We're going to get started. So, again, welcome, everyone. This is Qrvey's first in a series of virtual panel events under the banner of an afternoon with the analysts. If you're meeting Qrvey today for the first time, We are the only embedded analytics solution purpose built for SaaS companies and the multi-tenant use case.

SaaS companies integrate our solution to power their self-service analytics experience for their end users, and we're continuously incorporating Gen AI features into our offering so our customers can build less software and deliver more value. For anyone who just jumped on, my name is Kerry Pearce. I'm the head of marketing here at Qrvey. I'll be guiding the panel discussion, but let's go ahead and introduce our panelists.

First up, we have Howard Dresner. He is the chief research officer and founder of Dresner Advisory Services. He's a revered thought leader in business intelligence and performance management and even coined the term business intelligence in 1989, and he also led the business intelligence research practice at Gartner for thirteen years.

Moving along, welcome Myles Suer. He's a research director at Dresner Advisory Services.

Myles is a well known technology journalist and has covered AI extensively in his work. He is the number one CIO influencer according to LeadTail, and he facilitates the #CIOchat, connecting CIOs and senior leaders across industries worldwide.

Next up, we have Erin Peck. Erin is a senior product manager at Resolver, a Kroll business, where she leads product development for dashboards, reports, and data warehouse solutions with a focus on delivering data driven insights and enhancing user experience.

She's passionate about leveraging emerging technologies like generative AI to drive innovation and software. Erin is also a customer of Qrvey.

And now we have Arman Eshraghi. He's a serial entrepreneur and the CEO and founder of Qrvey.

He is known for his ability to recognize emerging technology shifts and making developer tools to leverage those opportunities.

Before Qrvey, he founded Logi Analytics, one of the most well known analytic development platforms for web-based applications, and Logi was acquired by Insight Software in 2021.

So welcome, and thank you to our distinguished analysts and panelists. Thank you for participating today.

Before we jump into the discussion, I invite everyone to use the chat feature as well as the q and a feature for questions and comments. We will cover as many questions as we can at the end of the discussion.

So let's go ahead and get started.

So why are we here today? Right? The topic of Gen AI has certainly boomed in the past few years, but where exactly is the software market in this GenAI journey? The goal of the panel today is to help engineering and product leaders cut through the noise, get a clearer understanding of how and why Gen AI is being utilized as you either speed up or cautiously approach experimenting with Gen AI in your product offerings. So we've paired some of the latest research in Gen AI for the software market with AI experts and tech product leaders to dig into the topics in more detail.

So it's my pleasure to kick off today's panel discussion starting with Howard Dresner, who will share the most recent industry research that his firm has conducted on generative AI.

Thanks, Kerry.

So just a little bit of background about us. The Dresner Advisory Services, we are a primary research organization that focuses on data analytics and performance management, and we collect lots of data. We've been around for eighteen years. We've got about fifteen years worth of data.

We're a bunch of data geeks like many of you. And I'm gonna share some of our analyses today. Obviously, not everything. We published over three thousand pages of research last year alone, but we've been collecting data on generative AI for a couple years.

We were actually early adopters of generative AI as well, and we are big fans of what it has the potential to do. So I'm gonna share some of those statistics with you right now to give you a sense of what's actually going on out there in the real world. And let's go to the first slide. We, collect data twice per year.

We're actually just embarking on our new year data collection project as we speak. And every time we collect data, both new year and mid year, we ask about generative AI. And we always add more questions, of course, because it is a very quickly evolving space.

Maybe evolving is even the wrong word. More like a revolution, I guess, in many ways. But one of the questions we ask is, how closely are you paying attention to this whole phenomenon of generative AI? And I'm guessing in the last few days, that probably has spiked.

So for those of you that have been living under a rock somewhere, this thing called DeepSeek was announced the other day or came to the fore. I've actually downloaded it and started playing with it. Many of you may have already or at least have read about it. But you can see that the interest in Generative AI going from q one of last year to q four, so the end of last year, has spiked dramatically.

So more than, you know, I would say the greatest majority are at least paying attention to it or reading about it often, if not constantly, which is, you know, pretty dramatic. And once again, I think, collecting that data in Q1 and Q2 of this year, we're gonna see it jump even more so. So lots of interest out there. Let's go to the next slide and just take a look at the perspective, and you can see it's it's pretty positive.

Right? Organizations, almost fifty percent are saying they're excited about the possibilities associated with generative AI, and they wanna be an early adopter. You can see that's a pretty big jump from where we were at the beginning of last year. Once again, I expect that to increase because it's, you know, very approachable to folks.

