Listening@work 2025: Listening in the Age of AI

Yes, me too. So everyone, can we start to give a warm welcome for this next section? I think one thing that I'm really excited about here, and, and some of you maybe, uh, have, I've heard about this whole thing about ai, right? And it's coming into the world of work. And I'm really excited just to dig into like, what does listening look like in the age of AI and what's next? And I'm hoping one thing that we could do with you all today is go be like a little bit deeper than maybe some of the headlines and the big kind of statements about ai. And we're actually just gonna dig into actual, like use cases and ways that you could start leveraging it today. And maybe some basic ways, but also more advanced ways that you could start to look at it to enhance what we're doing with listening and the insights and the data people analytics and things that typically would require an entire people analytics department in the past, right? So you, as a one person HR team, or a one person people leader, you now have the tools at your access of global teams and departments that you typically need an investment for. So let's give a warm welcome to first up niru global employee relations business partner. And, uh, I got a sneak peek into the slides already, so I'm like, okay, this is gonna be good. I'm really excited for this session. So lemme stop sharing, and Elise pull you up and bring you to the stage with me. Uh, thank you so much for being here. And you want me to kind of drive it, right? So I can pull them up? Yeah, you can do that, yeah. Okay. First off, thank you for being here though. And, uh, yeah, I would love for you to introduce yourself and then the floor's, essentially all yours. Hey everyone, thanks for having me here. I'm really excited to be here today. So I am HR B VP and also your partner in one of the tech companies in bay. Um, I hope you all are having a great time in the event so far. So today I'll be talking about listening and how you can use some of the AI ways to really have that data into action and major business business impact. Now, before we get started, you know, I have a question for you all, and feel free to type your responses in the chat. Now, when we say employee listening, what is the first word that comes to your mind? You can type your responses in the chat. Just one word. What is the first word that comes to your mind? Survey. Okay, I see some responses coming in here. What else? Okay, so I see a lot of survey. Okay. Okay, sounds good. Understanding communication, meetings, connect, hearing, being up, open. Okay, that's fine. I, I understand that as well. Clarity. Anything else that comes to your mind? Engagement, input. I see some great responses here. So thank you for sharing. Uh, now most of us picture annual surveys or maybe a pulse check as the only way to listen to our employees, but the reality is employees are speaking all the time in surveys, in chat tools and exit interviews. Even in the way they show up at work. The question is not if they are speaking, the question is, are we truly listening? And what are we doing about the data we gather? So with that context, let's get started. So now these are the top HR priorities for the current year as per Gartner's research. Now, what's really striking here is how each one connects back to how well we listen and how effectively we act on what we hear. Now, leader and manager development is the first priority. Managers are often the first line of listening. They hear employees concern on a regular basis. It's really about building that connect and relationship with their team members through listening and working on it, right? Then our culture lives in conversations, sentiments, behavior, values. Now, how do you decode that? Unstructured data like chat, exit, interviews, open next tech surveys. One-on-ones is how you really reveal culture strengths and friction points. Then workforce planning, change management. Now any change can succeed or fail. It, it just, it is dependent on how employees adopt that change, right? Listening really helps leaders understand the employee sentiment in real time. And you can really adjust your communications, your strategy on the basis of the data you get. So really have to keep listening continuously there as well. And HR technology is where the AI sits at the center. Now, technology is no longer about just efficiency, it's about insight. How do you predict basis data and how do you take an action there? Now, while these all look like independent priorities, but they are all connected by a single thread, which is the need to listen continuously. We can move to the next slide here. Now I have a question for everyone again, the next question for you, how do you collect listening data? You can share some of the sources. One you did share survey, any other source you can think of, feel free to type your responses in the chat. Focus group, coffee chat. Okay, water cooler. I love that. Round table, amazing one-on-ones. I love that. All hands meeting. Okay, anything else that comes to your mind? Okay, Solan. Okay, interesting. What else? Okay, team channels, great life movement sessions. What else? Daily, weekly, monthly meetings. Okay, slack channel. Great. Anything else? How do you collect data? So with that, I'll just show you a framework that I have on the next slide here. While Mo most of you covered a lot of these responses. Now I'll just summarize everything on this. Uh, uh, using this framework. Now, what people say, what people do, what people say they do, are entirely different things. Now, what people say are the conversations that you have in your workplace, right? Formal communication, like you have your regular one-on-ones, you have your performance reviews, you do formal checkpoints, and then you have informal, uh, conversations. Like one of you mentioned water cooler, right? That's how you get some of the interesting insights from your employees as well. And then you have ethnographic research data for which you have your data scientists, your analysts, they really work on your organizational level data to understand your employee sentiment and behaviors and attitudes, right? So what people say they do are the surveys, like you rightly mentioned, engagement survey, and then filling out performance reviews or feedback form after you conduct your training sessions, either we can do structured survey questions, right? You ask a question that can be answered