Paula Rivera: 

Welcome back to AI Factor, the podcast where we explore how artificial intelligence is transforming the way we work, connect, and innovate. I’m your host, Paula Rivera, senior director of Public Relations at IntelePeer. Today’s episode is part of our AI Unleashed series where we dive into real-world applications of AI with the people making it happen. Joining me is Derek Boudreau, our VP of AI and data product management, to talk about conversational analytics. From improving customer service to driving smarter business decisions, we’ll explore how this powerful tool is reshaping the customer experience. Derek, welcome. 

Derek Boudreau: 

Hey, Paula. Glad to be here. 

Paula Rivera: 

Glad to have you. Before we jump on in, I’d like to sort of start up with this little segment I cleverly call AI in the news and it’s football season, so I thought how apropos I will find something about football. So Silicon ANGLE actually just published an article about the NFL’s use of data and analytics. The article notes that the NFL’s unified data ecosystem rests on five pillars: data governance, data engineering, data solutions, operations, and end-to-end delivery. Derek, in your opinion, which teams should use more data and analytics to improve their performance on the field? 

Derek Boudreau: 

Oh, man. So this is a good one. And for 2025, I think it’s a little early still. I think historically, if you look at, I think it’s well known that folks such as the Steelers and Tampa Bay, and unfortunately my New England Patriots are also some laggards in the use of data and analytics. So I’m hoping specifically New England is getting better at doing so and we see the results on the field. 

Paula Rivera: 

Well, I appreciate the fact that you’re a Pats fan. I, being an improper Bostonian, am also a Patriots fan, so I’m going to keep you on for the rest of this show, Derek. 

Derek Boudreau: 

Well, I knew I had to call. 

Paula Rivera: 

So out of the five pillars, which one do you see being most difficult to deliver on? 

Derek Boudreau: 

They’re actually all challenging in their own ways, but I would say most recently the one that’s uniquely so is the governance. And there’s a few reasons for that. The primary is that just the ever-increasing amount of data that is available to us. And so it’s becoming just even more important to have the necessary governance processes in place to control that data securely, to be able to make that data and keep it in forms that are useful for the business. And as businesses are in ever-increasing pressure to make use of that, I think the governance is sort of really becoming the most challenging of them all at this point. 

Paula Rivera: 

So I find that interesting and I’m wondering, and I’m very quickly going off script. I like to go off script. It usually happens halfway through the episode, but as you were speaking, I was kind of thinking about guardrails and then I started thinking about, wow, if they’re not careful and they being the NFL and if they don’t have the guardrails in place, I could see their LLMs getting confused or getting caught up in fantasy football stuff that’s out there. Would that actually happen? Could AI sort of hallucinate its way into the fantasy football league and then start giving bad information back? 

Derek Boudreau: 

Well, possibly. And I think just data governance in general is about just preventing just that, preventing the bad data from influencing the outcomes in undesired ways. So I think that definitely, certainly guardrails falls within the data governance framework and making sure that the data that you’re using is providing the necessary guidance that you’re seeking it for it to do, so. 

Paula Rivera: 

Excellent. I think between you and Matthew Caraway, one of your colleagues who I’ve had on before, I think maybe we’ll do a three-way podcast sometime in the future where we talk about AI’s use in sports because I find it so fascinating. If you look at the sailing races, all of the big tech companies sponsor them. And so I’m going to have you guys back on, I promise my listeners, we will discuss this in depth because it is pretty fascinating where big tech plays as it pertains to sports and how sports are integrating technology into it. 

Derek Boudreau: 

That’s right, and we saw that early days with moneyball and baseball, a stats-driven sport can begin with and just how technology just for the past couple of decades just accelerated that even further. 

Paula Rivera: 

Yeah. Excellent. Well, that’s actually not why I invited you here today. So let’s get back on track before someone reaches out and reminds me of my mission in life. Conversational AI, let’s get down to the basics. For those who may not be familiar with it, Derek, what exactly is conversational analytics? 

