Qlik Connect 2025 | Ritu Jyoti, IDC & James Fisher, Qlik

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Ritu Jyoti

Group Vice President/General Manager, Worldwide Artificial Intelligence, Automation, Data and Analytics Research Practice

IDC

The clarity layer: Qlik’s trusted-data playbook for the AI age

Artificial intelligence is forcing a reckoning across the enterprise data landscape, and QlikTech International AB has staked its claim with a vision grounded in precision, openness and execution. The company is positioning itself as more than a participant in the agentic AI era, aiming to become the platform that delivers real-time answers from trusted data.At Qlik Connect, theCUBE Research’s John Furrier and Bob Laliberte analyzed how Qlik’s strategy meets the moment

play_circle_outline Evolution of Qlik platform and strategy for driving transformation and change.

play_circle_outline The Crucial Role of Data Readiness, Intelligence, and Integration in Analytics Market: Preparing for Agentic AI Trends

play_circle_outline Emphasis on business outcomes, value creation, and AI maturity for success.

play_circle_outline Shift in services engagement towards advisory capabilities and partner ecosystem.

play_circle_outline Utilizing TAM Expansion and Data Modernization for Strategic Growth

play_circle_outline Qlik's Continued Investment in Use Cases Sets the Tone for Future Strategy Success

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Ritu Jyoti, IDC & James Fisher, Qlik

Ritu Jyoti

Group Vice President/General Manager, Worldwide Artificial Intelligence, Automation, Data and Analytics Research Practice IDC

Ritu Jyoti & James Fisher talk with John Furrier and Bob Laliberte at Qlik Connect 2025 in Orlando, FL.

Ritu Jyoti, general manager and group vice president for AI, automation, data and analytics at International Data Corp., and James Fisher, chief strategy officer at Qlik, join theCUBE’s John Furrier and Bob Laliberte at Qlik Connect to explore the evolving relationship between AI and enterprise data strategy. Their conversation highlights how Qlik is integrating generative and agentic AI to drive meaningful outcomes for customers.

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What has been the focus of Qlik's evolution and platform development over its 30-year history? add

What market does Qlik sit in and how is their positioning within that market viewed in terms of the current era of AI transformation? add

What is Qlik's focus in terms of product development and market positioning? add

What has been the evolution of services and engagement alongside technology projects, particularly in the realm of data analytics and AI capabilities? add

What is the modernization with data look like from a research and market perspective? add

What are some possibilities for Qlik's future strategy in terms of acquisitions, organic growth, and new developments? add

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Ritu Jyoti, IDC & James Fisher, Qlik

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>> Welcome back everyone to theCUBE's live coverage here in Orlando, Florida for Qlik Connect 2025. I'm John Furrier, host of theCUBE with Bob Laliberte, with theCUBE Research, bringing down all the analysis, bringing the data to you, doing it differently this year with great analysis. We got IDC here, and of course, the chief strategy officer, James Fisher; Ritu Jyoti, group vice president, worldwide artificial intelligence, automation, data and analytics research practice at IDC. You got the data, you're tracking the market. James, you're setting the strategy for Qlik. Welcome back to theCUBE.

>> Thank you.

>> Welcome to theCUBE.

Ritu Jyoti

>> Thank you.

>> James, last year, we talked about the strategy. We saw answers kind of coming onto the scene. The strategy was seeing gen AI. We talked about that. Now, the goodness is here. You got the cloud, analytics cloud, and get the open data lake. You manage service. Standard formats are coming. What's it mean, all that strategy coming together? Explain why that's important.

>> Yeah. We often talk about this being the moment that Qlik has been built for its entire life. In our 30-year history, we've kind of been building an evolution of the platform, embracing all new technologies that are out there, but most importantly, working with our customers to understand what they need to do to get value. And value is created when you do things differently. Value is created when you drive transformation, when you drive change. And we've been listening to the market, listening to our customers, and then building out the platform to give us all the tools to help them do that. We announced answers last year. We delivered it in less than six months after we acquired the technology, but then we didn't stop. We continued to evolve it, and that's what it's all about. That transition from strategy to execution is all about listening, responding, doing things differently, and then executing on it.