in yes or no, agree, strongly disagree, structured wherein you have closed-ended answers or open-ended survey questions wherein you give an option to really write comments or psychometric surveys through which you really understand the psychology of your employees. Now, third component is what people do, which is your system data. Now that's also really important because here, there is no scope of biases, perceptions, or judgements. This is the data that you pull out from your HRIS, your performance management, your LMS, slack, Microsoft teams, zoom, Google Docs, and how do you really, you know, your timekeeping, your expense system, your ticketing system, you can bring in all the data and really see, uh, measure the, you know, the listening of your employees there as well. The good thing is system data exists everywhere when you hire or when you offboard from performance management to learning. It's more about the passive data. It's more of passive data. So it's important to have both active listening data and passive data to really see how you are listening to your people. And overall, you have to bring your or your hr, bps, your employee listening teams, your analytics team to really work on one common end goal. That how do you drive those workforce decisions that can make business impact? All your data has have to have some correlation with the revenue productivity. How do you keep your people engaged, happy, productive? And how does it get revenue for your company? Right? Now, while we are talking a lot about AI data here, I would say there's one common principle that we all need to remember. That everything has to be grounded in empathy and the human aspect of decision making, right? Understanding and really supporting our individuals that make up our organization is core to who we are as hr that makes us human grounded in empathy. Now, I'll show you some use cases on the next slide here that, you know, we'll just begin after this. Now, where do you get started? I would say start small. Do a pilot, run in one of your teams. Look at the data, look at the impact, and then scale it to your entire organization, right? And remember, success is not just about inside generation or presenting dashboards to your leadership. It's really about driving business impact where your employees are productive, engaged. And I'll show you some of the use cases, how we are working on listening data using ai. So this is the first use case that how we are using AI coaching for our go to market teams. We have collaborated with Clinch a GI to build this AI driven listening tool for our go to market teams. And this is a real example of how we are using AI in listening, in this case, how we are use coaching, our go to market teams. After every client or customer calls, you can see how the, the employees get their feedback report. So after each meeting, AI sends this feedback report to the sales rep highlighting call quality scores strengths, which is what the sales rep did well on the call opportunities, which are the areas of improvement with clear and actionable feedback, and then broader value, and then feedback on objection handling as well. And then manager receives a summary. You can see at the bottom of the slide with overall objective performance feedback for the team member, the sentiment call call quality engagement, right? So this is how we are using AI to really Where sales is on a in real time. Now, for example, what is the objection raised by the customer on the call? What was the employee's response? And only employee can see this data. That's an interesting part. The client cannot see that what, what the, the rep is seeing on the call live right now. So here AI is doing all the listening and sharing feedback on an ongoing basis, which directly has an impact on revenue. The best thing is this is all tailored as per employee and their individual performance. It's not a generic feedback at all. As you can see on the right side, how we generate the AI based listen, listening, feedback and coach our sales reps. Now we've seen examples of how AI supports go-to market teams and sales reps. Now let me show you an example of how AI can empower managers as coaches on the next slide. Now this is an example of how AI can act as a virtual coach for managers really helping them handle tough conversations, coach them on how to deliver feedback to their team members and help them become better leaders. Now, if you look at the first box here, manager types in a question to AI coach asking, how do I give constructive feedback without sounding critical? And then AI is asking question to clarify and understand the context. If you look at the second box here, and then the third box, if you look at it now, AI is coaching on delivering feedback using SPI model after understanding the context, which is situation, behavior, and impact framework. And then then AI is, you know, sharing a script of how you can have that conversation with your team member, right? So AI is doing all the chat here, helping manager to really draft the feedback script that you can have with your team member here. So this is another use case, how, you know, on the basis of our listening data and feedback, we have, uh, uh, coaching our managers. So these are three examples of how we have really worked on it. Um, now I will show you a case study, as in on the next slide, that how a software company called Sage has used listening data analysis to really drive business impact. So what they're doing, they're using AI powered listening through Microsoft Viva Glin, through which they have identified, uh, the drivers of attrition. They have been able to turn that data into action, which has helped in lowering down their attrition, their they've increased NPS scores, they've improved employee productivity and overall engagement. The other message I'm trying to con convey to you all here is listening only matters when it is connected with the action and business outcome. Otherwise, the data is of no use. Now ai AI can only help us, you know, being faster, being better, but how do you measure in terms of business terms is, is how you really drive an impact. Now with these examples in mind, let's bring it all together and look at some final takeaways from the session. Now, I would say you should be listening continuously, not just periodically. It should not be a quarterly or an annual survey or a focus group discussion. It has to be a continuous way of really tracking the data and