Name: Why you need modern workforce management in the era of AI
Uploaded: 2025-09-12T18:06:42.729Z
Duration: 41 min 4 s
Description: Why you need modern workforce management in the era of AI

Transcript for "Why you need modern workforce management in the era of AI": Hi, everyone. For those of you joining us today, we're gonna get started in a couple of minutes. But while we wait for folks to trickle in, please drop into the chat, with our ice breaker question. Introduce yourself. Let us know where you're dialing in from, and then share with us, what's your new favorite AI hack that you've been using lately? Alden, what's the what what have you been doing lately? Any new AI hacks that you've learned, new tricks? Oh, I mean, I'm living a very suburban life these days, so I've created a new GPT project all around landscaping and lawn care. And then I've I've given it, like, my precise location and, like, the current state of things and, you know, what I'm trying to do with it. And it's that's pretty cool. It's, like, not only come up with a bunch of recommendations that solicit my feedback, but then actually created a a dot ICS calendar file. They're like, here is your lawn care plan throughout the whole year. Nice. That's awesome. I know. I told you it was very, very suburban. No. I mine is on a similar note. I've been in, redecorating and redesigning condo mode. So I'll send chat g p t just, like, images of, like, my bathroom and living room, and it'll do, like, near perfect renderings and, like, different paint swatches and new hardware and lighting and everything. Like, it's it's been obviously amazing. Just to envision what it could look like. Well, typically, you'd have to pay somebody a lot of money to do that. So Yeah. No. It's great. I can test out any colors I want without having to buy paint. Nice. Some things coming up in the comments, job descriptions, review writing. Looking for the thing is a killer use case. Oh, yeah. That's awesome. Nice. Alright. Well, why don't we get started here? It's a couple minutes after the hour. So thanks everyone for joining us today, our webinar, why you need modern workforce management in the era of AI. By way of introduction, I'm Bree. I do product marketing here at Assembled. I'm joined by Alden today, our product manager. And then we've also got Bea in the comments, in q and a on standby. So she's one of our technical experts. She's here to help answer your questions. So some quick housekeeping items before we dive into content. Please make use of the the chat and q and a boxes, that you'll see on the right hand side of the module. Like I said, we've got Bea on standby, so don't be shy. Drop in any thoughts and questions as as we go on here. Also, this session is gonna be recorded, and we'll be sending out the recording after the event. So everybody will get a copy today. A quick peek at our agenda here. So we'll start with, a little background on the state of the support world, do a short introduction to Assembled, and then I'll kick it over to Alden to cover our support orchestration product launch, walk you through all of our new features, and then we'll round it out with, some customer stories before we jump into live q and a. So let's get started. So we hear this all the time from support leaders out there that support leaders have been testing out AI solutions. They're trying to find new ways to continue to make support more efficient, more cost effective, and yet teams just aren't actually seeing the returns they expected. So maybe, some of you out there can relate and that's why you joined us today. So let's, take a quick peek at what McKinsey's latest research tells us. And so this is a a survey they ran about where companies are actually standing with their AI investments. They surveyed a bunch of c suite executives about AI results, and they found that just most companies just aren't seeing the ROI that they had expected as early as they had expected to. So if we look at the left hand side here in the revenue column, 36%, are saying that they're seeing no change at all in their revenue from their AI investments. And only about 19% are seeing revenue increases above 6%, so anything remotely significant. And then on the right side of the cost is even more concerning. So 74% of companies are either seeing their cost stay flat or they're actually increasing after implementing AI. So this is the exact problem we're here to talk about today. That companies are deploying AI tools, but they just aren't able to connect those investments to their actual business outcomes just yet. And so this quote from Boston Consulting Group also kind of underscores what we're saying here that only 26% of companies have developed the necessary set of capabilities to even move beyond proofs of concept and to generate tangible value from AI. So for a lot of teams, it's still really early days. And then a final data point from McKinsey. So they found that only 1% of companies are describing their AI rollouts as mature. So this tiny little sliver you see on the right is 1%. So that means that 99% of companies still have to figure this out. And so if you are in the 1%, congratulations. But if you're like the rest of us in the 99%, you're in good company. Almost nobody has cracked the code on making AI truly transformational for their business yet. And so what are these 1% of people doing right? They're approaching their AI and human support with a unified orchestration strategy. And so everybody knows that we're gonna be using AI for, you know, the future forever. AI is here to stay. Support teams are rushing to deploy AI. A lot of you have probably been given some sort of AI mandate that's like, just figure it out, just do AI. But a lot of people don't know how to actually build out that strategy. And so a few truths that we've learned here from talking to folks out in the field. So number one, we know that 100% automation isn't actually the goal for anybody. So a lot of people are focused on how high resolution rates