>> Precision with trust was a big theme, I love that in there, and also access to data is a big one. More data makes the AI better. Great strategy now playing out with a lot of customer use cases. I think we have a lot of customers coming on theCUBE. Last night, I talked to a lot of the execs at Qlik, and the theme was very positive about where they're at. You're tracking the market. Gen AI is still just getting going. We're starting to see production workloads hit in the cloud and on-prem, the data is the crown jewel. Where's the growth? How are you tracking the market? Give us an overview of the market that Qlik sits in.

Ritu Jyoti

>> Yeah, yeah. So I think as James said that, and you heard it in the morning, they are in a very, very important place because they're not just doing the data readiness and the data intelligence and the data integration, but they also have a very, very powerful suite in the analytics market, and both the markets are going to be impacted by the agentic transformation. And gen AI was the step in the right direction because as I was mentioning this morning with gen AI, it was amplifying the human productivity. It was in the flow of the work and it was giving... Suppose an analyst was able to extract insights from five different sources and plug them together, gen AI could make them faster, but agentic AI is going to take it to the next level. And I'm super excited to hear what they're trying to do in the agentic era because I just attended a session, analyst session, and they talked about what's their vision. So I think they're in the right place at the right time, and I love the comment what he said is that they were preparing for this era. And there are very few companies that you can count on your fingers that they're very well prepared for this era. So I think they're very well suited.

Bob Laliberte

>> Yeah, I think that's something that I'm always looking at when there's a new technology shift, it's being able to look at organizations that have maybe more mature solutions than others. I've been impressed with what I've seen today with some of the things, the closed loop that you have, the feedback loop, the source, the ability to show all your sources and things like that, to build credibility with the technology. I'm wondering if you could comment a little bit on where you see Qlik as far as their AI maturity, the gen AI, the agentic AI as far... Because the thing that I always look at from the maturity models, it's always about look for those leaders so you can learn from them so you don't hit the same mistakes that you hit going through it. So I wonder if you could comment on where you see Qlik right now and as it pertains to their AI maturity for the market.

Ritu Jyoti

>> Yeah, great question. So I'll tell you something like I mentioned today in the morning that in a five-stage maturity model, most of the organizations in stages two and three, I have to tell you, the tech companies are in the stages four and five because they actually had the basis and the foundation, right? I mean, who could do it better in scale than them? But if you think about it, what I love about them is that how methodically they're kind of approaching it, right? They're not rushing it, but at the same time, just the way I was giving the example of a pharmaceutical organization, you have to be not sitting on the side and watching it all play. You have to integrate it, but the degree of autonomy has to increase with the trust technology and policy maturity. I think other things which I like is that when you know all these years, AI is nothing new. You and I, we all know that, but in the past, the focus was just on structured data. Then with unstructured data, with their acquisition and all of the augmentation, we started scratching the surface and unlocking the insights with gen AI. So they prepared well for that both organically and unorganically, so kudos on his strategy. And then now coming back to... When we are headed into agentic AI, very few people are thinking about it that it will all be about real time event driven. So think about their upcoming acquisition. So all of that is going to make them a very, very good center and foundation for all the different types of AI. Technically, AI is not going to work in silos. People for certain use cases, they're going to make use of gen AI. Some may be predictive AI, some maybe agentic AI, and we are going to crawl, walk, and run in the agentic AI maturity.

>> Yeah. I like how you bring up the acquisitions because I'm sure there's a lot more you guys are eyeing. Last night in front of the Qlik execs, James, I was getting the vibe and the word unlock comes up, unlock value. There's two conversations we're seeing in gen AI, platform engineering. Let's get up the platforms, the data lakes, all the abstract, the way the databases, and then the predictive sides, the business logic. So where's the unlocking going to be happening? Is there a platform first as the analytics come in here? Because the business logic and the domain expertise is on the analytics side because that's where the dashboards are, that's where the business is running. Now, you guys use the term decision engine from dashboard to decision engines, answers, ask a question, get an answer. This is kind of where gen AI shines. So talk about this unlocking value because if company doesn't do it, their competitors will. So the speed is going to go fast in this area. What's your thoughts reaction to where is that going to come from?