listen to your employees through one-on-ones, through focus group discussions, town halls, you know, any mode wherever you can get the data. It has to be a continuous listening, uh, way and get data from multiple sources when use AI to turn data into action. AI helps us move from just collecting data to actually acting on it in a faster, smarter way and at scale. And then prioritize actions with the highest business impact. Not everything employees say can be acted on at once. I would say prioritize the issues. Identify where you see the highest business outcome or the impact there and measure impact in business terms. Listening is only credible if we can tie it to productivity, retention, engagement or revenue outcome. That's where a lot of companies they miss out on. You know, when, when it comes to business impact, they would have a dashboard analysis for the leadership. But what are we doing with that? The action impact and business outcome is how you really drive impact in the company and not just active data. Focus on passive listening and use AI to make it even more impactful. So when you combine passive listening, like you analyze open text, chat, exit data with ai, you can really uncover hidden insights that leaders would really want to look at, right? So, uh, but this I will, uh, leave you with a thought here. The the final slide that the future belongs to leaders who not only listen, but act and employees are already speaking, remember, is how I started my session. The future belongs to the leaders who really act on what you hear and the data you see. Um, and I'm happy to answer any questions. If you have, you can connect with me on LinkedIn. Anything you wanna share or ask, I'll be happy to connect with you all. Wow, thank you so much. Niru. Uh, I'm gonna keep this slide off for everyone who wants to connect with her. Def, I mean, blown away. Those were some incredible use cases. I love kind of the live AI assistance that can come from the meetings, specifically the sales scenario and example that you provided. But I could even see that more for just our internal calls, our internal conversations with our teams and our one-on-ones and our check-ins. Same way a lot of you use like certain plugin tools. You know, you see all the AI bots here that are like listening that help recap and provide insights. But also think about like the grand scheme of, okay, if you have an AI agent that's assisting all your meetings, it's gonna be able to consolidate a lot of those insights and pull themes and larger things that are popping up and surface those as a organizational wide, Hey, this has came up six times across these three different teams. Maybe this is something that we should start to look at and take action on. Right? So, um, Niro, I'd love to ask you a follow up question. Something that you've led with at the very beginning that I think is really important, and that's, as we embed AI and technology into our listening ses uh, initiatives, it's really important that we stay rooted in the human element with all of this. How do you balance that or keep that top of mind as some of these HR leaders who are listening, they're going, okay, cool, I want to have this AI coach and this assistant to help us with listening as well as the action piece, but we wanna maintain that human piece. How do you, how do you maybe approach that? Great questions X. So yeah, while we do have AI coach for managers, you can, you know, get the data, maybe the, the script through ai, but you still have to have one-on-one conversation with your team member to, you know, deliver that feedback. And you really have to understand any concerns that your employee may have. How do you guide them? You have to understand their sentiments, their emotions, what they're going through, if they have any follow up questions and that needs a conversation. So one-on-ones can never be automated in my view, that human connect, the empathy, compassion we bring in the company that can never be automated. AI can help us and drive those decisions, but ultimately you have to bring in that human touch there. Yeah, Yeah. 'cause I think a lot of people, when they think about, especially even talent about AI being embedded into the organization, our first kind of reaction or impulse sometimes is how can we just automate and take things off our plate? And I think sometimes we need to reframe it as what you're talking about more of like, no, there's still a human action or like at the forefront of it, but AI is more of like an enablement and another tool to enhance the quality or the, the impact of what we're doing with it. That's right. And in fact, I'll just add here some of the, the activities which we thought we don't need humans for, we have automated them. So we are now trying to involve our, uh, HR team in more of strategic tasks like, you know, communicating about promotions, workforce planning, and really looking at how we want to plan our next year. What are, what are the goals? So really involving in them in those kind of conversations instead of sitting on just working on reports, excel sheets and those kind of things. They've all got automated with AI now. Yeah. So, yeah. So I'll ask one more closing question to you. And I think where a lot of the people, leaders and the audience are asking themselves, where should I start? Like what is some of the easy lower lift use cases that I could probably take action on to build momentum around AI and its impact with listening? Are there certain maybe low lift or media areas of focus you would encourage people, uh, to kind of start with? Or maybe, you know, think of, I would say let everyone start using ai. I, I see that some people are not really using it in the company now. We are encouraging every employee in our company to start using ai. Play around with chat, GPT, just to begin with. Ask you questions, see how AI works, educate yourself, test it out, try and see how it works, and do some pilot run. Maybe you can pick a survey data, put it in ai, get some insights and see how it works. Just play around, I would say. And, and we have also built custom GPT in our company to keep the data secure, because that's really important, right? You have to ensure whatever data you putting in AI has to be safe and secure. We, we cannot put our financial reports or performance data, then again, it'll be against the ethics and legal, uh, compliances. So we really have to ensure, uh, that we are adopting ai, educating our workforce, start