Caller is the New King: Service AI Without a Screen w/ Indresh MS, Aquant

00:00
Gerardo Pelayo Rubio
Hello, good morning, good afternoon. I am Gerardo Pelayo, Chief Research Officer at Service Council. Welcome to another edition of our In Service podcast. And today I’m happy to welcome Indresh. He’s the Vice President of Product and Technology Labs at a Quant. We’ll get to know him in just a second as he’s going to help us navigate through a topic that is very exciting, I believe, which is the role and the potential that Voice AI can have in the whole service ecosystem.

00:45
Gerardo Pelayo Rubio
I hear more and more conversations about multimodal AI and going beyond those initial intuitive use cases that we tend to gravitate towards and also as we move from theory and the potential to the learning curve that many organizations, many of the solution partners have gone through, as there’s more and more activity in the translating those pilots into scalable implementations that are not just doing the right thing, but that they’re paying off, that the effort is justifying the investment and that the technical deployment is being met with a sound strategic approach. That one of the things that I’m excited that we’re going touch on is the whole change management and how do we map the possibilities that we have with the needs of the people that can leverage this technology. So it’s going to be a packed half hour.

01:57
Gerardo Pelayo Rubio
We’re going to go through different examples and lessons learned, as I said. But first, Indre, I’ve had the pleasure of meeting you through some of the planning. I’ve read some of your recent blog articles. Really fresh, well communicated perspective on the topic for the audience that may not be so familiar with you yet. Can you provide a little bit of background about yourself and more generally the role that you have within a quant?

02:26
Indresh MS, Aquant
Yeah, yeah. Thank you so much for, for this and thanks everyone for joining from across the time zones. My name is Indres. I, I, I run the Labs team here at Aquant and the Labs teams, one of the primary purposes is to look at the market and the technology and the domain, especially given the fact that everybody’s running so fast, to be able to continuously stay on top of what’s happening outside of the company and then find opportunities to bring those technologies into our product and really see through some of those initiatives from ideas to productization. That’s what I’ve been spending a lot of time. Last year, one of the big topics that I’ve spent most of my time is figuring out how to make voice, which is super cool, super amazing technology. From being super cool to super useful.

03:30
Indresh MS, Aquant
Today I’m super excited to share our experiences, where we are headed as a company, but also where the domain is headed in general.

03:40
Gerardo Pelayo Rubio
Yeah, that’s great. You just reminded me this was two, three years ago, but leader here in the Austin community, he started off a meeting by saying hey, cool is cool but it still has to work. I think you encapsulated that perfectly. Before we jump into it, Aquant has been a great partner in this thought leadership sessions and many of our loyal fans will be familiar. But for those again who are new to it, can you just share a quick overview of what are the type of challenges, what’s the type of problems that Quant engages in with the service community?

04:21
Indresh MS, Aquant
Yeah, that’s great part. So Aquant was making service technology, AI technology useful for customers even before AI became a thing in the service domain, even before LLMs became so popular. We are broadly a service intelligence platform that helps customers throughout the service life cycle, whether in call centers, whether it is in field, whether it’s for executives to drive insights. And then recently we transformed ourselves from being service centric to more asset centric and more agentic platform again for service, but now with more modern technology, both for us to build service offerings, but also for customers to think about service delivery as a combination of this powerful agentic platform and then marrying that with their real business use cases. So we have been in the domain for the last 10 years.

05:32
Indresh MS, Aquant
Started out with troubleshooting use cases, but eventually we now have about a dozen different agents doing troubleshooting, helping customers with parts, helping customers with managing use cases that are not just service but still these are complex asset manufacturers. They have use cases that span across product engineering, customer engagement. So that’s where we are today. We are this modern agentic platform for service that is helping complex asset manufacturers drive their service operations.

06:16
Gerardo Pelayo Rubio
Perfect. Thank you. For that context, I think it’s very useful as we navigate your perception on voice, AI and especially the lessons that you’ve learned along the way along the journey that you just described. Quick reminder to our audience, today’s podcast is being recorded and we’d like you to be a participating audience. So if you have any comments, reactions, any follow ups that you want to have as we move through the conversation, especially to our LinkedIn audience, feel free to comment and we’ll do our best to respond either during the conversation itself or we’ll follow up in the comment section after the event. Without further ado, let me jump right in and picking up on something that you said that you’ve been at this for a while, Right.