I mean, a lot of folks are playing with various generative AI applications out there, and they can see the value. And it sort of gets the juices flowing, gets people excited about the potential and what other areas that they could possibly apply it to. Very few are saying they they don't get it, or they don't see the value associated with it or would use it. Almost really almost no one is in that, category.

Moving on to the next slide, plans.

And here we saw a pretty big jump, especially in the in production. Now we didn't ask them what they mean by in production, but almost thirty percent are saying now that they're actually in production with generative AI, or another fifty percent are saying they're experimenting with generative AI. Once again, if you go over to the right, virtually no one is saying they have no plans at all. So this is really broad based. It's pretty significant. It's across all industries, across all functions, across all geographies, and, folks are just excited about, once again, the possibility and what it brings to the fore, within their respective organizations.

And then if we just take a look at the priorities, for generative AI, what's notable about this, and I've I've highlighted two of them, is that organizations are focused both internally and externally in terms of the potential of leveraging generative AI. So internally, you know, increased productivity, efficiency, obviously. How do we do more with less resources? How do we do more more quickly?

And and then externally focused, how do we better serve our customers leveraging generative AI than we could do before? So that's great. I mean, obviously, there are other things we wanna use generative AI for, but those are the top two, for organizations.

Great. Let's jump back into the panel.

And the first question actually is for Myles. So the research is showing a pretty substantial increase in excitement around GenAI with more companies experimenting with it over the past year. What do you think is contributing to that trend?

Looks like Myles might be muted.

I'm sorry. Let me try this again.

Take two.

Thank you. I think the first thing that excited people was the ability for generative AI to go after unstructured data. And so there are a lot of really interesting use cases that people really couldn't do well before. So pharmaceuticals being able to track and analyze data about, you know, testing of of new drugs and, therapies, was an early adopter. And and then, you know, obviously, a lot of us did experimented with it in terms of marketing purposes and writing and things like that, generative AI's proof. But I think where we're going forward on are two things. One is, productivity.

And so, you know, where it's gonna change, you know, how we do different things in our jobs, how we manage, on and on and on. And then the other part is transformation.

So how do we use generative AI to extend our our products and what we do?

And so, you know, that includes customer experience and things like that. But in manufacturing, it includes you know, one of my favorite is seeing the spray work and actually go and detect, you know, where there's a weed and not put pesticide everywhere.

So there's just lots and lots of applications that are happening, and I think it's gonna transform everything we do. And it will transform everything that Howard and I are concerned with in terms of BI, in terms of how do you go get data and make use.

Thank you. Erin, how are you incorporating GenAI into your product offering today, and what was the reason driving it? And can you tell us a little bit about Resolver?

I'd love to. Yes.

So just a quick background on Resolver.

We are a SaaS platform serving two main industries. We've got security and investigation, so that's dealing with a lot of health organizations, university campuses, retail, pharmaceuticals, helping them manage their security operations processes as well as our governance, risk, and compliance product line. So that's looking more at, internal audit, risk professionals, compliance management, a lot in very heavy regulated industries like financial institutions.

So when we're thinking about GenAI, we're really looking to target the most intensive and repetitive tasks that users come to our system to complete. So two flavors.

With the security customers, we're really focused on streamlining that incident triage process.

They get a high volume of very repetitive incidents. So helping helping them really make sure they have complete data and triage as efficiently as possible.

We've sounds found some good fit with AI capabilities in that area.

And then governance, risk, and compliance, it's a little bit more on dealing with really complex, fast changing, regulatory frameworks.

So leveraging AI to reduce the mental load and help them recognize patterns within their data. And we believe that this is gonna help risk teams be more efficient and work together better.

That's great. Thank you. Arman, you work closely with a lot of SaaS companies that have been exploring how to incorporate GenAI into their product. What do you see as the most common reason supporting the trend?

I think it's mostly because users are asking for it. So there was a day, maybe two years ago or a year ago, that people were looking at GenAI as a luxury item. So if your application and your product had some GenAI capabilities, it was like, wow. That's very advanced, very modern.

That perspective and that expectation is changing now to a way that people expect that to be there if you really your company is and your product is on par with the technologies in the market. And that is changing very drastically as, you know, Howard showed it in their slides at the same ratio. Actually, user expectation is changing as they are using everyday applications, and AI is part of it. Analytics is not an exception.

That just drives it. Now, also, you need to look at it from super users, power users, end users perspective. Right? So each of them see AI in a different way to empower them to increase their productivity.