using it, build trust, and I would say, and, and involve yourself in more strategic tasks rather than administrative, uh, activities. And I'll add just another maybe simple idea for all of you, especially if you're trying to figure out like, how can I use AI to enhance my listening or insights? And I found that, yeah, there's a lot of these custom GPTs or tools that you might be, have an access to are really powerful from an analysis standpoint. Like, something that I even do after all our programs, and even I'll do after today, is I'll often take the chat log that we have here today and I'll plug it in and copy it or upload it in the chat GBT or a certain tool. And I'll ask, Hey, based on the communication and activity from today's program, what are some of the biggest challenges that people brought up that they're dealing with right now? Yeah. And for me, as a community lead that helps guide the future programs we develop for the network. So that's like an interesting just plugin idea where you could probably look at your Slack channels, Microsoft teams ongoing threads where you're like typically having to read everything and figure out what it's telling you. Those are easy kind of data sets that you could use given it's safe and data protected, where you can help leverage AI to give you insights and ideas or things that you might not have seen from the, the communication that is a form of acting on the listing, right? So, um, Nero, this was, uh, awesome. Thank you so much for sharing just the use cases, the screenshots, it's incredible the work you're already doing and I appreciate you sharing that with us today. Thank you for having me, uh, Zach and Craig, and yeah, thank you. Make sure to connect with her. I'll put her LinkedIn in the chat here. Uh, obviously incredible human and doing some amazing work. So give it up for, for niru for sharing that with us. Alright. Alright, everyone excited for one of our final sessions for today. Uh, obviously I think you just heard from a, a practitioner who's bringing this to life within their organizations, but up next I'm really excited to welcome someone who's a partner at Achieve Engagement, both, uh, at a organization that's been doing this work with an AI kind of an enablement component to listening. Uh, but they also just teamed up with a long friend and partner of ours at Engage at least. So that's really awesome. I'm super excited about that partnership that they've established. But I'm really excited to dig into this both on how they've actually been building technology and how that's been embedded and enhance what we're all trying to do here. So let's give a warm welcome here to Simon, one of the co-founders and CEO at Butterfly AI that is now also power to buy ly. So we're gonna just unpack these things in a conversation between the both of us. So Simon, let me pull you up here. Good to see you, uh, again, and, and glad you're not running around the streets of New York for this call. Um, but, uh, appreciate you taking some time outta your day and doing this with us. You are My pleasure. Thank you for having me. Awesome. Well, as we start to dig into this, I have some different questions I'd love to unpack with you, especially as someone who's leading this from an AI technology standpoint. But, uh, maybe just start, just introduce yourself, like what is some of the stuff that Butterfly AI is doing and, and how is like some of your initial perspectives on like enhancing listening, especially for some of those untapped, maybe employee pockets that we're, we're typically not able to get into? Of course. Um, yeah. Well, nice to meet you everyone. My name is Simon. Um, I'm one of the founders of Butterfly and we started about 10 years ago, uh, with a very simple observation, which was the way employee listening is done is broken, uh, once a year, surveys, 30 50 questions. By the time you get the results, it's a whole different world. So we actually introduced very early on, and we might have been the first company to do that, um, pulse surveys, three to five questions on a weekly basis. And our point of view was always that managers should be at the center of the information. HR should be aware of it, but managers add a key to retention. Like people don't even a company deliver managers. So our point of view was how can we rethink the way we listen to employees and making sure that managers get everything firsthand in terms of data. The second thing we noticed was it's a lot of data. Uh, and if you think about year surveys, again, it's very strategic and strategy is great when you have long-term planning, except that about 10 years ago, you started to see the shift in the workforce, um, where the, I would say the world is moving faster than it used to. And what used to take four years. Now it takes less than four years. Like you, you saw the jump in AI for the past two years, right? Nobody knew what the chat GPT was. Nobody knew what an NLM was. Not everybody does, even my parents. So that says quite a bit about how the, how fast the worlds evolve. Um, what we decided to focus on was an untapped market in terms of positioning, but as well in terms of servicing, which is a frontline workforce. Uh, so for us, what is frontline is everyone that is coming in contact with either the product firsthand or with the customer firsthand. So that means retail, manufacturing, distribution, uh, and it's actually 2.7 billion people working in that space. It's 80% of the workforce market. So it's actually the majority of employees. And what we noticed was they are the forgotten layer. They're the most important layer of every type of companies, but they're the forgotten one because it's tough to get a hold of them. It's often, uh, we used to have clients or prospects say, oh yeah, we, we have a listening to strategy. We do a survey once a year. Oh, okay, how many employee you have? Oh, we're on 15,000. Okay, how many responses you get? Last time, oh, we had 80% response rate around 2000 response, oh, 2000 response 80%. So, oh yeah, we're not serving the people in the warehouses. Why we cannot get a hold of them, which is telling you a little bit that the system was broken. So we decided to tackle that. Um, and that's what actually, um, said, if I, for the lack of better word, seduced gly. And, um, that's how we decided to join them because they have a very strong vision that was quite aligned with ours, and we felt that joining them was the right move for us and definitely is to