07:05
Gerardo Pelayo Rubio
And Voice AI, in some ways it feels like the new kid in the block within the whole AI conversation. Because in our day to day we tend to interact, engage with others via voice naturally. But from a work perspective, because of the limitations that existed before and a number of reasons, Voice AI was not the natural way in which we’ve learned to deal with the problems and to look for help as we leverage technology. So as that has progressed, as the technology has evolved and gotten better, how has your own perception regarding the why Voice AI is a good thing and how should it be implemented? Can you take us a little bit through that journey for yourself?

07:56
Indresh MS, Aquant
Yeah, no, that’s a great question. I think historically we have always primarily relied on a very traditional way of interacting with software. You have browser applications, you have desktop applications, you have mobile applications. And innovation around Voice has been pretty stagnant for a long time, especially in the last three years. However, there has been tremendous amount of investment, excuse me, critical thinking, and actually advancing voice as a feasible way to interact with data and technology. You would now see voice being more natural. It’s not as close as how humans would interact with human, but it’s a big difference between what was voice three years ago versus now. When we looked at Voice last year, one of the things that really excited us is how we can leverage the existing AI applications.

09:05
Indresh MS, Aquant
And it is now possible to add a layer of voice on top of it and still make that experience useful for customers. It’s really how you and I are talking and taking turns. Voice can understand. Voice can seamlessly respond in a way that humans can respond. It is super high performance. You don’t have to put that under rule. There’s a big difference in how Voice is today versus voice is three years ago. But it is still a new kid in the block. Like you said, it doesn’t mean that it can do everything. We just have to be As a SaaS providers, we want to be at the forefront of educating customers on what makes sense today, what makes sense in the future, how customers could should think about, you know, deploying voice.

10:00
Indresh MS, Aquant
But yes, you know, it has come a long way, but it still also has a long way to go. So the question is, where do we start? Right?

10:07
Gerardo Pelayo Rubio
Yeah. And so on that question of where do we start and I’m going to keep you accountable to the hey, how do we move from cool to useful and actionable? What Is it that you’re seeing in the market regarding the use cases that are really resonating where you say the technology is interesting, I think it can help me with this. How is it that service leaders are thinking about this and you helping them in that approach?

10:36
Indresh MS, Aquant
Yeah, yeah. When we started last year, we felt we could solve everything with voice. Right. We were so excited about the possibilities of voice, the promise it has, the vision that it has help us drive over a period of time. We’ve learned to. We learned that it has great potential, amazing possibilities, but also it has its own. It can also become a challenge if you don’t deploy it right. So working with customers, we have figured out like, okay, what is an organic journey that we can take our customers through? Do they have to be at the super sophisticated level of voice, AI or other use cases? We know today that they are solving it traditionally, but they’re not able to solve it the way they want to solve. So we think about different use cases and this is what we’re engaging with customers with.

11:40
Indresh MS, Aquant
There are a few themes that are emerging through the last year. We have, we put them into different buckets. There is challenges around efficiency, around the process automation, challenges around scalability, and also some challenges around operations. So we kind of put them into different buckets and then engage with customers. Okay, these are the different themes, these are the different use cases. Now let’s look at that from your perspective. What is a, in a low hanging, high impact, high value use case that we can start with? So to your point, you mentioned you’re spot on. Right? You know, it’s beyond, it’s just not technology. It’s about change management. It’s about how customers can become comfortable with interacting with technology in a reasonably new and different ways.

12:32
Indresh MS, Aquant
So our learning has been, okay, look at, you know, educate customers on these use cases, you know, educate them on complexities of those use cases and then work with them to identify one use case which is like I said, high impact, you know, low effort in overall and then walk customers through the use case, the change management, you know, validation of the, of the concept and then, you know, put it in production. Right. And then go after use case two, use case three and so forth.

13:04
Gerardo Pelayo Rubio
So, so let’s address that high impact and I want to connect that to our research. There’s three areas that I’d like to pick your brain on. The first one we conduct every year our voice or field service engineer survey. And over the last three years alone we have over 8, 000 responses on that. One of the things that has been constant across these three years is that at the top of the list of the activities that technicians do not like in their day to day is data capture. If you peel the onion a little bit further, we ask, hey, we get it, that this is the biggest problem. Has it gotten better? Only 30% of the technicians agree that data capture has become less time consuming over the last five years.

13:57
Gerardo Pelayo Rubio
So can you perhaps speak a little bit to the application, the state of Voice AI in this context, any specific implementation or results that you’ve been able to achieve here?

