Name: AI That Writes Reviews vs. AI That Understands Performance: What HR Leaders Need to Know
Uploaded: 2026-02-25T18:16:08.761Z
Duration: 1 h 1 min 28 s
Description: AI That Writes Reviews vs. AI That Understands Performance: What HR Leaders Need to Know

Transcript for "AI That Writes Reviews vs. AI That Understands Performance: What HR Leaders Need to Know": Hello, everyone, and welcome to today's webinar. Thank you so much for those of you who joined already and are already participating in the chat. But we do wanna give everyone just maybe a minute or two to trickle in. But as Sarah said, we would love to hear from you all. Let us know where you are tuning in from. Maybe you'll meet someone who is across the world, or maybe you'll meet someone who is ironically in the same city as you. And, also, would love to know from you all in honor of the Winter Olympics just wrapping up, what your favorite sport was. We, here at Better Works on the marketing team, had quite a few teammates who, watched the Olympics, whenever it was on. You know, it was always what we were talking about. And I will say, admittedly, I only watched really the gold medal matches for everything. But my, I think, really shocking, you know, performance was the figure skating. I think, you know, any Olympic sport is just phenomenal. I don't understand how anybody does any of those sports, but figure skating just blows my mind. I don't understand how people can do that, even jump and twirl while being on ice skates. And so I think that was just the most fascinating for me. And so would love to hear from you all. What was your favorite? My name is David. I am on the marketing team here at Better Works, and I am calling from Jacksonville, Florida. I know there's probably a few other Floridians in the chat here. It probably explains why figure skating is so mind blowing to me. But really excited for you all to be here today. Really excited about today's conversation and to hear from Cheryl and Caitlin. But before we get started, we do have a few just couple housekeeping items really just regarding the platform that we use. And so as you all, you know, are chatting away, this is the chat. We do love our webinars to be engaging. We'd love to hear from you all. Drop your LinkedIn. Meet a new friend. But, also, we'd love to hear what resonates with you all in the conversation. We'd love to hear your thoughts on the conversation, what you may be doing, where you may be seeing some things in your organization. And so really use that chat. We love to hear from you all. And then right above the chat, you'll see two different tabs. One says docs and one says q and a. And so in that docs section, we have today's webinar presentation slides, which I know many of you will ask for. And so those are there for you. We also have a registration to an upcoming virtual event we do every year as a half day virtual event. Really excited about that one. And so go ahead, save your spot for that. We'll be announcing our speakers shortly. And then a few other articles and resources relating to today's conversation. And then we have the q and a. And so we may have time at the end for some questions. And so as the webinar happens, put your questions there. They may get lost in the chat, and so make sure you use that tab. And then last but certainly not least, we will have polls pop up there. And so whenever we launch a poll, you'll see them there. And so we'd love for you all to answer, participate. And then last, we have our HRCI and SHERM codes. And so those, we will be emailing out after the webinar, and so stay tuned to your inbox for those. And so with that, our housekeeping items are finished. And so let's get into today's topic. And so let's start with the term performance reviews. You know, they bring up a mix of emotions for employees, managers, leaders, and especially HR, as I am sure you all know. And so just to get our poll practice in, we're gonna launch our first poll today. Love to hear from you all. Really, overall, how would you rate your organization's performance management process? And, you know, you may have just finished your process at the end of last year. You may have finished at the beginning of this year. You may be looking at your 2026 plans and thinking, oh my gosh. We have to do something different this year. And so we'd love to hear from you all in that poll. This is a safe space for all of these poll questions, and so answer honestly. We'd love to hear from you. But let's talk about why we are here as you all answer this. You know, AI is quickly becoming a part of performance reviews, goals, one on ones, honestly, that we're doing. But writing better reviews isn't really the same as building better performance. And so many organizations are still using AI really at the surface level, improving language and speed without changing how performance is truly understood. And we really believe that the real opportunity comes when AI is embedded across goals, feedback, and ongoing work. So that way you can surface pattern and alignment gaps that gives you all actual insights earlier. And so that's the shift that we're gonna be exploring today. Again, really excited to hear from our speakers, but let's go ahead and look at those poll results just to see where everyone is at. And so as you can see here, you know, it looks like around 40% for the most part say fair. We have 30% needs improvement, 20% strong, which is phenomenal. Hats off to you all, and then 10% weak. So, yes, as you can see, mixed reviews, but most, in that fair and needs improvement category, which, you know, we see all the time. So thank you all for answering that poll question. Now you all know where those polls will pop up. And so I am thrilled to introduce today's speakers. And so as Cheryl and Caitlin come to the stage, I'll go ahead and read their brief bios just so you can hear a little bit about their background. Cheryl Johnson is a visionary leader, advocate for diversity and inclusion, and change agent with a proven track record of driving growth in disruptive software companies. She is our chief product and technology officer here at Better Works where she brings her wealth of experience and strategic leadership to the organization. And beyond her role here, she's dedicated to diversifying the c suite across the tech industry and is really actively involved in empowering women and other underrepresented engineers to maximize their leadership potential. And then we have Caitlin Collins who is an organizational psychologist who serves as a strategic adviser and a Better Works product and platform expert for the largest, most complex organizations. She spent most of her career consulting for Fortune and FTSE 500 companies across various vertical markets, developing