Strong Leaders, Strong Tech: Equipping Leaders to Grow in an AI World : Future-Ready HR - Mini “Impact” Sessions (3 × 20 min)

Our very first, uh, presenter, very, very excited. She is from Tarion. She is one of our customers. Heather is here to present for us. Heather, thank you so much for joining us today. How are you? Doing great. Thanks for inviting me and, and having me a part of what is an interesting topic from an HR perspective. So (laughs) I'm excited. Yeah. Well, now I'm curious, what are you finding so interesting about it? AI in general. I think, um, uh, I think it was, uh, Bethany that made the comment that so many times we think that, um, AI's gonna take our job. Um, and I just... I, I actually... Part of what I'm gonna share is just thinking about that human component that so much of AI will do. I'm gonna call the, the work part, and we're gonna do the human part. Um, and so there's a lot that we s- we still have to do. Uh, we get to do. Even better to say, but we get to do it. (laughs) I hear you. Well, I think that was a perfect sneak peek. I don't know if we need to steal any more of your thunder. I don't know if you agree, disagree, Zach. Should she jump right into it? Let's jump right into it. Heather, the floor is all yours. Awesome. Let me hit that fancy Share button, and it looks like we are sharing and doing all the things. So, I am so excited to be a part of this. So I will say good morning, good afternoon. I don't know where everybody is from a webinar perspective, but I am definitely excited, uh, to speak on driving consistency, agility, and impacting f- through really a unified HR platform. Um, so we'll just dig in. I wanted to first take a second, um, to introduce myself and also Tarion, 'cause we're, um, we're three years old, a little over now. And so, not everybody knows what we do and, and where we are. But first I'm gonna talk about me. So, um, give a quick introduction. I'm Heather Fuquay. I'm in Louisville, Kentucky. Um, yes, home of the Kentucky Derby, where we get that spotlight once a year. Uh, but I started my career in 2005, uh, just a few years ago, and I was in benefits. That was my first exposure to the HR world. Over my career, I've supported many different industries, and these industries actually closely align with Tarion's verticals. You'll h- hear that in a sec. Um, I am a huge field hockey and golf fan, thanks to my two incredible kiddos. But let's be honest, I'm an even bigger Disney fan, for those of you that know who I am. (laughs) So, I do wanna very quickly high-level give an introduction to Tarion. Um, over the years, it's been found, um, that there's notable gaps that exist within the security industry. And we, as Tarion, have worked to close those gaps and provide services that help us stand out. That's w- what we want to be known for. So we offer a comprehensive range of security solutions while maintaining agility, ensuring swift decision-making, and preserving that personalized client-centric culture and value. So we approach bridging those gaps and delivering the best of both worlds where we can through safety and security. Uh, the industries that we, really that vertical-wise we provide is healthcare, aviation, education, commercial real estate, and petrochemical. And yes, I, I've actually been exposed to all of those sectors, which is kind of fun and interesting. But now, let's, let's truly dive in, into the topic at hand. So during our time together, we will review how a unified HR platform can integrate talent management, compliance, and risk an- uh, mitigation. This helps create a reliable source of truth for human resource, um, operations. Standardized processes ensure consistency and responsiveness, while agility enables rapid adaption to evolving business needs. AI-driven insights and automation streamline routine tasks, bring HR teams for more strategic initiatives, which is something I know we all want to have more time for. Um, these capabilities empower HR leaders to scale human potential effectively and manage organizational challenges with confidence. So, many HR departments operate with fragmented systems that create inefficiencies and increase risk. These disconnected tools lead to inconsistent processes across regions and departments, making compliance management even more difficult. Fragmentation also slows down recruiting and onboarding cycles, which negatively impact the candidate and employee experience. Ultimately, these challenges reduce operational efficiencies and hinder, uh, strategic decision-making. Addressing these issues require a holistic approach that unifi- uh, unifies HR processes under really kind of that single platform. Um, as mentioned, fragmented, uh, systems lead to inconsistent processes across the region. So, how many HR professionals on this call learn about a different process only after the transaction or action has taken place? Me, for sure. And then we scramble to respond, then we create new communication plans, we then have to create new SOPs. You name it, we're making it happen, because that's what we do. We're magic behind the scenes. Um, we also have higher compliance risks when we don't have those connections, and then onboarding. When it's slow, it's everybody's problem. In the security industry, hiring quickly is necessary. When onboarding's not working quickly, employer and employee are not making money, right? The result is lower efficiency and a weaker employee experience. As we all know, a great experience is priceless, so we work towards efficiencies there.Unified HR platforms can also bring together essential HR functions. As mentioned, recruiting, onboarding, but there's compliance, background checks, I-9, E-verify, immigration, enterprise risk management. The list continues to go on. We can put this all into one integrated system. It's a goal to deliver a seamless experience for HR teams and for employees by breaking down silos and enabling centralized oversight. This integration ensures smooth data flow across processes, minimizing errors, and enhancing transparency. From an HR perspective, transparency wins trust. By adopting the idea of unified platforms, organizations can drive operational efficiency and align HR strategies with business objectives. How many times have we heard, "Does this align," or, "How can you make this fix and, and, and match what we're trying to do as an HR goal or as a business goal?" Um, I think we all know that data helps run strategies for companies, so the more you know, the better. Um, this das- dashboard here, um, that you're seeing, it's very generic, but all usable data to have an effective conversation. So think about this. You're in a meeting with a people leader who's concerned about team performance and engagement. Without a dashboard, you might spend time pulling reports, explaining metrics, and manually comparing trends. This can make the conversation slow and reactive. But with a dashboard, you open a real-time view that can show turnover, retention by tenure, exit reasons, and that engagement score that can help the leader understand why performance might be low. So now the question is, how does this data guide the conversation? You can quickly say to your people leader, "I noticed turnover on your team is trending higher than the company average, especially among those new hires," or maybe, "Most exits cite career development as a reason. Let's talk about development opportunities we can implement to improve retention." There starts your proactive conversation. We can use AI to help with the creation of a dashboard, which leads to making our conversations effective and efficient, which I think are two E words we all hear from C-suite often. (laughs) So, it's a great opportunity and a great resource for us. So, now let's kind of talk about some of the benefits and, and how we can start to think about this- these pieces. So consistency is crucial for maintaining compliance and delivering a unifo- unified, um, employee experience. That unified HR platform standardize works- standardizes workflows across the enterprise, ensuring that policies and procedures are applied consistently. Centralized compliance management reduces the risk of re- uh, regulatory violations, while unified data enables accurate reporting and analytics. This consistency builds trust among employees and stakeholders and supports the culture of accountability. Accountability, what a big word, right? So I have four rules in HR, document, document, document, and be consistent. Consistency truly guides our ability to drive policies and procedures. When technology can help with consistent delivery, disciplinary documents, for example, we can use our document, input the situation, and then one less administrative task that's on our plate. We want to use technology to our advantage, but still, I can't imagine AI delivering our disciplinary document and an employee gets sassy. I don't think they're gonna know how to handle that. That's where we come in with the help of the delivery. We utilize the tech to help us in a quick turnaround and response to our people leaders, our client. So let's talk about agility. I bet we've all heard, "Let's be more agile," and sometimes in HR that means new SOPs or