14:11
Indresh MS, Aquant
Yeah, yeah, it’s a great question. And, and you know, I, I think it’s a twofold problem, right? One is, yeah, the data entry is inefficient for more than one reason. One is it’s laborious. Most often than not, technicians won’t have time to capture it. As soon as they are done with their work, they’ll park it and maybe come back to it later. Now that leads to more challenges around do they remember everything and did they capture everything? So one leads to the other. So overall, technology is not helping them, right? They don’t have enough, right, enough tools to do it. And then they’re also going from job to job and they’re optimizing it around, doing the job more often than not and less on capturing information. But I think it starts with technology.

15:10
Indresh MS, Aquant
So if you look at, if you were to use, let’s say voice has been there for a while, there’s a dictation that you can do on a keyboard of a handheld device and then speak something and then it produces text. But that is more technical solution like can I convert my voice to text? Is different from saying, can I debrief using voice, for example? And we hear this from customers not only for capturing work information, but also to capture knowledge. The expert technicians, for example, they have so much knowledge that you would want to, you want to find a way to capture them or a technician who just finished a job, they figured out a more efficient way to do it and you want to capture that as a knowledge article for somebody in the future to refer to it.

16:03
Indresh MS, Aquant
So you think of different use cases to capture information and then you now see, okay, what are the gaps in the tools that can help us bridge the gap. Fundamentally, voice is going to change it, right? Because Voice AI understands what you’re saying and not what you mean. Not, not, not. That’s what you’re saying. There’s a big difference between the two. So today, for example, you know, with Aquan, a technician who debrief finished their job, they can pick up the phone, they can tell Aquan mobile app saying, hey, I, I finished this job. I did, I replaced these two parts. I, you know, I recycled, you know, the filters, the pump, so they can describe everything and then say, go, you know, complete the work order for me.

16:53
Indresh MS, Aquant
And according to the backend, takes that information, translates that into more structured information and puts it in the right places, in the database. Right. So what used to take 15, 20 minutes for them is now, you know, in many cases less than a minute. So just to wrap it up, I think because the voice has become more natural in understanding what someone is saying, it’s able to take that, interpret the intent of it, and then translate that into what you would otherwise have to do it manually and laboriously. Does that make sense?

17:32
Gerardo Pelayo Rubio
It does. And I want to emphasize one of the things that you said because I, I think it has very deep implications, which is what this technology and the approach that you just walked through has on the customer engagement and closing the loop on it. One of the complaints that I hear about, not just through the survey, but also when anecdotally, when speaking with our community is the complaint that you’re asking me to fill in all these forms and spend all this time and then I don’t see you doing anything with it. I tend to speak about it as the innovation funnel where what’s being asked versus the perception of, is that being addressed? Is that being implemented? Do I, do I have enough time for drops significantly? So now you’re making it easier for them to capture the data.

18:26
Gerardo Pelayo Rubio
Yeah, but it’s also making it easier to do something with the data and for that something to be visible to the frontline themselves as well. I think it helps with one of the biggest myths, which was true for a while, that all these forms and CRMs was where data was sent to die. Now you can actually do something with. So I, I just thought that was a, a critical point and I wanted to double down on what you said.

19:06
Indresh MS, Aquant
I’ll add one more, one more thing to that, Gerardo. Meaning, you know, because you will now make it easy for people to enter data. They are going to, you know, they, they will be motivated to do more often. And, and the beauty of that is now you have a significantly super high quality data than which we had access to previously. Imagine if every time technicians complete their job, they’re able to say something for 30 seconds and then AI takes it and then translates that into more structured, useful information, complete information. Now we take the same information and then train the models downstream. Higher quality data upstream means higher quality AI downstream also. Right, so it has like ripple effect in the chain.

20:00
Gerardo Pelayo Rubio
Absolutely. Yeah, I agree. Great point. Adding to that not just from the perspective of the frontline, but what service leaders are measuring. We just released our KPIs and metrics survey a couple of weeks ago. And within employee metrics, workforce productivity and workforce utilization were both amongst the top three employee metrics that service leaders said that they’re going to be focusing on during 2026. One of the things that gets in the way of that productivity is commute time, for example, like all the time that the technicians spend not in front of the asset, not in front of the customer. And voice AI brings a possibility to increase the value or to generate value. While this is going on now, like all good questions, it’s not straightforward. Right. It has implications on safety.

20:55
Gerardo Pelayo Rubio
Is the technician gonna feel like, now I’m being asked to do even more. But on the other side, we also hear, hey, there’s less chance of overtime and more satisfaction at the job.