be the case for them. Well, it's been, it sounds like an incredible journey and I appreciate kind of the focus of what you're doing and really getting to the voices that are impacting the business the most, right? If they're engaging with the product, they're engaging with the customers, that's, those are pretty essential roles and people that you need to be in front of. And it kind of makes sense to me about like the challenge and the disconnect just because of how organizations are structured, right? When there's layers and hierarchies, oftentimes the top of the hierarchy is dependent on the next layer in communicating and listening to the layer below it, right? So now you're asking your leaders to be extremely strong listeners as well as communicators on what they're listening. And I know we've all played like those games at retreats of telephone and you start a sentence and you try to see how it ends up at the end of the, the chain. It's usually a whole different sentence, right? So I mean, it's, it's a, a credible challenge and issue. So let's get tactical a little bit. I'd love to break down different strategies and ways, okay. How do we actually start to access those voices and have mechanisms and even AI tools, uh, to enhance how we're listening with the frontline. So first you obviously pointed out the very broken system with the traditional techniques and, and how they missed the frontline voices. Um, now that we've realized that it's broken, can you talk about just certain methods and ways we could start to tap into those voices and then yeah, any ideas on how AI enabled tools could start to close that gap? Of course. So I think if you take a step back with the idea of employee listening, it's all going back to, uh, employee engagement. And our vision, and I think should be everybody's vision, that employee engagement should drive business outcome. That's the reason why you do it. Like the more engaged you are, the more you're willing to give, the more you're willing to do. And that's why it's a win-win for everyone. The big issue when you do a yearly survey usually is the amount of data you get because it's so much data, so many cuts. If you look at filtering, if you look at, I wanna see the gender, I wanna see the tenure, I wanna see the different department, I wanna see everything. So you are kind of overwhelmed with data. So what do you do with it? You're supposed to come with an action plan. Uh, so add a few more months, a few more weeks to do it. So by the time you collect the data that you can actually present it to leadership, then you can give it to the managers you already lost around three to four months. Now, if you go back to the front end workforce, uh, the average turnover is around 50%. That's in North American average. So by the time you do it once a year, if you do it again, over half of the people are gone. And you can't even measure anything that you have done over that past year. So if you think about a very tangible use case of ai, and I, this is where we've started to invest heavily, it has to do with action planning. And the way you do action planning is based on the data of course. But the beauty of ai, and you had that in the, with the previous presentation, is the opportunity to take so much information from a lot of different sources in order to give you the most accurate picture. 'cause if you look at data in a silo, everything looks to be disconnected. When you start to put the context around it, that's when you can tell a really nice story. That's why you can actually coach the managers, coach the leaders based on the actual data. So our point of view, and actually I could say my point of view is that AI is gonna be extremely helpful with the world of action planning. Not only at the holistic level, at the very top level of the company where we used to do OKRs, right? What is your objectives? What are the key results? And you pass it on to the next layer, the next, the next layer. Now with the use of technology, you can almost instantly give an action plan to every single layer of leadership from your front end leader all the way to your CEO. That is one, one use case. Um, now that goes hand in hand with other concepts because not information is relevant for everyone. So if you think about, I'm gonna use my example of front end workforce. Again, the way we structure data and we always had is based around the locus of control. So what does that mean? It's means that you have three different type of information. You have the information that you can control. As a manager, for example, I don't feel supported. I don't see my manager, I don't feel that my work is being recognized as a manager. I can take that information and I can take an action immediately. The second layer is your area of influence. In short, you can actually control it, but you know, someone that can actually influence it. For example, uh, the, the communication from the leadership is not always clear. Okay, as a manager, I can do my best to do that. Or I can just go to communications department and say, Hey, my team feed are not really aware of what is happening. Can you do anything about it so I can influence this outcome? The last layer is actually quite important, but is the most complicated one is the area of concern. You cannot do anything about it. You cannot influence anyone about it. But you know that in the long term, it's gonna be damaging to your team. Uh, the example I love to use as I think it's a bit out of fashion today, but it's inflation. Like you cannot control inflation, but you know, it's gonna be a, it is gonna impact your team on a day-to-day basis because the cost of life is gonna be more expensive. They're gonna complain about the pay. So you have to keep it in the back of your mind. So whatever you do as a strategy to listening to employees, try to bug it the information in a way that makes sense to you. For us, it was the locus of control. That's how we build our whole platform. And that's how we managed to, to get like quite a ni a nice following on the customer side, but make sure that whenever you start to do it with an intent, you understand why you do it. And most importantly, what is a frame of reference that you wanna share with everyone across the organization? Because at the end of the day, you have to de you have to develop an RI like, you have to show them why you do it and why it makes sense to it. Now, if there are questions, you're not ready to listen. Like you don't want to