policies. However, agility allows HR teams to respond quickly to regulatory changes, market shifts, and organizational growth, which we are in deep here at Tarion. A unified platform provides scalable processes that can adapt to global expansion without friction. I once had a boss that told me, "Friction is good," and I was like, "Ooh, friction is bad. It creates that spark." And then I... Got it. We wanna create a spark. Spark creates conversation. So sometimes that friction, when we go through these, is important. It helps us rethink, reorganize, reshift, all the words I've said, of what we need to do. Let's talk a little bit about the onboarding piece of it. So faster onboarding, talented deployment ensure that organizations remain competitive and responsive. By streamlining operations, HR leaders can focus on strategic initiatives rather than administrative tasks, just talked about. I will share, Metrotech has a product we're currently reviewing, Tracker i-9, that keeps HR in compliance through streamlined regulatory updates and efficient I-9 collection, which I think we all are hoping to have better processes around with all the new regulations for sure. I'll just say it's looking to be a great tool to help with all the scaries of what could be with I-9s.So how do we deliver the impact we want to see? Well, impact can be measured by improved employee experiences, reduced operational costs, and enhanced risk mitigation. As an organization, we are taking small steps to improve our employee experience such as a recognition program that uses our policies and values. By incentivizing employees that follow our values, we're ensuring that others see what should be done in the field. Reward the good, right? As we've discussed, a unified HR platform enables HR leaders to develop these outcomes by integrating processes and leveraging data-driven insights. The dashboard's a great example of leveraging data to help that impactful conversation. Improved candidate experiences lead to better talent acquisition and that turns into retention, while operational efficiencies reduce costs, and example, less spent on multiple background checks because someone didn't know they had, were already sending that request. It happens. We're all in a rush. We're trying to do things quick. And then that strong governance and risk management, um, piece protect an organization from compliance failures and reputational damage. Artificial intelligent- intelligence enhances the capabilities of a unified HR platform by providing predictive analytics, automation, and actionable insights. Predictive analytics helps forecast workforce needs and identify trends enabling proactive decision-making. So how many ... I'm curious, how many are using AI to help with that workforce planning? I think back several years ago, when I would send an email to my people leaders and ask them what roles they thought would be needed for next year, it was a spreadsheet each month lined out, several rows, titles. And it would cite what month will we be deep in project work for the client, and then we'd plan backwards to post the role and begin recruiting. When we have these capabilities from an AI standpoint, it's trending the workload and the workforce availability, one less metric we calculate, so we're providing that strategic component needed. And I believe many of us have already experienced some automation in our daily tasks, but automation streamlines those repetitive tasks, again, such as background checks, that I-9 verification, freeing HR professionals to focus on, again, strategic priorities. We are HR business partners. That is our opportunity to help support our clients. AI-driven insights allow leaders to anticipate challenges and optimize processes for those better outcomes. Ultimately, our goal. (clears throat) So as we all know and respect at this point, metrics and data help us improve across the organization. We can make tweaks and changes, but knowing if the change is impactful is where we find ourselves often. Organizations that work towards implementation of a unified HR platform experience tangible benefits. Metrics often include a reduction in onboarding time, a decrease in compliance errors, and improved employee retention rates. These improvement rates translate into cost savings, enhanced productivity, and really stronger organizational resilience. By leveraging integrated and AI, integration, excuse me, and AI, HR leaders can demonstrate clear ROI and position HR as a strategic partner in business successes. So the numbers on the screen represent an idea of what we could set as initial metrics to measure what success looks like. We don't know until we try, but it's important to start somewhere, and so we focus on what can we do, what can we increase, what can we change? Right? So really, what's next? I'd recommend that HR leaders evaluate their current systems and identify opportunities for integration and automation. (clears throat) Metrotech has many options to help support growth and build readiness. Consider how a unified HR platform can help scale human potential and navigate complexity with confidence. Integration and AI are not just technology trends. I think we know it's here to stay. Uh, they're strategic enablers that empower HR to deliver enterprise-wide impact. By embracing these tools, HR pro- uh, professionals can transform their organizations and drive sustainable success. So let's think about how HR, how HR, how you can and your organization can leverage a unified HR platform to scale human potential and navigate the complexities with a little bit of confidence. I tried to talk fast. I appreciate your time, your attention, so thanks for that. I know we're a little bit behind, so I wanted to make my points fast. (laughs) Yeah. It was absolutely fantastic. I really appreciate the roadmap you laid out, what you did particularly at Terion and what they can take away and implement themselves. Uh, it's exactly why we're all here today, to get those, uh, tangibles and those action items that we can implement Monday, next Monday, two weeks from now, whatever that looks like. Right. Um, before you go though, I am gonna put you on the spot and ask you one question, if you don't mind, Heather. Sure. Mm-hmm. Um, like we said at the beginning, and you even alluded to a bit throughout your, uh, presentation, you are a Metrotech customer. You use our background screening solution, AssureHire. Been a customer of ours since, uh, 2023, and I know that you have grown at an outstanding pace. I think adding like 3,500 jobs in the last year. Yeah. (laughs) That's a lot of background checks. Um, so I would love if you can kind of share a bit more about your experience, uh, partnering with Metrotech. Absolutely. I'm honestly excited to, to share this. I think the most important word you just said there was partnership. Um, they truly do partner with us. Um, I will, you know, I, I think everybody knows you're never gonna have a, a, smooth process until you start to have the conversations. And so, we had a little road, uh, hiccups, I'll call it, not roadblocks, but hiccups in the initial. And it was, "Let's talk about it. Why was this happening? This doesn't seem right. That doesn't seem right. Why are we duplicating this and this and that once more?" Right? All the questions, all those things that could happen if you don't take a second to look at the details and then ask the questions. Um, but they've done a great job putting everything together for us and allowing us to ask the question and then really saying, "Ah, we've got you. Let's make this different. Let's make this package a little more ... Um, let, let's make this one easier," let me say it that way. "And then this one's a little, um, a little more robust for this piece." It's just simply sharing what's needed, but again, that word partnership has been what's really impacted our relationship and the desire to want to continue the partnership. There's such a ... AssureHire is just ... It, it's, it's a wonderful opportunity for us to be with somebody that supports us so well. Wonderful. Well, we appreciate you. We appreciate you showing up, uh, for us here today. Thank you so much. And, uh, Zach, I will turn it back to you. Thank you again, Heather. Thank you. Awesome. Give it up for Heather. I mean, talk about, I, uh, one thing I loved about her session was how the power of having this integrated foundation that can also be enhanced by AI from, like, an analytics and data insight standpoint, it unlocks the, the potential of, of human potential. Like, as Heather shared, she's like, "When you do this, you can truly get strategic with unlocking human potential." So, I thought that was an awesome kind of closing remark that she shared. Uh, something that I'll launch on the screen here as well for everyone, because Heather shared, like, yeah, they hired 3,500 people in the last year. That is incredible. Talk about filling job availability at a s- at a massive scale. So, if you would like to kind of see how you can navigate some of those things, especially from, like, a background screening standpoint, which is (laughs) extremely important to that process, and especially if you're trying to move fast and add velocity and fill