How AI is Re-writing the Employee Journey

Monisha Saldanha (00:00) Welcome to Colorado Tech People, where we talk with the leaders shaping the future of technology in Colorado. I am your host, Monisha Saldanha an executive with 15 years of experience in product management. Today's episode explores how AI is transforming HR from administrative overhead into a strategic advantage. I'm joined by Poornima Farah, Chief Product Officer at Rival, a company building an AI-powered platform to manage the entire employee lifecycle. We'll dive into the hard decisions behind building enterprise HR software, how AI is changing the way teams hire and develop talent, and the real impact this has on employees and organizations. Poornima thank you so much for joining me today. Poornima Farrar (00:50) Thanks for having me, Monisha. It's a pleasure. Monisha Saldanha (00:52) And let's dive in. So first question for you. What is the problem Rival was created to solve and what is the solution? Poornima Farrar (01:00) Yeah, so Rival has always been an automation first mindset as a company. We've thought about what is it that HR really needs in terms of providing the best in class employee experience that requires the tactical manual steps to be fully taken off of the hands of HR teams, whether it's recruiters or sourcers or onboarding specialists or... HRBPs and automating that. As part of that journey, we have now become best-in-class, what we call orchestration layer, for a variety of use cases. If you're thinking about how to build the best pre-boarding experience, connecting all the systems in your organization, whether it's your HCM, your payroll, your benefits, what have you and bringing a single layer to life that is fully personalized and configured. Or whether it is as people are off boarding, what are the tools and systems they need globally? You know the laws in one country or one region may be different. The needs of employees in different parts of the globe might be different. So Rival just really takes into account the varied complex nature of the employee experience and focuses on automating that from end to end in a scalable way. Monisha Saldanha (02:11) Wow, fantastic. And Rival evolved, right, from early onboarding tools into a fully AI-powered talent suite. What drove that transformation? Poornima Farrar (02:22) Yeah, good question. So yeah, actually, you know for anyone that was born in the 2000s, you probably at some point filled out a paper. you know, offer a letter. Like you probably had to come in and sign something that said, okay, I'm now a part of this company. So Rival actually first started off by creating the technology that automated it. So this was like, you know, over 20 plus years ago. So it's kind of in the company DNA to always think about what is it that needs automated? How do we really need to think about the opportunity to scale a business by taking the manual steps off? And so the natural transition from there became, okay, now let's think about what that means for recruiting. In the space of recruiting, you're looking at... you know, a big pain point around how to actually find the best talent, as well as how to match the talent that is applying to your jobs, to the relevant roles in your organization. So being able to really tie those two pieces together from a recruiting experience via automation and integration became kind of the next meaningful chapter. We've also recently launched the same kind of journey for the performance layer. So if you think about performance, often a common pain point is I know what my employee is doing today as a manager, but I have recency bias. I have proximity bias. I know some people better than others. I may not know all the details of what's actually going on. So, know, Rival brought in the performance layer, but then it's now bringing in AI to actually make kind of that entire performance review experience with Human in the Loop more concrete and focused on the ability to really drive value to the employees by giving their leadership team as much information about them as possible in the system so that their feedback, reviews, check-ins are all more meaningful and transformative. Monisha Saldanha (04:10) And HR software has traditionally been fragmented with different pieces of software to solve particular problems. What made you believe an integrated platform approach would win? Poornima Farrar (04:21) Yeah, it's a good question. So I've spent, you know, over the last 17 years of my career in product management and I've gone back and forth between data products and people products and data products and people products. And at the end of the day, they're both the same problem. You are looking at data on people and you're looking at, you know, at people's data. And the common theme that I saw, regardless of which, you know, organization I was working with was that systems are at the end of the day very complex and often in an organization created based on compliance, regulatory needs, governance needs. There are tribal knowledge layers that require people to maintain certain things in a certain way. So it's almost impossible to say, okay, I'm going to consolidate every single thing into one platform. And even when you're trying to do that, it's not... as efficient as you might think because that kind of a platform is incredibly robust but also complex. Nobody wants to touch it and mess with it. You're bringing in third party implementation consultants for millions of dollars. And so you're in a set it and forget it mode, which means that you're having to create shadow processes to make sure that the different systems work together. Think of like a tuition reimbursement policy or a parental