21:08
Indresh MS, Aquant
So, yeah.

21:10
Gerardo Pelayo Rubio
What has been the reaction that you’ve seen from leaders and from the frontline itself around the application of voice AI during their downtime?

21:22
Indresh MS, Aquant
Yeah, yeah, I think as we started to introduce voice to some of our early customers, this exact team, you know, started coming up, right, Meaning how to utilize technicians, you know, downtime. But they are still, you know, they’re still driving to the job site, for example. And, and you know, the use cases varied from, I just want to know, you know, the history of the assets I’m going to, you know, I’m going to work on today or I want to know what has been done, what do different technicians have done or think about the issue that I’m going to now pick up and continue. Or it could also be, hey, I know I’m going to these two job sites and I know what are the high level. I understand whether it is a maintenance or I’m going to fix, doing a break fix.

22:31
Indresh MS, Aquant
Let me prepare for different scenarios. And the third use case is about you flip the equation between the AI and the user. In most of the scenarios, I am the technician. I’m going to drive the conversation with AI. I’m going to ask you a few things. It’s basically driven by me. Some of the customers when I spoke with, they said, okay, why don’t we flip that equation? Why don’t AI ask me the question? It’s almost like a training scenario. You think of tactically CRM data, access to CRM data to understand our service data, to understand the job I’m going to deal with throughout the day. Preparing for procedures and troubleshooting, but also just learning. Right. All these things can be done while driving. They don’t need to be, they don’t need to type. They look at their iPads and iPhones.

23:43
Indresh MS, Aquant
So these are themes that we hear frequently and we happen to work with some of the customers that agree to be design partners with us and to figure out, like you said, how to make it useful, but also how to make it safe, how to make it worth their time while they’re driving. And, and I think this is only going to get, you know, there’ll be more and more requests around these type of, we call them, we put them in the efficiency, how to utilize technicians time and their skills more efficiently. So this is the time, you know, time bucket.

24:26
Gerardo Pelayo Rubio
Yeah. And, and a quick comment on that. We still see that there’s a gap between safety and productivity in terms of how technicians feel that technology, not just AI technology, is helping them be more productive versus safer. Say about 20% points gap. What is interesting to me is that if we actually think about how this works, we’re helping the technicians not have to scroll through a screen or typing while driving. It reduces the stress that you have to prepare ahead of the arrival. So spending less time on site because you got some of the information ahead of it and therefore you’re not driving with such a hurry to the next, to your next appointment. So it has to be deployed responsibly. Absolutely.

25:30
Gerardo Pelayo Rubio
But there’s a lot of benefits that I could envision relative to the way that they might be leveraging technology today, how voice AI could actually create for, create a safer environment for them.

25:47
Indresh MS, Aquant
Yeah, and it’s also like, you know, one thing is like you said, you know, they have to scroll through and there’s a lot of responsibility on them to get it done. Right. Whether it’s, you know, access to information, troubleshoot, whatever that is. Voice flips the equation. You know, it basically it puts in front of you an interface where you can keep asking and it’ll go figure out where to find information, how to present it to you, how to organize it for you, how to repeat it for you. You know, you know we have use cases where as simple as sometimes when voice reads out a long digit it just says 1, 2, 3, 4, 5, 6, 7, 9, 9. And someone hey, can you slow down so I can, you know, I can understand or can you repeat? Right.

26:33
Indresh MS, Aquant
These things are, these things are so natural in voice interface and it just de stresses the whole experience. Compared to know, having to read through and you know, spreading in front of a monitor or screen, it just adds more natural way of how people try to access and you know, store information within themselves.

26:56
Gerardo Pelayo Rubio
Yeah, no thank you. And building on it and this is not, this can be anywhere in the process really when the technician is actually working on the asset. But this interaction of seeing AI as the expert is a very interesting application to me. We’ve seen through our research a rise in the technicians willingness to self troubleshoot, go to the knowledge base and try to find the answers when they get stuck on the job. But the number one action that they go to is still calling another technician. 54% in last year’s survey say that this is their primary action. So voice calling somebody is, it is a primary mode to get this solved. That speaks to the efficiency and the effectiveness of it. Doing it with an AI agent might feel different. So first off, what have you seen?

28:09
Gerardo Pelayo Rubio
What have been the results of the frontline being willing to engage an AI expert as they would a colleague?