roles quickly, you need to be able to do that with a strong foundation. So, if that's you, uh, make sure you sign up and say yes to this, and just take a demo with Mitratech and really learn more. How can you leverage something like that as you go into 2026? So, um, Kim, anything to share on that front before we keep the program rolling? Uh, no. I think we will keep moving it along, right? We got a lot of information to still cover. (laughs) We do. We do. So, everyone keep yourselves strapped in. Hopefully you still have your notebook out. We're gonna keep it rolling. I'm gonna invite, uh, I'm a personal fan favorite of this individual, someone that I deeply admire, has spoken with our programs in the past. I consider her a, like, kind of a thought leader, a lighthouse to this industry. And just hearing the stories and the ways that they've already approached integrating AI and standing it up and empower, empowering their people to also be in the driver's seat and leverage these tools has been really interesting to watch. And this individual also presented at our AI at Work program earlier in the year to share some of that, and now we're ... I mean, that program was in February of this year. So, we're nine months later and I kind of got an update, and she really shared, like, how important the intersection of the CTO/CHRO relationship is, and even some big reset buttons they have had to hit since that implementation. So, let's give a warm welcome to Dezalynn Wood, global chief people officer of Syntex, to, to the stage with us. We're very lucky for this opportunity. Dez, welcome. Thanks for being here with us. Uh, it's great to see you again. Thank you so much. I'm gonna do the, the famous ... Thank you, and thanks for that wonderful introduction. I will do a screen share. Hello, everyone. I'm glad to be back. I'm just making sure I get all the right, uh, desktop sharing. Everybody here knows that it's not always obvious. (laughs) So hold on. You're just going back and ... It's so sensitive. All right, there we are. (laughs) Okay. You can see? Yes. Okay, they can now see my screen. Yes. There we are. Um, I wanted to just kick in here is that, I, I had a great chat with Zach about, about how fast this changes. So, if you present any kind of material, you're pretty much sure that a few months later it's not relevant because that's how fast things are moving. And so, what I call myself is like a presentist. I'm not talking about the future, I'm not talking about the past, I'm not nostalgic. I'm just gonna talk to you about where the journey feels like it's right now. And I feel like right now, if you look back in the, over the summer and into this, the fall, all these articles were popping up about this intersection between people and culture leaders and technology leaders, and that's really because there's two groups that are really spearheading this co-creation of, of AI at work. And we come at it from slightly different perspectives. So, I wanted to share a little bit about our journey, and of course get some of your questions after that. So, you know, if I think back to the beginning of the year, I'm a big consumer of podcasts and news, and there was like, this endless sort of doomsday scrolling of-... about how we're all going to have, like, no jobs. We're basically going to be eliminated from the workforce. And, you know, what are we going to do with this fact that AI is coming in and it's going to take our jobs? And we know from a, a leadership standpoint, that's not really a great sell to people, which is to hear that this technology w- that we're urging you to adopt is eventually going to replace you. Now, I say I'm not a futurist because I think we're kind of terrible at predicting the future, and we really don't know exactly where it's going. But we do know that people are expecting to understand how to use AI, but at the same time, getting this message about, you know, what might come for them. So, I really believe, in our roles as leaders, we can position it as a savior or as a villain. And you have to make sure that the t- track that you're talking about in your organization really reflects what AI does right now. And what does it do is it says, you know, there's these reports about, you know, employee morale, and often it's about people being burnt out because a lot of their time is spent on admin functions or low-value work. And so, if you're in any kind of coaching role, everybody wants to contribute high-value work. And so, if we really lean into, you know, the adoption process in our organization is going to be to eliminate low-value work, and that you will be able to contribute at a higher level, it definitely is easier to adopt. But it's also true 'cause that's where we are right now. Obviously, if you're in a, you know, the customer service industry and we're talking about checkout lines, there are some li- the roles that have been automated. But overall, automation, you know, since the time of the Industrial Revolution, you know, we were all farmers and then we transitioned into different kinds of roles. And that doesn't mean that we had no jobs, we just had different jobs. And so, I'm really talking to our team about, how do we evolve our work? Keep up with the changes without the . So, something I think is really interesting about how AI was is we basically launched it, think of it as most of us, we launched it and then we learnt about it after. And that's an unusual thing in technology because generally when you're buying, let's say, a software solution or a platform, you've got someone who's, you know, training you on how to use it. You have use cases, you have an understanding of the destination and the outcome that you're trying to get with that technology. But most of us, when it comes to AI, we just had it, whether it's at home with a, you know, tools like ChatGPT or whether you have an internal tool or something like a Copilot. It just appeared and you were supposed to learn how to use it on the fly. And this doesn't necessarily track to how people pick up on technology when it comes to actually having measurable outcomes. So, I'm gonna give you a second to read this quote here. Just be quiet for a moment. So, uh, thinking, you know, what we saw starting, I'd say, middle of last year and very strong at the beginning of this year, is the technology team really telling the people team, "This is how work is gonna change. This is how work is gonna get done," and introducing tools. And this happened even in our case is that, you know, there's town halls and I'm being introduced to tools that we had developed. So, we're very lucky. We're a technology company and our CTO is very creative and very innovative. And a year before Copilot came online, he launched Gandalf. And Gandalf was basically a chatbot that was put in teams that you could interact with. And we launched it at a town hall but it was really like, our first virtual employee was not introduced by the people team. It was introduced by our technology team. And I want you to think about that because it's a really interesting thing 'cause typically, that's not how, uh, not how, how changes in work are often brought in through some sort of change management, but we were introduced to Gandalf this way. And what we did is we started showing heat maps by how often people were logging in, and it really was encouraging use. And this is in the early stages of AI when people were quite nervous that if someone knew that they were using AI they might think they weren't doing their jobs. Now, we know that the narrative has shifted greatly, that now the narrative is not that if you use AI you're not really a valuable employee. The narrative has shifted that if you don't use AI you're not gonna be a valuable employee, and I want you to keep that in mind. So, what we did is that our technology teams w- brought in the learning. So, we showed the tool, we encouraged use. Back here, we had, like, leaders like me and our CEO talk about using Gandalf and really trying to take away this feeling that AI aiding you and assisting you is you not really working hard and really championing it. And we also did a lot of education on the difference between what, let's say, gen AI does versus agentic AI, and get everybody really comfortable with the terms. And this was, again, done in a very grassroots way. It's not like I had plan. This was just happening at town halls as technology teams were educating us on this technology. So, I kind of like to think of it as like, you know, you throw every ba- you throw babies into the lake, and then you start talking about how to swim after they already have the technology in their hands. And we did the same things as I think many of you might have done, which is to do hackathons. And in these hackathons we basically said, you know, "Submit your best idea in terms of what can be made." Now, we did this early in our process because we wanted to test our own ability to develop AI internally. And initially, we had a really great uptake. We had a lot of people who were, you know, using our chatbots and getting used to proposing AI agents. And then, we had some results, which is our developers were able to build a bunch of different agents that