people centric strategies for performance, development, alignment, and change to help those organizations become more nimble, capable, and productive. She holds a bachelor's of science and a master's of science, both in organizational psychology with a certification in training and development. And so needless to say, you are hearing from some experts here today. And so with that, I will pass it over to Cheryl and Caitlin. Amazing, thank you so, so much, David, for such a robust introduction. It's funny to have your bio read back to you. Don't you think, Cheryl? Always. Right? Yeah. But welcome, welcome, everyone. We're super excited to be here. I am Cheryl, I'm really excited to be talking through this topic with you. I think it's a very prominent topic for us to go through and will prove to be quite interesting as far as what we're gonna deep dive into today. Before we get really going, we are and get talking about what AI can do in performance and what that means. I think first, what we want to do is really acknowledge where we're at today. What does that look like in the world of work? How is AI being used or applied within performance programs really across all industries? And most of the leaders that we talk with, most leaders and managers and what we're seeing in in data from employees and managers is that there's no longer this debate that should AI belong in performance. It's more about how do we wanna utilize it in the right way because it's already happening. Employees are already out there. Managers are already out there using it. Leaders are too. It just might not be in a way that is intentionally designed or for purpose within the program. What we're seeing and showing these quotes that we've pulled from various places up on the slide is that employees and managers are using different types of AI tools to help communicate and help really get to impact faster and what that looks like. So drafting self reviews, helping to craft emails, helping to provide feedback to other people if they feel a little in about how to write that effectively, summarizing the work that they've done together, all for helping to really relay the value that they have brought to their role, to their department, to the organization, and really trying to more clearly lay out impact. And I think that makes a lot of sense because we're all trying to get to the result and show our value in the right way, especially especially at work. And there's going to be an increasing amount of pressure as time goes on to better articulate performance clearly, and that's gonna keep growing exponentially. So I think the big question that we're gonna unpack together today is not whether AI can help write faster. It's whether AI is in a position in your performance program and in your organization to help understand performance in the right context? Is there the right input to create the right output, and how are we utilizing that, not just to help with performance reviews, but how are we then cycling it back in to help with inform h I HR processes and with the right insights? But, Cheryl, from your perspective on the technical side, how is that resonating? Like, what are you seeing when it comes to how people are using AI today at work? Oh, yeah. That's why I'm so excited to speak with you, Caitlin. And y'all can hear me. Right? Because Goalcast is telling me I'm having audio issues. We're good. Great. Yep. So that's why I'm so excited to talk with you because you bring this sort of organizational expertise. Like, you understand all of these processes and how they're supposed to work and, you know, how humans are human, which is great. And I live in the world of tech and have a deep understanding of technology, and that's why I just, like, love working with you because I think we make such a great partnership, and we do work together all the time, which is great. And what's interesting about these quotes is that from a technical standpoint, they are scratching the surface. Like, this is just the surface level of what AI can do, and that's what we're excited about talking about later in this in this talk is just all the ways that you can use AI to really transform performance that goes just, you know, a thousand times beyond these wonderful examples. Like, great that you're, you know, saving time, right, writing your reviews, but there's so much more potential. So that's what I'm really excited about getting into with you today. agree. And yes, we work together so well. I'm very excited that we're talking about this again. And I'm excited to unpack that. Before we get to the unpacking, we are gonna launch a quick poll to really set the stage for everyone that's joined us here today on how the AI is being used across our per performance program across our organ, excuse me, and what the breadth of that looks like. So we've got just really quick four questions, where we're looking at do employees use public AI tools independently like ChatGPT, Claude? You know, are they using it outside of policy, let's say, within the organization that you provide as a company, provide company approved AI assistance for general productivity? And whichever one of these matches closest to you, please select the one that feels the same and not might not be exact, and that's okay. Is AI embedded into what you're looking at? And or is it still just being considered? So it looks like as the results start to start to roll in oh, it looks like I can control the scroll. I didn't realize that. Hopefully, you're seeing the scroll too. So what we're how we're seeing this come in is, that it looks like most people are saying that as a company, you provide AI assistance for general productivity, which is very cool. What we're seeing across a lot of organizations globally is that, as Cheryl mentioned, we're starting to scratch the surface of what that looks like. But it's pretty it's a pretty good mix bag across the board. So the fact that everyone here has some consistency on providing approved AI assistance is actually really cool and helps give us a place to start from. I love that. Okay. So I think what we'll do oh, thank you for closing the poll. Is look at just a few data points to help ground us on what we are going to be exploring as well. So from from different reports, we can see that 95% of employees use AI for basic service level tasks, like search and document summarization, kind of what I've mentioned early on, helping to organize either their day to day work, providing summary feedback summary reviews, feedback, whatever that looks like, helping them to write goals or maybe change the focus of their goals, which is awesome. It's helping to write and get that work done faster. And then we're seeing that 93% of employees believe that AI's potential is