Derek Boudreau: 

So it’s a great question and I’m glad to have the opportunity to talk about it here today. So what I like to think of conversational analytics as very simply is it’s a feedback loop that turns these raw customer interactions. So think chats, texts, and phone calls into oftentimes you hear actionable insights. I think that’s overused. I like to think of it even more specifically as decision fuel. So this information that allows us to use conversations as impact levers that we can use to make and measure change all about using the voice of the customer. So the customer’s telling us what we’re doing well, what we’re doing wrong, what they like, what they don’t like, they’re telling us about problems in their experiences, and we’re able to use this technology to drive this conversational analytics where we can track and measure these things. And so at the end of the day, conversational analytics is really all about turning these conversational signals into really business outcomes or business metrics. 

Paula Rivera: 

Excellent. And that’s super important. For as long as I can remember, I’ve been in this corporate world for a very long time. Companies are always talking about voice of the customer. I’m inclined to say conversational analytics would play a big part of knowing and understanding the voice of your customer and/or subsequently the customer journey. Again, I’m throwing Derek a total curve ball here. We did not discuss this prior to today, but here we are. 

Derek Boudreau: 

Yep, absolutely. 

Paula Rivera: 

Wonderful. So why is it becoming such a critical tool for businesses today? 

Derek Boudreau: 

And so I think as you just mentioned too, that it’s been around for decades, this idea of analyzing conversations, however, it’s only really been accessible to those that could benefit from the technology and usually this very expensive or complicated technology. And so that’s changed drastically even over the last few years. With the advent of some of the latest AI capabilities, it’s really leveled access to some of these technologies. So as businesses look for edges and differentiation and using insights into their interactions with their customers as a way to drive business decisions that it really make impact on their customers, I think that’s really driving the criticality of this. And so yeah, and I don’t think it’s going to stop anytime soon. I think as we get access to more interaction type data, being able to use conversational analytics to make improvements in the business and drive where the business should head and go and where they should focus is just going to continue. 

Paula Rivera: 

I think that’s such a valid point that you just mentioned and the whole democratize people like to throw that term around, but it is kind of democratizing the data and the analysis. And I’ve found, again, I’ve been in business for a while now and yeah, it’s only been the past few years. I mean, I’ve been clamoring for years now of wanting more data, but getting my hands on it has never really been a reality until, yeah, I would say LLMs, large language models, popped onto the scene specifically with ChatGPT. The amount of information I now get in my little niche of the world is pretty mind-blowing and it really does definitely help me determine what I’m going to do in my job. 

Derek Boudreau: 

That’s exactly right. And it’s providing access to the insights that we gathered from large amounts of data in ways that, as you said, were only available to the few in earlier times. So yeah, it definitely has democratized access to these insights. 

Paula Rivera: 

Wonderful. Can you share any examples of the types of conversations it analyzes? I mean, is it everything across the board? Are the products out there that actually do this? Do they do everything? Do they just do voice? Do they do voice in chat? Can you give any examples for that? 

Derek Boudreau: 

Absolutely. And so you do see all types of modes of conversation available here. So there are folks that specialize in certain modes and digital modes, folks that such as IntelePeer, that specialize in multimodal, including voice. And so it can span SMS and chat, email, voice conversations. Really any type of conversational medium is really a candidate for conversational analytics and voice is a unique problem of its own and one that is more difficult than the others because of the nature of voice-based conversations, because of the needs to transform a voice into something that can be analyzed with the methods available. And so voice is certainly unique in that, but any of these conversations can span also different domains. So across all of our available articles that include topics from ranging from support to scheduling to technical help to frequently asked questions in any kind of conversational context, these are all types of conversations that can benefit from conversational analytics. 

Paula Rivera: 

As you were speaking I thought of… So for those who may not know, IntelePeer will work with its customers and we’ll conduct something called an intent study, which is we’ll go in and help you analyze the conversations your customers are having, why are they reaching out to you? And that really helps develop a map work for, okay, where can AI best implement things? And it’s funny that you were just talking about the different industries because Derek was kind enough to share some of this data with me. He’s a very sharing gentleman, and the number of items that fell under the support category was almost mind-boggling. And you could kind of see that there was similarities across industries, but there were also differences. If you’re a veterinarian, one of the supports you may need is refilling your dog’s prescriptions versus if you are a dentist and you are just want to make appointments or one of the biggest support things is appointments. So I appreciate the fact that you mentioned those various industries, Derek. 