basically did these- this, this work that no one wanted to do. So, what was really great is that we were living the actual example of what I'm sharing, which is, you know, suggest agents. And when these agents come in, they don't replace you. They're doing things like, you know, um, a task manager reminding you to do things. We had ones that was doing RFPs and doing collections and contracts. And this is really the message that we wanted our people to feel. But what was happening also is that while we were getting hackathons and suggestions from our team, uh, largely this was being done by our developers. And so, I had this challenge from our CEO which was basically, we need to accelerate our AI adoption and not measure just the amount of tools people are using but the outcomes we're getting. Now-This is an interesting thing, because you have to think, this is, for most of us, this idea of AI enablement or measurable outcomes are coming a good year after people are using the tools. And so, I think a really important question you need to ask when you have essentially this technology already used in the company, there's already a bunch of different ways to learn it that are sort of, um, I would say, uh, unstructured, is to align with leadership, be it your CT or your CEO. And what exactly are we trying to accomplish when we're saying everybody needs to be leveraging AI? And if we're not just measuring how much people use our tools, what are we measuring? And in that discussion, I had this, a real epiphany. Because what I ended up finding out was the understanding was that they aren't just gonna use assistance. We want to have major productivity gains. So, whether that's hours saved in the week, or the idea that you don't need that new headcount, but there was an understanding that we're supposed to have major productivity gains through this grassroots adoption of AI. The other thing that was an expectation was that people would understand how AI can be embedded in their roles, and how they can create their own solutions to have AI do part of a workflow or part of a job. So, if you think that you're just gonna introduce technology and people are gonna, uh, get major productivity gains, understand how to transform their roles, and build their own solutions, what I said is, "Yeah, no, that's actually not how technology's adopted." And what ended up happening is in this rush to not feel like you're being late, because I think if I said hands up to you all, I'll do it now. Hands up if you think that you're late in your company in terms of AI adoption and AI use and AI outcomes. Who here hears from their leaders they're late? Everybody's saying they're late. Everybody thinks everyone else is ahead of them. Well, I'm here to tell you, I see a lot of hands, that it's impossible that we're all late. It's just impossible. We're not all late. What's happened is that we have these tools out here with these assumptions attached to them that are actually totally unrealistic, given how this tool was i- it was basically introduced from a technology perspective. So, even though we did things like create LinkedIn learning paths or whatever learning tools you have, and leveraged all the great sort of free content or content that we had in our library, this does not turn into these outcomes. And I think this is really important, because when you think you're late, you grab whatever you can fast. And I got caught up in that. I got like this real nervousness of, "Oh my gosh, we need to get everyone trained." So, we did a pilot, even just with my own team, and I realized that, you know, watching a bunch of generic videos, um, doesn't necessarily transform your adoption of a tool. And what I ended up finding out is what we ended up seeing was although people were using our tools, they were really just generating a lot of stuff. So, I'm giving you some examples of the kinds of stuff that was popping into my inbox. I don't know, raise your hands if this is what you see all day long, is you just start to see more and more content coming from people, but it doesn't have this perception of being high value. And in fact, the message we were giving people, which is like, "We want to see you using AI," started to backfire, because people who are, you know, filling up s- their, their email or teams or Slack with this kind of look, I think people started to feel a little bit tongue in cheek about like how much AI somebody was doing, and the value of it was actually not seen quite as high as we had told people. And we're not alone. You know, this con- this word AI slop, it didn't win w- word of the year. I think it should have, you know, in the, uh, in the Oxford Dictionary. But this idea that we tell people to use artificial intelligence, but the perception that there's a lack of effort, depth or quality to it, is a reality. And I'm not just talking in my organization. I was hearing from my colleagues, even from my husband is, you know, "Oh my God, this person just sent me a bunch of AI stuff." I don't know, hands up if you heard that. And they weren't like, "Wow, they're so amazing." It was more like, "Wow, it's exhausting to go through it." So, I started to think how is it that this message we gave, we put this tool out and told everyone to use it, but now what we're seeing is just a lot of stuff, but we're not seeing a lot of productivity gains. Is it just me? And the truth is, it's not. Um, MIT reported recently, just found this article just a couple months ago, that 95% of these, these pilots, companies are failing. They're failing to see revenue from it, they're failing to see measurable impact, and that's because, you know, quantity doesn't equal quality. And I think the approach we made of just introducing tools and telling everyone to use them doesn't actually create measurable outcomes. And that's really a very frustrating thing for a lot of leaders, because most companies have injes- have, uh, have invested as, you know, I'd say, um, i- from small to large amounts in AI solutions. So, what I did with us is I said, "You know what? I'm going to press a reset button," which is tough to do, because you have to admit that what you did got you to a certain point, which is a lot of excitement. And I would say a small group of users were very engaged, to what are we trying to actually build? And let's do enablement that really, uh, I would say brings everybody to the level of consistent use in your company. So, I found another article that just helped inspire me, saying that winning organizations are those where HR leaders and to- technologists share the mission. So, we want to create a digital first, but also human-centric workplace. And what I find is that when you're just talking about tools, it's not very human. And in fact, what we had done was very low touch. So, what I said is, you know, how do we balance this desire to automate, but an understanding of how humans actually adopt technology? And so this was when I switched to a very human approach. So, away from tools or apps or, um, you know, p-... things like the, you know, LMS type of training, and really said, you know, "What is it that we want our people to be doing with our tools?" And I based our training on some research I did, which is really how technology is adopted. So whether you look at Excel, Outlook, or Salesforce, or any program, they're remarkably similar. And that's it. There's a small group, but there's a large group of people who use it for basic use, and then there's a group that uses it, let's say intermediate, which is that next level. So if you think of Excel, you know, it's those pivot table people, and then there's a small group who are advanced, who, those are the ones who have, like, multiple sheets that all link together. Just so you know, that's not me. And what I thought was interesting, if you look at how technology is adopted, is that, you know, you could be the kind of person on Outlook who has, you know, files for everything, uses OneNote, has everything come in and automatically get sorted. Put your hands up if you're that person. Or you could be me, who has, like, 2,000 unread emails. Never before did we tell people that if you're not the person who's using every feature, you're not gonna be relevant. That was never said before. It's more like, "Wow, you're so organized and I'm not." So, what I thought in our enablement is that we really need to have a basic level that brings in everybody to a basic understanding of what we're expecting you to use, and then we should layer our training to follow how technology is naturally adopted, and not believe that every person who uses AI is gonna get to that 10% advanced. So what is realistic? And then build a training to that. So this is a framework that we put together for our AI enablement, and that was that we would have a beginners for individuals and leaders, and the beginners would really be on just using the productivity features. And so whether you're using your own tool or you're using Copilot or a ChatGPT, it's really how do you use it as a chat assistant to help get your administrative work done faster? And just hyperfocus on that as level one, because that's really the large w- uh, belief right now, is level one is people