can get. Like, you'll see everyone out there promoting, like, fifty, seventy, 90% full automation. But we know that you're always going to need live people for the complex cases, for those judgment calls, for customer relationship building. For some of those VIP cases, we don't truly wanna automate every single case away. Even OpenAI, they walked back on their no humans and support strategy. And, McCormack famously announced they were going AI first, automating everything, but they've, backtracked and have started rehiring after that. So it's not about automating everything. It's about automating the right things and figuring out where AI fits best into your overall strategy. Number two over here. Despite what we just showed you in the charts, we believe AI will eventually save you money. But in order to reach those savings, it becomes a workforce management problem because you're gonna have to reallocate your humans effectively. And so the only way you can really do that and know that it's working is through a workforce management tool. So you need that orchestration layer that's gonna help you coordinate your in house agents, your AI agents, your BPO agents, all in one place. So to really get that value from AI agents, it has to be connected into your staffing and headcount plans. You can't do those things separately. And third here, we know that the core of what makes a great customer experience just really hasn't changed, but it's the new tools that in the picture that mean you have to adapt your strategy. So customers are always gonna expect those fast, high quality resolutions. But now you have to make sure that your AI agents are staying on brand, that handoffs between AI and human are seamless, and just making sure that you're continuing to deliver those high quality, consistent on brand customer experiences across the board. And so, at Assembled, we're driven by this guiding philosophy, and this is at the core of why we're building what we're building. So we believe right now, what modern workforce management is more important than ever, and that serves as a critical orchestration layer for hybrid support. So you're gonna hear us throw around this word a lot, orchestration. And so what does that actually mean to us? It means that you're forecasting and managing your human agents and AI agents in the same platform, in the same interface, not in separate silos. So teams are gonna need to make decisions on deploying AI based on insights that are only available in a workforce management tool. And you're gonna need to revise all of your staffing plans based on how much AI can actually automate. And so the AI point solutions you'll see out there just don't have these workforce management insights. So teams using them maybe just aren't optimizing their staffing like they could be, and that's why they're not actually seeing that ROI yet. So if you've automated some workflows that, you know, in theory can take 20 to 50% of the cases off your agent's plates, like, what comes next? You know? How do you know how much support volume and agent workload has actually been reduced by AI? And do you know if you've actually shifted your staffing plans accurately for the cases that still need humans? And then on top of that, how do you plan for the cases that are escalated between AI and humans? And so and then the legacy workforce management side, a lot of those providers, they've bolted on AI as a solution, but but they haven't truly integrated AI, and human planning together. So, essentially, you're still working out of two separate tools. And so we believe that only a true orchestration platform can give you these answers that you need. And so this is why we built Assembled as this modern, unified orchestration platform that can help you manage those in house agents, all of your BPO agents, and especially your AI agents all in one place. So we're the platform that can connect your AI automation with your workforce management and help you get to that ROI by intelligently balancing your AI strategy with all of your staffing plans. So in other words, Assembl'd still puts the right agents in the right place at the right time, but now just with the addition of AI in the mix. And so with that, I'm gonna hand this off to Alden, our product manager, to show you exactly how this is manifesting in Assembled product. Cool. Bree, thank you so much. Hi, folks. I'm Alden. I'm one of the product managers here at Assembled. I have to say, I think that this is one of the most exciting times to be building workforce management products. I I spent a lot of time with customers from Fortune five hundred companies, small start ups. Pretty much every conversation I hear the same two things. Alden, we've started automating some chunk of support cases, but we don't know where to go next. And I'm now spending money on AI agents and human agents, but I'm not seeing any ROI. Assembled, you're the experts on support. What should I be doing? So I'm thrilled to walk you through how Assembled makes it easy for you to scale up your AI automations and to reap the ROI of your AI spend in this new hybrid world. And it really comes down to three things. The first is understanding your case data and subsequently how to scale up AI automations. It will be hard to discern any ROI if you're only automating a small percent of your easiest cases. You'll see it when you start to scale up, and all of this starts with understanding your case data, which lives in a modern workforce management platform. The second is how to adjust your staffing strategy in light of AI automation. No one wants to enter 2026 with the same level of spend on their support team and an incremental line item for AI. AI needs to lighten the burden on how many agents you need to provide amazing support, especially in a world where your business is growing year over year. And lastly, support is often your best representation of your brand values. How can you scale up AI without compromising on your support quality? No one wants