Qlik Connect 2026 | James Fisher, Qlik

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James Fisher

Chief Strategy Officer

Qlik

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James Fisher, Qlik

James Fisher

Chief Strategy Officer Qlik

In this interview from Qlik Connect, James Fisher, chief strategy officer of Qlik, joins theCUBE Research's Rebecca Knight and Rob Strechay to discuss how enterprises are moving from blanket AI mandates to deliberate, data-driven decision intelligence. Fisher explains that the enterprise AI conversation has matured from top-down directives to a focused search for meaningful use cases grounded in quality data. He underscores that trusted, governed data isn't just a technical requirement — it's the catalyst that prompts individuals to act differently, take info... Read more

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How are companies currently thinking about AI architecture (in 2026)? add

What role do governed data, trust, and individual adoption of AI play in driving transformation and creating value? add

How should enterprises evolve their systems, infrastructure, and data practices to support AI, create reusable data products (with a semantic layer), and quickly deliver trusted, value-driving outcomes? add

How can trusted, contextual, and governed data be reframed as an accelerator rather than an impediment to organizations moving forward with AI? add

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James Fisher, Qlik

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Rebecca Knight

>> Hello everyone and welcome back to the Cube's live coverage of ClickConnect 2026 here in Orlando, Florida. I'm your host, Rebecca Knight alongside Rob Strechay, the analyst. Rob, we're talking data strategy.

Rob Strechay

>> I love this.

Rebecca Knight

>> When do we not talk data strategy?

Rob Strechay

>> Well, I think this is the root of... Data is the root of AI. You can't do it without trusted and quality data. You can't really do AI effectively. And then you have to have all the things that go around it to help you govern it and make it trusted AI going forward. So this is so much fun.

Rebecca Knight

>> Who better to talk about how to do AI effectively than our next guest? And that is James Fescher, Chief Strategy Officer at Qlik. Thank you so much for coming on the show, James.

James Fisher

>> Thank you for having me.

Rebecca Knight

>> So why don't we start big picture and have you talk a little bit about how companies are thinking about AI architecture right now in 2026?

James Fisher

>> Yeah. So look, we've been through a period of time where there's been a lot of top down directives around AI. Just do AI, find a solution. AI is the answer before you've even asked the question. There's now been a shift, right? So if I stay away from the technical side of the architecture, but put it in business terms, what we're really talking about now is finding meaningful use cases, use cases that can drive value from AI for an organization that can drive change, that can drive transformation. And as you were just discussing, what is the root of that? It's the data. It's the magic that powers those AI use cases. And we've seen this big shift from trying to just apply AI to a problem, to thinking about what is needed architecturally to bring that data together at the right latency, in the right format, and deliver that in a way that it's consumable to AI applications. And I think that's a really positive shift that we've seen over the last couple of years.

Rob Strechay

>> Yeah. I mean, I loved the message coming off the main stage this morning in the keynote. I think that one of the things that I've been saying for, it feels like months now, it's probably only been weeks, but this changes so fast is really it's not about the models. The models are going to change and you're going to want to plug in this model, plug in this model, this different assistant to help you. It's really around the data and the evolution and really having governed data as a foundation. As organizations are really worried about their AI spend, how do you show them the value of having that governed data foundation that will help them more so than chasing a model or chasing something else? How do you communicate that?

James Fisher

>> Well, I think you're exactly right. The pace of change, the level of investment, what's coming out of the labs on an almost weekly, daily basis, is such that we'll never keep pace. If you don't build an architecture that's responsive and can work in that changing environment, then you're never going to be in the game in the first place. But look, I've been on stage here at ClickConnect for a number of years now. We talked about the fact that it is about doing something different. It is about that change is what drives transformation, and it's that act of doing something different that creates the value. So back to that notion of a governed set of data that can support a use case that people will actually use, that people will actually engage with. Well, if they do that and they trust it, then they'll do that thing differently. Then they'll take that action, then they'll drive that transformation, then they'll create that value. So again, there's a lot of technical elements we could talk about, around governance, around lineage and all of those great things, and they're super important. But it really, for me, comes down to the individual using AI to do something different and embracing that. And that's really the power of where this can take us in the future, I think.

Rebecca Knight

>> Right. Because yes, having the governed data, it's all about having the individual be able to trust what the data is saying and the context around the data, and then being able to take action based on that. And it's a mindset that you're describing. It's not necessarily, as you say, there's the technical aspect, but there's also the individual worker who is going to be using that to move the needle within his or her organization.