underutilized for strategic tasks. And what the the difference between these two data points is really bringing to life for me is that we no longer have this unwillingness to use AI. We can see that adoption is not a problem. I'm willing to bet, especially given my own personal experience, that even more than 95% of employees are using this for more than just work related tasks that it's starting to, you know, really become a part of day to day part of your function. I mean, I use it for gardening tips and tricks. There's so many ways to be able to expand the scope of what this looks like. So adoption and usage is no longer the question. It's more about depth and clarity of prompts. How how can we use it to make sure that performance is viewed in the right context? And this dissension is what we are going to unpack together today to make sure that we're we're prompting in the right way. We've got good data in and really great data out with the right focus of insights. We've got one more quick poll for everyone. So whereas the first poll was about depth of usage, this or breadth of usage, this one's more about depth. And it's ordered in a from tasks to insights for what we're looking at here. But how then is AI being used in your program within your organization today? So whether it's helping to write goals, feedback, or reviews, summarizing notes from discussion to discussion or meeting to meeting to review inputs. Are you using AI in your performance program to help surface insights about your talent strategy, about your structure for performance or risk assessments, or is it something that you're still experimenting with? So we'll give just a moment, and here we go. So it looks like across the board oh, we've got some interesting consistencies where we've see where most of you about 30 oops. That switched. Most of you, but it's a close run, are showing that you're still experimenting with AI. So I'm gonna gather from the previous poll that it is in part of your process. AI is part of the conversation, but using it in a meaningful way hasn't really been determined yet. And then we see that the close second is that currently AI is helping to write goals, feedback, and reviews, and trying to get tasks done faster. So that's pretty consistent for what we see with all the organizations we work with and and what that looks like. What don't you think, Cheryl? Absolutely. Yeah. And that's why I think so you're in the right place. Like, I think we have a lot of, you know, great things we're gonna be sharing. So, yeah, excited to to get into it. Yay. Okay. I think then that closes our foundational piece. Cheryl, I'll advance it to the next one as we talk about how it's being. Awesome. Thank you, Caitlin. And, you know, I'm you you know, right now, as we've been saying, most of the AI chatter is in performance management is really about rip writing reviews. So people are using AI to draft their self reviews, to summarize feedback, to polish their manager evaluations. And, honestly, that's fine. Like I said, I think it's great. It saves time, and time is money. But look at where that is happening. It's happening at the review documentation time, generally after the work is done. So AI is being used to summarize history. It's operating at the moment of reflection and not the moment of performance as we know, which is happening continuously. And that's, like, the metaphor I like to think of. It's like using AI to edit a final draft of a book instead of helping the author, you know, during the process while they're writing it. So in these scenarios, we're applying intelligence really at the least leveraged moment in the performance cycle. So which is why we're excited to get into what's really possible. So we know performance doesn't happen in these, you know, annual or semiannual moments. Performance unfolds continuously, and AI's real power is in the flow of work. So as we know work is unfolding over time through achieving your goals, you know, having your one on ones, giving feedback, developing skills, and momentum builds across these moments. And, traditionally, we're evaluating these these things in isolation. Right? Goals live in one place. Feedback lives in another. Maybe skills somewhere else. And then reviews potentially even somewhere else all at the end. And then we, as an employees, as managers, really have to connect the dots manually once or twice a year. So I've I've lived through many a performance cycle in my career and and sort of know that pain, especially when you're where you're doing that for many, many employees. And AI has the pen potential to change that. So what we're seeing is that it can be present while work is happening. It can observe patterns across goals, feedback, meeting cadence, skill usage, and it can connect these signals in real time and surface the insights before performance stalls. You know? We're waiting till the end, like, way before the end. AI is going to optimize momentum instead of these moments. I think it's what we've always imagined the sort of holy grail of performance to be is this continuous loop. And AI you know, and and doing that I'm sure you know this, Caitlin. Right? Like, doing that as a manager for a lot of employees, it's very taxing. Like, we just don't have the capacity in our busy days to do that synthesis in real time. But now as managers, you know, we have these tools that allow us to be sort of super managers, really. Right? 100%. And without especially when you've got a lot of direct reports in a in a really complex hierarchy, Like, your brain is always moving to the fastest result. And on purpose, it does not remember every detail of every day that all your employees do. So I think. that the powerful usage here that you're describing is really creating more objectivity and really helping to reduce that bias and halo effect that just happens by Exactly. you know, unintentionally. Yeah. Absolutely. Exactly. And we're gonna get into, like, very specific use cases. So we're we're going you know, we're talking about the, you know, sort of the vision here, but we're also gonna get into very specific tangible real world scenarios that we are already seeing out, you know, out with our customers. So AI implemented well can nudge managers when feedback is imbalanced. It can ensure goals are aligned with strategic priorities. It can surface skill gaps early, like, not when you know, too late, right, when it becomes an issue. So that's what we're excited about. Now I'm gonna get into, like, some specific scenarios. Kicking off with skills. So we're really excited about what we're seeing with skills inference because, you know, in a lot of times, skills are they're self declared or they're based on maybe a isolated learning moment. But what we're able to do with