Alright. Let's go ahead and kick this one off. Thank you for joining us today. My name is Juanita Oguin. I lead product marketing for our platform solutions at Coveo. I am really excited to be here with our guest, Isaac, who'll introduce himself in just a minute here. But did wanna let you all know that we're gonna take the next thirty minutes to give you our view and our perspective on beyond the quadrant using inside engines to drive enterprise growth. We'll break that down a little bit for you. If you have questions along the way, please feel free to chat them. We are going to address those at the end of the session today. And so, again, wanna thank you for taking the time to explore this very important topic for us. Isaac, over to you to introduce yourself. Greetings, everyone. My name is Isaac Sikolic. I'm known for being a CTO and a CIO that now works with organizations around the digital transformation efforts. I partner with the best technologies, in terms of, technologies that can really transform how organizations are operating. And I do a lot of writing and speaking. I, write per c I o dot com for InfoWorld, for my blog, and, hopefully get to meet some of you on the road as I'm usually keynoting at different events this time of year. Awesome. Thanks, Isaac. Alright. Let's jump right in. So for those of you not familiar with the Gartner Magic Quadrant for insight engines, it's actually a report that's been around for Coveo the last ten years since two thousand and six. And as of the time, it's actually changed a few names. It was actually the information access technology report, then it changed to enterprise search, and now it's referred to as insight engines. And for those of you wondering what does that mean, you can see a nice definition here. It's about applying relevancy methods to discover, analyze, describe, and organize content and data, enables interactive and proactive delivery or synthesis of information. And the whole idea is to deliver great experiences to those customers or employees engaging with your information. There's a lot there in that definition. So, Anusik, I wanted to ask you as a former CIO and CTO, how did you think about the magic quadrant for Insight Engines? Well, first, I'm gonna do what you all do, which is look at who's in the upper quadrant and to make some notes and say, okay. I see Coveo in the upper quadrant. That's great. I'm gonna get a dip dig deeper. Right? And I'm going to look at what Gartner has written as the strengths and weaknesses, of any of the vendors, that are listed in a product category. And the reason I'm doing that is, you know, there are there's first is it's gonna give me some sense of how to go shopping, how to ask questions, what are the capabilities that are truly relevant to me. A lot of times they'll get, high marks for something that are very I'm very interested in, so I'm gonna follow-up on that. And sometimes I'll see things that are, low marks on something that is just not relevant to my business. And so, you know, that's gonna bake into the question how I'm really thinking about the category. And and lastly, I really wanna see how they define the category. Right? We you know, they shifted language that happens a lot in technology. They come up with, their own jargon or use industry jargon. But what I could see from this report, you know, the ability to ingest content and data, the ability to enrich the content and data. Juanita, we've talked about that in previous, webinars that used to be separate platforms. You'd have to buy a a platform for ingesting, a separate platform, for enrichment. Now they're putting this all together under insight engines, delivering results to multiple UI. So I'm not just building one search interface, ability to evaluate and tune relevance, ease of use, which we'll talk about, very important to me, multiple languages, and probably the most differentiating thing that I think you should be looking at is personalizing experiences. Right? We're not a one size fits all in terms of what questions we're asking, in terms of where we're asking it, what information we're looking for. And so that's really, to me, the hallmark of a really good insight engine. That's great. You covered a lot of important points there, which I know we'll, get back to throughout the presentation as well. Thank you for that. For those of you not familiar with Coveo, we, you know, we wanted to highlight some of the things that we're proud of that came out of the report. For one, we've been a five time leader in the insight engines, while it was named that particular category. But since the report's inception back to two two thousand and six, we've been named, a leader seven times. Also, we're very proud to have been recognized for the broad coverage that we provide across industry verticals and the different areas that we help our different customers, including on their commerce sites, service and support, workplace, customer facing websites. And for us, that really means, you know, we believe that we've we've been very consistent in our ability to provide a vision and execute on that vision, but also to continuously innovate because, you know, the world is changing constantly, and we're we're trying to stay on top of that and sometimes, you know, provide, a more advanced more advanced options for our end users. And, also, we really take our domain expertise seriously. We know that it's important for companies to get guidance to know where to start. So these are just a couple of highlights that I wanted to share, that Coveo was sort of rated for out of this report. Isaac, from your perspective, covering the bullet point number two here, when it comes to industries and especially in this, you know, economic environment, do you think that there are any particular industries that are prime for, digital transformation or prime for using insight engines? Yeah. I mean, I mean, traditionally, we see a lot of, insights engines being used in, e commerce and in media. I've certainly done my fair share of those. But I think