Right. And, you know, a common question that would come up is how can that be avoided? Because it's clearly happening, you know? So what steps do companies take to prevent AI from becoming biased? And, and,
00:32:35
Speaker
You provide a really interesting and an incredibly valuable service. um But oftentimes, you know, even in this, you know, i would I would think at least conceptually, right, when we talk about surveys, which is basically we're asking questions, we're trying to gather feedback, we're trying to gather information, um there can be bias that creeps into this. So is this something that Raventa is taking steps against? How do you look at this problem?
00:33:05
Speaker
Yeah, very very, very good question. So I think there are, I guess, two levels of bias when it comes to AI that need to be considered. One is the level of bias at the model training level.
00:33:18
Speaker
And this is obviously something that us as applications don't have direct control over. it's It's the AI research labs. But of course, they're aggregating huge, huge amounts of data. And so that includes a lot of online data, which sort of bakes into it a lot of human biases. That said, I think the models have actually done a very good job at mitigating that as much as possible. And, you know, i I wouldn't be able to quote a study or anything like that off the top of my head. But generally speaking, I can make the claim with a high degree of confidence that the average LLM is a lot less biased in the way humans are than humans are. And so that's already a very good thing. And so
00:34:01
Speaker
the way that it's used for, you know, those types of basic, you know, age-based biases, race-based bias, gender-based biases, et cetera, that's already mitigated to an extent. um But then there's also the application layer bias, which is up to the applications and how they are built. And so...
00:34:21
Speaker
Thinking about bias in terms of surveys, which I think the survey model inherently introduce and introduces bias, um is something that's you know very important to you know obviously build into the product that we're building.
00:34:39
Speaker
ah But I think the very nature of how we're addressing how survey data is collected um by, first of all, focusing on the timing in which it is collected. So, of course, if you're, for example, if you go up to your employee's ah desk and you ask them what they think about your Q3 strategy, like,
00:35:02
Speaker
Unless they tend to be very assertive as a person in general, they're probably just going to agree with whatever it is that you're you're putting forward. um Same goes for like a general survey where you're asking them a general question and you're not really giving them an incentive to be honest and tell tell you what they're actually thinking. So having a more conversational approach to this, where it's not only, hey, you know give us this data so that we can then you know fire you or whatever, which is real a real fear that a lot of employees have. It's more, you give us this data, we can actually help immediately.
00:35:40
Speaker
And that's not you know a fake promise that we're making, because if you say that you have this problem, the AI can actually help you with it as fast as possible. And so that... reframe of you know what the actual experience of partaking in a survey is like for the employee reduces the bias on the employee level because they have an incentive to be authentic. And so that's a level of bias that I think we've been especially focused on mitigating. But then, of course, yes, there's also the level of bias in terms of the system prompt that we provide for the AI to generate the questions and the best practices that it's using to create those questions. And so that's also a part of building building the product.
00:36:22
Speaker
You know, um thank you for that. I love hearing that and I think it makes a lot of sense. um it it think I think, you know, in all this kind of talk about ai and how businesses are are using and adopting it,
00:36:38
Speaker
it makes me wonder, you know, really to kind of get the most benefit, I think, from from all these new, shiny, you know, AI-powered tools and platforms.
00:36:51
Speaker
um You know, HR is typically pretty slow to adopt a lot of innovation. Typically a horrible buyer persona. um I'm just saying out loud what, you know, what we all already know But what do you think HR leaders need to learn, ah you know, or or what should they prepare for to remain competitive in the in their roles? You know, you already, you know.
00:37:20
Speaker
mentioned that that trope, right, to to to use your word about, um you know, h or or AI won't replace, you know, workers, but people using AI will replace other other workers.
00:37:33
Speaker
um So if people want to stay competitive. What do they need to know? What do they need to learn? How do they prepare to be able to advance their careers and not go backwards? Yeah, really really, really good question. And one that I don't have the definitive answer to, but one that I will try to, one I've thought about, so one that I'll try to answer.