Better Works is really infer skills from contribution over time. So we are able to see skill development, skill building through goals, through goal accomplishment, through feedback. What am I hearing from my peers? Through one on ones? What am I you know, my manager and I discussing on a weekly or biweekly basis? And, yes, still from learning activity. But we're able to synthesize it across all of these channels. Right? So these are that's a lot of signal, Mhmm. you know, that's really coming in and and and really helping employees understand, okay. Where are my skill strengths? Where are my opportunities for development? And, again, as an employee, trying to synthesize all that information in real time, you know, kinda near impossible. Let's be honest. But with AI, we're able to do that. So in our case, for example, we are able to look at the context of the employee. So what is their role? What job family are they in? We can see that performance evidence that's been accumulating over time, and we're able to recommend skill and skill development leveraging these very transparent sources. So we're gonna say to the employee, right, the AI, let's say, to be honest, you know, we see you have this skill, and this is why. Right? These are the sources. These are the, you know, transparently showing that to the employee so it makes sense, and they're able to connect those dots. In the way we have implemented AI in our platform, we always want there to be a human in the loop. We understand that, you know, ultimately, the manager kind of an employee have a relationship, and there's a lot of conversations and a lot of support and coaching. And, really, we think of AI as a way to kind of enable more of that. So less tactics and more strategies, more coaching, more help. So we we always try to keep manager, in the loop. So, for example, you can have manager verification of these AI surface skills, just as an example. And, you know, this isn't just a feature story. So we're not just talking about technology because one of the things that's so true about performance and performance management tools is, you know, the tools should really be facilitating the the human work. Right? The human work of performance. So, you know, when skills are inferred continuously from real work, it's not just resulting in better reviews. It's going to enable stronger internal mobility. Right, Caitlin? I mean, the faster, yeah, the faster you can see that, you know, you have these skills to develop, maybe that you want you wanna go into a different part of the organization or whatever the case may be from a career standpoint, that really allows you as an employee to take action, It does. It, without having to kinda wait. yeah. Having the ability to have an objective view on what to prioritize and focus and why, especially yeah. As you mentioned, if it is lateral into other parts of the organization, it might you know, everyone's career path looks a little bit different. So how they might be able to contribute their skills and focus on their development in different ways, I think, is so powerful from a people planning perspective. Exactly. One of the aspects that we're talking about here that I think resonates with me, deeply is the idea that AI can really, facilitate fairer talent decisions. Okay. I remember working, you know, for a very large company where we had a quarterly promotion cycle, and every manager would have to present their employee for promotion. And I I saw a pattern that, you know, the the individuals that have managers that were stronger communicators, you know, or whatever the case may be, maybe a little bit more aggressive, tend to get they tend to get represented well during those cycles, and then others might suffer, really, for no reason, right, of their own. Or conversely, some folks are maybe better self promoters, and so they're kind of getting, you know, getting, the eye the eyeballs when it comes to promotion time. But what we're finding with AI is that we're able to surface this data and information so that all of these things become, you know, way way more transparent, way more equitable and fair, so that we're not just kind of, you know, relying on people who tend to be better self promoters or better communicators or whatever the case may be. So that is an area that, you know, I'm very excited about. The other is we kind of been talking about is really earlier intervention. So that you know, I, as a manager, always thought it was my you know, I wanna help my employees kind of indot identify those gaps early, you know, not to the point of where it becomes a real performance problem, but more of that opportunity phase. Like, here's an opportunity for you to build some skills. You wanna be there as opposed to waiting for it to be a problem where they're not able to kind of, you know, carry their their weight, kinda, know, maintain, the level that maybe their colleagues are maintaining. So I think that is another area that I'm really excited about. And then finally, beyond the kind of manager and employee, when you just think more broadly of the organization, it's really gonna enable much more kind of evidence based work workforce planning. Right? So that's pretty important to just have that kind of real time visibility and to skill kind of supply and demand in a way that's more much more automated, way less manual. And, you know, research is showing that leveraging AI to infer skills for from performance data can drive meaningful improvements in overall performance of the organization, so not just of individuals. So there's an employee and manager story, but there's also, I think, an organizational story that's really compelling because you're moving from this kind of episodic talent management, you know, muscle to something that's or akin to, like, continuous intelligence, you know, where we're able to, through the power of AI, sort of synthesize those signals and deliver the insights that can help you make smarter decisions about your organization without the kind of mental load that would have been required in the past. Yeah. What says know, that's a hard one to? follow. You did so well. As you were talking, what it was making me think of too is, you know, a lot of organizations struggle with employees be for for nefarious reasons or unintentional reasons being able to game the system. That happens in a lot of places. And I think, you know, as you were describing, even in your past experience at other organizations from promotion time, what dawned on me as part of this is that bringing that transparency and that objectivity to the front so everyone has an equal weighting and an equal footing. as far as that goes. And I think from an organizational standpoint, when I think about strategic direction, like, how