So super users, developers, they expect some capabilities from AI. Power users may not be coders, but they are the savvy data users.

And then they want some productivity out of GenAI and also end users. They want self-service, but now a better self-service powered by GenAI.

Great. Thank you. Alright. Let's jump back into the research, and Howard will be sharing more on adoption and top use cases.

Great.

Okay. Let's go to the first slide. And, you like with every phenomenon in the marketplace, you gotta follow the money. Now where is the investment going to be within organizations?

And these aren't the vendor organizations. These are the user organizations by and large. And what they're telling us and I circled the bottom three. I mean, there's, you know, what, about fourteen percent of organizations saying, yeah, we're not allocating any budget to this.

But the promise of generative AI is so compelling that organizations are definitely, putting where their money where their mouths are, so to speak, and they're investing even if it's early days just to experiment with it, understand it better so that they're a little bit more conversant in it, and then understanding the use cases. What are the use cases around it? Where can we get the most value from generative AI in the near term and then longer term as the technology, of course, evolves and continues to improve. So it's pretty exciting to actually see real money and real projects associated with generative AI because that's actually what moves things, forward.

Let's take a look at the next slide, and you can see, that it's still relatively early days. Now once again, this is Q4 data that we're taking a look at. And once again, we're collecting data right now. We'll see what it looks like in q one and q two.

Those numbers are definitely gonna shift. We're gonna see things going from the left, which is under ten applications, over to the right to more than ten. I don't know what those folks that are in that fifty one to a hundred category are actually doing. It'd probably be a really interesting conversation to speak to them to understand, well, where are you getting the greatest value from generative AI?

What is the what is the curve actually look like? So it's early days, but organizations are getting started and trying to see what makes sense for them, which use cases deliver the most value with generative AI.

And then if we take a look at use cases on the next slide, these are all really interesting ones. And, of course, we've all played around, creating correspondences, creating maybe marketing stuff.

Some have been writing code or using it to write code. So these are all interesting use cases associated with it. But the top ones, really, once again, is that internal and external promise of what generative AI can bring to the table. So internally, once again, we're dealing with our internal customers and our external customers. How do we better serve those two sets of constituents better than we can do or have been doing previously with other technologies or with, with people for that matter. So those are certainly the top two use cases that, we've been seeing.

Kerry?

Great. Let's jump back into the panel.

First question here is for Myles. The two most prominent use cases, as Howard had shared, focus on the internal and external support. How are you seeing companies leverage GenAI for these use cases? And then, also, what are some emerging use cases that you anticipate gaining traction in the year ahead?

Yeah. I mean, I think I I think, obviously, marketing is is where a lot of this is going. I mean, the ability to transform how you interact with people is enormous. Obviously, we we talked, beforehand about energetic AI, and how it will transform customer and services experiences.

I mean, we've all had the problem of calling in, and there's this buffer before you can actually talk to a live agent, and and you get nothing out of it. In fact, it slows down your resolution problem, time. And yet I've seen some examples where you call in and the agents actually able to, address your problem and solve it, and you never have to talk to human beings. So we are seeing some some some real potential as we move to agents. And I know, Erin, you have a kind of a compelling agent story that you can talk about. So it it really is gonna affect a lot of different things. As I said before, it's gonna affect personal productivity, and it's gonna affect how we do things.

And so we're we're just at the beginning of this thing, but I do think this is as big as, you know, the Internet, when it all nets itself out.

Thank you. Erin, what use cases did you set out to address with your GenAI features, and then what challenges did you face along the way?

Great question.

We brought our first GenAI feature to market in 2021, so I think pretty early.

It was an intelligent triage tool, and the idea was it's gonna help our corporate security users triage incidents and make connections, within their data, really ensuring, like, complete, accurate, efficiently triaged incidents.

Sounds great.

Our users met this with a little bit of skepticism because we work in very highly regulated security industries. So one of the initial hurdles was around how do we get them to trust and buy in opt in to these features.

Our legal and sales team did a great job of laying out really easy to interpret documentation for our users to opt in. And this documentation covered four main topics.

First, data usage. So how is the AI tool going to be leveraging their data to train the model and deliver better services?

Data regionality. Arman, you know this is a big one.

Where is my data gonna be processed? Especially working with a lot of government organizations, we need to ensure that the data is not leaving their geographical region.

Data security, it's always at the top of our mind. We need to prove that any AI subprocessors we're using are still compliant with our security standards.