Derek Boudreau: 

Yeah, absolutely. And with the technology and conversational analytics, you can dive and create multi-tiered intents too. So you can get very specific about the types of appointments and if they’re a new customer appointments versus existing customer appointments and really pinpoint the specific contact reasons within this intent study to really understand the types of interactions that are being handled, how they’re being handled and where things can be improved and so on. 

Paula Rivera: 

For someone like myself where I, not that I take things for granted, but you just figure, oh, you need to just set up an appointment. But when you really start to think about it, and as Derek and his team has done, they’ve really mapped it out. It’s not just an appointment. Are you a new patient? Do you have the insurance? Is this an emergency? So that’s pretty darn fascinating. I’m going to switch gears just a little bit and start getting a little bit technical. Derek really is one of our technical experts. I’ve heard you say previously, Derek, that there are four key steps for how conversational analytics work: capture, process, analyze, and then continuous improvement. I don’t know, I think that was maybe a year ago or so where you were talking about that. I don’t know if there are maybe five or six if any other steps have come into play, but can you kind of walk us through these steps and maybe how would someone out there in the real world need to think about these steps to be able to take off on an analytical project? 

Derek Boudreau: 

Absolutely. So I think to answer your question, I think these four main pillars of conversational analytics are still in play. There’s probably variation amongst them, depending on the medium or depending on some other specifics. But I think for how we want to discuss them here today, I think these are perfectly suitable. So capturing is all about how to make use of the data for the other steps. And so if we use our voice as an example of capturing, and so a support call that we want to analyze, we have to capture that voice as it’s occurring. So we do that through a recording and IntelePeer has the ability to record conversations end-to-end as they traverse different parts of the call. Then we need to process that, and that requires a number of steps. So first is converting that voice into a digital form that can be processed by different parts of an NLP pipeline that includes a bunch of AI steps and others. 

And that helps us to convert this voice data into data that can be measured and counted and analyzed. So we do that through a various number of steps in a processing pipeline that are specialized around voice and around conversational analytics specifically. And then once we’ve created this data from these voice conversations about support, now we can analyze those. So now we can use that to understand the business impacts. So what not only the intents, as you mentioned earlier, Paula, about what kinds of support calls are coming in. Are they talking about a new product that they’ve received? Are they talking about questions about how to use a new service? Are they talking about specific problem areas with the product that they received? So allow us to correlate what’s being said to specific business interests, different KPIs and different measures as well. Did we resolve that question in an effective manner? 

Did we do so in a satisfying way? And so on. And then this data can be used for that fourth pillar, which is continuous improvement. And so how do we use this data to make improvements and then measure against that same data? And so this goes back to what I said earlier about conversational analytics being this feedback loop. And so what we’re demonstrating here for these four steps is exactly that. I capture the data, I process it to make it be able to be analyzed. I analyze that data and then I use that same data for continuous improvement and to measure the impact of any business change I made to how I’m handling these support calls, did that make effective change in the direction that I wanted to? And so on and so forth. And so these four pillars are really just an example of this conversational analytics as a feedback loop. 

Paula Rivera: 

So would it be fair to, as an example, would it be fair to say if you manufacture consumer goods and suddenly you have a rash of calls because the iPhone has broken down, Apple probably would not appreciate that example, but there’s a glitch in the iPhone system and then Apple gets all these calls, it’s probably all automated, but Apple gets all these calls and realizes, “Oh gosh, we now have a spike and everybody’s calling about this particular thing, stop what we are doing, let’s go in, let’s look at this and fix it.” Is that a fair example? 