to see horror stories on Twitter of your AI spitting out nonsensical information or introducing a new level of friction that wasn't previously there in a world where your support team was handling a 100% of cases. So all three of these necessitate a tight integration of AI and human agents, and you need a modern workforce management platform that handles this orchestration like Assembled does. So let's jump in. The first is analyzing case data to power your automation journey. So workforce management platforms are great for understanding the volume and the nature of support cases and then orchestrating staffing to your first response times, that delays, quality metrics. But all that data is also an untapped well of insights for what can be automated with AI. So I'm excited to announce our new automation insights dashboard. Assembled users, you'll be familiar with this. This is an extension of our existing forecasting tool that helps you understand the volume and nature of cases that will come your way based on historical data. But we still get the questions of how much volume is AI automating today, and what is the low hanging fruit that we can pick off next. So let's walk through some of the newest features here. First is an extension of this dashboard that shows how much volume is being resolved by AI today. And that's plotted on top of all the cases that your team is handling. And one of the exciting things that you'll see here is that AI is not only lightening the load of work that hits your support team during a typical workday, but it's actually handling a huge chunk of cases that tends to come in outside of normal business hours or in this case over the weekends where you might not be staffing support. And AI is really a a key unlock to be able to to being able to provide great twenty four seven service even if your agents aren't in their seats. And as we've built this, our customers have said, okay, Alden, this is great, but I want more and awesome. I love that. Our customers are ambitious. We're ambitious too. So next thing that we've done is run an analysis on all of your WFM data to help you identify where to go next. For most customers, there are a lot of knowledge cases that can be automated. And you all know better than most maintaining knowledge bases are hard. It's difficult to keep everything up to date. Fear not. We are not telling you your knowledge base needs to be perfect. Everything needs to be up to date. Rather, we want to identify where you could be automating more if you were to build out a knowledge base for a particular type of topic or case category based on your highest volume of topics that aren't covered today by AI automation. And not only that, we can scrape information from these cases to help you build up those knowledge articles, publish them, and connect the workflow to it. So I know that there's a lot here, but the key takeaway is that the first step of maximizing the value out of your new AI solution is knowing where to automate and having a confident plan to go from a small percent of automation to a large percent of automation for your cases. And you need a modern workforce management solution that leverages your case data and provides a tailored strategy for scaling that. Alright. The second is, how do you now optimize staffing? You're automating now. That's great. But arguably, the most critical step to get right is how do you adjust your support team as AI is doing more? If you're paying for AI automations and you have the same number of agents steps to the pre AI world, you will be double paying for AI and human agents. So I'm excited to hear two capabilities that work together in tandem. The first is adjusting your staffing requirements based on how much AI is handling in various channels and queues. And users of Assembled AI and workforce management products can attest to how this already works today and how they are able to reallocate and upskill agents to more complex cases now that AI is resolving more of the tier one type cases. But the second killer feature here is how we're applying AI to help you rebalance your team staffing based on real time criteria. I've heard time and time again, Alden Assembled is great for understanding real time issues or staffing imbalances for an individual queue. But how can I, as a support leader, zoom out to see my whole operation in one place and make thoughtful decisions on how to rebalance across the whole? Well, our new command center provides a snapshot of your most important views of your support operation in one place. You can look at all your queues or channels or teams from one dashboard, configurable, modifiable as you see fit. And you can see, in this example, we've got four queues from one customers and one customer. And what becomes immediately obvious from this aggregated view is that some queues are overstaffed and understaffed. And fortunately for you and fun for me as a product manager, this is ultimately a data and user experience problem that is very well suited to AI. Because our workforce management platform knows the entire state of your operation, we can programmatically spot deltas and over and under staffing and surface recommendations and actions to take. So for example, in this exam for example, in this scenario, you can see that the billing and the refunds queues are well overstaffed, whereas technical support and trust and safety are are pinched either chronically in the technical support case or at certain times of day with trust and safety. We will surface recommendations for how you may be able to rebalance these queues by looking across what agents are available and the relevant skill sets, and then allow you to take actions within this dashboard to immediately reassign people to the right queues and to hit your SLA targets. There may be reasons to not make these adjustments. Ultimately, you as support leaders have the best pulse of your operation, and, you know, there are reasons to give you control of this rather than doing it