Make AI move the metrics your PE firm watches: an HR playbook

HR leaders keep hearing the same instruction: adopt AI, lead it, make something happen. What rarely comes with it is a line to the numbers a board watches. Melissa Lemberg has spent nearly 30 years on experience-led transformation for companies like Delta, Ford, Apple, and a string of Fortune 100s, and now leads digital transformation and AI strategy at LogicMonitor. Her position is direct: AI earns its place only when it moves a business metric, not when usage climbs.

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Should AI be its own strategy or part of the business strategy?

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AI belongs inside the business strategy, tied to metrics leadership and investors already track, not run on its own. Lemberg starts from company priorities and the numbers the PE firm cares about, then asks which parts of the business can move them. Revenue per head and total EBITDA become the targets, and AI use cases get chosen for their pull on those figures. "So not just an AI strategy alone, but it has to tie back to the metrics that matter," she says.

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How do you keep a human in the loop as work gets automated?

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Break each role into two piles: repetitive tasks a machine can take, and the judgment work that stays human. Lemberg finds the line through stakeholder interviews and journey mapping, watching where people lose time and where trust carries the value. AI drafts the content or clears the queue, then a person reviews it and owns the call. "building trust, right? That's something a human can really do well, right? Not so much machines or AI," she says.

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Why isn't high AI adoption the same as business impact?

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Adoption counts activity, not outcomes, so a company can reach near-universal usage and move nothing. Lemberg's warning is blunt: "people could be using ChatGPT to make recipes all day, but it would still show high adoption, right?" The fix is to make adoption prescriptive, naming which tool to use and toward which result, and to set a benchmark before you start. "You have to put a stake in the ground in order to show evolution, right, or impact, if you will," she says. Without that baseline, ROI claims fall apart under financial review.

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How does HR lead the rollout without making people fear for their jobs?

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HR leads it as a change-management problem: set culture from the top and tell each person what is expected of them and why. Lemberg is candid that "AI can be scary, right? Everyone is starting to worry," and that fear is what stalls adoption. The counter is steady communication and a clear line from company goals to each role, plus enablement and a real what's-in-it-for-me so people engage instead of bracing.

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What should an HR leader with no transformation team do first?

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Start with the business, not the tools. Lemberg's sequence: learn the metrics that matter, put up a heat map of where AI could move them, starting where headcount concentrates like go-to-market or engineering, then run one or two experiments with a human in the loop, such as AI-assisted hiring to pull down time to hire. She scales it through a champion network, operators who justify each idea with a measurable outcome, backed by IT for security and access. "You don't have to reinvent the tool every time," she says, the point of the network: spread what works instead of rebuilding it everywhere.

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Lemberg expects AI to be written into every role and function inside the next 12 to 18 months, which turns today's scramble into a standing expectation. As Logan Rivenes put it, "right now AI is as bad as it's going to get," so the operating model HR builds this year is the one that compounds.

Logan (00:02): We want to chat about AI in 2026. We've kind of gone through the 2025 introduction of mass AI adoption. But before we get into some topics, I'm here with Melissa Lemberg, leading digital transformation in an AI strategy at LogicMonitor. Melissa, is there anything else you would like the audience to know about you?

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Melissa Lemberg (00:26): Absolutely. I've spent almost 30 years thinking about experience led transformation. So helping large organizations like Delta Airlines, Ford Motor Company, Apple, think about who they want to be and how they want to engage with their customers, with their employees, with their partners, and how do they use data, technology, platforms, processes, and human centered change to drive true transformation so they can reach their objectives. They can be engaging their employees, partners, customers, and otherwise to achieve their objectives, be that drive efficiencies or fuel growth. So this area of digital transformation and automation, right in this case with AI, is an area where I have deep experience in working in lots of different markets, lots of different kinds of companies, small and large, some of Fortune 100s to truly drive transformation and how work gets done.

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Logan (01:21): Awesome. Well, for our audience here of HR leaders in often private equity backed companies, we wanna help empower you in how you're going to lead AI and HR. But I wanted to kind of kick the whole conversation off with, so I've heard from multiple HR leaders that often they're being told adopt AI and do something with it. So I was just talking with Dani Woods and Kristin McDonald about that and like how to translate that into actual initiatives. Your opinion, when we kind of prepped for this, was tying AI to business strategy. So let's start there. Let's talk about like, how do you think about embedding AI into business strategy rather than just AI as a strategy on its own?