leave policy. Ultimately do some of those things in your system of record but a lot of the work actually happens completely outside of it. And so to me, it became clear that we need to think about it more from an ecosystem and orchestration perspective, right? So we comment that arrival is really about empowering our customers to rival the status quo. know, HR becomes the rival of their organization by getting the tools that they need, automation components that they have, so that they can rapidly iterate and build the flows that they need. Whereas all the systems would have the relevant information flowing through. Monisha Saldanha (06:13) And What were the hardest product decisions in expanding from onboarding into other workflows? Poornima Farrar (06:19) I think, good question. Let me start with what was easy about it. The easy thing about it was that our customers were already approaching it that way. So, you know, the product was called onboarding, but as we kind of worked more with our customers to understand how they were leveraging our product, we realized that they had myriad other use cases. Badging and credentialing in healthcare, offboarding when it came to large global companies, policy acknowledgments, what have you. So as we kind of learn more about that, it became clear that it was more about how the work should flow, hence work flow. And so the naming piece was, you know, in one way relatively straightforward that we still have clients who sometimes refer to us with the older names. The second part of it that was, but The part that was really challenging was actually trying to figure out how we can build the right sandbox for HR because a name like workflow is so broad that it can apply to anything, right? Like you can think of procurement workflows, you can think of supply chain workflows, HR workflows. So for us, the mission really being we are there to give HR the sandbox that they need in order to make their workflow is the focus. And this is kind of what we do. All we do has been a, you know, an area of focus. but also an area that we're continuing to market in the broader ecosystem for people to know about. I would say the last piece there is often there are solutions that talk about being able to do, let's say one of those use cases, but they don't necessarily talk about the orchestration. So one of the hardest challenges that I've seen as part of this is for us to really be able to explain what we mean when we're connecting different systems, outlining the orchestration layer versus just enabling an organization to tackle one use case at a time, which is what a lot of the market does today. Monisha Saldanha (08:13) Yeah, and When you look at your competitors, are they taking a platform approach too? Are they following your lead? Poornima Farrar (08:22) It's a good question. It depends on what we mean by competitors. Yes and no. So I would say that the best in class, you know, vendors in this market are focused on, you know, empowering people to work across the ecosystem because you don't get into this industry unless you're really focused on serving people well. And so there is this common ethos of making sure that the different systems work well together, play well together, that they really have kind of a platform mindset. Where I think there's some divergence is probably most in terms of, how technologies are actually being built. So at Rival, we've taken a very strategic and meaningful approach of built-in AI, built-in platform, built-in automation, in order to make sure that clients have a clear understanding of everything that is already coming directly as part of the system for them. Often, we've seen... prospects who come to us, you tell us stories about how they get stuck because they don't, you know, they have to buy separate like modules and separate, you know, buckets or they have to have, for example, an offer module and they have to have a, an integrations module and they have to have a scheduling module and an AI analytics module. And so you don't quite know what you're actually buying, like when you're actually getting the software. So that piece of, you know, how you're doing the overall structure becomes critical in terms of telling the story to the clients on how we can play in the ecosystem. Monisha Saldanha (09:48) And How do you decide what to automate versus what should remain human led in HR workflows? Poornima Farrar (09:54) That's a good question. And it depends is probably the best answer. It starts at two layers. One, there's the use case level, right? And then there's also the organization size or a challenge level as well. So the use case level is kind of... probably the one that most people talk about. Common one these days that we see is scheduling. Enabling people to be able to come in and schedule the interviews themselves. Good use case there for AI to be able to come in and say, okay, let me find the people that are actually available, that are best fit, what have you. Summarizing interview feedback, matching candidates. There are different use cases that can be, leveraged in order to really have that good structure for time and cost savings back to the organization. But at the same time, there's a thoughtful approach to it because how we're matching candidates to jobs is an obvious one where we really want to make sure that we're providing the relevant tools for skills-based hiring or understanding what the organization is, but not necessarily making that full decision on behalf of the human in the loop. So in terms of how we make that decision, it's gathering a lot of customer feedback, thinking about where the relevant... human in the loop layer comes in, what the governance