00:37:54
Speaker
um To start with, from the HR point of view, I think this is actually an incredibly exciting time, if seen the right way, because the very nature of how... Let me see how best to put this. so Because we're having this kind of overhaul of, once again, to use the trope, how work gets done, um there is more than ever a need to understand what that means at the individual level and at the first principles level. So for AI to be effectively adopted in any company, sure, there's like the technology aspect of
00:38:41
Speaker
you know, this is what it can do. And these are like the direct parts of our workflows that it can help. But even that process of understanding that requires employee buy in, it requires thinking about how you're factoring in, you know, various departments, how you're querying them, understanding what problems they're going through, how you're then seeing through their, their feelings about that, how um they're adopting what they're thinking about adoption, etc. All of these things become so much more relevant and not only an afterthought or something that, you know, maybe executives will think about at some point down the line. No, it's something that they need to think about right now. And so this is where HR people for what they, you know, really
00:39:27
Speaker
can be and what their role really can be. i mean, a lot of times it doesn't end up being, and that's not always the fault of the HR. A lot of the time a company will also undervalue the HR or see it as just like a, you know, an operational necessity rather than a strategic lever. um Now I think more than ever, it is a strategic lever if both the company and the HR
Future-proofing Careers and Predictions on Work
00:39:51
Speaker
sees it that way. And so this shift in how HR jobs are, especially HR leaders are thinking um is is a really exciting one if it's one that they're ready to embrace. And I have you know been fortunate enough to meet a lot of great leaders who you know are thinking that way. And and it's always awesome to to be able to chat with them. But I think there is ah a larger kind of shift that needs to happen ah in the industry. More generally, in terms of how
00:40:23
Speaker
And I'll answer this, I guess, from a perspective that I can speak to with a higher degree of confidence, and that is early careers. um Knowing, you seeing obviously, most people around me are, myself included,
00:40:38
Speaker
quite early on in their career, how they kind of pivot and what they focus on in order to, I guess, future-proof themselves. And I actually wrote a LinkedIn post about this, which um i'm I'm forgetting what I wrote exactly, but I'm going to try and recall it to my best st ability so as to stay consistent here. um But i i there was this kind of central focus on
00:41:09
Speaker
first of all, picking what I call an idea space, and this applies for startups, but it also applies for people who are looking for jobs in general, is this kind of generalist skill set of, yeah, I can do this, I can do that, I'll i'll do whatever they they take, you know whatever the company asks or whichever you know job takes me, ah is a mentality that I think is going to be not rewarded to be put lightly.
00:41:40
Speaker
um And so getting really obsessed with an idea space puts you in a place where you can actually guide AI in a particular direction and you know be a part of innovating how it's used.
00:41:53
Speaker
And so that also requires obviously a lot of agency and and the willingness to say that, yeah, I want to actually take creative control of something.
00:42:03
Speaker
um But with that mental shift, I think that is probably the biggest thing, because for one, like knowledge, as you say, and like knowledge mastery is not something that's going to be rewarded anymore because it's available at the click of the fingertip. And sort of general ability that I can do this and I can do that and I can, you know, I can fill in where I'm needed also isn't going to be as rewarded anymore. And so the creativity of choosing what to do and choosing where to focus, especially when you think about when AI, you know, reaches, let's say, AGI, however you see that, we'll save that topic for another podcast because that's, you you know, a whole beast of its own. But even if you think about just taking further the capabilities of ai as we're already seeing them rapidly evolve, um the part of the human economy um or the part of the economy in general that's going to be most relevant, that's going to stay relevant ah in terms of the
00:43:05
Speaker
role that we have in it as humans is taste. And so focusing on, you know, how do you get good taste in a particular area ah is, I think, a really, really important one.
00:43:19
Speaker
So let's distill for for everybody listening here. I want your... Yeah, please do. That was very rambly. we will distill. I want you to give me top three predictions for the future of work 2026 and beyond, what do you think some of the biggest changes are going to be? And it include anything, how we work, where we work, worker classifications, anything that you expect to see in the in the coming years. Give me your top three in as few words as you can.
00:43:54
Speaker
Sure. Yeah, so the first one that comes to mind, and I'm just going to spitball them as they come to mind. Fire them away, yeah. The first one is I think, a slightly controversial take in that we often talk about how AI is going to replace entry-level roles.
00:44:13
Speaker
I think what it's going to do before that is sort of flatten a lot of organizational structures. So a lot of kind of middle roles, especially in large enterprises, are going to be, I think, made redundant more so or faster at a faster velocity than entry level roles because there's fidelity concerns, safety concerns, etc. etc So that's that's number one.
00:44:37
Speaker
um Number two is I think we've we've been shocked by the pace of technology evolution, but at the same time, in a lot of ways, we've taken it for granted. Like we complain about a model hallucinating for performing slightly worse than this in insanely high standard that we've come to expect.
00:45:04
Speaker
And so I still think in a lot of ways, we're underestimating the capabilities and 2026 going to see evolution in a way that, you know, technologically might already be there, but we haven't seen what that looks like when it's actually adopted in a workforce or in a business situation. So that's number two is we are underestimating the pace of technology capability when it's ah sort of materialized in the workforce.