28:21
Indresh MS, Aquant
Yeah, I think one of the learnings for us deploying AI as an expert is more than anything else is just the change management. It’s great that I can ingest all of the information and it’s able to interact with the caller, but I think it’s a new way to interact with technology and then we realize that we need to guide our businesses through the change management. How do we deploy this? How do we go from hey, this is not working to this is useful now, this is usable. So how do we take them through the journey? I think for us, I would say majority of our efforts goes in that just that’s why identifying the use case is important and then working with customers to kind of create almost like a plan which is grounded in change management for them. Right.

29:23
Gerardo Pelayo Rubio
And, and just specifically because as you said, you’ve gone through this journey. There’s a difference between engaging an AI expert in general. But are we engaging via text like a chat function versus voice, which is what we’re talking about today? Have you seen the introduction of voice really moving the needle in data change management?

29:52
Indresh MS, Aquant
It certainly has the promise of that. That’s what we have seen. But every customer is different. And that’s why we said, okay, if you think about AI export and then slot the export into different tiers, right, okay, this is a Tier 1 export, this is a Tier 2 and Tier 3, Tier 4. As you go from Tier 1 to Tier N, the expected expertise becomes higher and higher. The right. And we said, okay, you know, if you’re, if your technicians today call another expert for 10 things out of 10 things, three things, you know, you really don’t have to use the real experts time on those three things. Okay, here, let’s put AI in front of them and then we go from tier one to tier two to tier four. I think jumping into tier two, tier 10 is not the right.

30:43
Indresh MS, Aquant
Basically thinking that, okay, let’s put this expert in front of all our technicians. And after this point, you know, everybody is supposed to go to AI first for all kinds of, you know, collaboration or guidance. I think that’s a no, right. I think just the community is not there yet because they all have to feel confident about, okay, AI is actually helping me. I mean, if you think about the bias today, like, you know, if we have a third technician and let’s say you have 20 years and I have two years, the technician will invariably go to you all the time, you know, because, you know, there is a bias that this technician knows that this Gerardo has 20 years of experience. So most likely he’s going to give me better answer than I think that’s the same thing with AI.

31:37
Indresh MS, Aquant
So we need to graduate everybody to kind of first understand how to interact with it and then just generally gain confidence in A’s ability to respond to them in a way it makes sense. So our approach has been don’t go to the end state and deploy it. Start with something basic and then go from there.

31:59
Gerardo Pelayo Rubio
Good, good. So I’ll share with you a quick data point to give you confidence that you’re on the right path. I know you’re very passionate about this. I said 54% of the technicians are calling one of their colleagues when they get stuck. Only 24% of them are texting a colleague. So this is with non AI technologies. But we see that voice is more than twice as it’s being used, more than twice as much as texting. So it feels like the most natural way of engaging. And if instead of asking technicians, now you have to trust an AI and you have to switch, correct your preferred mode, if it’s meaning them halfway. Now the part that we talked about at the top, which is do I trust the data?

32:58
Gerardo Pelayo Rubio
Is the data that I’ve been capturing all this time in the past is that being incorporated into the this AI tool. That’s where the story starts to come together. So just like with safety, it needs to be responsible. But there’s a lot of potential to meet missions where they are by leveraging.

33:18
Indresh MS, Aquant
AI I believe it’s almost like it’s a self fulfilling prophecy. Right. So you start with a technology that can capture high quality data and then you feed them back into the same technology to kind of push the quality even higher. So it’s kind of, you know, you have the right tech and the right data and one needs the other. You can’t survive independently. So for us it’s about okay, that’s why you know, reduce the friction early on, figure out what is the shortest path where it something like wise can be. First of all the users feel usable, it’s useful and it addresses one of those buckets efficiency and some of the things that we talked about, as long as it fits that I think there is a general willingness across the board to try it and deploy it in some capacity.

34:13
Indresh MS, Aquant
That’s what we have seen. We have not seen a customer so far comes to us and says oh this doesn’t work, it’s for them about okay, help us understand like you know, this is a, this is where we are, where should we start? And you know, how do we deploy, how do we do change management, how do we measure. It’s more. It’s more has been how rather than.

34:34
Gerardo Pelayo Rubio
I would say so. So on that two quick questions to wrap us up. First off, are there any common pitfalls to avoid or key success drivers? As you’ve spoken about change management a couple of times. So whether it’s finding the right use case, defining where value is going to come from. Do you have any advice for our community along those lines?