And then last, third party access. Anywhere AI gets involved, we need to ensure that they're processing their data with the same kind of rigor, that we use around security.

Mhmm.

That makes sense.

Question for Arman. Analytics itself was not called out as a use case in the slide, but there are several of those use cases that have ties to an improved analytics experience.

What's the impact in your perspective of GenAI and analytics in the near term and long term for software providers?

So, analytics is actually part of, you know, many of these applications, as you know, and regardless of what vertical they are serving. So the way I personally see it short term, meaning the next year or two, and midterm, meaning five years from now, and long term, meaning five maybe ten years from now, I would say in the short term, definitely app builders and application makers and SaaS companies, if they have not yet, they will form their vision.

They will know what they can do with GenAI. They will create some demo assets.

They will create probably prototypes, and they may even go to market, some of them, and implement and go to production.

So that's what I see in short term will happen in the next year or two. In midterm, if you are talking about five years, definitely, AI will be a must have extension for analytics in many aspects. We expect end users would expect easier, better ways, and extended way, not replacing what they have, but extending essentially what they have with AI to offer a much better self-service.

And long term, ten years from now, I have no idea. I don't have that kind of crystal ball. And with this kind of fast paced technologies, I don't know exactly what would happen.

Alright. Excellent. We're gonna jump back into the research. We've got another set another section here on areas of concern.

Yep.

Super. Well, there are always gonna be areas of concern. Right? I mean, it's with any new technology, any new innovation.

And you can see that in surveying all of those user constituents within our community, there are a lot of concerns. And so you see at the top, and, this was reflected in what Aaron had to say around data security and privacy, legal and regulatory, clearly ethics and bias concerns.

And I circled one at the bottom because it's it's pretty far down in the these, folks in their thinking that responded.

And I think it's gonna climb, which is what are the use cases? Right? Right now, it's kind of like the wild west out there a little bit, and, we're kind of generalizing in the way we look at this technology. But there are some use cases, as I mentioned before, that I think are gonna be higher value for organizations, rather than other use cases. So I think maybe not necessarily starting with use cases, but, you know, why not? Understand where you might get the most value and near term and long term in leveraging generative AI. But these are all obviously valid, concerns, and they need to be factored in to, anything that we do with generative AI or AI generally in the organization in order to be successful.

Excellent.

I'm gonna leave this slide up for the first question here. And this is a question for everyone in the panel. Looking at these concerns, which one do you take the most seriously, and which one would you toss?

We'll start with Erin.

I'm going to keep data security.

It's always at the top of our minds, and we need to ensure that our customers can rest easy working with any AI feature that we build. And I'm tossing talent and skills availability.

I think it's a very teachable skill, so anyone who's really, well experienced in the software industry can likely apply those product skills to an AI feature.

Thanks. Myles?

Yeah. I I think, the data and security privacy areas are are big. I think that we in some degree, we have to have new tooling to help help with those areas, vector databases, for example, store things completely different ways. But I really wanna focus on Howard's one because I've had a number of discussions with CIOs, and one of the fears that they have is that organizations have forgot everything they've done over the last many years of developing concrete use cases, and and they just wanna get into action.

And every time that happens, things fail. In fact, there are some numbers I've seen in some research that indicates that one of the reasons, people have failed is this particular issue. They've got a bunch of prototypes going, but they don't go into production because they don't have value. So, I think use cases are really important to, to get well defined and and figured out.

Which one would you toss?

Oh, which one? Oh, that. Probably no quantitative measures at this time, but it is hard.

Great. Alright. Arman.

Yeah. So looking from SaaS company's perspective and looking through that lens, I would say that definitely they deal with multi-tenancy and external use cases for most part.

So at a totally different level, it brings the data security, data privacy, you know, to their attention because it would multiply. Also, the other thing that would be magnified from SaaS company's perspective is the cost. Because as soon as they go there and add these capabilities, since they are working with multiple tenants, sometimes tens of thousands of tenants, I mean, the cost needs to be justified. And especially if they are looking at analytics in general and GenAI as in particular as a revenue source as added value to their product this needs to be justified as well that goes back to the use cases part that needs to be well defined so the cost can be justified and customers and users get the value that they, you know, they pay for.

Did you tell us one too?

I would say, probably, again, since it's a SaaS company, it's not internal use cases. Probably organizational policy would be, probably not, you know, what they can have control over it.

So Okay.

Great. And, Howard, from your perspective, which one do you take seriously? Which one do you toss?