can like, really where I'm looking at this is how does AI serve your talent strategy, in turn, serve your business strategy? And the world is continuously evolving faster and faster. Our business strategies have to match that, our market, our consumer. So if this if we can leverage this, because this is where we're going, in order to help make shifts, be more nimble in making sure we've got the right skills lined up for how people are performing, how that needs to evolve and transition so it keeps pace with the needs of how our businesses change is gonna make I mean, it's just that's just such a powerful concept to be able to do that faster and? more effectively. Yeah. You're so right. I mean, things are evolving and moving so quickly that it's hard for all of us to stay on top of. So I think it's. we need these tools. Right? We need these tools to work on our behalf. And so now we're gonna get into to goal setting. But before I get into the product technical side of things, yeah, I'll hand it off back to you, Caitlin. Yeah. Okay. Yeah. I I think we have to leverage what we can, so we're not trying to just rapidly keep up. So. when we think about from a goals perspective, in goal most organizations have some type of goal program and what that looks like. On paper, goals are meant to really create alignment throughout the organization. Is each department, each individual focused on the right thing at the right time, and also growth and development. We wanna use goals to make sure that not only there's an execution from a business standpoint, but also our people growing and developing in the right way or the opportunities for them to take advantage of certain elements. But in practice so there's one is an intention. Then in practice, what I hear from a lot of organizations is that they experience them more as episodic exercises. So whether they have an annual goal program or a quarterly goal program, it becomes an exercise where it's as a check the box. We have to get goals done. There's not a lot of follow through, and there's an end of year to making sure that, hey. Did you you know, marking progress to make sure they're shown as complete or whatever that looks like. So it's not folded into our day to day focus as we would want it to be or hope it to be. And I think that's not because there isn't for a lack of caring about that these goals matter, there's just a lot of focus going on, and priorities are always shifting. And maintaining that level of alignment at scale can be tough, especially in some cultures and in some environments. So what's changing now with AI, what I've seen in practical use and what that looks like is that AI is really helping keep that context, that why that that, connectivity front and center. So it's more than just helping employees write goals in the right way, which I think there's a lot of value in that is important. But it's making sure that it's being brought the why is brought to the surface where employees can see, like, right in front of them why their work matters. So it's it's not so much anymore about asking the question, what goals should I create or what goals should I be working on? It's more about what is the organization trying to do differently, what are they trying to impact, and how do I contribute to that? What does that look like? If I can see and have visibility and know what my department heads, my functional unit heads, what the organization is really focused on, it creates a better picture and more context for how I as an an individual employee can contribute to that. And I think that is really exciting to have that right in front of you without any guessing or trying to figure out from nothing what what that contribution. like. That's great. I keep Yo. oh, sorry. I was I keep going back to my past experiences, but I I remember even for junior employees, like, sitting side by side with them and helping them write their goals. Right? Because that blank screen, Yeah. is so daunting. And, and, yeah, it would've been great to have the tools that we have these days, back then. Yeah. I had that same experience where we're sitting down, and I didn't even even as a manager, I didn't have clarity about what the organization was trying to achieve. So you're trying to balance what you think you know to help your employees focus on the right things. And sometimes that can create a lot of misalignment, especially since everybody has their own ideas of what that looks like. So this is, I think, yeah, a really exciting time to be able to execute faster and have prior right priority focus. Shifting that oh, sorry. Into how goals. didn't know. Go ahead, Carol. Awesome. Yeah. So, you know, we've been talking about how AI is being used in performance, primarily focused on writing, you know, drafting reviews, and polishing language, and saving employee time, which is great. And what we are talking about here is something different. It's really pulling AI upstream into the process, right, in goal setting. We know that the quality of performance conversations ultimately are only as good as the quality of the goals and the kind of work that's done that is being discussed. And, you know, many employees, they're starting the quarter with that blank screen. You know, what am I gonna be working on? Hopefully, they're talking to their manager. They know they're on a team. They they know what they're gonna be doing. But perhaps the better question is, you know, what is the organization trying to move? And then how am I gonna contribute? And it's so important, right, for employees to see how their work, no matter what where they are in an organization, to see how their work contributes to the ultimate success of the company, it's really, really important, I think, to connect the dots for that employee. And, it can be a lot of the times too much sometimes for a manager perhaps who has a lot on their plate to help connect those dots. I think a lot of managers are good at that and getting better and better at that. But, again, there's an opportunity with AI to really make it a lot easier. And so, you know, you're thinking maybe you're helping the individual move from really something more tactical, task based to really thinking about organizational impact and how can I be impactful? Mhmm. And, yeah, and that's what I think makes the these sort of AI suggested goals interested interesting because it's not just you know, there's so much context we're able to see about the employee. Right? We know what their role is. So we know, you know, what are other people with that role doing, you know, at this point in time? We know what the hopefully, manager goals are, what their top company goals are. So now there's, like, sort of baked in alignment. Right? Because we have that built in. We