James Fisher

>> Yeah. I mean, look, I think I was here on the Cuba event a few years ago, maybe 10 plus years ago, and the conversation was about a weather app, right? And the weather app tells you, this famously happened to the BBC weather app a couple of years ago. And it said there was like hurricane force winds in July. It was a data problem and the app was serving up the wrong data and everybody looked at it and nobody trusted it because they have context. They understand what the weather should be doing in July. And it's that understanding. It's that context that we as humans bring, but we can build into AI to help us understand what it's actually telling us to help make it more consumable. And a few years ago, I never thought I'd use that analogy again, but here we are.

Rebecca Knight

>> Right, right, right.

Rob Strechay

>> And it's wonderful weather outside here in Orlando.

Rebecca Knight

>> In Orlando.

Rob Strechay

>> So I would say that it's a great analogy. And I think it's a good analogy when you start to look at it because I think things like data fragmentation is real, right? And I think a lot of the talk about how you get across silos, how you leave it in place, things like iceberg and other things, like you said, what are some of the architectural decisions that determine the outcome of AI for an organization, whether it's going to be if it's going to compound over time or if it's going to be fragmented? What are some of the things that you see that people should think about?

James Fisher

>> Yeah. I think people need to understand what our enterprise systems are being used, where that data is coming from. I think we like to think with the pace of innovation around AI that the enterprise is always at the forefront. I've talked to customers only last week that are still working in mainframes, talked to customers only last week that are still distributing PDF reports. So there's an evolution that we have to go through in terms of how we build our infrastructure around AI, how we identify those use cases, deliver value quickly, and then drive trust from it. All of those things will, I think, will compound over time and create a unique opportunity where data sets get reused. As you solve one problem, you'll go onto the next, and that will create even more momentum for the organization.

Rob Strechay

>> Yeah. Like the whole idea of data products and being able to bring the semantic layer to the data products and things like that, so it becomes reusable and you're communicating in the same language.

James Fisher

>> Yeah. It's about understanding, I think, what the use case and the user of that data is. I always talk about the notion of data products as bringing the ideas of product management to a data set. Who is the consumer? What problems are they trying to solve? Then you create that product that then enables value for them. But if you create it for one group, then someone else is going to find a use case for it in the future. And I think that's one of the big investments around reusability that will become increasingly important.

Rebecca Knight

>> There's a big message here at ClickConnect around avoiding lock-in. I think your CEO was talking about how if you're not a customer, you're a hostage if you have a lot of parameters around these things. How do you advise companies balance that need between flexibility and adaptability with stability and governance?

James Fisher

>> Yeah. There's been this notion of the data gravity into singular enterprise platforms for a number of years, right? It started with, dare I say it, some of the big ERP vendors, right? I'm going to get one single instance of my ERP and it's going to solve all of my problems and I'm going to get one single instance of a data warehouse or a data lake and it's going to solve all of my problems. Well, look, you heard from Ingersoll Rand this morning on the main stage, right? That is a highly acquisitive organization. They're constantly acquiring new companies, bringing those into their infrastructure. They can't possibly bring all of that into one single ERP instance, right? So that ability to span and work across what has always been, and I think will continue to be a very fragmented landscape, but also work in an environment where to the point earlier, we simply don't know what AI is going to look like tomorrow, next week, let alone in a couple of years. So you've got to build that flexibility, that adaptability into the infrastructure to future-proof your investments long term.

Rob Strechay

>> So one of the things that I hear from CIOs and CDOs and things of that nature, the C-suite, when they're looking at this, they go, "One of our big challenges is when we bring up governance, people think we need to slow down and it's going to slow everything down." How do you see and reframe trusted contextual and govern data as an accelerator, not an impediment to people moving forward with AI?

James Fisher

>> I'm a big fan of the phrase go slower to go faster. And I think that applies really well in this case, right? By creating and taking the time to build that foundation, to think about where it's going to be used, how it's going to be applied, just that little step, that little extra time you take there will provide exponential benefits long term for all of the reasons we've talked about, whether that's performance of the AI application, whether that's performance of the agent, but then you start to think about the evolution of agents longer term and the autonomy of an agent, its ability to access and generate its own data within that same governed structure and framework. So that I think is another case where the snowball rolling down the hill will grain more and more momentum as we go.