guardrails are, what the modeling guardrails are, and then scaling from there. Monisha Saldanha (11:16) Was there a key turning point where you realized AI needed to be central, not just additive to the product strategy? Poornima Farrar (11:24) it's always been the way I've approached things. I'm not, I'm a platform person, right? So I, I don't, I, want to be able to go to clients and have a conversation with them about, here's everything that's being used. Let's show you the full story end to end. the, Probably the biggest turning point in terms of embedding versus not, actually probably originated not with AI, but with analytics. So often analytics got treated as an afterthought, right? And if you're going to think about 20 years ago, you're kind of putting together some of your dashboards and maybe you're sharing it at the exact level, then they're making recommendations on changes and those changes are getting percolated down across different layers of management. And then eventually, like in the long run, like changes are actually being made. That meant that it takes so much longer than it should for any meaningful change or transformation to really take place. Because by the time you've actually analyzed the data and disseminated the action, the data has already changed. you know, Coming up on a lot of these analytic tools, what I saw was that democratization of the analytic tools meant the decision-making was so much faster. Leadership could outline strategy and provide guidelines on how someone should think about the data and then kind of leave the actual actions in the hands of the people that were doing the work. And so the same thing also applies now to AI, right? It's less about you know, if we kind of outline, here is the AI process that we want every single person in the company to follow versus outlining guidelines and then having, you know, the individual departments and the individuals leveraging different tools to say, okay, I understand what my vendors are offering. I understand what the use cases are that need more automation or self-reliance. I understand how the different systems should be orchestrating together and scale from there. So that... We've kind of seen this trend coming and we've been building towards this built-in model from day one. Monisha Saldanha (13:20) And at a practical level, how is Rival using AI to change how HR teams operate day to day? Poornima Farrar (13:26) That's a good question. think There's two responses on the practical side. Internally, obviously, we're AI consumers, but I think you're asking about the in software as well. Internally, it's really interesting because with the development acceleration with AI, we are seeing just our ability to really move so much faster on being able to actually add value from a technology perspective to deliver capabilities and tell the story in the market on what we're doing. In terms of the actual HR teams, we've kind of been very strategic about where we are adding specific capabilities. So in the talent acquisition space, really top of funnel, sourcing, recruiting, and outreach being the core AI use cases for us in the employee journey. Just being able to come in and have HR take any and all use cases and say, okay, let's come in and build the journeys within minutes. By the way, that process typically takes months. Giving them knowledge agents so that they can learn in an accelerated fashion about what's actually happening within their organization. Giving them employee agents so that their employees can, you know, real time get responses on any questions that they have as part of their pre-boarding or onboarding journey. And then on the performance side, you know, giving them the ability to really improve their review process itself. A lot more planned, but those are some of the highlights today. Monisha Saldanha (14:46) Yeah, great highlights. Recruiting is notoriously time consuming. How does AI actually improve outcomes just speeding up processes? Well, let me say that question again. I didn't say that quite right. I'll edit this out. Recruiting is notoriously time consuming. How does AI actually improve outcomes versus just speeding up processes? Poornima Farrar (14:48) Thank you. That's a great question. So let me tell, let me talk a little bit about how recruiting typically works for the audience. You know, kind of in historically you're talking about all the roles that potentially will be getting created and filled. Then you're creating that job template. Then you're posting the job template on your career site. You're potentially distributing it. And then you're waiting for applicants to come in. And then as those applicants come in, you're taking a look at who those applicants are. You're trying to find the best match. There are expectations in some organizations that recruiters must look at every single applications if you're high volume, forget it, right? Like it could be huge. And so. Some of the things that the industry has done is to say, well, for those high volume cases, let's actually make it automated. Let's say I'm trying to hire for certain roles and all I need to know is, does somebody have a driver's license and do they live in a particular area? If that is the case, then just have a chat bot ask those questions, and then they can be quote, automatically hired. Okay, great. We can automate some of those things, but that doesn't really solve the core recruiter pain point, which is, people make up the business. You care about the quality of the experience that they're having, you also care about the quality of the people that you're bringing in, the onboarding, the nurture experience that you're giving them through the process, and