Ritu Jyoti

>> I think we've seen so many experiments with AI that now people are really focused on measuring a return, and they're trying to do that by embedding AI and agenting into the existing business process rather than trying to do it on an island. So it is closer to the business function. It's closer now, I think, to the teams that are working with it. But there was a reason that I kind of went before Drew in the keynote this morning, which is you kind of got to start with the business outcome, what problem are you trying to solve? And then you start asking the question, what data do I need? What infrastructure do I need to support that outcome? And that's essentially what we've been trying to do. The notion for me is this idea of data for AI. And so what do you need to do? What infrastructure you need to get data ready to support AI use cases, but then how do you use AI to support data? The idea of preparing data, driving access to data, lowering data literacy, driving AI literacy, all kind of factor into that. So there's no kind of silver bullet around value, but I think sort of we've now turned the market on its head a little bit.

>> Go ahead.

Bob Laliberte

>> On the roadmap, how do you see the priorities for you now that you see kind of some fruit coming off the tree, so to speak, you're starting to see some business value, again, domain expertise, the human in the loop, that's where you guys are playing. Congratulations. What's next? What are you eyeing on the roadmap? What's your to-do list?

Ritu Jyoti

>> Well, the first thing with any roadmap, and I'd spent nearly eight years as Qlik's chief product officer before I took on the strategy role, whenever you deliver any technology, the first thing you need to do is stop, make sure customers are using it, make sure customers are getting value and drive that adoption. So my message to our product teams will be, right, we've delivered it. It's coming. Now, we've got to turn that into real business value for customers, learn from that feedback and then adopt it. So we can always keep one eye on the future and where the market is going. Think about how we continue to expand across the value chain. But look, one of the things that sets Qlik apart is we're focused, right? We don't do computer games. We're not trying to do CRM. We're not trying to do an ERP system, right? We're focused on helping our customers get the data they need into the right place so that they can use it for AI and agentic use cases. That's it.

Bob Laliberte

>> Yeah. Now, that makes a lot of sense. And it was interesting, some of your customers today, I think the gentleman from Truist came out and said, "Start with your... What's your North Star?" So figuring out what it is you're trying to achieve, and I thought a lot of the demos that you did today did a really nice job of highlighting what the business outcomes and then backing into how that was collecting from all the different data sources. I thought there were some great demos in there around the zero code and being able to update to get additional information on the fly and things like that. So obviously, you're listening to your customers, you're trying to develop the products there. Are there any others than ones that we saw today that come to your mind that are particularly satisfying for you as someone driving the strategy, seeing it being fulfilled and delivered as an outcome for businesses?

>> Yeah. I think, for me, the use of the entire platform has been really something that has had an impact on how we've thought about the world and reflects a lot of the work that I've personally been doing with the team. We went from being a 30-year-old BI company to extending our capability across the value chain, but building a customer journey that allows them to see value and then adopt new technologies. The cloud enables that. Our capacity model enables that. We don't force a customer to do anything. We create a value reason for them to do it. That's a slightly longer game than trying to force the issue. When you see the returns in it, you see people like Tom coming up from Truist, and Tom's got a big team of folks here this week and how they look at it, how it's solving problems for them. That's key. I've just left our executive advisory board where there are 20 CIOs and CDOs that help us with this all the time, and it's seeing those pennies drop and then it get put into real production that I love.

Bob Laliberte

>> Great.

>> To talk about the numbers because I hear surveys and stats. I saw, I won't name the names, but a cloud company said, "Hey, this is an X percent adoption on the cloud," and I see an on-prem supplier has a high number too. You can't have too high numbers. So a lot of squinting through the numbers means is activity. There's POC. Some say POC purgatory. There's a gap between production workloads and gen AI than actually what's going on. So can you help us understand what that means? Is that accurate? Is there more adoption in certain places? Obviously, it's a great place to start when you know what you want. You're applying data too like James's answer there, but generally the macro level, what's the adoption and what's actually getting into production? What are some of the real numbers?