Oh, boy. I'd be hard pressed to toss any of them, but I would underscore the cost factor. And once again, I'd I'd go back to the current sensation surrounding DeepSeek and the fact that they were able to, you know, train their model with a fraction of the resources and drive cost down dramatically. I think cost becomes far less of a factor, and it's going to really democratize this, technology.

And so small and midsize enterprises are gonna be able to take advantage of it just as larger enterprises, would be able to. So if I had to toss something, gosh, I think model maintenance and management is gonna get a lot easier, honestly.

But, you know, we'll see how the technology develops. I mean, I do think you need to be cognizant of all of these things.

Of course. Here's a question for Myles. What's it going to take for some of these major concerns to subside?

Well, I mean, obviously, the tooling has to has to mature.

You know, we talked about privacy and security concerns.

A lot of the sector databases don't have native protections to them, for example.

So so there's some, there's some things that need to happen, there.

I think, you know, to get to, you know, the synthesis of the other things, people absolutely have to industrialize their data.

And, you know, some of our research shows that, you know, a very large percentage of organizations still have to go do the fundamentals. They don't have their data house in in order.

And and then I think, finally, you know, being able to show tangible value. Now, I mean, the one of the things that was I was thinking back earlier that was really important was the fact that, this this technology is really good at sorting and analyzing things. So, like, thinking about, you know, the use cases that, Erin talked about. You know, it's gonna be, you know, transformative because it's really hard to go incident case by incident case and try and pull out trends. And so it'll be able to pull out trends for you very rapidly. So I think I think these issues will get solved. I think they're in the process of getting solved, and it's just moving really, really fast.

Great. We've got, one more question for the panel before we open it up to the live q and a.

And so I'd love to get everyone's perspective on how you can minimize risk and improve your chance of success when embedding Gen AI into your product.

And I'll start with Erin.

Thanks, Kerry. I have a few thoughts on this one. First and foremost, let's not forget to do the PM job. You need to be very close with your users and how they operate both within your system and in the market.

So make sure you're taking them along for the ride throughout your development process.

The worst thing I think we can do is just shove AI into our product, without being thoughtful about it. So make sure you're solving real user problems, and you're having that impact you intended.

Two more ideas. First, I think we need to consider AI as a companion and tool for users to be successful in our systems, but it's really important that they maintain a sense of autonomy. Right? You're not replacing their critical thinking skills. You're just augmenting it and allowing them to be more efficient.

And then my last tip for product managers is to start small. The first AI feature that we brought to market took a really long time to build.

And by the time we did get it out, it was trickier to nudge that functionality in the right direction.

Thanks, Erin. Myles?

I really liked what what Erin had to say there. It reminds me, of what happened at GE, you know, roughly ten years ago. They started streaming data off the jet engines, and the CEO, at the time said, well, what does this mean for us? And they they went through a whole bunch of discussions, and they finally realized that they didn't wanna sell jet engines anymore. They wanted to sell services.

So I think for all of the folks that are on here, it it really is a moment to say, okay. What does this really mean? How does it transform our value that we deliver to customers in much the same way that Erin talked about? So this is a value discussion.

It's getting closer to your customers and and making sure that that they get the value. It's not just bolting it on. That's not gonna get you anything. It's about how will this make their lives better.

Mhmm. Absolutely. Arman, do you have anything to add about minimizing risk and improving chance of success?

Honestly, you know, that voice that Erin and Myles provided all right on. I mean, hard to add anything to it.

But I can just say from SaaS company's perspective, again, they are working with a lot of customers, and it's not internal use case. So they may not know exactly the requirements.

So they need to really do a little bit of extra work to really go out there and ask their customers and all of the tenants what matter to them. It's a little bit more tricky, more difficult, but it's doable. And especially if they really launch something as a prototype first, as a demo, and then show it to some of their key customers. Definitely, they get good ideas, and then they can come back and add it to the product. And, of course, you know, working with Curvy or any other kind of, you know, company that, you know, can be helpful giving them some kind of advice and demos and other, prototype asset, it can accelerate and lower their cost and risk to do that.

Thanks, Arman. And, Howard, do you have any advice to share?

Yeah. Sure. Couple of things worth noting. I mean, I would view this generative AI as something as really as a strategic initiative within organizations.