who've mastered how to prompt and mastered how to upload documents and to do this kind of work. And what was very interesting for me in doing this is instead of sending them to the never-ending amounts of YouTube videos or whatever you could do, or an app you could pay for, we just made a very human approach. And you'll be happy to know, because this is free, is we just recorded ourselves on Teams. So you can see me in the box. I'm explaining our strategic framework as the introduction, so it's me. It's not somebody from, who's a, a thought leader, who's, like, you know, maybe AI generated. It's me talking to them about what we're doing here at Syntact. Then we have in the top corner here our CTO, who's actually sharing with him how Gandalf was made. So it's a high, high-touch, highly, I'd say, analog approach to introducing people to what AI is at Syntact. But why we did that is we wanted people to really feel a connection to the tools and to our use cases, and we even did things like put up these big questions that were on everyone's mind. You know, what are you really worried about GenAI? Are you worried that it's gonna take away jobs? And then what we did is after these series of questions, we just showed, uh, our, the CTO and myself chatting about AI. And just so you know, this is the intro module to enablement. So instead of just going into prompting 101 or getting you right into tools as if the tools themselves are gonna have you connect to the technology, we wanted faces from our company to talk to people and to get them to feel really ous with what we were doing, understanding that if you feel emotional safety and you feel valued, then your mind will open up and relax to technology. And then what we did is instead of just showing generic use cases, we actually created our own use cases on what we thought you should be using our tool Gandalf for. And then we had our developer, who actually, again, recorded himself going back and forth, videos of each of these are from 10 to 15 minutes, doing a real dialogue with our AI assistant. So instead of just doing what we call a single-shot prompt, which is when you ask one question, you get one answer, you see sort of the evolution of the conversation from a developer's standpoint. But the key is, here we were using our own strategic paper, we were talking about our own goal setting process, we were talking about how to use, uh, Gandalf to coach you through talent mapping. So we were using our own documents, our own screens, our own language, and letting people understand that this is what we mean when we say productivity gains. So when people were saying, "Hey, I use AI to write job descriptions or to edit my emails," we were like, "Guess what? There's a whole other way to use it," and this back and forth really taught people what level one looks like, and it greatly increased use and the quality of use. And then when we got to our level two, this is where we're saying, "Can you transform your role or process?" This is actually much more challenging, because asking people how to transform their role with AI is very challenging b- based on their current knowledge of what AI does. So what we did in that session is we ex- we introduced a roadmap assistant, which is an assistant that talks to you and helps you understand how to break down your job or your process into AI chunks, and then helps you complete a roadmap for yourself. Now, w- we didn't do this initially, and guess what we got back? People weren't really thinking about how to use AI in their jobs because they were limited by their own knowledge. So by having your development team build this kind of chatbot, it helped educate people on how to build their roadmap. And then we brought back our videos showing people how to build an agent if they wanted to, but we also did something that I feel very proud of, is we designated a small group of people champions. So if you remember my other chart, I had the, you know, beginner, intermediate (laughs) , and then these advanced people. We basically asked every department, who's that person who adopts technology really fast? We all have a super user, the person who knows all the features of whatever your software tools are, the one who understands reporting, how to fix any bugs.... the person who interacts often with the vendor. So we said, "Choose that person and we will do this boot camp for them, where we will bring them in and teach them how to build agents." Now agent building is open to everyone at Syntax. We have over a thousand agents that have been built. But the idea that these champions would be able to execute on a department roadmap is more realistic than I think asking every single one of your employees to become a developer, which is largely unrealistic and, well, probably lead to a lot of agents that are actually not effective. So, that was our level three. So imagine this, basic productivity use, then interacting with an agent to understand how to transform workflows, an option to try to learn how to do it yourself, or you can now go and be literate to talk to a champion and have that built. And this is much more realistic because you can scale this, whereas having your developers can't really do an AI, uh, roadmap for every single leader. So, oh, I already came to this stuff. So, what I want to end on 'cause I know I'm getting to the end of my time, is that one part of AI enablement has to be telling people what is important to keep as human beings. And this is something that came to me personally because I realized that we were getting a lot of AI generated content, and leaders weren't seeing it as that talks about what humans still need to do. (laughs) And I think that this might sound silly, but there are some that really resonate. And I use Yuval Harari's message, which is basically our brains haven't had a major software update in 70,000 years. So, why am I bringing that up is that we have this misunderstanding that data causes you to make decisions, I call it the data myth. And that's the idea that seeing a lot of information and seeing a lot of charts is gonna propel you to do and see things differently. Well, that's in fact not what happens. If that was the case, there would be no cocktails at the end of meetings because we'd be finished drinking 'cause we know it's bad for us. We know s- we know sugar's bad for us, we still eat it. So knowledge doesn't create change. What creates change is that very human understanding of how to package information and how to connect to people. And right now, AI doesn't do a great job. So what we really wanna make sure in our enablement is that people don't just take what AI generates and throw it back at you, because that doesn't lead to change in decisions. So I'm just gonna bring you something funny as I end. One of my quotes that my team knows I use all the time is, "A squirrel is just a rat with good PR." And that's essentially, understand that a lot of what AI puts out feels ve- very rat-like and not very squirrel-like. What do I mean by that? It just has facts and people are left unmoved, and I really came to feel that the amount of AI content that my team and other people were showing me felt kind of rat-like, and I literally used that. I was like, "Ugh, I just feel exhausted when I open a message and I see all those bullets, all those dashes and all that bold." I don't know about you, but I'm like, "Oh, I guess AI wrote this." I didn't care that maybe somebody... took a lot of time with the AI to get it to me, but here, you have to then work on it after human being did it. So what is the squirrel, is this connects to me. It reinforces it, it gives me a sense of urgency, it makes me want to act. And I told them, "No amount of PDFs and FAQs and guides make me want to do anything." In fact, don't open most of my attachments. So how are you still selling to me even though you've used AI to make this tool? How are you adding value to it? So here's an example of, like, a team charter activity. Wanted to do team charters, and I'll send, in five seconds everybody's throwing me this stuff. Well, you know what? I'm not really excited by this. I don't know about you. It just looks like everything else out there. And so what, I was going back to the team and saying, "Hey, I still want you to present it to me." I want to see, I want to see you distill all of that information into a simple visual. All right? So I don't want to see this. I want to just see a simple process. I don't want to see this kind of stuff in an email. I want to see branded images. I still want clean, branded, simple data. Even though you have all of this behind there, I don't want to see all of that. And finally, I still want a human being to book a meeting with me. I still want them to present to me. I still want to be, um, explained things, and I don't want it to be like, "Hey, I sent it all to you, you can go through it," because I won't. And so I really wanted to let our team understand that while it's important to master the tools to get the information, if you want the information to mean something to me, it needs to be distinctly human in how it's done. And that has not changed. So, as I conclude, this is a great quote. You know, it's talking here about