34:59
Indresh MS, Aquant
Yeah, yeah, it’s not plenty. It’s actually two or three. One is voice sounds super exciting. It’s like suddenly I became a kid in the candy store. I wanted everything of voice and give it to customers. So I think where we are focusing and where we always have advocated our customers and prospects kind of focus on is what is that we are trying to solve with voice and are we ready together us to deploy to you as a customer and are you customer, are you ready to kind of deploy voice? That basically goes back to identifying all different use cases. But pick I would Say at the beginning, just one use case that we feel most confident that we can deploy.

35:56
Indresh MS, Aquant
We can, we can do change management and we can measure, I think all the three, if we can, if we can find a small enough, but a good enough use case to kind of do all these three, I think we have a plan, we have a game. Right after that, it’s just repeating that template across multiple use cases. And over the last year, we have, at a quant, we have identified about, I think, a dozen different use cases that we talk about. We talk about how to help. For example, after hours is a big challenge for many of the customers, like 9 to 5 to help. After that, what happens? The technology that is not really competing with anything. It’s complementing where there is nothing today, for example. So some customers talk about process automation, some of them talk about after hours. Yeah.

36:48
Indresh MS, Aquant
So pick one use case, figure out how to do change management, verification and measurement. And then from there on, together, we’ll figure out how to repeat that across all different use cases.

37:02
Gerardo Pelayo Rubio
No, thank you. And one of the things that you said especially resonated the avoiding being spread too thin. Right. It used to be that, like, do not put all of your eggs in one basket and try to spread the risk. I, I’ve been hearing what you said quite often of let’s focus on the right starting point and then build from there. Final thing, I know this is a big passion of yours, but so what is your bet on the adoption the value of voice AI? How maybe is there a way in which others can better understand also what voice AI means for them?

37:48
Indresh MS, Aquant
Yeah. I have a philosophical explanation to that, but I’ll also give you a practical explanation of that. I think if you look at the history of humanity, all technology, as advanced human society, right forward, like, we have tried to do things better. If you think about voice, it’s very, it’s. For me, the moment I realized this, it was like, you know, my aha moment, that voice AI is the only thing which is taking us backwards to our roots, of how we have always interacted with everybody. Right. Like only with technology. We chose a different approach. We started typing, we started, you know, doing all of that stuff. But the best way we all know to interact with each other is voice. Right. And without voice, we evolve, become, I would say, largely inefficient in how we interact with the world.

38:39
Indresh MS, Aquant
So voice AI is actually taking us back to the roots. It’s making us interact with the world the way we Always knew the most efficient way to interact. So I think I’m super excited about what it is doing. It’s kind of taking us full circle back. I think purely from a technology and looking forward standpoint, I think I see a lot of potential in voice solving problems in modern ways. Like, you know, we always, if you keep innovating with candle, innovating on candles, you get a better candle. You never get a good light bulb. Right. So we need to change the approach to get a light bulb, which is the next evolution of candles wise AI. Is that right? We have been trying to solve some of the efficiency, process and scale and operations challenges with largely traditional approaches which didn’t fit very well.

39:34
Indresh MS, Aquant
What I’m more excited is, okay, is there a way for us to help call centers operate better? Is there a way we can onboard technicians faster than we have done always through lmss and things like that? Is there a way we can get access to, Is there a way we can push this idea of left shift of customers resolving if not everything, but more issues themselves than they’re doing today using voice? So there’s tremendous opportunity to look at existing problems through the lens of voice. And I believe because of the nature of the technology itself, many problems. If not completely resolved, you almost go from spending 40 minutes to spending 5 minutes type of efficiency. I think it does change how businesses look at most of the problems today. Does it make sense?

40:46
Gerardo Pelayo Rubio
It does. And I didn’t want to wrap up before thanking you not only for the clarity that you’ve provided on this and your perspective, which I appreciate how grounded it is, but also for making available the resource to our community on how to test drive what voice AI actually means. I saw it in the description of the podcast. I believe I saw it in the comments as well. Appreciate letting our community play with this and see what those interactions could look like. But Indrash, again, thank you for being here, for helping me and the community really see where this could take us separate from the noise. Thank you to our listening community for sticking around. I know went a little bit long but, but I have a policy of not cutting off an expert when they’re on fire. So it’s been a fun conversation.

41:55
Gerardo Pelayo Rubio
It was recorded so you can go back and share it with your colleagues, revisit some of the context and see you soon in our next edition. Hope you have a great rest of the week.

42:05
Indresh MS, Aquant
Thanks everyone.