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Melissa Lemberg (02:09): Sure, absolutely. So we really look closely at company-wide metrics, priorities, company values, and let them guide us forward in determining the strategy for leveraging AI, achieving objectives. So not just an AI strategy alone, but it has to tie back to the metrics that matter. So what the PE firms are looking for, what the company wants to achieve, and then saying what areas of the business can we use as levers to drive those objectives? So fuel our growth, looking perhaps at a go-to-market team, accelerate product development, looking at an engineering team, and then identifying opportunities where you can insert the right tools, the right processes, the right methodology, so that you're driving adoption of AI tools to achieve those business metrics, be it revenue per head, looking at total EBITDA or otherwise, so that you know that you're affecting the metrics that matter for your business.

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Logan (03:13): So what are some of the use cases that you're looking at in that? So if you're trying to drive revenue per head, because that's a big one in PE-backed environments, what's a typical use case you would start to think about? How can we implement it? Or going through that so then you're tying AI use case to business metric?

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Melissa Lemberg (03:32): Absolutely. Looking at stuff with go-to-market, if we're looking across go-to-market teams, where are there opportunities to do things like better forecasting, account planning, identifying the right relationship mapping, conversations, communications, and product, right? Cross-sell, up-sell. And how can you use AI tools, data, gathering information to make those activities easier, more efficient, more data driven so that the time that account executives, for example, are spending are developing relationships, right? They have all the right information in front of them. The ways of working are more efficient. They're not having to do everything manually, right? So that they can be smarter in their jobs, in growing their accounts, offering new products, right? And the product team can be using AI tools to be developing new products, because they're using code acceptance tools, security tools, et cetera, to be able to drive their work more efficiently. They have time to think of new products, new capabilities, test new ideas, and then bring more products forward so that go-to-marketing can sell more things that we know that our customers need. So looking at those kinds of functions and identifying where are there manual processes now, where are there repetitive processes that could be automated through go-to-market tools, CRM tools, using tools like a ChatGPT or otherwise to make people smarter, better, faster, right, more informed so they can do their job to grow revenue.

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Logan (05:11): Yeah. And so in that whole thing, so you talked about making AEs more efficient, you know, product development, being able to develop more products, get more revenue. You talked a lot about human in the loop AI when we talked about it. So in doing all of this, like, how are you thinking or trying to get the rest of your executive team to think about AI and human in the loop way to do it?

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Melissa Lemberg (05:25): Yes, absolutely. And when I talked about my experience, I talk about experience-led. I talk about human centricity, right? Always having that human in the loop. That plays a big role in incorporating how work gets done in an organization. So ensuring that when you look at an individual and you look at their role, identifying which tasks could be automated. So what are those repetitive tasks that they're undertaking that could be automated? And then what are the tasks that really need to be undertaken by a human? So developing relationships, having meaningful conversations, responding to needs, understanding friction points, right? And be able to have those conversations to grow accounts, right? For instance, as an AE, building trust, right? That's something a human can really do well, right? Not so much machines or AI. So how do we do that effectively?

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And so for us, it becomes a combination of looking at job roles, the tasks engaged, piecing apart where the human parts and the digital can best work together for decision making, but also actually having conversations with people. So conducting stakeholder interviews, so understanding where are friction points in their job and where do repetitive manual tasks slow them down. Right. Then figuring out which tests are most suitable for AI. So where can you understand where the systems breakdown, where the tools are not available, where people find that they're spending a lot of time that they don't need to. So in the olden days, when we used to talk about marketing and production, we'd have people called producers, right? Who would take like a piece of copy or a photo and then take it to someone else for production, for checking, take it to someone else, right? Check a box, bring a file folder, do all the things. Looking at processes like those to automate and say, how can we have someone set up in a queue? How can we automate workflow around decision-making? How can we enhance writing, content development, personalization by using AI tools as a starting point? And then a human can check in, can reread, can add points that they need, but they have something to react to as opposed to starting from nothing, right, in a content development example.

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Logan (07:54): Yeah. So I want to get to the automating tasks piece, because I think that'll lead us nicely into how HR can think about this. First, so you have the automating what we're already doing and making it more efficient. I'm curious, like, what your take is on unlocking new capabilities, so things we aren't already doing that now allows us to, like, have deeper research so we can create new products or things like that. Unlocking different capabilities that we're not already doing and how you're thinking about that.