automatically. But we are highlighting these gaps, surfacing the actions that you can take. And from one dashboard, you can reallocate qualified agents from one queue to the next. So summarizing this feature, Assembled helps you realize the ROI of AI automations by adjusting staffing requirements for your team based on the volume that AI is automating today and using AI to help you detect and take action on staffing imbalances that will improve your SLAs. So that's all good and dandy. But what I also hear from customers is, hey. I'm planning for 2026 right now, and I'm under pressure from my c suite for how I can keep support costs low without sacrificing quality. Well, we're also rolling out an updated version of our planner that helps you understand where you are today with AI automation and where you likely will be or could be as you continue to scale up AI. And this applies both to AI automations that are containing cases and therefore never going to agents and the efficiency gains of our AI Copilot that makes your agents more efficient. And, again, this is a data problem and Assembled as a modern workforce management solution, has all of that at our fingertips. And we're here to make it easy for you to plan for a very exciting year ahead as AI only continues to ramp up. Alright. So now we've covered how modern workforce management provides a clear, confident path to scaling up AI automation based on your case data, and how you can reap the benefits of AI through data that's natively integrated into your support team staffing tools. So lastly, I wanna talk about the thorny one, which is how do I ensure that quality doesn't fall off a cliff as we ramp up automation? And it's a very valid concern. I'm sure all of you have had a bad experience with the chatbot where the bot doesn't answer your question and you can't get through to an agent. Or if you do, you then spend another five minutes regurgitating all of that same information to an agent again. Well, those experiences will be a thing of the past. And one of the great things about modern workforce management platform that is built for in house BPO and AI agents holistically is understanding how well staffed your support team is. And if an AI agent can't easily solve an issue, who they should route a case to. There are some black and white examples that you probably already know. If your support team only works 8AM to 5PM, you can leverage AI agents to handle cases outside of these hours. And while AI can resolve a lot of these cases, it can't resolve all of them. And so we provide the ability for AI to capture customer information for when your net agents next come online, they can reach back out to those end users. So is AI a silver bullet in this case? No. But addressing two thirds of customer issues outside of business hours is a big win. But one of the examples that we hear a lot from customers is, hey. I'm running a sale this week, and I don't wanna temporarily temporarily hire more agents. What can Assembled do to help? And this is a great example if you don't have enough support agents to handle all of the inbound volume and AI cannot automatically resolve every case. So we built a new feature called dynamic handoffs that puts you in control of how and when AI hands off to a support agent. For example, if your team is slammed and a customer wants to get through to an agent, we can accurately share back with the customer how long the wait time will be to chat with a representative. And if they don't want to wait, we can capture callback information. Or conversely, maybe one of your teams is sufficiently staffed and already exceeding their SLA targets. Well, you can set thresholds of when into whom your AI should transfer the call. And if your phone team is overstaffed that those agents have the skills to handle chat, Assembled can intelligently route those cases to available agents. And this is really the power of modern workforce management again that's built for AI. AI agents provide twenty four seven coverage. They lighten the load of the cases they can hit, can continue to provide a stellar customer experience throughout the day, and intelligently bridge the gap between a conversation that may start with AI and ultimate resolution with a human agent. Alright. Now remember that painful experience I talked about a few minutes ago where you provided all the information to chat agent only have to do so again to a human agent? Well, that is a thing of the past. Agents who are now picking up a conversation that was started with AI receive a detailed audit log of the prior conversation along with an AI summary of the key issues and where they need to pick up the conversation to provide the best support and resolve the customer issue most effectively. And this ensures that your customers get the fastest, highest quality resolution in the case that AI can handle all of it on its own. Okay. I know I covered a lot of stuff, so I'll quickly summarize. Assembled modern workforce management platform helps you understand your case data and provide a tailored journey for scaling up AI automations, including identifying gaps in your knowledge base, helps you optimize agent staffing to realize the ROI of your AI automations, quickly rebalance teams that may be over or understaffed, and help you plan for the future. And then finally, leverage real time workforce management data to ensure dynamic handoffs to the right agent to provide the highest quality experiences. So very excited to be able to share all of this with all of you. We definitely want to get your hands on these features as soon as possible. So please, in the comments and, Bria, I'm not sure if we're gonna be running a poll here. We definitely wanna hear your interest, so that we can follow-up. Yep. So, we've got a a poll live now. So if you I'll just wanna drop in, or choose your selections for any features that you're interested in learning more about. That's available now. So, using this kind of three pronged approach that, that Alden just