about finance. I mean, you think about the wealth of information that a typical financial company has, a bank and wealth management, and asset management, tremendous amount of information. And I'm gonna come in and I'm gonna start asking some questions. And I need you to know something about who I am, what types of investments, what types of products I might be interested in, what my risk profile is. And you're gonna give me some very different answers, based on that context and what I'm asking for. So I think financial, and, finance is a a pretty hot area for this. Health care, I've been doing, workshops with hospital systems and care, providers around the notion of patient experience. Right? And, you know, we have our work with our doctors, that we do when we're sick or when we visit them or at the hospital. But there's a lot of things that we do outside of that where we're asking questions. We're trying to figure out what an ailment means. Should I take a drug or should I not take a drug? And we're doing this primarily with Google. Right? But how important is it to have relevant information for my background, for my health conditions, something that I can do collaboratively with the nurses and doctors that I trust with the brands I trust. So it's, it's come up quite a bit in health care as we moved out of the pandemic era and into how do they improve patient experience, but also improve the lives of doctors and nurses, who are servicing them. I also think about some lagging, industries. Manufacturing comes to mind and manufacturing tremendous amount of information in the product catalogs, how to service products, what the products are. And you think about the different audiences that need to see this information from different perspective. Sales, distribution dealers, sales channels. So, you know, you talk about moving a product out of commodity status and being into the hands of of of multiple buyers. I think there's a lot to be done in manufacturing. And then I'll share the secret in high-tech. If you have a SaaS platform, one of the things that I do regularly is search and say, you know, can I get answers to a question without having to read through tons of documentation or go through a lot of training videos? If I can get a simple answer to my question, so I can move forward with my implementation, That's a high, watermark for for high-tech companies, but I think it's a very important one. And this is where Insight Engines play in the high-tech market. Thank you for covering that. A lot of interesting industries there that you kind of poked at and gave some great examples. I wanted to move into asking you or going a little bit deeper into you kinda talked about how you use Insight Engines or how you would use it as a former CIO, CTO. But what does this actually mean to you? Yeah. I mean, I think the first thing is the word itself insights. Right? I want answers. And what I found, I have a history of implementing, search indices and church indexes. I find there's a lot of upfront work, that some platforms put you through just to have the data in a place where you can start analyzing it and testing queries out and categorizing it, handling integrations with SaaS platforms and things like that. So I'm looking for ease of use. How quickly can I start using the capability with as little upfront work, to be able to get there? I'm looking for one source. You know, a lot of even the the providers in the quadrant have separate, use cases. They have, you know, maybe one platform for employment employee experience, a different platform for customer experience, or maybe they are only covering one of these areas. And I'm looking at this and saying, you know, if you're a customer, a customer support agent, somebody working for the company, I wanna be able to ask a question and know that the information that's feeding it is consistent, that I'm all looking at the same information. Not that, I'm not gonna have I'm gonna have security around this. I'm gonna have privacy around this. I wanna make sure that when we look at product information or pricing information, that this consistency between what an employee is searching and gets to see, and what customers can get access. I do think personalization plays an important role here. Personalization is taking into context of when I'm doing a search, who I am, where I'm doing it for from, there's some security angles around this, but mostly it, it gets down to how quickly can I start looking at answers to my questions? And then lastly, when I think about the employee experience, I think about, you know, is this being presented to me, this search being presented to me in the tool that I'm using to do my work and is the data behind it comprehensive? My worst scenario is I'm in platform, you know, a a CRM platform or an ERP platform, and search only looks at the data that it stores. That's a problem for me. Or if I'm in that platform in sales or marketing, I wanna do a search in that platform, but, my CIO or our IT team is telling me go somewhere else, go to this intranet or go to this portal to really do your searching. That's breaking my work up and not very useful. So those are four things I'm really homing in on and saying, can we bring this together and make this easier for people to do their jobs and find information that they're looking for? Yeah. That's great. And, it's really important to consider both external and internal perspectives for sure. So I'm glad you covered on the employee experience side. When we look at that quadrant, which we opened up with, there's actually a lot of different companies on their fifteen, actually, in this report. How should companies and digital transformation leaders think about this growing landscape of vendors? And, you know, I see some of them that are sort of build versus buy approaches. Can you talk about that? How should people think about this? Yeah. I mean, the land of search really started in the the area built, and I've that's where I started from. I think my first search engine was a predecessor to the