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Melissa Lemberg (08:28): Sure, absolutely. And this has been a key area of focus my entire career, right? Because we used to call it lift and shift, right? When someone would take the exact same thing they did by a new piece of software and then try and translate the exact same thing. It doesn't work, right? So you have to think about new ways of working. So this is where we start to identify like, what does good look like? Let's focus on outcomes. So like, what are the outcomes you're trying to drive? Understand perhaps what's working now and what's not, right? To drive those outcomes, to reinvent ways of working and new processes so that those can be enhanced with AI. We're not just taking the same exact activities and automating them, as you mentioned. And part of that is having conversations, watching people, what they do every day, what tools are they accessing? Where are they getting tripped up? Where are they spending a lot of their time? Understanding some of their fears, and I know we're going to talk about this, right, around using AI, understanding AI, having access to the right tools, but also, like, what are those outcomes we're trying to drive, and what would be the most efficient and effective way to driving those outcomes, which can lead to activities like journey mapping, right, from those stakeholder interviews, understanding opportunities, understanding emotions at each phase in the process, and the outcomes that you're trying to drive to create new ways of working, which is really about transformation with a little T or transformation with a big T when we're talking about enterprise-wide.

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Logan (09:56): What's the difference between big T and little T transformations?

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Melissa Lemberg (10:00): So little T transformation can be a step change, right? Access this tool instead of that tool, right? In some cases. And then big T transformation can be like, are overhauling our main system of record, you know, in the past it's been X, now it's going to be Y. Like this is huge, right? Everyone's got to shift and change, right? And sometimes it's swivel chairing from one system to another. Sometimes it's changing what's important to the company, right? And what they want to be and what kind of products they're developing. That would be a big T transformation.

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Logan (10:31): Yeah. Okay. And so then going back to how we kind of started this, tying it to business metrics, how are you then like actually measuring these various things that are happening to metrics? Cause I think the sentiment I've heard, and this may or may not be true, is that oftentimes head count right now is the leading measure of AI in the early days. Like, what are other ways that you're thinking about measuring AI? Not that you're necessarily doing that, but.

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Melissa Lemberg (11:01): Sure, that's okay. So really thinking about benchmarks. So where are we now? You have to put a stake in the ground in order to show evolution, right, or impact, if you will. So really understanding what are the benchmarks now. So when one looks at an engineering team, I use that as an early example, like how much are they using coding tools right now? What are the tasks they're using them for? And then where can we move the needle? And behind that is really the tracking.

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Melissa Lemberg (11:29): Right? So understanding what is the ROI, right? When you're developing something new, do you buy something new? Do you invest in existing tools? You are evolving tools? Do you build something on your own? Right? Like what is the effort in adopting something different? And then what are the outcomes you're trying to drive? And then how is that different from the benchmark where you are now? So putting a stake in the ground and understanding benchmarks for measurement, understanding the outcomes you want to drive and then being able to do ongoing measurement to see if those changes that you're implementing are actually making an impact.

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Logan (12:07): Okay. Yeah, those are good tidbits. And that's going to be, I think something that HR leaders are super interested in, especially as at least on this show and from us, we're encouraging them to get involved early in the AI conversation. But I guess that's kind of our second topic is how can we start taking AI as a empowering business or influencing business metrics and get HR involved to help lead that because often AI does impact people and headcount. So therefore HR starts to get into the conference or should start getting into the conversation.

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Melissa Lemberg (12:47): Absolutely, and AI can be scary, right? Everyone is starting to worry. Like, you know, it's a lot of new information. It's new technology. It's new ways of working. People might fear for their roles, right, or their job, their livelihood, right, as they believe AI might take over their role. So to combat that, partnering and having strong engagement from the HR department and from leadership. So we're talking like CEO, board level leadership around culture. Like what does being an AI first company mean? Like what does that mean? How do we translate that? So behind any good human centered change management strategy is communication. So having and starting at the leadership level to say, this is what's important to us. This is the culture we're driving around using AI and automation and ensuring that everyone feels comfortable.