Ritu Jyoti

>> Yeah. So I think I'll agree with the point that it's pretty early days right now. And as a ballpark, if I would say there's only 20 to 22% of the adoption happening in production right now. There are lots of POCs and proof of values and concepts going on. The real pivot, the first two years, we saw very few industries and organizations who were able to move fast because they were better prepared with their foundations of data. They were better prepared with their foundations of governance and they're able to scale. So this is the year where you might have heard it loud and clear. This is the year when people are moving their experiments into production and proof of value in gen AI. But if you think about agentic AI, it's very, very low right now. There are very few people who are doing it, and I don't blame them because I think they'll be first movers and early adopters, but they're all mindful of risks and they have varied levels of autonomy, but they're not sitting on the side. Just the way Mike said, "Leaders don't wait, they act". So that's what we are seeing. But having said that, I did quote a number today that the value, the amount of spend that is happening on AI is extremely mind boggling. We are predicting that it's going to cross almost 816 billion by 2028. So people are being advised to fail fast, learn fast from the mistakes, focus on the high value use cases. And as he was saying and you were asking, figure out what is the outcome that you're after. I love to joke that you don't hire a CEO of a company and put that person to do an RPA automation job, a data entry job. So don't try to force feed. These technologies are phenomenal. They're nascent. They're expensive. You don't need to put it in the areas where... I will also tell you one another quick joke here. One CIO of a company, a very large company came to me and he said, "I'm very proud of my team. We ran a hackathon. We identified a hundred productivity use cases. Do you have a decision framework as to how can I prioritize that?" And I said, "With due respect to you, you'll be written off from the industry very soon because productivity gains will be table stakes, right?" So focus on what business outcomes you're after from your corporate strategy, what moves the needle, focus on those, focus on high value use cases. And I'm sure James must be also looking into there are a lot of greenfield opportunities where organizations can actually be. You have to be with the right safeguards and the guardrails and the policies, but move the needle in those areas faster. My favorite example for an agentic analyst role is that a revenue analyst can go and ask, how do I actually drive my company's revenue? What kind of a pricing strategy I have so that I can actually drive my company revenue by 10%? It's not there today, but it's coming. And I'm sure-

>> It's a question that could have an answer.

Ritu Jyoti

>> Exactly, exactly.

>> There's answers.

Ritu Jyoti

>> How can a fully autonomous revenue analyst can be working hand in hand with the marketing person and figure out what can be my marketing campaign tweaks to drive my revenue or improve my profitability or whatever? It's coming there, so.

Bob Laliberte

>> So I think those were really great examples. One of the things that I wanted to ask you, James, along those same lines, when you're thinking about driving those business outcomes, all the experience you have working with industries, multiple different industries, one of the things that I hear often when I'm talking to organizations because of the lack of AI resources that they have internally, is they're all looking for help in some form, whether it be a blueprint, something like that. From your strategy perspective, is that something you're considering, given all the experience you have, being able to start packaging things up of you need to get this data and this and put this together and helping to template a little bit to help accelerate the adoption for certain use cases?

Bob Laliberte

>> I think there's been a natural evolution of the type of services and engagement that has to sit alongside any technology project, but certainly around data analytics and AI capabilities. I mean, when I first started in the BI space, it was time and materials. It was implementation, right? Well, there's no really such thing as implementation anymore in a cloud world. There's some good configuration that has to happen. And I think as a result of that, we're adjusting our services to think about more advisory capabilities, working with the huge partner ecosystem, a lot of which is represented here, folks that have very specific expertise in very specific industries, in very specific geographies that can bring together the requirements around cloud, around data sovereignty and around business use cases. So it's really a shifting framework. I think blueprints are great. I think they can help ease the path to a use case and a value proposition, but they will only ever get so much of the work done for you. A lot of it has to be specific to your use case, specific to your need, and making sure the right skills that exist in your organization is key. And that's always been the biggest barrier to any BI project for years. I mean, data literacy is the thing that's held us back in the BI world for 20 years. And now, AI literacy I think will have the same impact if we don't address that as well.

>> And we have agents to help us do that. I got to say, I love this conversation. I got two analysts and a strategy czar here in theCUBE, so I have to kind of throw out the kind of strategy conversation we would have had five years ago. James, what's your TAM expansion strategy? But what's the TAM? And you guys would quote some numbers, but what's interesting was coming up here on theCUBE, here at Qlik, and other events is it's a modernization effort, not a shift, not a migration. So the migration's happening, but they turn into modernization. So it's not about TAM expansion. We might argue maybe this is more TAM, obviously numbers are huge, but it's a whole nother thing. Can you guys comment on... I'd love to get all your perspectives on this. It's not about the TAM because the market's there. There's demand, unlocking a value that's going to be a forcing function, be very competitive. What is the modernization with data look like? That's a big theme in the keynote, big theme for customers because whoever unlocks that value makes money. They do it faster, productivity everywhere. So what is the view from a research and market perspective and a product market fit conference? What's your reaction to that? Or do you... Is that off-