your own branding as well. So, Rival has taken a different approach in that... You know, instead of kind of following everybody else that's kind of focused on improving the post and pre model, we are flipping it to basically think about it more from a proactive sourcing perspective. So instead of waiting and saying, okay, I posted this role, I'm going to wait for the first 500 people that apply before I close this role. Why not have the recruiters and the hiring managers collaborate, look at, you know, the full pipeline of people that are coming in or available in the market and then say, okay, who are the best people for this role? So we have a 750 million passive candidate database globally that allows people to able to look at different profiles across the globe. They can slice and dice the data. They can look at who's actually available. And then we also drive the actual outreach in a personalized manner as well. So you don't want to just send them you know, a generic message. You want to learn more about who they are. So scaling the personalized outreach, scaling the nurture campaigns, scaling the candidate matching with skills are all ways in which we are, you know, shrinking down the top of funnel experience because we believe that that in particular is a good way in which we can complement the other applicant tracking system solutions that are available in the market today. Monisha Saldanha (17:47) And Rival copilot introduces a conversational interface. How important is natural language interaction in HR tools? Poornima Farrar (17:55) Yeah, it's a good question. Just for context, my background is in linguistics. I have a PhD in stuff, I spent a lot of time thinking about computational linguistics and how natural language really works in the context of software. And our approach with Rosi, which is Rival OS intelligence, has really been to think about AI and the natural language layer strategically from where it makes the most sense to provide either form, content, or automation. So let's say we're talking about the personalization outreach, right? It's not just about creating that personalization. It's really about like understanding who the candidate is, what they're interested in, why they might be a good fit for my organization and then surfacing that back. So you're looking at kind of that full data layer. Another example might be, you know, if an employee is stuck as part of their process and wants to talk to Rosi, in that moment, we need to be able to give them responses to questions like, okay, what are my outstanding tasks or tell me more about, you know, what documents I need for my I-9. I don't understand section one. And just being able to get more conversationally. comfortable with getting the responses quickly and efficiently. It goes back to then, you know, that prompt engineering that we're all slowly becoming masters at in our daily lives. Coming into HR organizations in a way that A, makes their lives easier, but B, really improves the employee experience and productivity as well by giving them tools that they can leverage easily as part of their way of working. Monisha Saldanha (19:30) And What are the biggest risks of applying AI in HR, especially around bias, fairness, and decision making? Poornima Farrar (19:37) I think those are the three big risks, right? We think about them and talk about them all the time. Governance and compliance are always number one priorities when it comes to any organization and HR, along with other departments, is responsible for it. So it then becomes critical to make sure that when we're... launching AI capabilities that HR is in control of, you know, whether they want to adopt it, how they want to adopt it, what that looks like, full transparency in terms of, you know, the model itself, as well as what's entailed in there, the explainability layer of the model and also providing segmentation, right? So you and I should not receive the same kind of responses from our systems as managers in an organization because we would be running different functions if you will. So a lot of the guardrails that have historically been in place in terms of analytics or automation kind of continue to apply. Where it does get critical is that the level of trust with HR is much higher than with... other agents. So I saw a quote recently that stuck with me, which was that when it comes to an IT agent getting it wrong, you log a follow-up ticket. But when an HR agent gets a critical issue wrong, you may have a compliance issue or a damaged employee relationship. So we see a few things. We see our clients be very meaningful about, sorry, intentional about the approach that they take in selecting use cases where they will apply AI. We see them testing rigorously. We see them giving us a lot of good feedback around how we should be prioritizing the experience itself. We see a lot of adaptability on AI capabilities when it comes to the HR manual steps and experiences and more, you know, time if you will for for assessment when it comes to the employee side which naturally makes sense. Monisha Saldanha (21:36) Where does AI still fall short in understanding people, performance, or potential? Poornima Farrar (21:41) Still fall short. That's a good question. I think I mean, At the end of the day, you need a human in the loop, right? Like what AI can do well is really help us with the scale and automation that is required for an organization to be able to understand its data and its people. as we know, AI is as good as the data that is being surfaced up to it. So when there is context missing, when there is different external systems that are not speaking well to each other, then AI naturally falls short. And