00:45:34
Speaker
Yeah. um Number three, and this is my last one, so I'm going to take a second to think about it a little bit more.
00:45:45
Speaker
I like some of your first two, and i I thousand percent agree, especially with your first one about how it's going to hor like um flatten a lot of a lot of companies, a lot of more complex organizational structures. I think you're finding that um What an entry level looks like is going to be different. It might even be managerial level, but you might be managing agents. And as you gain that experience, mid-level might look like somebody who's more strategically involved in how that they're used.
00:46:17
Speaker
Um, but, uh, but absolutely. And those are going to be skills that are just going to have to become part of any sort of higher education or certification programs so that when folks enter the workforce, they're like, you know, formally educated on how to best use a lot of these new, you know, agentic products and services. But, um, I don't mean to interrupt you or steal your thunder. Go ahead with number three when you're ready.
00:46:45
Speaker
Yep. Um, Yeah, no, you you you gave you gave me a second to to consolidate the thoughts that were in my head. And so I'm going to end it on a positive note.
00:46:56
Speaker
And that is, i think, that 2026 and beyond will be a golden era for innovation. think there's a lot of fear mongering surrounding, oh, AI is even taking...
00:47:11
Speaker
on the more creative roles and you know they're also being made redundant in some ways. I don't think that's the case because that last bit of saying that, hey, this is something that people are actually going to like or that's actually going to be useful um is still incredibly important. You can just get to that stage a whole lot quicker. And so you can test new ideas faster than you ever could before. And so it's going to be, yeah the best time for innovation that we've seen perhaps ever.
Advice for Leaders and Conclusion
00:47:43
Speaker
Perhaps ever. You know, and what's really funny is even at ah as a byproduct, we might see customer service improve. We might see you know, time to ticket resolution improve. We might see this these incredibly, you know, compelling,
00:48:04
Speaker
you know analytics that show us how much better customer experiences are going to be you know and the outcomes that the customers are going to be getting because of the new technology that exists that helps the business deliver whatever products and services is that they do. Even if in there is something that, like for example, home services and many of which is not going to get replaced by AI at least anytime soon, right? We all need a plumber. We all need an electrician.
00:48:31
Speaker
yeah But the software that powers these businesses, right? I mean, it's so ripe for for disruption right now. um You know, it's going through you know, this this transformative period. You just just talked about it, right?
00:48:46
Speaker
um And ultimately, those changes could very well, I mean, i don't know that they will. I think that there's certainly room for it to happen, but they could really positively influence, I think, our customer experience. um So obviously, a'm really yeah a really, you know, that would be a nice byproduct of all the things that you just talked about. Last question, really curious.
00:49:11
Speaker
single piece of advice for a business leader, right? When it comes to collecting and leveraging data in this sort of human resource process, right? And and in their people ops, right? Which is what it sounds like kind of where Reventa sits, right? We yeah we want to, you know, pull the audience, collect all this feedback so that we can help businesses improve in different ways, which can really stretch from ah hr all the way through ops, right? Depending on what these...
00:49:40
Speaker
these different things are that we uncover. What do you tell them? What's the one piece of advice that you give these business leaders? What do you leave them with so that they know how to better leverage all of that new data that they might get? So I think this one is perhaps counterintuitively very simple in that my piece of advice would be,
00:50:12
Speaker
Basically, just be honest with yourself. Are you prioritizing this or are you considering it as something that's a core part of your strategy or a core part of your role as as a leader?
00:50:28
Speaker
um Because i think a lot of people say that they are and it's easy to say, oh, yeah, I did my annual survey. And also easy on an intellectual point of view to say, that yeah, okay, all of this data would be very, very helpful. um But what I have found is that there's a whole lot that's being left on the table when it comes to even the data that they already have available. um And that's why I think this is an area, a space that you know even if there are 10, 20, 30 Reventa copycats that come out of it, I mean, first of all, I take that as high, high praise, but even if there are in the next year or so, ah i i don't think that's going to be anywhere near enough to actually fully harness the the potential of of the data that, for one, hasn't even been uncovered yet, but also the data that has been uncovered and and what can actually be done with it. So my my piece of advice would be like,
00:51:29
Speaker
check your your your processes. Are you actually making use of it? If not, yeah might not be with us, but do it. ah that That is good advice.
00:51:40
Speaker
i appreciate you taking the time to join me today, Anish. Really, I'm grateful for all your insights. Yeah, thank you so much. um And of course, thank you all for joining us. This is Mustard Hub Voices Behind the Build.
00:51:53
Speaker
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