Derek Boudreau: 

Absolutely. Yeah. And so because you can identify the reasons for calling through conversational analytics or the reason for making contact through this intent identification in the sub intents under those, as we spoke of earlier, you can understand the different rates of occurrences of these. And so if there’s an anomaly such as that you just described, where people are calling at a peculiar rate for a certain intent or topic, it’s certainly an indicator that there’s something wrong that needs to be paid attention to. And so it’s just early warning indicator to the business with all the information needed to really understand what’s happening, to make decisions, and to measure the impact of those decisions accordingly. So that’s a fantastic example, yes. 

Paula Rivera: 

Yeah, it kind of hit me. In my previous life I worked for the Hertz, the car rental folks, and I used to talk to the fleet guys quite regularly, and Hertz used to be one of the first indicators of, “Hey, you have a problem with XYZ car.” And the reason why we could do that is because we would have thousands of cars from any manufacturer that were being used regularly, and we kind of had that one clear channel, the fleet department, who would see, oh gosh, I don’t know, the Hugo seems to keep breaking down. We need to tell the manufacturer this. So it’s very much a more sophisticated way of identifying potential issues and addressing them “in real time” versus the good old days where it was someone making a noticeable observation. 

Derek Boudreau: 

That’s right. It’s across all the conversations. It’s not just where I’m looking at a sample of interactions that may have occurred. It’s because of this technology, we can look at all the interactions. And so in the same way that you could look at all the cars of a certain type across the fleet, this is the exact same idea that because we can look at it at scale, we can identify these same kind of trends. 

Paula Rivera: 

Excellent. Here’s one of my more favorite topics because I do think it’s really interesting sentiment. So what role does sentiment and intent analysis play in this process? And I know we are here to talk analytics, but how is AI able to identify and adapt to sentiment? 

Derek Boudreau: 

Great question. So we talked a little bit about intent analysis and how that comes into play. So we can focus on the sentiment side. And sentiment is an important indicator of the experience being provided, whether that’s an experience by an AI agent during the course of an interaction or a conversation or a human agent during the course of a conversation. So it’s a good indicator of the customer’s experience, whether that’s a positive or negative experience either at the moment or with a specific past experience or a specific device or a specific service, sentiment is a good tie to understanding the customer state of mind relative to the context. 

And so language models and different technologies that allow us to identify and quantify and qualify the sentiment across this large scale data that we were just talking about so we can understand trends and insights relative to sentiment based on how specific AI agent is handling a customer’s issues or a human agent is handling customer’s specific questions or a product we just delivered or a service we’re delivering or a promotion we just rolled out, how customers are reacting to that and using their own words in the form of a sentiment to really understand are we having the effect that we want to? And if not, what do we need to make change? 

Paula Rivera: 

So do you think consumers acceptance of, I’m going to say use the technical LLMs chatbots, however, here in the industry we always talk about how chatbots have advanced and the chatbots of yesterday really are no longer applicable. So I’m envisioning as you were speaking, I’m envisioning getting on the phone, you start going through the phone tree or whatnot, and a lot of people immediately have a negative connotation of dealing with an automated or an intelligent virtual assistant simply because they’ve spent the past however many years using these chatbots that in hindsight were relatively primitive. So is there automatically a skewed sentiment towards frustration because people are like, “Oh, I’m going through the system,” or has that kind of gone away? 

Derek Boudreau: 

Done right, I think it’s gone away. So I don’t believe the AI agent itself is a frustrating thing any more or less so than dealing with a human. Oftentimes it’s less frustrating to deal with a well-designed AI virtual assistant that can understand your specific needs, that can navigate efficiently through information to respond to your needs, that it can do so even if… They don’t have a bad day, an AI virtual assistant isn’t having a bad day. And so they are a consistent experience that is personalized to your needs. And one of the real benefits too is it’s always available. And so if you want to make an appointment to your dentist, you don’t have to do so during business hours when you may be busy with other things. Where you can do so at any time that’s convenient for you. And so I think just that mix of capabilities, just I would say out of the gates, makes it a more pleasurable experience than it could otherwise be with trying to deal with a human doing the same. 