that, you know, AI isn't replacing jobs. It's really, it says here, "Rather than replacing people, AI reshapes roles, shifting employees away from tedious tasks towards strategic, analytical and creative work." And so if we're really going to help our people be augmented humans, we have to first define what success looks like, second, create enablement that actually speaks to them as human beings and feels compelling and very highly personalized, and third, we have to remind them what we still want from them as human beings to do with that AI output so that what they're doing is continued to be valued as opposed to being dismissed. So that's my story of the intersection of people and culture and technology. See if you have any questions. All right, everyone. Give it up for Dezalyn. Dezalyn, thank you so much. I feel like the quo- the closing quote here is like, be the squirrel. (laughs) Like, can you as, like, spearheads in your organization, be the squirrel for the rest of the company and lead that way? Um, I guess I have a closing question. We are a bit behind time, so we're gonna keep it rolling, but I guess-You and I had a, a more personal conversation about being sometimes in the messy middle of things. Yes. And, and it sounds like even with your own AI integration experience, you had to hit a reset in the messy middle moment that got you on a different or more on a, a best on track. Just tell us a little bit more about like, for those individuals that maybe are starting this within their organization, they're going to experience some type of messy middle. What can they expect and how do they make sure that they stay in the path and kind of break through that? Well, I think the thing that we were chatting about is that, you know, when tools are introduced and you're being told, you know, everybody needs to be using it and there's all these, you know, there, there's, you know, we don't need to train them because there's so much information out there, just pause and remember that people don't adopt change that way and still require the same kinds of, let's say, squirrel-like treatment, uh, for technology no matter how compelling it is. And I think when technologists introduce technology, they naturally love it, they adopt it fast, they play around with it and they learn fast. But this is technology we're expecting normal people, everybody to learn, like me. And I don't just play around with things. I need somebody to, to encourage me and to humanly talk to me about it. So what I say is my messy middle was, you know, if you hear people say, "We're not seeing any ROI from this. We're not seeing, uh, any real business impact," don't just panic and think you did it wrong. Understand that in order for you to get from playing around with tools to measurable outcomes, you have to bring people on a very, I would say, um, structured journey so that they understand what it means when we say we want you to leverage AI, and people who leverage AI are more valuable than people who don't. It's not because you use it. It's how you use it and how you package what you used. And I initially felt like, oh my god, I got it wrong. But I just paused and said wait a minute. I think what I did is I didn't fully understand A, what the expectation was, and B, how to get people to that place. And when I went back to my roots of training in a highly human and high-touch way, very customized, we got back on track. Yeah. Well, thank you. That... I mean, I second everything that's coming in, in the chat, like, wow, thought-provoking. (laughs) This was incredible. Uh, so thank you for just taking us on that journey with you and breaking down how you navigated that and even got people a little bit more on track of like, what do we mean by this and what are we looking for? Mm-hmm. And what does actual quality output start to look like and feel like? And also, the social learning experience that you're doing that with each other, right? So, thank you so much. Thank you, uh, thank you so much. Have a g- have a great afternoon. All right, everyone. How about that? Our second Impact Accelerator, I mean, packed with some insight. I feel like that should have been a session where you could literally model the maturity kind of levels, start to work in that champion's network that she talked about, and follow that same kind of int- integration path. I mean, that was incredible. So, we're gonna keep it rolling. Next up, we are gonna welcome also another person that I'm like a fan favorite of. She's led tons of playbook master classes within our leadership network. And as I kind of talked to her, I was like, "Hey, w- w- you know, as Desalin even shared, you know, a lot of times people are pumping out a ton of output from these AI tools, but it's n- actually quality outputs, a lot of sludge, or there's a lot of things that are just kind of disengaging for people." So one, how do we continue augmenting the people reality? What are some lessons learned? But also, how do we, how do we automate and integrate AI and reduce the risk that comes with maybe being too dependent on it or allowing it to run freely? So, let's welcome Vanessa to the stage, vice president, talent management and operations, also goes by V. So V, welcome. I will add you up here. I appreciate you being here with us. Uh, yeah. We'll, we'll let you up. I know I see you unmuted. Are you able to turn your camera on as well? I am... There we go. Hello. There Hi, everybody. Uh, thank you, Zach, for having me here, and thank you to the Mitratech team. I'm going to share my screen. Um, can everybody see that? Yes. Awesome. I am gonna jump right in. I have so much to share with you all. So if you catch me babbling, please just interrupt me and slow me down. Uh, I am so glad to be here again, and I'm gonna be talking about what it means to build a connected enterprise, um, not only through technology, but through the people, the processes and the systems that enable the scale, the consistency and the culture that we've been talking about through these many Impact sessions. I have lived through tech transformations firsthand, uh, from our main goal at Intellerad of being an AI disruptor in our workplace, uh, to our HRIS implementation during hypergrowth and organizational transformation. So my goal here is to make this session actionable for everybody. I'm here to share lessons learned, pitfalls to avoid, and frameworks you can bring back to your organization today. So a little bit about me before we kick off. I'm Vanessa. As Zach mentioned, please call me V. I am the VP of talent management, uh, at Intellerad. For those of you who don't know, Intellerad is actually a sister company of Mitratech, uh, and we are both related to the HG network. I have been working in private equity or PE-backed companies since 2019. I am a generalist at heart, and variety is the spice of life for me. Uh, and I speak English, French and Italian. So you can approach me in any of those three languages and we can have a conversation. Um, I will be sharing my LinkedIn after the session, so feel free to connect and share any feedback that you have with me about this session. So what am I gonna talk about with you all in a little bit more detail, very quickly, a snapshot of the journey I want to bring you on. This is kind of my roadmap from fragmentation to flow, which is something I personally experienced at Intellerad, where we took disconnected data sources and reconfigured them to translate them into meaningful insights. So each of these themes builds on the other because you cannot scale culture or experience without scalable tech, systems or people.So let's talk about augmenting the people reality. Um, AI controversially might take, when used properly, can enhance the employee experience. AI allows us to be better listeners, it can help us better anticipate employee trends, and it helps us design a better employee experience overall. As I mentioned earlier, at Intellerad it was my team's most critical mission to be considered an AI disruptor at our business. AI disruption which, in my opinion, starts with HR, a lot like what Dessaline mentioned, because we own the systems, the processes and the behaviors that shape how people grow, perform, and adapt. When organizations start integrating AI into their work, it's HR who has to rethink the skill strategy, performance models, talent reviews, succession and workforce planning long before the tech ever reaches customer-facing teams. This pushes talent management to be more evidence-based and continuous rather than reactive or anecdotal. The biggest shifts here are cultural. As AI tools become embedded across our business, employees need new mindsets around experimentation, learning agility, and working alongside intelligent systems. We as HR set the tempo here. Whether AI adoption succeeds or fails is determined by how well we as people leaders guide managers, redesign roles, build confidence, and ensure equitable access to systems and development opportunities. And as AI takes on more administrative load, especially in our realm, from calibration prep, to skills mapping, to succession analytics, the function itself of human resources evolves. Talent teams can spend less time compiling data