hear the, I say the answers to don't ask them, just don't. But if you ask a question, and I think this is another part where we've seen that people kind of lost faith in employee listenings because they feel that their voice is not being heard. It's not that it's not being heard, it's because sometimes people feel they cannot do anything about it and therefore they just don't do anything about it. Like, I'm not gonna touch it as an employee. It's very frustrating. Like, oh, you ask my opinion, I give you my opinion, you don't listen to me anymore. So I think the use case of AI will be heavily targeted towards the action planning, towards the idea of prioritization. And if you tie all this information together, so think about employee listening, employee performance, business performance, and why not customer performance. If you have all this information in one place, you can give the most accurate information to your managers to take the right action. And even beyond that, help your employees understand the impact they have. Those people don't leave because they're not paid well enough. Like people leave because they don't feel appreciated. People leave because they don't like the manager. So it's very important to understand how, what triggers people, what motivates them. And you have all this tool in your hand today, except there's too much data that you cannot sort through it. I think what one thing that really stood out that, you know, extremely powerful is yeah, access to the action planning and the resources essentially immediately in many cases, right? And, and I also think about kind of the experience that the modern world is in with social media, with, you know, instant click purchase on Amazon. We're kind of wired to get immediate access to the things that we want. And if we have a challenge or something that we wanna surface and we wanna kind of, you know, get immediate support or action upon something, especially the new generations and millennials, we're kinda wired to get it right on the spot, right? So if we're going on traditional models where we're doing an annual engagement survey and it takes months, that's dis that's extremely disengaging based on our experience in life. So can we start to leverage ai, if anything, to buy us some time as an organization, right? Like at least give them some resources, some action planning, some feedback based on what the voice and, and the listening's engaging with that maybe at least gets them through the initial kind of needs of a response for you as an organization to kind of build a larger response or a larger action plan on some of these things. A hundred percent. And I think what you have to be careful when you start to think this way is about change management. And change management goes on to communication. Um, if you force something, it's like love, if you force it, it might not work. Uh, so the idea here is make sure that if you decide to take the path of ai, like a lot of people are scared by ai. And then you have another category that think that AI is just a chat bot. No, AI is beyond that. Like attrition intelligence is actually everywhere you use it on every day, uh, from driving, from even going to the office. Everything that you do has a component of ai, whether you know it or not. So it's important that if you want to include it as part of your day-to-day, as part of your programs that you share the benefits of why you use it. Again, do it with intent. That's very important. And you have to do some part of the work where you have to reassure your employees as well. Uh, I think Nero mentioned, uh, you have to lead with empathy. Well, in a lot of people minds, if you think about AI and robots, they always go back to a terminator. Like, we're all gonna be robot robots. They're all gonna be scary. Well, yes, there is some part of it that is scary, but there is a huge upside that is gonna help you to give you superpower. Um, if you think about, again, I'm gonna go back to the front line. AI is very present, but robotic is even more present. What people are actually scared of being replaced by robots. What we have to think about is more about what do I have to do for myself to upskill so that whatever the meaning, I won't say the meaningless task, but the task that takes a lot of brain share in terms of time, but makes it very boring to do, can be done by ai. You can focus your attention elsewhere. Yeah. Can even live in a world where you can do several things at the same time and be done in your day in like three to four hours. Now what you have to be careful about is companies have to be okay with that. So how as a company you accept the use of AI every day. Uh, you see it in school already, like, Like I finish in 2007, I'm gonna turn 40 next week. I had to write my dissertation, sitting in the library, open big books, ordering magazines. I'm so happy there was not AI back then. 'cause I know that with the right prompt, I have my dissertation written not in two, three months, but literally in the space of a week. So I think the role of education is how do you use AI every day so that students get better and develop new skills rather than just copy pasting, which had new skills that you didn't have 20 years ago. So as an employer, how can you coach your employees to use ai? Not in a dumb way, not in just replacing meaningless tasks, but how do you help them get this superpower that brings them to the next level? Yeah, it makes me think too of, okay, if you do overly lean on AI to do your listening, let's say it becomes this agent that's pulsing and checking in and doing all these things that normally a leader would do or you would hope a leader's doing with their team. Yes, maybe you've offloaded then a lot of time and also skillset that you to rely on a leader being able to have you necessarily, they, they don't need to have that skillset. Then if that's your philosophy and you're leaning on AI to do it, does it actually result in the engagement score of I feel heard, you know, by the organization? Does that go up, right? Like at what balance do we need to maintain the human element of it being at the forefront as Niru was talking about? Or do we lean on AI kind of doing a lot of the heavy lifting? So I think that's like an interesting kind of, uh, like reality that maybe we'll find out more as organizations, how aggressive they go and where they land with their strategy, what ends up being some of the results. Um, so, and I'm curious for this next