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Trust, you're having the right kinds of decisions from a security standpoint, from a trust standpoint, right? To ensure that your data and everyone has the right information and it's secure. But also what does that culturally look like? So how are you leading from a leadership executive standpoint, those communications for the vision for leveraging AI and how it helps to align to company goals, which we talked a lot about, and also thinking about employees. So how do you create the right enablement plan and outline expectations for how people are going to engage with AI and also helping them understand what's in it for them, right? So talking about rewards, incentives, right? And how they might be assessed in the future around their capability to do their job in the framework of leveraging AI and ensuring that they understand what success looks like for them and their role, and then how it ties back to the company objectives. We often talk about what's in it for me, metric of like, I need to understand how I'm tying to those big goals of the company.

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Logan (14:44): Yeah. So how are you starting to see the incentives and AI adoption being brought into an IC's role and how they should start thinking about it and using it in their job?

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Melissa Lemberg (15:03): Sure, so I think starting with the company goal, right, of being an AI-first company or however they want to articulate it, and then bringing that down to a leadership level, right, a management level, and then the IC in terms of helping them understand what does good look like. So perhaps, right, they undertake a talent assessment at some point, right, throughout the year or multiple times throughout the year, but really laying out an understanding about expectations. So in your role, at your level, our expectations are X and Y around how you're going to use AI tools, right?

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And in the beginning, those expectations might just be around curiosity, right? Creativity, innovation, right? People who want to learn, right? You may not know everything and that's okay, but we're looking for you to show me signs that you're interested, right, an AI, and that looks good right now. And then as we move forward and start to create methodologies for how each role and how work gets done, then our expectations might change around adoption levels for a certain way of working, a certain tool that you're using, or expectations around efficiency in your job. And making that really clear to an IC, to a manager, to a leader, of what good looks like from that perspective so that when you start to assess talent on their AI fluency or otherwise, they know what you're looking for, right? They have access to the right tools, to the right data, but also the right enablement.

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So a big part of partnering from an HR standpoint is what's the enablement strategy? So if you're using, let's say a tool maybe from OpenAI like ChatGPT, are you teaching people how to use it, how to create prompts, how to create their own GPTs if necessary or otherwise, so that they have the wherewithal, the tools, the enablement, the information to do what you're asking and can understand it. And I know we'll talk a little bit about this, but also creating things like champion networks to help peer enablement, to provide coaching, not just to ICs, but to leaders too. They need to understand how to use AI.

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Logan (17:18): Yeah, so what's your champion network?

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Melissa Lemberg (17:20): Absolutely. So we created a champion network to really help the broader organization think about empowering every business unit at the company to leverage AI for efficiency, growth, for innovation. So we cultivated this network of trusted skilled AI champions who accelerated adoption and enable transformation and shaping this AI first culture. So really their job is to accelerate efficiency. So automation improvements, right? Drive adoption, scale capabilities, speak to their leaders. Like, what is it that this group is trying to achieve? You know, I've identified a number of tasks that I think we should automate as the champion or going around and understanding pain point where they can do that. But we also require that they measure impact, that they come and say, we want to build this agent. We want to automate this skill. We want to use our enterprise network of agents to leverage this data. We say, why? Like, why put the time into it? Why put the energy into it? What's the outcome? Right? So they're measuring business impact, but they're also there to encourage experimentation, right? These are our folks who are already curious, who are already getting fluent in AI.

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Melissa Lemberg (18:37): So how do they help the rest of their business unit get smart around AI, ensure responsible AI usage, foster innovation, right? But also build enthusiasm, right? And be able to speak to one another about the projects that they're doing, the technologies that they're leveraging, how they're innovating. So creating hackathons, You to call them sparkathons, right, to bring ideas to bear, but also to leverage what other people around you are using. You don't have to reinvent the tool every time. So we're using this network of champions to help drive automation forward, knowing the IT department is very busy, right, focused on organization-wide initiatives, right, and all the tools we use every day. So how do we use these champions, right, as just that, champions of AI to drive our objectives forward, and help ensure that in each business unit we're prioritizing the right initiatives, we're bringing AI fluency, and we're driving those metrics that matter to the business.

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Logan (19:35): Yeah, it's interesting you say that IT is busy and I don't want to make this like a, partnering with this department and that department for AI and all of this stuff. Like in identifying champions and getting people to start using and getting curious, how is IT making sure that it's not just consumer level products that, you know, we're dumping company information into in order to like, even sparked the curiosity to get champions started.