Ritu Jyoti

>> Maybe I'll go first and then you can respond. How's that? So you talked about the maturity model earlier this morning, and I think maturity models are great, but I think there's a danger with a maturity model that everybody just goes to the last thing. Everyone tries to just do the shiny object at the end and forget that actually there are so many use cases, the types of enterprises we are working with, the user personas, the different types of data, the different functions. You kind of have to be able to do all of it. So it's not a rush to the end. You've got to do all of the work, but we've got to help our customers support all of their use cases. And I think that creates a rising tide for what comes next. So for me, our ability to help customers no matter where they are in that journey, and then to help them get to that next step is kind of key. Don't force them to do anything but enable them to get to the value.

Ritu Jyoti

>> Yeah. Yeah. Well said, James. Thinking about what you just asked, John, I'm going to kind of spur up a little bit of a controversial commentary here because we have been talking about... When we spoke about gen ai, we said there were a lot of scenarios in which creating a marketing blog or doing a quick assistance and certain things, that definitely expanded the time to some extent. But when you think about agents, I think the one important thing we have to remember is that with agents, we are going to get digital labor. And the digital labor is not just going to invade into the software budget. So we, IDC, when we do our software forecast and all, we are actually going to look into how is this going to this time going to grow in terms of actually invading into the headcount budget because we are looking for efficiencies and the digital labor will not be just coming. So stay tuned. At some point in time, IDC is going to announce this, but this is going to be much broader than that.

>> Yeah, I love how you wrote the labor. I want to ask you just a side question that popped in my head. Is labor the right word? And that's more of like I see on an income statement, labor is an expense. Or is it more workforce? Are they the same thing? Is agents a digital workforce or digital labor? Is there nuance there? What's the-

Ritu Jyoti

>> So when the gen AI assistants came, people started using it as a digital worker, or RPAs were called as digital worker because they were not really kind of working as individual on its own. They were working hand in hand with the human. So the new terminology that has been floated is digital labor, just the human workforce or digital workforce. So the labor and workforce can be used interchangeably.

>> So it's seem the same thing?

Ritu Jyoti

>> Yeah, but essentially there's a nuance between digital worker and a digital workforce. Digital worker could be doing some parts of the work and not really kind of augmenting a human. There's a joke around that, whether we'll have to look into whether they get 401(k), whether they get retirement.

Bob Laliberte

>> Who manages them?

>> How old are they? They can get it until they're like 200 years old.

Ritu Jyoti

>> But management is a very important thing. And we'll see the rise of AI managers for all of these agents and all. So basically, this is going to be treated like just you had an army of your team members. You will actually be you looking at them as an extension of your team. So that's why we call it as a... And that budget is also going to kind of help drive the software TAM.

>> What do you think, Bob, on this whole TAM versus modernization? Are they two separate things? Are they one greater than the other? I mean, the TAM is huge, right, I mean?

Bob Laliberte

>> Obviously, there's so much around AI right now, gen AI, agentic AI and so forth. So the market for that is going to, absolutely, continue to grow, but as you said, it's going to be based on real value that organizations are getting. So it's going to take time, it's going to come in stages. You can't jump from stage one to stage five overnight. You've got to go through each of those stages, build... I call it time to comfort with the technology. You need to be able to spend time in there doing it. And I think that ability, once you have, that's what's going to allow you to move. Once you understand the technology, you're comfortable with it, you know how it works, that allows you to move to the next stage, get more business value. Again, hopefully, you're focusing on the business value quickly. So it then opens up the budget for more projects that leads to other growth, other opportunities to drive greater value possibly.

Ritu Jyoti

>> If I may add something quickly to that, it's one thing is to look into the different stages, but there are dimensions to those stages. You have to think about strategy, you have to think about people, you have to think about technology. You kind of teed up about is it happening on the public cloud? Is it happening in the edge? Is it happening on premises? What is your deployment scenarios? So there's a whole bunch of you are upskilling, reskilling. So all of that needs to be factored. And as James was saying, there's no starting point. Every customer, every use case will be a different journey, but ultimately, the goal is all about don't kind of go after just force fitting the technology, focus on the business outcomes that you're after.