Paula Rivera: 

Yeah, I think you hit the nail on the head and it’s a matter of doing it right. There was recently an MIT study that showed that indicated a lot of AI projects fail. And I personally would say, well, there’s an easy explanation. It’s because the product or the project wasn’t thoughtfully implemented. So I think you hit the nail on the head with it needs to be done right. 

Derek Boudreau: 

That’s right. And that’s where conversational analytics plays a big role as well. And so by understanding how customers are interacting with the organization today through any of the various channels that they do so to have these conversations, we can use that to understand what goes well, what has done well, and what is an area of improvement, and use those insights from those conversations to drive the virtual agent experience and make sure that we deliver the experience that will provide the outcomes that are being sought. 

Paula Rivera: 

Going back to that continuous improvement loop. I love it. 

Derek Boudreau: 

Exactly right. 

Paula Rivera: 

Wonderful. So let’s talk impact and the benefits companies can expect when they start implementing conversational analytics. How does it improve customer service and agent performance? 

Derek Boudreau: 

And I think we touched on some of these things here and so understanding… And so in order to change something, you have to be able to measure it. And so being able to analyze the conversations, again, whether they’re conversations through any of the mediums that we talked about, through any of the channels that we talked about, within any of the domains, in order to make impact on those conversations, you have to be able to measure them. And so conversational analytics allows you to do just that, to measure these conversations in the ways that you need to measure them, whether it’s the sentiment, whether it’s policy adherence, whether it’s delivering a sale outcome as needed, measuring is the first step to making change. And so I think conversational analytics is just the vehicle by which you can improve the customer experience. And so I think this is just a continuation on the theme that we’ve been discussing throughout here today. 

It’s what you can measure, you can change. And so conversational analytics helps improve customer service and agent performance because we can measure the service being provided. We can measure the performance of the agent, whether that’s an AI virtual agent or a human agent, we can measure them against any metrics that the business is trying to aspire to and then make change accordingly. And again, it goes back to that feedback loop. Did we make a change in the way the agents are handling policy questions in a way that impacts the outcome desired, whether that’s a sentiment outcome or a policy adherence outcome? And we can use the ability to look at the conversation over time and view that yes, the change that we made is making the impact that we desired to or it’s not making the impact and again, make additional change as needed. 

Paula Rivera: 

I’d like the fact that you talked both about being able to change, whether it’s the human or the machine. You said earlier in the segment about the fact that AI agents don’t call in sick. And in my head I was like, nor do they take vacation. But the other interesting thing, you do kind of need a human in the loop, but with an AI agent, you make that change once and it’s done sometimes when you’re dealing with the human, we’re all human as we like to say, but sometimes it might take two or three times before that change actually happens simply because the human’s kind of working through it and embracing whatever change is being asked to be made. So to a degree, it’s probably a lot easier to train a machine than it is to train a person. 

Derek Boudreau: 

I would agree with that, that it’s certainly more consistent that you know as you said, that the results received today will be the same tomorrow, will be the same next week, next month. So I would absolutely agree with that. 

Paula Rivera: 

Yeah, unless you’re my husband, I’ve been trying to change him for about 20 years now and I’m still working at it. Let’s talk about operational efficiencies. Does conversational analytics, would seem natural that yes, it definitely does lead to operational efficiencies. Can you give a couple of examples of the type of efficiencies it unlocks? 

Derek Boudreau: 

Certainly. Well, going back to this decision fuel, I think it really unlocks the organizations and business to be able to make faster decisions without having to rely on heavily manual processes to try and understand what is important, what is being talked about without anecdotal, using anecdotal evidence. It’s database evidence to make faster decisions and understand the impact of those decisions more quickly lead to these operational efficiencies. 

Paula Rivera: 

Right. Which probably then conversely helps to increase sales and strengthen brand equity. 

Derek Boudreau: 

Absolutely, because you’re very focused on understanding, again, going back to the voice of the customer and what they’re saying, what they’re telling you about your business, about your operations, what they like or don’t like about your product. And you can make a quick decision, make the necessary adjustments, understand the impact of those adjustments, and really focus on how do you improve a sale for a customer based on what they’re telling you that they need and they want and expect with just again, delivering this experience that strengthens the brand equity. 