question too, because you've developed these systems now and there's different techniques to surveys and listening. Um, you know, I think overall, even with our community giving us feedback, we've seen engagement and survey responses decline in recent years. Um, so I'm curious, what, what do you, what have you seen as the drop off and are there certain ways that you've seen AI or even your technology or even other passive kind of listening method methods maybe fill in the gap or reengage people? Yeah, of course. So I think survey fatigue is inevitable. It's gonna happen, but that's something that you can do in order to mitigate it. And it goes back to the action planning. It goes back to acknowledging the feedback. And again, do everything with an intent, right? If you tell people this is the time of the year, again, we need to do the yearly survey and you wanna have a hundred percent response rate because I need to tick my box, People are just gonna do it without thinking about it, you're gonna have a hundred percent response rate. But that is not meaningful data that you're gonna be collecting. Now I, I think if you go back to why surface survey fatigue is real, it's not really about too many surveys. Like employees don't disengage because of the frequency alone. They disengage because they don't see an impact. So it goes back to that you can have a survey every week. If you show them how the responses, how the feedback was collected and what you did with it, I guarantee you they're gonna keep on responding. Like we have clients that have been with us for like eight, nine years and they still have the same response rate. The mix of people is different because people sometimes feel they have have nothing to share and that's totally fine, you have to accept it. However, when feedback disappear into a black hole, that's when people conclude nothing changes. So why change that? Why, why waste time? And if you go back to employee like, uh, net promoter score for example, the highest promoter score you're gonna get from an employee is within the first three years, actually the first year, like the honeymoon period. Then it goes down, then there is like a bit of a peak on the four, five year. Why? Because that's usually when people get promoted. Again, very excited to work here and then it goes down again, listening is the same thing. If you don't do anything with the data and you don't show that you do anything with the data, people are gonna stop responding. Now, if we think about the what can drive the drop off as well, you can have some cultural conditioning. Well, people are used to respond to surveys. There's no follow up. That's cultural, right? It's the culture of the company. It could be about, uh, mismatch of the format or the reality. Like traditional system assume very long form desktop base, email base, um, where actually the workforce now has shifted. People are on the phone more often. So make sure that if you do a survey that's actually pleasant to do it on your phone. Uh, the other thing is the wrong id. That one size fits all cadence. It doesn't like if you have a mixed workforce, even everyone in an office base, people have different needs based on what they do. There is different personalities based on different departments. So make sure that whatever cadence you decide to use, whatever format you want to use is adapted to the people that are responding. Now, if we think about the solution, um, make sure that whenever you do collect the inside the feedback, you do it in the flow of work. Like it's the same if I ask you, Hey Zach, can you please respond to your survey? Go to the break room. Like, uh, I'm doing something right now. So, no, it's better to make sure that we are embedded your day to day. A lot of companies do it through the intranet. As soon as you connect, there is one question that pops up, you respond to it. It's part of your day to day. That's why we beat a butterfly as well. We are part of the clocking system. We're part of the activity trackers. Like we don't want employee to go anywhere else. We wanna be part of the day-to-day. Um, you can have, again now the role of ai, AI driven personalization, adapt the question based on the role, where they are at, what do they do, who they reporting to. So that allows to a avoid repetition and have a bit of diversification in the type of questions that you ask. Um, define what is the difference between passive and active listening. Uh, NRU mentioned it, right? If you have a structure that is only about, I'm asking for your question, I'm asking a question, I'm asking a question. What is lots of more information happening in a passive way as well? And there's a way for you to create that information. Um, and the last part is I think the most important is close the loop visibly, you said we've heard we do. Once people understand that that is the best way for you to re to reduce the cell fatigue. And it's not about asking fewer question, but it's really by asking the right ones at the right time and making sure that every answer matters. It makes me think of like, I know some of you have either checked out online or at a store and there's always like those middle questions they plug in there before you could finalize your checkout. And sometimes that can be annoying, right? But like I think about as you shared, like the flow of work, think of what are those like little micro moments that you could plug in listening nudges and, and kind of check-ins, whether that's when they log into the computer, when they cl in at the portal, at the, at the shop. Like what, whatever that is. Like, think of those like little micro moments that they're already going to be engaging with that you can leverage to gather insights and listen and get feedback from your people. Yeah, a hundred percent. Yeah. So we're coming up on time here. This flew by with you. Uh, I guess I'll ask two closing questions with you. Yeah. Um, one, you brought up a really interesting point, our, our kind of call before this about that organizations have actually trained people not to give feedback and we've conditioned people to not speak up. Um, so I'm curious of just like your point of view on that and then yeah, as we kind of do some closing remarks for the network, what's kind of the blueprint forward? Like what are some of the maybe key strategies and things we should be thinking about when it comes to ai, human oversight, transparency, all these different things to kind of move, especially