>> I'd love to get your guys' thoughts on structural changes from strategy, because when you have strategy with execution, we're in a shift. Everyone kind of agrees. Everyone sees the shift here. There's always structural money-making opportunities, value. I love the unlocking value conversation. The business logic is key. Scale is great. Scale, speed, all good business logic. Is there new opportunities on the strategy roadmap? And do you see in the market new things emerging? Like for example, indirect channels are booming because they're close to the customer, a long tail of specialty partners. I've talked to a partner here in the hallway. They do AutoML and that's all they do, and they do it really great. They're a small partner, but they have a lot of customers, so they're operating. So how do you see that into the strategy? Because that's almost definitely an expansion of customer reach. So you mentioned where they are on their journey, but the channels are changing. What's your thoughts on engaging customers? What structural things are you eyeing that you're looking at the strategy? Okay, we got to make sure we cover that. Or is it relevant? Can you guys share your thoughts on new structural things?

Ritu Jyoti

>> Yeah. I think the ecosystem play around this is kind of key, right? I think we were thinking the same thing. Traditionally, the ecosystem around these types of technologies is it involved vendor, advisor, consultant, implementation, infrastructure providers. And I think that's all very, very relevant still. But certainly what's been emerging over the last couple of years, the role of the marketplace, the role of how the ecosystem and different components of technologies come together. And I think in the agentic world, that's just going to become much, much more important.

>> More engineering, more integration?

>> Well, as you start to think about how agents interact with each other across different enterprise platforms, that's going to change the nature of that kind of collaborative landscape a little bit. I think it'll be very interesting to see how that plays out in some of the co-petition landscape we see in the vendor community, the role that the SIs play in that. I think that's going to be really interesting to watch.

Ritu Jyoti

>> Yeah, I couldn't agree more on that. I mean, in the agentic era, we are just scratching the surface right now. But essentially, more and more of these agents will be available in the marketplaces. Some of them will be embedded in the ISV solutions or even in their solutions, for example, but they cannot... It's going to be a very complicated ecosystem, and they all have to work with each other. So we are hearing about all the protocols for interoperability, whether it is MCP or A2A or A2C or whatever it comes up, but that standardization needs to be done because in one way, we saw that the bot sprawl happened, right? But there's now upcoming agent sprawl, so ecosystem, coexistence, interoperability. The other thing, which James kind of alluded to, is that there was a traditional method of actually getting things done. You get in professional services company. Professional services company is poised for massive amount of disruption. When gen AI happened, they were actually being brought in to kind of bring asset based, bring those assets and get that thing. Now, they'll all be coming into the... They'll be playing a role in the business, value engineering, but then later on, they'll all be transitioning a lot more into the software side.

>> I saw a study that McKinsey has all their advice, all in a chatbot over the story. So a lot of change. Guys, thanks so much for coming on theCUBE. Really appreciate the insight. Great to have the research analyst and head of strategy as we connect the dots. I mean, the customers are on multiple journeys, meet them where they are. Good strategy. I guess my final question is, what's next? Share a little bit of peek into the mind of the strategic thinking, Qlik, more acquisitions, organic growth, new-

Bob Laliberte

>> Well, look, all of those things are possible, but look, you know me well enough to know that I'm not going to give you a great answer to that. Look, we're really excited about where we are right now. I think we're only just scratching the surface in terms of the use cases that are there. There's so much opportunity in the market, even for more traditional use cases, right? There's a lot of value still to be created from that. And I think all of the investment and excitement we've seen around gen AI and agents, that's great, but it's creating just a great wave of investment across the entire space. Whether that's learning, whether that's skills development and technology. Our job is to continue to be part of that ecosystem, to continue to work with partners like IDC as we understand what's happening in the market and put ourselves in the right place to help customers get where they need to get to.

>> James, thanks for coming. Thanks for sharing the data and giving them a good grade. If you can give a letter grade, B+, A-, what would you say?

Ritu Jyoti

>> A+.

>> A+, okay. After you got that compliment, that's a sandbag right there. Thanks for coming on theCUBE. Of course, bringing you all the data here in theCUBE, I'm John Furrier, Bob Laliberte, getting the strategy, connecting the dots. We're putting the puzzle together for you. Thanks for watching.