Paula Rivera: 

I love it. I love it. So typically, for those listening, you all know I typically shy away from being overly promotional about IntelePeer. Obviously I work here, I love the company. I think it’s great, but you don’t necessarily need to listen to me talk for a half hour, 40 minutes about how great we are. So I try not to do that. But on this topic of conversational analytics, we actually recently introduced a product called SmartAnalytics and analytics has been a focus of ours for a while now. It continues to be a focus, but I was hoping Derek could talk us through SmartAnalytics and how it helps customers harness the power of what’s going on with their customers and how it helps our customers improve their performance. Derek, could you talk a little bit about SmartAnalytics and how it supports the high volume customer interactions? 

Derek Boudreau: 

Absolutely. And so SmartAnalytics really takes advantage of our unique platform here at IntelePeer. Because we are an end-to-end platform for the entire voice experience, we’ve actually built a pipeline that is able to support this platform. So it’s a purpose-built pipeline that uses the recordings generated from the voice calls on our platform, leverages our AI hub, AI infrastructure to analyze those recordings, uses our data hub that’s built on top of Snowflake to store and be the analysis engine, the consumption engine of those analytics for our users, and all the while providing these conversational insights from the entire customer experience. 

So that means the moment that they call in and interact even with the legacy IBR, right? So predating the current AI agents to traversing one of our AI virtual agents to, if they need to be transferred for whatever reason to a human agent, we capture that entire experience within our SmartAnalytics platform through all the components that I just mentioned and make that available through our SmartAnalytics BI layer where you can view insights from the complete interaction end-to-end or any segment of that interaction and understand, again, the voice of the customer, understand where the experience being provided is optimal and where it’s not optimal and make the necessary change and enter that feedback loop and so on. So SmartAnalytics really is an exciting area for us here IntelePeer. 

Paula Rivera: 

So would it be fair to say that, let’s say, and I believe I saw this in one of our demos, but with SmartAnalytics, you could go in and look and say, “Oh, the AI agent keeps dropping this call when the word unique is said, let’s go in and look at why this is happening and let’s fix this.” Would that be a kind of an example of how this would be helpful and impactful to a business? 

Derek Boudreau: 

Absolutely. Or it could be that people are clawing in and talking about a new product that the AI virtual agent hasn’t been trained on handling yet. And so we’ve seen an influx and transfers from the AI agent to a human agent, and we can easily see the reasons for that. Or conversely, if the AI agent is transferring to a human agent and the AI agent wasn’t able to handle an intent as we expected, but what is the human agent doing that ultimately met the outcome of that intent and we can use that as a way to train the AI agent so that the next time that call comes in of that type, the AI agent will be better suited to handle it as well. And so having access to this complete experience allows us to use conversational analytics really to drive the optimal performance of the AI agent using all the surrounding experiences to do so. 

Paula Rivera: 

I love it. That’s great. And I personally am excited. I think SmartAnalytics sort of rounds out the offerings that we have and really makes our smart agents and our smart office and smart worker products that much stronger and more valuable for our customers. This is Paula with the little cheerleading pom poms on. I do think what we do have to offer is pretty darn powerful. So it’s exciting to see this come to fruition, and I’m excited to see are there any developments or anything new that will be kind of introduced to SmartAnalytics over the coming weeks at all? 

Derek Boudreau: 

Absolutely. And so we’ll be releasing and you’ll be hearing more and more about analysis agents. And so we’ve talked a lot about even here today about virtual AI agents that help you during the course of the call to schedule your appointment. But built on top of SmartAnalytics are a different type of AI agent called analysis agents where they are looking at all the data that’s being collected and automatically extracting insights on that data to be used to make these decisions. And so not relying on a human analyst to dig into the data and understand trends and outcomes and scores and understanding of the landscape of the data, but AI agents that are scouring constantly the data as it comes into our SmartAnalytics platform and automatically surfacing insights that are important to the business so the business can focus on making decisions instead of digging into all the data available. 