have that knowledge, and and we weight that quite high. Right? Because that alignment is so key. We understand their performance. So, you know, in our tools, we're able to suggest professional goals, so goals where you can help drive impact in your organization, but also personal development goals. because we have all that performance context and all those signals. And so it's so much more than just, like, helping me write my goals. It's truly having this extremely intelligent, you know, tool that's gonna synthesize all this information and suggest goals. And not only goals, but milestones. So and if you use OKR language, you know, it's gonna suggest an objective and then a set of key results that could help you achieve that. No. It's so simple, and, you know, the experience as a user is so simple, and I think it it hides a lot of a lot of sophistication and a lot of, like, true intelligence that I think can make every individual way more impactful. Yeah. I love how I you're right. So right. It's making them more impactful. And I think, really, what we're talking about too is bringing meaning to people based on the data and the information that we have. With, you know, with strong input comes strong output, but, like, being able to serve up meaning and value to the work that employees do is, like, huge for engagement. Yeah. And that's all you know, there's so many things that that impacts, and it's pretty foundational to what we want people to feel, at least where. we're coming. Yeah. So. so as we shift then from goals, I think goals creates direction. Direction, but then talking about one on ones is where momentum really starts to happen in the manager to direct report conversations. What you're seeing here up on the screen are some questions that managers commonly ask themselves during review time. And in the regular one on ones discussion, that's where really context of performance lives, where coaching lives, where there's visibility of blockers, conflict, development or shifting of focus. And oftentimes, those what we're seeing today sometimes is those aren't captured in the right way, and that leaves then at the end of year review time, these questions about managers having to go back into their memory to pull out information about trying to synthesize themselves a whole year's worth of conversation. So what we wanna look at is more than just goals, but then how does AI help support those ongoing dialogues where all those critical little bits of conversation are so important and really bringing that to life so we're not relying on our memory so much. Right? Yeah. Yeah. Go ahead and take it away, Cheryl. Yeah. I was just as you were talking, thinking, like, my memory can't can't do that anymore. So if it ever could. If it ever could. Yeah. And I remember I was so diligent back in the day of having a notebook for every employee, and I would take notes in their notebook. Right? Because I wanted to be a good manager and make sure that I could look back. I know I was one of those, Caitlin, I know by know. eleven because that's me. Great. Great. And but, really, are we expecting that in you know, the anyone, any human could just go back and really synthesize all those handwritten notes if you can read them because my my writing's not great to detect patterns, etcetera. That would be challenging. Right? And that's so this is another exciting, you know, use of AI that we think it you know, because it you're able to kind of look across all those meetings. And if you're meeting weekly with an employee, that's a lot of context. And we're able to summarize and synthesize that information over a defined period set. So maybe you're trying to look at, okay. What did we talk about over the last quarter? Right? Like, maybe you have an employee that's really doing great, and you wanna understand, okay. Was there something going on in these discussions, like, that you know, what are some of the themes coming up? Or maybe you're having an employee who was struggling in that quarter. You can kinda get a sense of maybe we didn't talk enough about x y z. So we're able to kind of detect and surface different kinds of patterns, you know, coaching patterns. Are we talking about development? Are we talking about skill development, or are we staying super tactical, like, in every one on one and just talking about tasks? You know, are we you know, is the am I as the manager, addressing performance issues in one on ones? Or, you know, you know, kind of, hiding away from doing that. Right? Yeah. Yeah. Because that that's a challenging part of the job. Are we able to see sort of growth happening for that employee in those discussions? And then importantly too, this is visible to the employee. You know, it creates a shared memory. And employees, obviously, they have there's onus on them as well to to kind of own, you know, their career, their skill development, their performance. So we need to give the tools to them as well, right, to to help them to get better, right, day day over day. So having that kind of shared, memory levels the playing field and, you know, gives the power and information to the employee and to the manager. And then when it comes to let's circle back to what we opened with the performance review. Mhmm. This is yet just more information. Right? That is it's more information that's gonna contribute to the ultimate work that is the performance review that's grounded in what really happened and not just what you happen to recall, you know, or that, you know, that might have happened over the past month. tells a different story sometimes in reality. or perspective between the employee and the manager see things very different sometimes. So, yeah, you're right. Like, bringing more objectivity to that, think, is so powerful. Yeah. Awesome. And, again, we're going right back to where we all started with the performance reviews and AI and performance reviews. And, you know, the opportunity here isn't just generating more data. Right? It's activating the data you already have. You know, most organizations don't realize often how much performance data that they're already sitting on. You know, there's millions of conversations, hundreds of thousands of feedback moments, all the goals created and updated, the recognition shared, the one on ones logged, you know, the calibrations discussed. So the problem isn't really the lack of information. It's that often that information is fragmented, or is just too many signals and not enough, capacity to sort of synthesize, especially in not in real time. And if those signals especially live in silos, then, you know, that even makes the the the challenge even more daunting. If you have goals over here, feedback over here, right, one on one somewhere else, especially if you