Alta Vista search engine, and we spent a ton of time just moving data into it. We spent time, building the indexes up, being able to categorize information as it was coming in, and then doing a lot of work to integrate, that index with our our web platforms. And it was slow, and it was expensive. But probably the most important thing I I would take away from that world of build is, there was a disconnect. We were doing a ton of work on the engineering side to make the index work, and our product owners, our stakeholders had to wait, to to be able to see any results until we got all that overhead built up. And then we were going back and forth through multiple iterations after that to constantly retune, re rank, resort. We'd have dozens of, of discussions with different stakeholders who had different perspectives over what went into the interfaces, which parameters to use to rank results. And, we were constantly churning, and going through that exercise. So, you look at this today, think about the word insights again, and I'm thinking about, you know, how do we take a lot of the upfront scaffolding and a lot of upfront of the engineering work that gets data ready for search and really focus again back on the experience, for end users. There were three things that really caught my attention rating this report, Juanita, that I think if you're going to open this up, really look for, there are, some of the companies in the top quadrant, here's some of the things that Gartner said about them. One of them they said is lax vertical offering. And that translates to me that you probably have to do a lot of do it yourself work to do categorization, to bring in third party data sources, to present the information that's relevant for your particular industry. So we talk about moving up stack, making it easier, getting to our the right experiences, the vertical experience. I think about health care and financial services and manufacturing, very different paradigms to be working with. And I want some expertise on the back end from my partners to be able to guide me through that process. There's another, company listed in there. They have multiple search products, one for employee experience, one for customer's experience, and they said it is up to the developer to provide scaffolding to the pipeline. Okay? I translate that to I gotta work through two tools. I gotta navigate how to set up that architecture. I'm not in that one data source anymore. And my developers have work. Alright. I want my developers focusing on the personalizations aspects, on the machine learning aspects of it, and less around moving data around. And a third one really gets into how you think about the partner once you start implementing. Again, most search engines, most search platforms, inside engines I've worked with, you're gonna get to an MVP. You're gonna put something out there. You wanna learn from that experience, and you wanna continue to iterate. So I want to make sure that my partner is there to work with me post deployment, so that I can continue to improve the experiences that I'm putting out there. One vendor, it's it has the most variant of sentiment on the efficacy of post deployment services. That would scare the heck out of me, Juanita. That just basically means I'm left on my own, to continue to improve the product. I'm glad we have you here to try and get these complex statements. Thank you for that. That's great. So we've kinda covered what's in the report, what's what are your views on it, and even the landscape of players. I think it's important to go beyond the report, right, beyond the magic quadrant and talk about what's not in this report. And so I wanted to get into that with you. So I think, you know, between you and I, when we were discussing this, there's four key topics not in this report. And I wanted to know if you could just explain these a little bit more, the first being, digital transformation force multipliers. You actually wrote a blog on how insight engines become a digital transformation force multiplier. What does this mean? What are their attributes? Can you explain a little bit more here? Yeah. I actually covered this in one of the white papers I wrote for Coveo, and I do a a ton of writing around this, on my blog and on my website. It's actually a big part of my keynotes too. We're living at a time where organizations have to make choices about what areas that are prioritizing. And there's a lot of different factors coming into that. Right? We wanna grow revenue. But we're also at a time where cost cutting, and looking for efficiency is really important. We wanna be really innovative in terms of how we're using data and analytics and machine learning. But we also wanna be safer about, how we're using data and about the privacy of data. And then going back to something that we talked about earlier, being able to take an experience and realize it's a continuum from supply chain to customer, to customer service rep, to employee. I want to be able to make sure I could service all four constituents. And it used to be that when you look at these dimensions, I'd have to make individual investments that impact one of these strategies. Right? So is this a cost cutting strategy? I'm doing automation, or is this a revenue generating, technology and I'm improving customer experience? Or am I doing insights engines for employees and focused on SharePoint sites and file folders in different locations? Or am I focused on, you know, how do we really build a language that connects customers with our employees? So force multiplying digital transformation investments, recognize that at this time, I need to be able to make a big bet on something that's going to impact multiple areas. And so when I can impact both CX and DX revenue and cost, innovation and privacy and safety, I know I'm really impacting the organization in a transformational way. That's great. Thank you for that. And I feel like this also touches, the popular saying and request of teams to do more with less. Yeah. Thanks for that. The second