Rebecca Knight

>> So what you were just saying about go slower to move faster. I mean, I know it's a Malcolm Gladwell Maxim two, but it's a little tricky right now with, there's so much short-termism and making sure you hit those numbers and you are investing in AI and that the story being told to Wall Street is that you're investing in AI. Right now, as a lot of leaders are being a little more cautious about their AI investment, what signals are they looking for before they commit and what signals do you think that they might be missing that they should be paying and more attention to?

James Fisher

>> Yeah. I think a lot of people are just sitting back and looking at the initial investments they made over the last couple of years and are trying to find the value from that and trying to think, well, what return did I get on all of that money that I've just spent? You've got a PO in front of me for yet another AI project and how am I going to deliver value from that? And I think for me, that's one of the questions I've been asking a lot of customers here and will continue to ask over the next couple of days is, what is your most valuable Qlik application today? What is that diamond platinum layer in the medallion architecture that exists in click today and wouldn't that be useful for AI? So it is a lot about seeing that value, seeing that value come to live and then knowing that people can adopt and use it. There's another big part of this as well that I think is important is there is about the investment, there is about the need to drive a return, a need to demonstrate that you're keeping up with your peers in the market, but there's also the governance, there's also the policy around the use of AI, the protection of the data that sits within that environment that's equally important in the discussion. And I think people are trying to figure that side of it out as well.

Rob Strechay

>> So kind of on top of that is the whole idea of being flexible, right? And how do you bring flexibility as we were talking about multi-model, multi-assistant, multi-cloud, how do you see organizations really, I guess, making sure that they don't feel locked into yesterday's decision?

James Fisher

>> So I think there's, again, I talked about companies producing PDF reports earlier. One of the things that Click that we've always done is have this notion that you can consume an insight in any way that's relevant to you as a user. So if you want to look at a PDF report, great. I'm not going to stop you doing that. If you want to do that through a Copilot or a generative AI assistant or an agent, great, you can do that. So this notion of maturity that Gartner have talked about a lot, you move from prescriptive to diagnostic, descriptive, predictive, all of these different things. And it always implies you end up at the last shiny object, doing the coolest thing at the end of that spectrum. And actually, in reality, enterprise customers have to solve all of these problems for a really wide set of users in their organization. And I think that's a really key thing. Our job is to help you consume wherever you need to consume.

Rebecca Knight

>> So I know one of the issues that you're very passionate about both professionally and personally is data literacy and the opportunity gap that that presents for people who maybe don't know how to work with AI or aren't given opportunities to work with AI. Can you tell us a little bit about what your fears are and what challenges you see ahead for-

James Fisher

>> I think we have an incredible opportunity and I'm super excited about everything that we see going on here at the show and generally within the industry right now. But for a number of years, the thing that held value back was people's confidence in working with data. And we've started to have that conversation around people's confidence of working with AI. And they're really important dialogues. The question starts to become what happens to those organizations that for whatever reason can't invest. Those individuals that don't have access to education, that can't learn how to use AI for their own personal productivity or in their workplace. So there's an opportunity gap that potentially starts to evolve. So while we're all worrying about data infrastructures and building agents and the cost of deployment of those, I think it's always important we understand about the user, about the individual that's working with it and making sure that we not only democratize access to AI, but we democratize the value that can come from it. And I think that's going to be an increasingly important part of the conversation as we move forward. And as you say, it's something I'm super passionate about.

Rob Strechay

>> That's great. I think as we look towards next year and as we're at ClickConnect 2027, what do you hope that we can be talking about next year?

James Fisher

>> Well, look, I think I was asked a similar question at the end of the Cube last year. I talked about seeing customers using the capabilities that we've talked about, seeing those customers engaging with the release of Qlik Answers. Today we talked about a whole host of really exciting capabilities around new agents that we're delivering, providing access to that tooling for our customers that are on premise. So for me, it's about continued adoption. I'm a product guy at heart, so somebody using the capabilities, delivering value from it, telling me what works, telling me what doesn't work, that's what makes us better and that's what I'm most excited about.

Rebecca Knight

>> Two product guys at heart.

James Fisher

>> Yeah.

Rob Strechay

>> Absolutely.

James Fisher

>> I think you get it.

Rob Strechay

>> I love it.

James Fisher

>> Exactly. Well, James, a real pleasure having you on the show again.Thanks so much.

Rob Strechay

>> Thank you so much. I'm Rebecca Knight for Rob Strechay. Stay tuned for more of the Cube's live coverage of ClickConnect 2026. You're watching The Cube, the leader in enterprise tech news and analysis.