have one on ones in a notebook, you're gonna. be in trouble when it comes to performance for a few times. Exactly. Exactly. And then when AI is able to kinda connect these signals, then it becomes something way more than a writing assistant. Right? It becomes skills intelligence. So you become a, you know, a a skills expert, right, on every employee. It becomes pattern recognition over time. So you're you're able to see these patterns and and see the trends and where things are going and and interject in the moment way earlier, right, way earlier than when someone's kind of veering off the track. And it becomes more about early insight instead of just, like, a postmortem report. So that's what we're excited about the application of AI and performance reviews is exactly that. Yes. It's gonna reduce cognitive load. Like, we've talked about that a lot. That is something that's real. Yes. A time saver. Right? But, yes, reducing cognitive load, but also really just helping reduce kind of all kinds of bias, whether it's recency bias or, you know, you name it. Having these signals rooted in real data and information, real performance data that is connected throughout the year, it you know, your reviews become they stop being an exercise of recall, and it becomes for the manager and the employee an exercise and interpretation. I think that's what we get yeah. We get excited about because I think the vision for this is where AI is a tool to just make us all better. You know? I think. we would like it to not replace us. Right, Caitlin? Right. Yeah. We we want it to make us better. Yeah. Absolutely. And we want it to make employees better. So, you know, it's it's evidence first. Right? So AI is assembling that kind of base layer of evidence, reducing all the biases that I mentioned, surfacing the themes that we might have missed. It's structure without scripting. So one of the things and I love reading the stories of how many examples that maybe a professor gets something from, you know, a student, and then they'll get, like, the exact same thing or almost the exact same thing from another student. Like, just these examples where it's clear everyone's using AI and they all sound the same. Like, we shouldn't be doing that. Right? If an if you if employee senses that, like, if an employee senses that AI wrote this thing, they are gonna question, right, the value of Of their manager. the? content of. the yeah. Their manager of the review. So using the AI to really to as a tool to kind of, like I said, elevate your capabilities, but then you are still going to do the actual work. It's gonna take you way less time, and it's gonna be way better quality. But it shouldn't replace the manager because, you know, one, people are gonna see through that. But two, you know, it's it's the right thing to do. Right? So, it's gonna ensure consistency and fairness, which is great. And, essentially, you know, the managers are gonna spend less time data gathering, you know, less time sort of recalling, and they're gonna spend more time coaching, you know, more time waiting weighing trade offs, you know, more time kind of using what's uniquely human about us to help, you know, Mhmm. their employees. So. yeah. So that's what we're excited about. I think there's so much, potential. And I know we're getting pretty, but we're getting to the end of our slides. So we just got a couple more to to go. Yeah. So, We're, Caitlin, back to you. on track. I love that. I think my takeaway. from that, which is our huge our biggest focus is keeping, like, humanness at the center of this and reducing some of the administrative burden, but then, you know, making sure that there's greater focus on those judgments and the context of situations, which make us all human, and more strategic, allowing people to just really enhance their own skills and grow in the right ways. And and as we think about the evolution of AI and how it's expanding and how we really wanna think about it strategically in our performance programs and keeping the humanness at the center of it is like a bit of this maturity model that we're showing here on this slide. And I find that really the first point of entry for most organizations is the very top left box there, which is administrative relief, and that's helping people do things faster. Write faster, standardized language. If it's a global organization, even having, like, similar context from culture to culture, There's a lot of value in that, but we wanna be able to shift that to more augmentation of intelligence, where we're starting to identify then patterns, surfacing themes, and helping leaders really make more informed decisions across the board. So what we're looking at and how we're thinking about it is taking the foundation of how we're seeing people use it today and really taking it to the next level to say, how can we better improve this so that we're more we're better, faster, better judgment, whatever that looks like for organizations with stronger insights. And the shift really that we're looking at isn't anymore. It's not as Cheryl said, it's not AI taking over the human. We're not shifting from human to AI, but we're more looking the way we look at it, we're more looking at a shift from manual interpretation to informed judgment that allows us to be more strategic and being able to look future forward instead of always looking capturing information and looking backwards. Is that how you that's. how we both do better. Right? Even from a technical side. Absolutely. Yeah. Absolutely. And, you know, as as we as we were talking a little bit, I was looking at the chat and some of what folks are kind of asking about. And, definitely, I think if you're interested in understanding how Better Works works, you know, so you can get one on one time and and get a demo and sort of get insight into, you know, how we facilitate this. There and there's a lot that that we're doing to really leverage AI to make these you know, make all this as streamlined as possible for employees, but I just wanted to throw that out there because I'm seeing a lot of questions around, wait. How do you actually do this? I'm glad you Which is great. have a link to I'm so glad you did. And so to close us out, what we were thinking about how we would close out this session about all this stuff we wanted to share was what would next steps look like. And as you're thinking about how AI is applied in your programs at your organizations, wherever you're starting from, what does that what does that advancement look like, and how does that shape what you want success to be? And so I would say, like, the number one, starting with outcomes, honestly, I'm gonna tell you this is not just for AI. I apply it to absolutely everything. Starting with the end in mind, what