that is one of the common things that we see and one of the common ways in which Rival is actually coming into help. So let me explain that a little bit differently. Let's say that you're looking at an AI agent in one system of record. What happens is it only has access to that system of record. It's not really thinking about what's going on with the employee from a learning and development perspective. It's not really thinking about the data in payroll or benefits or some of these other systems. So you don't really have a central layer. So that becomes a key challenge in terms of AI agents not necessarily being able to do end-to-end flow the way that they ought to be able to because the data and the structures are not in place. So given that, we end up needing kind of a different way of working altogether where the agents should be coming in to kind of expect that the data is not the problem. But the actual experience layer is the problem, identifying that and then working from there into identifying the relevant data sources that should then be plugged up. Monisha Saldanha (23:18) And HR teams have very different needs across industries. How do you balance flexibility with simplicity in your platform? Poornima Farrar (23:26) Um, so that's, I love that question because, um, the, The common theme across HR teams across, you know, many, if not all industries is this passion for serving employees. Well, um, I can spend, you know, all day like in onsites with, with HR teams, identify you know, 25 different, um, optimizations, but then the top ones that they will prioritize are the ones that are going to make their employees' lives easier. So that's really where there's this passion that often comes in with HR teams that is amazing. The flexibility that they need that they often don't get from larger systems is for kind of their unique challenges. You know, if you're in healthcare, you're thinking about physician assisted onboarding differently than frontline assisted onboard, frontline onboarding. If you are, you know, bringing in a, an international cohort of nurses, then you, you have to start months in advance, sometimes years, and you want to make sure that you're nurturing them through that journey before they can actually land on day one and start working on the paperwork. So there are these experiences that HR needs to create in terms of, and that's just one example, but there are many, but these experiences that HR needs to create becomes really important in terms of how we can build out the overall structure of the organization. And, come in to help serve their needs for the different types of cohorts that they have. So that is where the majority of the flexibility is typically expected. Can you make my workflow for the different types of audiences that I have and not just assume that I am a one size fits all? And it's really where Rival shines relative to others in the market and why we complement other HCMs so well. Monisha Saldanha (25:16) And shifting gears a bit, let's talk about managing your product team. How do you ensure your product team stays close to HR users? Poornima Farrar (25:23) Yeah, mean, it comes in variety of flavors, right? I'm very fortunate to have a wonderful team that spends a lot of time talking to customers. We also spend a lot of time talking to our customer success teams. Different types of users, you know, there's the, you know, on the prospecting side, we learn a lot about who's out there in the market and what they're looking for. And on the existing customer side, you know, a lot of feedback in terms of what's working and where they would want to see improvements. So a lot of it comes down to really listening to customers and gathering their feedback on how things are working. The other piece that my team does as well is, you know, spending a lot of time in trying to think about how the way of work has changed. So we have flow of work diagrams in terms of, you know, how we see different departments or different roles working today and how a lot of that is being transformed from an AI perspective and kind of, you know, taking systematic approaches to how we can really, you know, attack that one at a time. if you will. So that becomes then a framework by which we can be focusing on not just the AI capabilities, but also the transformation overall, sometimes includes AI, but sometimes it just includes user experience or automation or analytics or what have you. Monisha Saldanha (26:36) What lessons have you learned about leading product teams in a fast-evolving space like AI and HR? Poornima Farrar (26:42) I mean, you know, every month the answer is different, especially in the last six months, like with a lot of the tools that have changed and evolved. Lots of, mean, The key lesson that I've learned, I would say is probably that we need to remain agile, right? Like. the, We can't lose focus on kind of the customer needs and pain points. Um, but are Some of the things that I've talked to my team about is the roadmap is a fluid document. You know, um, not that we ever were outlining like year long roadmaps, but honestly, like in this day and age, like being able to adapt and iterate becomes incredibly critical to, um, driving value to customers. So that's really number one in terms of kind of the product side. Um, The second is, you know, more of my team is vibecoding. They're also looking at being able to do sizing themselves by leveraging AI tools and saying, okay, I want to understand, given our current tech stack and architecture, what is the feasibility of something like this? I mean, that's fantastic. I mean, to be able to actually have that tool. Pairing design incredibly closely with product is