topic that's not in the report is around having analytics with unstructured data. You also wrote a post on how data scientists can improve analytics and machine learning with unstructured data. Can you share some examples? What do you mean by this? Yeah. I mean, if you look at the the tools that our data scientists are using and the data sources behind them, most organizations are working with the easy part of that equation. Right? The structured data in our databases. And that's where we usually start from. And then we start integrating structured data from our SaaS tools. And now we either have data lakes or data warehouses. We can use our data visualizations. We're doing our machine learning off a bit. And then when I ask these CIOs, these chief data officers, what are you doing to enrich your offering with your unstructured information? How is that fitting into your analytics programs? And I often get a blank spot around this. And so my answer around this is let the insight engine be your back end to be able to query and join information around your unstructured data. You know, I'm thinking about information from content management systems around your products that I wanna bring in, information from my, from my CRMs in terms of what customers are doing and what their actions are doing, my different marketing experiments that I'm doing. There's just a wealth of information. My customer support tickets, for example. There's just a a wealth of information out there that when we think that we're moving to a world where we're not just typing keywords in, we're asking questions, We're expecting to start seeing a verbose response coming back from from the the machine. We're starting to see language models being able to do this. We wanna make sure that we're not just looking at data and structure. We're looking at the complete information that represents the back of the problem, that the user is asking for. So I think, you know, if I have an analytics program and, seventy five, eighty five percent of the way they're working with my structured data, My next question is, well, how am I gonna bring in the unstructured data into this fold and insights engine become a vehicle to be able to do that? That's great. Thanks for that. The next one is I I feel like it's a popular one as well, and it's agile low and no code dev options. You wrote a blog on this as well. What are some of the benefits you would say are of having low and no code options? Well, first, I'm thinking again back to stakeholders and customers and recognizing it's really hard to talk about an insights engine if I'm only using a wireframe. Right? There's really no context to this. What I really wanna do is rapidly load in some of the information that I know is easy to do and important to do and start prototyping, what this interface looks like, put it out there in a POC or a pilot, and start looking at the analytics around what people are searching. So when I say ease of use, I'm looking at how can I do this as quickly as possible because I don't wanna give them a static wireframe? I wanna give them an actual tool to use, and I want them to come back to me and say, I need this other data source plugged into here. I need some categorization built into here to be able to do this. There's a different persona we need to think about. So I wanna do that really quickly. I'm also thinking about the developer experience. Right? One of the things that we were struggling with when I implemented search engines is, we could get a search engine built for a business and an enterprise that had a primary use case, or a customer set that we were ready to invest in. But getting into that second, third, or fourth use case became an incremental difficulty for us to be able to do. And so what I was looking for is, is there a time and place when I can rapidly develop a solution for my employees? And then also have another way of building APIs out, another way of building, customized solutions for my customers. And so I think this ability to first build a prototype using agile very quickly, Then number two, provide developers different ways of building the experience out. If I'm building something for employees, maybe I'm using a low code tool to be able to do this. If I'm building something for customers, it's integrated as a workflow against it. I'm thinking about customer journey. Maybe I'm doing a pro code interface around this. And then being able to do this again, from a dimension of customer segment or from a, persona. Right? So if I have my top hundred customers that are searching data in a specific way, I want may wanna productize that and build an interface specifically for those top hundred customers or those sales channels, or those marketers who are gonna use the product and the search a very different way than my average user. So again, giving me the flexibility of using low code to rapidly develop, low code to build, and integrate, and then bring those experiences directly to the user either in my portals or in my customer facing tools or in my SAS platforms directly in a CRM or an ERP that they're using. Those are the things, that I think are really important that the report didn't actually cover. Thanks for that. We're gonna get into the last one here, which is the impact of generative AI, LLMs, and chat g t, everyone's favorite topic. In a recent vlog, you claim that ChatTGT and LLMs, were going to ignite the consumerization of search. What does that mean? Can you explain what that means for product innovation and IT leaders? Yeah. I mean, you think about how the average person is doing a search today. They're getting a little box, you know, on a screen in the upper right hand corner. They're typing a two three word keyword in, and then they're praying that the results that come back are something relevant. And we're going back and looking at documents and things like that to see and fish out the information. We've just had an entire paradigm shift over the last eight months. We're asking questions. We're prompting. We're doing this