Paula Rivera: 

So someone like myself who data is not my strong suit, I really appreciate the analysis agents element. I’m so excited. We’ll be rolling that out and I can’t wait to have you back on once that’s live and talk about how it’s being used. So that’s great. Thanks for that little insight as to what’s coming down the pipeline. Appreciate it. 

Derek Boudreau: 

Yes, I’m very excited about it as well. 

Paula Rivera: 

Being the product manager, I would hope so. Or vice president, I would hope so. 

Derek Boudreau: 

Yes, very much so. 

Paula Rivera: 

So before we wrap up, we’re going to have a little fun with Derek. As those who listen in know I do this rapid fire. Three quick questions. There’s no overthinking, Derek, you can give me a one word answer or you could expound upon your answers if you would like. The three questions: what’s your favorite tech gadget that you can’t live without? If you could have dinner with any AI pioneer, past or present, who would it be? And what’s one word you’d use to describe the future of AI? So without further ado, favorite tech gadget? 

Derek Boudreau: 

Oh, gosh. So I’m not going to say my phone because I feel like that’s expected and sort of given at this point. I would say my whoop, because I like to know how well I’m sleeping, how far I’ve run, how fast I ran, and so on. So I would say my whoop. 

Paula Rivera: 

Okay, you just call this a whoop with the W? 

Derek Boudreau: 

That’s right. So it’s basically a screenless iWatch, Apple Watch, excuse me. And so yeah, it just tracks all of your bio data. 

Paula Rivera: 

Oh. That’s super cool. I’m going to tell my husband about that. He’s using a couple of different gadgets to kind of track this and track that, but I’m going to have him look into that. 

Derek Boudreau: 

It’s very interesting, yes. 

Paula Rivera: 

And once he starts focusing in on these things, he just kind of goes down the rabbit hole, but I think that’s an addiction I would support for him. So any dinner with any AI pioneer, who would it be? 

Derek Boudreau: 

Oh my gosh, I think I have to go original, like OG, like Alan Turing or somebody similar to that. 

Paula Rivera: 

Yeah. Yeah, I like that very much. One word to describe the future of AI. 

Derek Boudreau: 

I think there are many words here, but if I had to sum it up into one, and it’s not a very exciting word, but I would say exciting. So exciting would be the one word I would use because I think that’s what it is. The impact that it can make, the advantages that it can bring to businesses, to people, I think it’s just an exciting time for us. 

Paula Rivera: 

It really is. And it is almost a shame that when you look at all the media and the online platforms and whatnot, they sort of skew towards the negative. And because scariness attracts clicks, and don’t we all want clicks in our life, but it really is so exciting, and I’m actually looking forward to where we’ll be in 10 or 20 years because I think a lot of the promises that are being bantered about today will actually come to fruition hopefully sooner versus later. Excuse me, there’s the New England in me. So yeah, I concur exciting, and I might throw in fantastic and amazing, but it is exciting and I want those headlines to go more towards positive, but I have a little control over that. 

Derek Boudreau: 

Well, I think it will, and I think as we go through the cycles and it’s understood and how this can be used to make impact in lives, make impact in business for the positive, I think that’ll just get reinforced along the way. 

Paula Rivera: 

Wonderful. So Derek, I want to thank you. You have an amazing knack of taking your immense knowledge base and putting it into terms that Paula can understand. I always walk away knowing a little bit more after I’ve spoken with you, so I really appreciate your time today. 

Derek Boudreau: 

Always a pleasure speaking with you, Paula. I enjoyed it. 

Paula Rivera: 

Thank you. Thank you so much, Derek, for joining us today and sharing your insights. Conversational analytics is clearly more than just a buzzword. It’s a powerful tool for transforming customer experience and driving business growth. To learn more about how IntelePeer can help your organization unlock the power of SmartAnalytics, visit intelepeer.ai. That was my shameless plug for the week. Thanks for tuning into the AI Factor. Don’t forget to subscribe, share, and leave us a review. Until next time, keep talking, keep learning, and keep unleashing the power of AI.