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Melissa Lemberg (20:05): Absolutely. This is a partnership hands down with IT. Like this is not me alone, right? I'm just helping get the people together, you know, get the submissions for who wants to be nominated, right? Who can we nominate and otherwise and coordinate the group. But IT is the backbone, right, of this initiative to ensure that people have access to the right tools. We're following the right security protocols, right? We build in the right trust. We also created an AI governance model, right, to ensure that anything that's being built that engages AI and our data is secure, right, it has an ROI case behind it, we have the capabilities. And in many cases, our IT department needs to be engaged because the champions might need help, right, they need access to the right information, they might need APIs that exist or don't exist, right, to access the right tools, data, etc.

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Melissa Lemberg (20:55): So this is absolutely IT back and forth. But the way I like to look at it is it gives them more tentacles, right? More arms and legs to get things done in the business. And quite frankly, they're coming forward as experts, right? In their own business and understanding the work to be done so that they can come and say, I have a problem. This task needs to be automated. I think I would approach it in this way. What do you say, IT? Right? Like, is this the right way? Do we have something already that exists? How much effort is it to build it? Can you help me build it or I can build it on my own, but can you give me access to these tools? Right? So it's absolutely sure a partnership with IT. So when I say they're busy, yes, of course we all wish we had more great IT professionals in our organization, but this is a way to help extend their capabilities to reach their impact by creating this champion.

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Logan (21:47): Yeah, that's good. We're at like, we've got IT and we've got champions, we've got HR, we've got all these people. So like, what is your advice, maybe like a couple of tactical things an HR leader could do that has their board or their sponsor or their exec team saying, you know, they're in a meet, they're in exec meetings, you know, the HR leader start using AI, knowing all of these things have to kind of start working together. Like what are some tips you would have for a HR leader to kind of help lead this initiative if they don't already have like a dedicated transformation team.

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Melissa Lemberg (22:24): Sure, absolutely. So I think it's really about understanding one, the business, right? That's number one, which I know you've talked about on past podcasts was just like HR needs to understand like the business of the company, right? And what metrics are important, right? So I think that's number one. And then understanding from like an AI suitability perspective, like where are you prioritizing? If you put a heat map up of what parts of the organization do they think can help really drive those metrics forward. So I gave some examples right around go-to-market or engineering or where you have the most mass right from an employee perspective and then be able to say, okay, let's talk to those leaders. Like how can we help impact those numbers? Like what are the roles? What are the tools that they're using? What's the enablement required, right? What's the culture that's going to drive this forward and really partnering with those business leaders to identify areas of focus.

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Melissa Lemberg (23:23): So it might be one of two experiments around using AI, right? And that could be everything from hiring, using AI tools perhaps, and like a LinkedIn with the human in the loop, right? So AI is not making any decisions, but where can they start to show impact on like time to hire, right? If we were talking about the talent acquisition arm, right? Of HR.

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Melissa Lemberg (23:45): How do we take important metrics like that and then say, okay, where can we put AI and the human in the loop together, right, to move those? So understanding the business, the metrics that matter, creating almost a heat map of areas of focus, and then partnering with those leaders to identify opportunity for automation, and then providing enablement, right, and career pathing and role description to start to say, what are the expectations? And this goes back to the change management plan of communications, which is like, if you don't tell people, they don't know. So how are you telling them what's important to the business? Why is it important that we're using AI? What's my expectation of you in your role for using AI? And how am I equipping you with the right enablement tools, knowledge, access that you need to be successful? In my opinion, HR plays a big role in that, right? In driving this open, transparent culture around leveraging AI, about driving responsible AI usage, and enablement and giving people what they need to be successful.

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Logan (24:53): Yeah, I love that because it's starting from the top business metrics all the way down to what individual people, it's like the new version of OKRs for AI. You know, no longer 70s Intel, it's now the 2026, I think. And you got a ladder up from individuals all the way up to business revenue. And that's awesome.

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Logan (25:21): Okay, so for you, you had mentioned to me when we prepped for this how much adoption you have in your current company. What is that like to see AI adoption just spread like wildfire? The way you phrased it, I'm saying that, so I might just be putting words in your mouth that it was like wildfire, but that's the way I interpreted it.