iteratively. We're looking at multiple paragraphs of response and the answers are pretty good. And so now people are starting to ask, when is this going to come to the information inside the enterprise or to a B2B product that I'm using or to a customer interface that I can go in and ask a question. And in that universe of information that I'm asking my bank or asking my health care provider, or asking my SAS platform, I'm getting a direct answer to my question. And so, but fifteen years ago, Gartner coined the frames coined the frames that consumerization of IT back then. This was really referring to mobile. This was people coming to the office using, no phones, no mobile tools, but experiencing the iPhone. And then later experiencing the Android devices and saying, you know, when can I start being as efficient and having as good of an experience that I'm doing outside of the office with my phones? When can I start doing this inside my office? And that paradigm shift drove CIOs to say, we need to start thinking about, you know, whether we're providing devices, what apps we're using, how we're presenting information back, to our customers and employees. Mobile first application development came from that paradigm. And I think we're gonna see the same thing from LLMs with search. Right? How do we get rid of seven to twelve different interfaces, three to four search engines that the average enterprise has, and get to one source of data, multiple segments, multiple use cases, and then going out and being able to say, I can support large language models against my content, my data, my information, and provide really relevant results back to my employees and my customers. I think you're gonna be seeing that over the next few years. And what do you need to do to get started? You really need to have an insights engine that makes it easy to load content and then start working, toward improving experiences. Thanks for that, Isaac. So we just talked about what's not in the report. We're gonna ping back ping pong back to what is in the report. So wanted to highlight just a few areas that Coveo was rated for highly. The first being, that Coveo offers the best level of personalization of all the vendors in their market. The platform requires low and episodic cost to maintain underpinning the claim to be the relevance platform, which is what our product is called. And Coveo leads, in our ability to surface insights and platform mechanics and third party UI environments. What do those things mean, Isaac, to you? Again, putting your CIO and, CTO hat on. Yeah. I mean, I you know, you and I have discussed this and and, you know, I went digging into the Coveo documentation. I'm really interested in the personalization aspects, of an insights engine. How is this done? What does this look like? How much work do I have to do, to enable this? We've, you know, quite frankly, been talking about personalization for a little bit, of a of a period of time now, And I'm looking to see it implemented. I'm looking to see the machine learning that's behind it. How do we have, snippets of information? How do we make it easy to do recommendation engines? So I I really think about that aspect of it. And then at put my CIO hat on, you know, it was very common for us to have a dedicated team to either support the search engines that we built, or to continue to roll out search capabilities into other areas of our enterprise. I'm looking for how to do this easier. I'm I'm just looking for that easier button. And that means, you know, make it easier to plug into my different SaaS platforms. I I don't wanna have to build integrations out. Again, let me use low code, in places where that's appropriate. Let me bring the search interface directly into Salesforce, directly into the tools where people are using them. So I think those are the things that I'm really looking for. And then, you know, being able to put this together really quickly. Right? Somewhere in here, I'm gonna prelude a post that I've written that hasn't published yet. Somewhere in here, you have to make a business case around this. Right? You have to be able to show that I'm going to do this and put a return on investment against an investment around this, that blog post, I think is going to come out in the next week or two. And it really comes down to, can I take an existing capability? Cause you probably have searched somewhere in your organization already. Being able to take that one area, start doing AB testing, send some of your users to your existing engine, send some of them to your new engines, start measuring the impact and collecting feedback around it. And you're gonna see, right? A lot of the search engines that we've built and deployed over five, ten, fifteen years, they're just data. They have outdated information. They have outdated tools, and you're very quickly gonna be able to show impact, either by generating leads or selling more product if you're e commerce or be able to have greater brand loyalty when you talk to your banking customers. Lots of different ways that you could show an ROI off of this. And you'll see that in the next post, Juanita. Looking forward to that one. Thank you for that. So, we did wanna offer you a couple of follow-up resources here. We actually mentioned this, what we refer to as the CIO white paper written by Isaac on why you should revisit AI search for your enterprise tech strategy. So you can actually, QR code scan that right now. If you like what we covered today, if you liked our perspective and you wanna go a little bit deeper and even talk about specific use cases, how you can use AI to really transform and scale, you can also meet with Isaac and I directly. So I put my email here, j l geen at Coveo. But, also, there's a direct QR code for you to follow-up to, connect with us, more formally. With that, we wanted to thank you for your time. We hope we were able to give you a few different insights and ways to think about your business. Isaac, thank you for your time. Thank you. It's been a good, discussion today.