shared, a lot of our early customers are seeing some really massive gains here. So Alyce AI, they're an AI platform for health care and housing organ organizations. And so they were able to use Assembled to identify all of these knowledge gaps. They found new automation potential and ended up leading to a 50% automation of case triage. They used to be completely manual, so they freed up their agents from a lot of these more tedious activities. In the middle here, Flexcar. So they're a month to month car leasing service. So they were able to use our dynamic hand off logic to keep their twenty four seven coverage intact without having to hire a bunch of overnight staff members. And they still managed to cut their resolution time in half on top of that. So AI agents are able to resolve the low complexity tickets and then they only escalate the urgent ones to humans for those overnight staffing hours. And then third here, Graphio. So they're an Amazon brand aggregator and they've been using Assemble to automate a lot of their business decision making. So especially around seasonal peaks like Prime Day. So Graphio has a lot of, brands that sell through Amazon. So on Prime Day, they can easily weigh the trade offs of deploying AI agents versus hiring temp agents like they have in the past. So it helps them really do more effective long term planning. And so far, they've been able to save nearly $2,000,000 just from using that method. And so, a couple more points from some analysts just backing up what we've been saying today. So in Gartner's latest report, they mentioned 95% of support leaders plan to retain human agents, and that AI and humans in tandem is gonna be the most effective for delivering great customer experiences. And then, we've got another quote from McKinsey and Company that they say, workforce planning is more difficult than ever. Employers just don't know how fast AI is gonna force workforce rebalancing, retraining, and upskilling. And so if you think back to, the the staffing mission control that Alden just showed us, This is exactly the type of problem we're trying to address. So helping people make those decisions on that rebalancing and where to rescale and move agents around. And so this is why we're building what we're building. And finally, I'll leave you just Brief, if if you don't mind, I'm just gonna jump in because it looks like Matt Kosko wrote a a question that I think is relevant to this as well with regards to to Intercom and the platform. Intercom has a lot of this data. They are a contact service platform and they, you know, they can help you analyze case data. They, of course, have some automations as well. But I think that this quote is super super relevant, which is, workforce rebalancing requires a workforce management platform like Assembled that helps you understand the nature of where your internal, outsourced, and AI agents are working and most effectively orchestrating across the three of those to provide the highest levels of service and to help you optimize cost along the way. Sorry. This was good jumping off, but I'll hand it back to you, Bree. No. That was great. So finally, we'll we'll leave you with a a few key takeaways. If nothing else, I hope you've all realized that an orchestration mindset, is the way to go for future planning for your support organizations. And so a few key takeaways here to get started thinking about your own orchestration strategy. So first, you're gonna automate what you can. So find the low hanging fruit, analyze cases, look at your queues, try to automate the things you know you can automate first, step one. Step two, then you're gonna optimize your staffing accordingly. So they're gonna have to adjust to account for the cases that AI is taking on. And then third, you're gonna wanna measure and making sure that you're actually driving impact. So you're gonna track how AI is improving metrics, like resolution and handle time. And then as you measure, as you continue to find new opportunities, it sort of becomes this flywheel of positive feedback, where you can continue to improve. And with that, we can jump into live q and a. So, again, there's the the module on the right where you can drop in any questions. I see a few of you already have a few questions in here. Question about, does this account for multichannel like chat, phone, web, and email? Yes. It does. Assembled is multichannel product. What have you changed on your forecasting strategy to incorporate AI versus before AI? We now forecast both total volume and the resolution mix with AI in the fold. And so while on that one a a little bit too, maybe, Bree. Like, I think that this is one of the key things that our customers have been struggling with in this new world of AI is that everybody's buying AI and AI is is automating some percent of cases now. But unless you are actually making adjustments to to your required staffing calculations, you'll never realize the ROI of that AI spend. Like, you don't wanna be in a situation where you're retaining or, you know, or you're you're staffing the same number of agents and you're now paying for an AI automation tool. These two things need to to work in tandem. And there's a a kind of more sensitive topic here of, you know, is is AI eliminating jobs, and is it, like, taking out members of the team that were previously there? That's not really what we see. More of what we see is, hey. We have really ambitious growth targets for 2026 where we're planning to grow 20% or 40%. And AI is an opportunity to allow you to retain and upskill your team without needing to hire more for next year as AI automation continues to increase. I think that this is, like, one of the positive stories, very positive stories that, you don't see as much in the headlines, that's, you know, doom and gloom around AI. It's really how can you get your best people to do increasingly more critical, more empathetic work, and as your business grows, run a even higher retention, high quality agent team. Great. I