and more time coaching leaders, shaping strategy, and future-proofing capability. This is why AI disruption genuinely begins with us, because we are the only part of the business responsible for helping every other part adapt, grow and thrive in the face of constant change. We are also entering a phase where teams won't just be staffed with people, they will be staffed with AI agents alongside side working people. And that shift only works if we lead the way in designing the structure around it, redefining roles, clarifying accountability between humans and agents, setting ethical guardrails, and preparing leaders to manage blended teams. Without HR shaping the operating model, AI agents won't make teams more effective, they'll just end up creating c- confusion. With the right talent architecture though, they become force multipliers that let people focus on higher value work. So, if we look at this next slide, we can see directly some of the ways in which my team has been leveraging AI over the last quarter, and it goes without saying that AI has helped us first and foremost with our documentation. In fact, we've been able to push out a code of conduct, a global handbook, policy vaults for each of the regions that we operate within, and two playbooks, one employee-facing playbook and one for managers, all with the use of AI. We were able to create agents that retained all of our policies, our brand guidelines, our core values, and marketing guidelines, including the tone of voice that we use when delivering press releases. This way, all of the documentation that we have lives within the same voice, is worded in the same way, and is structured in a way that aligns with our values, our ethos, and our goals as a business. This, in addition to other major projects that we had going on at the time, like HRIS implementations, would not have been possible without AI accelerating the work that we do. Truthfully speaking, the slide that we're looking at right now is a real slide that I have shared with my team during our quarterly update. These are four agents that we've created in Claude, our AI tool of choice, that has helped accelerate the way we work every day. First is Intellibot. Intellibot was rolled out by our IT team and includes knowledge-based articles about our HR policies, finance practices, business systems policies and processes. Intellibot is available on our Teams chat and we can message it a question with, with or about any of the topics that I just mentioned. After Intellibot, you can see some HR-specific bots that we onboarded, and we give them cute little names for added value. So you can see Ima Wordsmith, which creates all of our job descriptions. In addition to branding guidelines, Ima has access to our entire job catalog and comp structure. That way, we can immediately align new roles to their job family and comp range. Connysolidate is meant to consolidate all of our HR feedback. In this spot, all employee feedback from exit interviews, check-ins, you name it, is consolidated into this bot. Any time we receive that feedback from employees, we can throw it into this agent so that it can deliver takeaways, predict engagement trends, and support us in making data-driven decisions when it comes to our people and our culture. Finally, our compliance agent, Cal Ender, supports regional documentation, requirements, uh, regional documentation and compliance requirements across our four countries. With this agent, we're not updating information or uploading information into it, but rather resources. Resources like SHRM, in Canada the CNESST, documents from our compliance partners that we work with in our international regions so that we can be sure we're building a robust and comprehensive compliance calendar across t- talent management, total rewards, and payroll. Using these agents has allowed us as a team to become more agile, has improved the way we use data, and it has supported the way we make decisions. It's allowed us to predict trends that we see happening across the business, and it's improved our ability to be compliant, which I'll talk more about in a bit. So what are some of the lessons learned here? First, overcommunicate. You're gonna hear me say this a few times throughout this talk. When we launched Intellibot, most of our employees did not realize that it needed hands-on training. Intellibot only gets smarter if people give it feedback when answers are off, and its accuracy is tied to how clearly our knowledge base articles are titled. If we don't share that openly, people will assume the bot is always right, and it absolutely isn't.We also learned that even in a tech company, not everybody is a prompt pro. A lot of the, "This isn't working," feedback boils down to low-quality prompts. Our HR team actually hosted an internal hackathon to help us level up on our prompt writing, and our talent development is now rolling out a company-wide hackathon to stress test and improve how we write prompts. Next lesson learned, feedback is data. At one point, I asked Intellibot a Canadian benefits question and it gave me information from Cigna, our US provider. Why? Because I put Canada in the article tags instead of the knowledge base article title. That one mistake became a crucial data point for us, which leads to the next lesson. Validating data is a chance to reverse engineer your AI. Take whatever your tool gives you, feed it back to the system, and have it critique itself. I've even recommended this to leaders with team members who are skeptical about AI. When you understand where the tool fails, you understand how to use it better. I had Intellibot read its entire knowledge base article, uh, entire knowledge base so I could see exactly how it was interpreting our content. It's the same approach teachers use when they have students bring in AI-written essays just so that they can tear them apart and learn how the tool works. And finally, brand matters. Build your brand into your agents and your prompts. Any time we create a new agent, we start by uploading our brand guidelines and employee credo. When I designed our succession planning framework, I uploaded our credo and recruitment ethos so every recommendation aligns with who we are and the culture we're building. So speaking of culture and agility, most organizations underestimate how much a disconnected tech stack can slow down your business. Although AI is now a critical component of our architecture, it doesn't protect our business from all the risks associated with a broken infrastructure. When systems don't talk to each other, you get duplicate data, inconsistent employee experiences, and slower decision-making. I have seen this firsthand. This time last year, our systems across accounting, finance, HR, and compliance were disparate, causing major headaches when it came to reporting, decision-making, and accurate financial planning. Building an integrated HR architecture is not about having a tool for each of these challenges. It's about designing the flow of data to marry to your processes so that you can build accountability, share ownership of outcomes, and furthermore, scale across your business starting with your tech stack. Building an integrated HR architecture is not about buying more tools. It's about the flow of your data, your processes, and shared accountability. Even though the technologies are scaling the process, your people are scaling your culture, and you need buy-in and adoption of both to scale your business. So let's talk about integration, the impact of which is huge. The symptoms of poor integration might show up as missed opportunities, increased compliance risks, lack of workforce visibility. Without connected data, leaders can't forecast, plan, or even see what's really happening across the org. And with AI emerging fast, integration becomes the foundation for insight. We cannot leverage AI effectively if we don't have good data. In my past life, re- quarterly reporting took an entire week. At one point, we were using our LMS for people data and then cross-referencing it to what was available in our payroll system, and then delivering that in a manual report to our finance team, who then stuck it in a larger, even more manual report. Getting consistency was impossible, and more importantly, the way we were working limited visibility at our manager and our leadership levels. So here's what we did. We standardized our tech ecosystem around, uh, our core HRIS system, called Hibob, that included reporting, compensation, learning, and then we built outward integrations. We integrated with payroll, with our ATS, with our benefits providers, and an engagement tool. Once this architecture was stood up, we set out to design custom workflows for onboarding, off-boarding, and compliance. Doing this allowed us to au- automate almost all of the HR processes that we had that were, at that point, incredibly manual. Before this implementation, administering hires, data changes, off-boarding, leaves, et cetera required manual data entry in at least six different systems, and we were able to reduce that to a single point of entry. This also reduced our margin of error across these different systems, and it freed up the role of our team members