product management, obviously super critical as well, to be able to really tie the two together. Design in my organization leads vision. And so many of the design tools today have gotten so far ahead that you can literally go straight from having the designers create the experience to... You know, having those components be brought directly into the code. But there are other ways in which you can essentially be having designers code directly and have even more involvement from the engineers at the kind of the final checkpoint stop. So that piece is fully evolving. I see a lot of evolution also in terms of product marketing and field marketing as well, where with a lot of the modern tools, we outline our strategy, our storytelling and our methodology. And then our field teams are actually taking that content and then transforming that across different industries. What does it mean for manufacturing versus healthcare versus legal, for instance, and then really being able to accelerate that storytelling. So it's less, you know, as we go further downstream, less about product features and more about really the use cases and what the needs are that it's serving. So it's an interesting time. I would say a lot of experimentation versus a lot of stable state, if you will. Monisha Saldanha (28:54) Yeah. And What are the biggest challenges you faced in scaling a product organization alongside a complex enterprise product suite? Poornima Farrar (29:02) I think the biggest challenge in something like that is just understanding the complex and suite yourself. There is often inertia in trying to make changes to that kind of a system because you don't want to break things for enterprise clients. You don't want to lose your credibility in the market by making a wrong move. So there's this need for experimentation at the same time as building something at scale and kind of the combination of the two becomes a critical part of how we can really build out the journey for the organization. So the first, I would say chapter was a lot about you know, identifying quick wins, identifying, you know, where the core gaps were, for instance, analytics, and really shoring those up while simultaneously looking further ahead at the horizon and starting to drive the agentic layers in the product as well. Monisha Saldanha (29:54) How does making HR more efficient change what HR teams actually spend their time on? Poornima Farrar (30:00) You know, it's the more time we can give back to them from a manual, you know, layer, the faster they can get to the more strategic parts of the organization. That's number one. But stepping back from that, and by the way, that's the company answer, right? Like in the sense of like, everyone says that for, I mean, you can probably say that for any tool out there. I will give you time back. You can be more strategic. But what that actually means in the context of HR is that when we see organizations starting to prepare for the future, whether it's bringing, you know, modern tools and technologies to their employees, whether it is... you know, understanding the skills profile of their organizations and how that can be improved or building out the overall experience for a particular journey, whether it's onboarding or offboarding or what have you. HR needs one, thought partner. And, you know, they need a way in which they can be independent and Agile, right? often HR systems move very slowly. Implementations take years. Transformation to implementations take even longer. There's often a promise on the roadmap that roadmaps that never get delivered. And in the meantime, they have to do a lot of work that sits in spreadsheets, bonuses, compensation, what have you. So building out, you know, a way in which workflows and employee journeys can be streamlined in a robust way, reduces turnover, which is the number one focus for HR teams, improves employee productivity, and most importantly then, like gives them an opportunity to really start to think more about what does the future state of that organization look like. Monisha Saldanha (31:37) Fantastic. This has been a great conversation. I have one last question for you. What is one book every builder should read and why? Poornima Farrar (31:46) One book that every builder should leave, read and why. I would say I really like the first 90 days because it's a good way to really think about understanding your organization. And it's also a book that hits me as organizations go through change management. So your first 90 days may restart depending on when and how your you know, your leadership team is changing or if there is transformation happening. But I think a lot of the strategies for an organization has to apply based on the actual steps, if you will, that need to be taken as part of that. Monisha Saldanha (32:25) Yeah, I've read the first 90 days multiple times, so it's definitely a good one to read, I agree. Poornima Farrar (32:28) Yeah. Yeah, yeah, I find myself going back to it more than other books, which is why it came to mind. Monisha Saldanha (32:37) Yeah, fantastic. Well, thank you so much for this conversation, Poornima. Poornima Farrar (32:43) Yeah, my pleasure. Thanks for having me, Monisha. Monisha Saldanha (32:45) And I'd like to thank our listeners for listening to this episode of Colorado Tech People. If this conversation on how AI is transforming HR and the employee experience sparked new ideas, consider sharing it with a leader or builder in your network. Don't forget to subscribe so you can hear more conversations with the people shaping the future of work through technology. Until next time, keep building systems that don't just scale, but truly support the people behind them.