think another one for you, Alden. Can you provide a high level overview on how orchestration will be set up when companies already have different CRMs, different contact centers? Yes. I think in many ways, the answer is that not much changes. So Assembled has always been an open platform that integrates with all of the top CRMs and and CCaaS platforms. So Intercom, Salesforce, Zendesk, Customer, many, many more as well as we integrate with kinda any custom solution as well. We have a a robust, open API platform too. So all of this data is is is already into assembled and works natively bidirectionally. The big thing that has changed is how do you account for AI as a third type of agent? If, you know, the first type of agent is an internal team, the second type of agent is a is a BPO team. I know AI is now a, a third type of agent here. And we have over the past, you know, quarter or two, basically refactored a lot of our platform to ensure that we can account for this new type of agent and one that operates a little bit differently from an internal and external, internal or or outsourced agent. So kind of same the same kind of flow of operations as before, you know, cases come in. We understand the nature of the cases. We can help route to the the relevant, agents to address them. In this case, AI agents may be taking, a large number of cases or be the, you know, the first to attempt to to resolve those. But I think one of the things that I I showed at the dynamic handoff feature is that AI cannot resolve a 100% of cases. And in those scenarios, you don't just wanna drop those customers on the floor. You want to figure out how can I get how can I transition from a customer conversation that started with AI to a live human agent who may have the requisite skill set, critical thinking to to be able to resolve that? And, Assembled has that now built in. We're continuing to build more and more features. So maybe, Pri, like, that helps, like, answer this a little bit more. Like, we continue to build more and more connective tissue between what AI is doing and what, your your internal human agents are doing. Great. So just looking through some of the the FAQs that we typically get. I'll just read through a couple of those questions as well. So we get asked a lot, like, I mentioned metrics, measuring, improving. So teams like to ask what metrics can we actually track to understand ROI and the impact on support KPIs. And so our best practice here is to focus on two different layers of measurement. So on the first side, you'll have the agent and customer impact. So looking at things like handle time, CSAT, escalation rates, to show whether AI is actually improving day to day support. And then on the other side, you're gonna wanna look at adoption and automation impact. So looking at your automation volumes, your resolution rates, and also at agent adoption will tell you how much work that AI is actually taking off of your team's plate. So together, those give you this really great balanced view answering two questions of, is the AI being used effectively, and is it making a real difference across the organization? And then another question we get a lot here is how do we get our agents actually comfortable with using AI alongside their existing existing workflows? And so we've, been through several deployments with teams, and so we've we've learned that the one of the best ways to build comfort across teams is just by starting small, setting clear expectations. So, a best practice is to introduce automation gradually, beginning with some straightforward, very low risk tasks so agents are seeing quick wins without too much disruption. So and then training, transparency, constant feedback loops just help really reinforce trust, because they're seeing the agents are seeing their input actually help shape the system. AI is not a thing happening to them. AI becomes their partner at work. And once agents are actually realizing that AI is reliably handling repetitive tasks, that they normally would find kind of frustrating, adoption and building their confidence just becomes that much easier. I'll also add, I think that this is great opportunity for all of you as leaders to set the tone within your organization around what the role of AI is relative to your support agents. And, you know, as I I said earlier, we don't view AI as a thing that is going to eliminate your team. I think that there's a much more positive framing here is the business is gonna grow 30% next year. You are all amazing agents, and we want to retain you, and we want to upscale you. So we want to move you away from doing, you know, password reset or what are the hour answering, you know, what are the hours of operations? Or can you tell me the the status of my order type questions, you know, kind of the tier one type cases? AI can handle a lot of those. Like, that is what AI is, like, just natively very good at. And we want to, you know, upscale you as agents into the more critical thinking, higher empathy type cases. And I think that that is that is really, like, the strategy for how you deliver a message to your team where AI no longer seems scary. It more seems like it's creating an opportunity for you to grow in your career as a support agent, and ultimately kind of move from, like, a customer service rep to much more of, like, an account manager or, you know, a support engineer who's getting more into the weeds, and and doing more critical thinking type tasks. Great. Alright. Let's just close out by quickly taking a look at some of our poll results here. I think we're having some technical difficulties. I'm not seeing the poll results show up. Sorry about that, guys. We might not be able to see the poll results live, with everyone here, but we definitely will be following up based on, some of the selections. Excited to hear that so many of you are are eager about the features that we are. And with that, I hope everyone has a great rest of your day, and thanks for joining us today. Thank you.