because at first, data entry was a huge chunk of our day-to-day. This implementation has allowed our team members to grow in their career, uh, some of them even earning promotions for broadening the scope of their areas of responsibility outside of data entry. Our HRIS system also offers HR and finance dashboards that are bespoke to the system. Data that previously took us a week per quarter is now available anytime, in real time, with the click of a button. And our cherry on top is that these dashboards are equally visible to our leadership, giving them better insights into their staff and their budget. So to wrap up on this, formalizing and integrating our HRIS allowed for one source of truth for employee data, cross-functional dashboards that leaders could actually use, real-time visibility into turnover, engagement, compliance, and productivity, automated audit trails, and early warning indicators when something was about to slip. Finally, because my team was no longer stuck reconciling data manually, we could redirect our time into strategy, coaching, and forward-looking planning.So a few lessons here became painfully obvious along the way. First, again, sound familiar? You truly cannot over-communicate. People need time and repetition to trust change. We were on a three-month pilot with weekly stakeholder meetings across HR, finance, IT, InfoSec, business systems, and payroll. And we shared takeaways from those meetings publicly so that everyone stayed in the loop. We also mixed our training formats. We had live sessions, recorded videos, and office hours so people can learn in whatever way works best for them. We also put people on the front lines of data integrity. Leaders were responsible for validating team data and employees re-onboarded into the system to ensure all their information was accurate. It also doubled as a sneak peek into our new onboarding experience, which people actually loved. Another big lesson: Data integrity is everything. Bad data destroys good systems. Building our infrastructure for our HRIS system was one thing, but the seven months we spent auditing and cleaning our data, in Excel no less, was the real grind. However, it truly paid off. Garbage in/garbage out is real, and I strongly encourage anybody planning an implementation to assess their data quality first. Next, stakeholders want to co-create with you. They don't wanna be surprised about integrations and rollouts. Bring them in early and they'll turn into your biggest champions. And lastly, architecture is never done. Build for iteration, not for perfection. We do HRIS reviews quarterly and are constantly refining our automations. So another area where these integrations have paid off very quickly is in risk reduction through automation. I've been circling around this topic through my talk so far, although it cannot be overstated enough. Compliance is, for us, about creating a level of trust where the business feels safe moving quickly. Our systems now make the single point of contact for any auditing body concerned with people data and processes. Furthermore, anything that concerns the employee is now centralized in one place. I'm very proud of the fact that our HR processes are often praised by auditors for their comprehensiveness, and I credit this to two critical factors that we've been talking about. One, leveraging AI to be proactive about upcoming legal and regulatory changes. Two, ensuring our source of truth is always up-to-date and fully automated so as to reduce our margin of error on data and process. More specifically, our new and improved systems architecture has allowed for uniform and standardized onboarding so that nobody falls through the cracks; role-based access permissions that give managers what they and stakeholders improved access to data without compromising employee privacy; automated flags for missing I-9s, expired work permits, overdue training, or employee-driven data changes like address changes, changes in VPN location, et cetera; and finally, year-round audit readiness instead of the usual fire drills. So again, some quick lessons learned. One, consistency reduces risk. Train everyone the same way and promote visibility into your process across the org. Also, don't automate for the sake of automating. Automate real challenges. We don't need to over-complicate the process and track for things that aren't audit necessary. In fact, we work with our auditors to trim the fat and better understand what we're rev- what we're reviewing is process improvement against what is a hard-line audit requirement. Next, anchor your process and your values. Compliance is easier when it allows, uh, aligns with culture and buy-in so the process becomes implicit if you're bought into the culture. Finally, cross-training prevents bottlenecks. Resilience is structural, not personal, and everybody should be able to jump into an audit, know the process, and pass it with flying colors. So lastly, let's talk about augmenting our people. Until now, we've talked about the AI, the tech stack, and the compliance side of wiring everything together. But the real magic, and the part your employees will feel, starts once your systems and data is integrated. This is when you can finally treat enablement as its own cultural pillar rather than something scattered across tools. If your HRIS acts as a growth ecosystem, then tying learning paths, performance insights, and recognition singles into a single workflow becomes essential. When these systems talk to each other, they stop being software and they start becoming accelerators of people development. A few ways in which we've done this is, one, through a leadership enablement series that builds soft skills and manager confidence, all available in our HRIS system. We also have short, punchy micro-enablement videos using mixed media to reinforce policies and processes to promote compliance in a way that's actually digestible. We have automated our learning journeys in our HRIS system, triggered by role changes, performance insights, or skill gaps. And we have dashboards that make development visible, showing where growth, promotion is happening and where it's stalling as well. We also, in the same system, have a performance management process that runs year round supported by systems that nudge these conversations and help leaders track goal- goals and growth in real-time. Then there's AI, which is quietly reshaping how we approach all of this. Skills techn- taxonomies are shifting from static documents to dynamic systems. HR is becoming the early warning system, spotting where roles are about to evolve, where talent might fall behind, and where re-skilling needs to start sooner than we could ever see before. AI is also transforming performance and development cycles. Instead of waiting for annual reviews, AI-driven analytics highlight patterns in real time, like productivity shifts, collaboration patterns, and early indicators of burnout, leadership readiness, flight risk, or even emerging strengths. It doesn't replace a manager's judgment, but it gives talent teams a far richer foundation for building people up. At the end of the day, the goal is not just plugging in the tools. It's using the systems to augment human development.So when you get this right, your ROI is tangible. Creativity, retention, performance, and eNPS all rise. Um, in our instances, the results were really palpable. We saw eNPS go up seven points globally, we had better retention, we had over 50 promotions at the start of our new fiscal year, we had higher performance outcomes, and you can see how leveraging these systems as part of our culture drove that development and that im- impact across the board. So a few truths showed up loud and clear. Low engagement is still feedback. If people aren't using your tools, the tools aren't always the issue. We can dig into capacity, bandwidth, and what people actually have room for, which means we're designing for a real environment. If our business is in a high pressure season, we lean on microlearning for higher touch development. Shared foundations matter. That's why we started with a playbook to level the playing field and layered in intentional learning increments. And finally, reward experimentation. Learning only what happens when people feel safe to try things, break things, and treat failure as data instead of something to fear. So my ask of you is simple. Don't treat these HR sys- systems like IT projects. Treat them like the operating system that allows your organization to scale. Start small, one integration, one automation, one AI experiment, and then repeat. Thank you. If there are any questions, I am happy to help. Thank you so much, V. Uh, that was incredible. I wish we could follow up and ask questions. You can stop sharing now, by the way. We see your, your chat screen up. Phew. (laughs) You're good. Um, thank you for that. I wish we could ask you a bunch of more questions, but we're kinda running a little bit behind time in the program. So for everyone that listened, I mean, talk about lessons learned, uh, I encourage you to reach out to her. Vee's one of the most amazing person of service. I'm sure she'd be happy to answer some questions directly. So thank you so much.