I think we're gonna find, certainly within user organizations, and I suspect in developer organizations too, lots of projects where people are playing with the technology or experimenting with it and doing proofs of concept. And I think organizations need to step back top down or really view this as something truly strategic and invest accordingly. And then I think the promise is that this makes certainly from a data and analytics perspective, it makes it so much more accessible. One of the things that I've been talking about since 1993 is what I call information democracy. How do we get data-driven -- timely, relevant data-driven insights to all of the user constituents, both internally and externally to an organization. And right now, penetration is, you know, hovering around forty percent, maybe a little less than that. And something like generative AI makes it so much more approachable.

And by embedding the technology, it gets us or will get us much closer to where we need to be in terms of information democracy. And and that means ultimately alignment with the mission and strategy of an enterprise.

Thanks so much, Howard. That wraps up the discussion topics for today. We do have some questions that have come in from the audience.

The first one here is for Erin. What was your process for experimenting and rolling out GenAI features?

Okay.

We recently ran a beta program for a newly released requirement summarization feature. So this was targeting our governance risk and compliance customers and helping them really rapidly respond to changing compliance frameworks by giving them human readable digestible, summaries. Right?

It's really high stakes that those summaries are two things, accurate and usable. They have to be really easy to understand. Otherwise, what's the point?

So we teamed up with a really small group of trusted expert users who were able, to help us and partner with us in ironing out any of the kinks for that feature. So highly, highly recommend beta testing your new features.

Thanks, Erin. We had a question come in for Arman. Has DeepSeek changed your working assumptions on GenAI? What's the role of open source?

Yeah. I think definitely everything that helps to democratize it, brings the cost down. And, honestly, I'm a software guy, so I will vote towards software rather than relying relying on hardware. That's the better way to democratize it.

So if really software and optimizing software is the way to go and optimize more, bring the cost down, you know, do better on that front, I think, that would be a positive development from my perspective in long run. And I know in short term, some people may look at it as more like maybe the investment has been done for some companies in the last year or so. Maybe it could be done better or so, but that's very short term. In long term, I think that will have a positive impact.

And the better, the cheaper, the, more available AI is and the better embeddable it is, and people can easier and lighter version of AI that can be embedded.

And, you know, it will help SaaS companies. It will help the market to utilize it better.

Mhmm.

Okay. Great. Another question here for Myles. What do companies need to make sure they get right about adding Gen AI into their product?

Yeah. I'm gonna add it again. I mean, I think I think this is really about figuring out exactly what business problems you're solving. I mean, if you think about what Erin has shared with us today, I think one of the things she's doing is changing the value prop.

And and as somebody who's been in a SaaS company for a number of years, you know, the thing that I think is really important is when you're presenting and you're sharing your value to a customer, oftentimes, you'll have a CIO in the room, and that CIO is gonna be looking for something that's gonna generate for value for him and for the organization.

Having the ability to take and actually turn what probably is workflow into analysis and then, decisions is is really empowering. So I think the opportunity is huge here for SaaS companies become more relevant and to be more relevant to senior decision makers, with what they're able to do here.

Great. Looks like we have time for, one more question. This one is for Arman.

How are people adding new revenue from GenAI? Do you have any examples you can share?

Yeah. So, again, they look at GenAI as a kind of extension of the analytics that they are doing and the self-service part. Now some examples are, in addition to what they do with analytics, not as a replacement, but they do what they do with analytics today. But, additionally, they provide this capability that adding a prompt that from end users' perspective, they can really go there and say, what kind of questions I should ask about this data, or what are the outliers about this data? From power users' perspective, rather than they go through the composer and start building these kind of charts, sometimes they can ask, you know, what kind of chart, what kind of visualization, what kind of automation, what kind of workflow I should create for this type of data. And then there will be some suggestions that many of them might be good or they can take it and modify it. And then so those are the most common, that I have seen amongst SaaS companies.

But at the end of the day, again, they also SaaS companies look at it as how to improve their products, to serve their users, compete better with their competitors, and also add new revenue streams.

So those are kind of the way they think in order to take advantage of this new technology.

Great.

Great. Thank you so much. I just want to thank everyone who attended today, and thank you so much to our panelists for sharing their stories and their insights.

Following this session, we'll make sure that we get a recording to all of the attendees, and we'll be sharing some additional resources that each of the panelists have gathered that they found very helpful, and, we'll be sharing those in the coming weeks as well.

Of course, check out Qrvey.com if you'd like to learn a little bit more about some of the things that we're doing in the embedded analytics space for SaaS, especially related to GenAI. And everyone go have fun with Gen AI, and and let us know, what you're learning, and stay connected with us all on LinkedIn. Everyone here is happy to connect. So thank you all again from Qrvey, and enjoy your day.

Thank you.