is the the outcomes that you'd wanna get from being more strategic with AI in your program within your organization? What are those results? What does success look like in the short term and long term? So it's about asking yourself what how how are how would managers behave in the end? What saves time? How would employees behave? How would HR be able to utilize it? Does it help us get to achieving priorities faster? Does it help us streamline or really advance how we're looking at talent and performance with the organization? Succession planning. You know, there's so much that can come into this. So really starting with the end in mind so that you know what you're driving towards, I think, is really important. Then the second one, which is audit where you're at. This is just understanding where you're at today. And it's not, like, not starting with the tool in mind first, which is part of starting with outcomes, but we wanna meet people where they're at in order to be able to make the right understand how they're making decisions and understand where the gaps might live so that we can create a better plan to get to the end result. So really taking good stock of internal tools that you're using plus external tools that employees might still be using in a very honest way, non punitive. But that way, it'll really help you determine, like, what are the gaps that we're facing and what are we seeing that employees are trying to do with this that we might not yet have policy or practice around. The third one then is designing for for signals versus annual events. So, yes, all of us want if you've got performance reviews in your tools, promotion cycles, whatever that looks like, we all wanna make those big heavy burdens easier. But I think before we really redesign what that process looks like is also making sure that are we capturing the right signals along the way in order to make that easier. Those regular one on one, Cheryl and I were talking about being able it's not just even writing goals, it's is our people working towards them? How are they pulled into one on one conversations? Are people pivoting and negotiating? Is is what the work that's being done and what's being communicated even through feedback, is it effective and meaningful? Or do we have the right signals in place that we know that our managers are really effective and driving the right information. So I think making sure that those are in place and we have clear line of sight will help then even out the whole year end to end process. And the last one is making sure, and probably the most important one, but making sure that human stays. in the middle, that we're not here to replace humans. I don't think there is an employee from any report or anyone I've met that wants AI to replace their manager. I'm gonna tell you what. Because we still have you know, judgment is still so critical. Creation, innovation, like, all of these things that bring value to our business, AI can really level that up and help us if we use it in the right way, but it doesn't mean that it takes that engagement out and makes us any less important. It's just enhancing what we're so good at, I think. Exactly. Something that we we think about and care about deeply. And I saw a comment, about, you know, using employees using AI to write self reviews. It that just takes us right back to the to the beginning. And the reality is whether we like it or not, employees are using whatever off the shelf, you know, AI chatbot right. to write their performance review, and it doesn't have that context. Right? It doesn't have that performance context, and it's going to be kind of, like, vanilla, you know, not very individualized, not very useful. I'm sure managers would be super frustrated, right, receiving something like that. And that's why, for example, the approach we have taken is we don't we don't ever write for the employee. They do have to write it. We will help the AI will help, refine, you know, so which is nice, but they have to, write it. What we are providing, though, is all of that synthesized information. So, again, they don't have to they might forget, oh, wait. I remember I had that special project I did four months ago and I crushed it, and maybe they forgot what that special project. Right? That's all gonna get surfaced so that they can use that information in reflection, right, to to do their review. So it's really important. from our standpoint that we design all of this with this human plus AI approach. Yeah. 100% agree. we recommend it for all of you. Yes. Yeah. We do. And for ourselves, we we drink our own Kool. Aid, don't we, Cheryl? Yes. I think that brings us right to the end. So thank you, everyone, and I'll hand it over to Dave. sort of addressed some questions, right, you know, naturally. So, yeah, back to you, David. Absolutely. Thank you both so much. I honestly can't believe we're already at the top of the hour. You both, as you can see in the chat, did a phenomenal job. I know anytime we talk about AI and performance, we need way more than one hour. And so definitely need to have you all back for part two, three, four, or even five coming up. But I will just wanna start by saying thank you to both of you, Again, sharing your insights, taking the time just to to engage with the audience. And I wanna thank everyone for joining us. Again, there is so much that you could be doing right now. I'm sure your calendars are full. And so just the fact that you took the time to join us live is just incredible. And we did launch a quick poll. You know, I see in the chat a lot of you have a lot of follow-up questions, a lot of, okay. So now what? How does this work? And so we would love to chat with you, share with you what we're seeing from our customers, what we're seeing from our conversations with other CHROs and people leaders. As I launch that poll, we'll have someone reach out to you, set up some time just to chat, see what you wanna learn, and how Better Works can really fill in some of those gaps and answers. And Sarah did just drop the link to register for our half day virtual event. And so please take some time, register for that. Again, it is one of our yearly events. We have it going on in America and Europe. We'll be releasing our speakers soon, and so make sure you register so you are the first to hear who will be joining us and speaking. Really excited for that. And so, again, thank you all so much. Keep an eye out for the email. I know there was a question or two about the slides in the recording, and so make sure that you, look at that email. Let us know if you have any follow-up questions. But with that, we will launch our feedback survey as you all leave, and we hope you all have a great rest of the day. Awesome. Thank you. Bye. Thank you all.