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Melissa Lemberg (25:42): So it is exciting. We a very high adoption rate, higher than most. And when we think about a particular tool that we're giving everyone to experiment with AI. And I know our chief performance officer loves to say, the first step was winning hearts and minds. So really thinking about how do get people excited? Let's just give you access at work to the tools that you may be using at home, right, to drive AI usage and answers and automation. So let everyone have access, give them enablement as part of onboarding, right? So they know how to use the tool, they can start to set up their own prompts, they can see how they can teach it, right, to be an assistant to them, right, a helper to them, to help drive work, right, and drive efficiencies. And so people got very excited about having access to a tool at work that can help automate some of the functions that they do, can help them make decisions, can help empower them with knowledge and data, right? That's just company specific, right? And really locked down from our company perspective, but can make them as a human smarter, more efficient, more effective at what they do every day. And so that's really exciting.

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We went from everyone using it to like driving efficiencies, but then it's, well, how do we take all those individual efficiencies and drive business impact, right? So that's where we are now, is wanting to drive business impact and looking at those broader success metrics from a company standpoint and saying, okay, it's great we're all engaged, it's great we're all using the tools, but now how do we create best practices, right, around leveraging company-wide tools, right, company-wide ways of working, best practices in approach? So getting people comfortable with technology is absolutely important. Starting to look at incentives, right, for people and why use the tools, setting up leaderboards, right, starting to think about ideas, mentions on all hands calls, things that get people excited about using it all help drive adoption and making sure people understand expectations around that adoption and ensuring it's adoption for good, right? Like it's adoption for driving those metrics because at the end of the day, people could be using ChatGPT to make recipes all day, but it would still show high adoption, right? So like, how do we start to look in a responsible way at how people are using tools to say, this is driving adoption in the ways we want to, this is driving efficiencies and growth in the way we need to.

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Melissa Lemberg (28:19): So let's start to articulate in a detailed way how to be using which tools at what point in the process to drive what outcomes so that we're very specific around expectation. And using these tools will make us as a company successful. So we're getting to that point where we're very much articulating why. So adoption should remain high, right? Because we're being prescriptive around how to use tools effectively.

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Logan (28:47): Yeah, that's really good. We've had a jam pack session. We've talked about, we need to tie it all to business metrics, all being AI initiatives, which starts with HR and folks understanding how the business makes money. We've talked a lot about that on this show. And then breaking it down all the way into how individuals can use AI in order to improve their jobs and how there's some kind of measure there. Before we, I asked you for your parting advice. I want to ask you to pick up your crystal ball. And we now have at least now we're having the conversation. We've got 2025, mean, AI was going before that, but 2025 seems like the rocket ship of AI. Like where do you think in the next 12 to 18 months, AI is going and within companies.

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Melissa Lemberg (29:37): Sure, absolutely. So I think AI will continue to grow within companies and how they use it. I think this notion of human plus digital will be clearer and more prescriptive. AI will soon be incorporated into every role, description, and function. And companies will start to really hone in on where can they make the most impact with AI in every role. And so looking at that might be different from leaders to managers to ICs and how they use it, but being more prescriptive around how AI is used in each function to drive metrics for their area of business that tie into the larger company metrics, as we've said.

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Logan (30:22): Yeah. Awesome. That is a good prediction going forward. Well, Melissa, is there anything else you would like the listeners, you would like to leave the listeners with today?

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Melissa Lemberg (30:34): I think really taking AI as one part of your digital transformation, thinking about how can AI drive your business forward and how do you bring along everyone that's a human in your organization to ensure that they feel comfortable, they feel empowered, they have the right access to tools and information so that they can make an impact and understand their impact on the broader organization because AI is here to stay, right? And it only keeps getting better. So it's important for us all to learn constantly to listen to what's happening in your organization and to gauge where we can all lean in on enablement, understanding and empowering our employees with what they need to be smarter, faster, more efficient and effective at what they do.

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Logan (31:29): Great. Yeah. And right now AI is as bad as it's going to get. Well, great, Melissa. I want to thank you for taking the time to chat about AI. Think it's still on everybody's mind. It's not going anywhere. Where would you like the listeners to connect with you?

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Melissa Lemberg (31:33): Yeah, that's very true. Sure, absolutely. You can reach out to me on LinkedIn, follow me on LinkedIn, get my thoughts, and also watch LogicMonitor as we continue to advance AI, both our product as well as as a company and driving our AI first culture.

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Logan (32:01): Awesome. Thanks a lot, Melissa, and enjoy the rest of your day.

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Melissa Lemberg (32:04): Thank you. Take care. Appreciate it. I really appreciate being on the show. I love what you're doing and I love the words that you're spreading to HR organization and making a broader impact as a whole. So thank you.