with the frontline employees. So I know that's kind of like a lot to unpack with two questions, but I'd love your thoughts. Yeah. Uh, well I answer the first one and I think it's quite short. I think the reason why it's a broken system at the moment, it's mainly because for one department, they tick the box and then please feel it. Because once they respond this, like I just mentioned, like their opinion goes in the ether. They have no views of it. And often when you have your review with your manager and they talk about the results as a team, you don't even recognize yourself anymore. Because when you do a once a year survey, you have your recency bias. Like I can guarantee you give a raise of a hundred percent to everyone today, you do your survey, nine months, nobody will thank you for the raise. That happens. So often, the data is dated, there is no follow through as an employee where like, oh, I've said that this is what came out of it. No, it's like, oh, I said something, they don't care about it. It's the same thing over and over and over again. So now if you think about blueprint on how we can make sure that moving forward You can set yourself for success, I think there's a couple of things that can easily be done. So try to shift the goal, like from surveys to actually an actual system of listening like this. The future is not only about training better survey, it's really about creating a listening strategy and system that capture the right signal continuously. And they touched on that as well. Uh, and it's like, if you think about it, it's think like moving from an annual health checkup to continuing monitor with your wearables, right? We all wear a watch. Well, I don't have my watch today, but instead of going once a year to the doctor, you have your watch that just tells you how you're doing all the time. So think about it as you, uh, set a strategy. Now, the use of ai, I think people should see AI as an amplifier, but keep the humans as the interpret as the interpreters. So AI is great at detecting patterns across thousands of comments, multiple languages, fluctuating sentiments, but it should never, ever replace the interpretation from the humans. Like you have the context, like that's why on Butterfly, for example, when we onboard a client, we try to under to capture the system design of customers. Because what the tone of responding to comments is different from client to client. Like, we want to make sure that you, you're still, uh, aligned to your own DNA as a company. And therefore the way interpret the data has to be very specific to your work base as well. So if you think about the blueprint, there is about a clear division of roles between AI listening and surfaces. Humans interpret and act, and employees see the feedback loop. Now what's important if again, with AI, guard rails and transparency and non-negotiable, like as a company, employees will have to know the three things. What data is being collected, how will it be used, and what protections are in place because people, again, are scared of AI today. Now, the most important person in your organization, so if I'm gonna offend a few people in the call, is not hr, it's actually the managers. And these are the people that are on the day-to-day with the team. So any blueprint, any blueprint will fail. If you bypass the managers, they're the ones that employees will look for action. Like if the head is not following, the body will not follow. So the approach you should have is really equip the managers on how to respond to comments or to look at the data, how to have your action plan in place so that that's where managers don't just receive the data, but we get them a guide on the next step that can run insight into conversation and action. Now of course, depending where you are, you have a regulatory design that you have to be careful about. But if you wanna be very pragmatic, first, start with an audit, pilot some AI listening tool, train your managers from day one, making sure that we're equipped, communicate transparently. So include your internal communications department about what you do with information and be proud about it. And then slowly scale. Like it's the same if you put your finger, how you call that in a, in a, in a, in a clog, in a like, oh, it's gonna go really fast. So make sure that when you're ready to go, you're gonna go really fast afterwards. But be gradual about it. It's always better to incorporate something new than taking something away. So if you decide to do a company wide survey for 10,000 people, make sure you're ready to follow through with it or start with 200 people, see how it goes. Adapt, add another 800, another thousand, and then you're gonna get to your 10,000 employees eventually. Well, there's your blueprint, everyone now is, uh, I mean talk about like literally the blue blueprint. I think that's an incredible way just to approach and think through these things. And I think the manager point is so important, right? Like often it's like HR kind of works in this silo. We get the buy-in of the C-suite and then we push out this thing and then afterwards managers are coming to us with all these questions about things that are coming up in their team that they don't know how to answer. Yes, you're gonna have some really progressive managers approach you for help with answering those questions. Others are probably gonna just say, ah, you know, it's kind of this HR thing, or it's kind of this other thing. Just let's just stay focused on what we're doing. You know? So I think it's so important, like bring 'em into the, the conversation early, train them if anything, co-create the listening with them. And then I think of even something as simple as like building a FAQ page of like, Hey, when this happens, you're gonna get these types of questions. Here's a resource and a playbook on how you can navigate some of those conversations. Like that's very simple as a way to like enable managers. So, um, Simon, this was great though. I appreciate you coming in, sharing some really input as a founder that's building these technologies, enabling organizations in this way, and also just tapping into a very specific area that's often overlooked, which is the frontline employee, right? And those are often the voices that we want to get the voice to, but as you shared, are all overlooked just because our, our traditional systems aren't built to access that. So I really appreciate you breaking that in and, and joining us today.