Welcome back to the Tech Talks Daily podcast, where today I'm recording at the Qlik Connect event in Florida. I can feel a shift happening in real time over here because this isn't just another event focused on what AI might become. For me, this is where the conversation is turning into what it actually takes to make it work for organizations. Because if you've spent any time speaking with business leaders over the past year, you'll know the story. There's plenty of pilots, plenty of proof of concepts, but very few examples of AI delivering consistent, measurable value at scale. So what I want to explore here this week is what is it that's holding organizations back? Is it the technology? Is it the models? Or is it something more fundamental going on beneath the surface? And today I'm joined on the show floor by Qlik CEO Mike Capone. He's a leader who spent decades working at the intersection of data analytics and enterprise transformation. And in this conversation, we're going to get into the reality behind the headlines. We'll explore why so many companies are still stuck in what he calls a 'ready, fire, aim' approach to AI and why the real bottleneck has nothing to do with models, and why getting the data foundation right is now the difference between progress and failure. And I also want to explore agentic AI. What does it actually mean in a very real business context? Not as the buzzword, but as something that can drive business decisions and most importantly actions across an organization, and the very real risks that come with getting it wrong. And perhaps most importantly, we'll look at what separates companies that will successfully operationalize AI over the next 12 to 25 months from those that remain stuck in pilot mode. So if you or your organization is trying to cut through the noise and just understand what it takes to move from AI ambition to operational reality, then I'm sure you'll get some value from our conversation today. But enough from me. Let me beam your ears directly to the show floor at Qlik Connect here in Florida, where you can sit down with myself and Mike Capone, CEO at Qlik.
So I'm here on the show floor at Qlik Connect in Florida with Qlik CEO Mike Capone. Mike, what excites you most about this event every year?
This is my favorite event every year. What excites me the most is the energy and the excitement we get from our customers and our partners. This year in particular, travel hasn't been easy. It hasn't been an easy time with all the things going on in the world for people to make the effort to get here. And yet so many customers came, and it's because they really want to understand what we're doing and how we can partner with them on this AI journey they're all going through.
And I think one of the big themes this year is about moving from AI ambition to operational reality. So when you look at your customers today and take all those conversations that you must be having with people all around the world, what is the single biggest reason that they're struggling to make that leap that they all want to do?
Well, look, everybody's experimenting. So there's a lot of pilots, there's a lot of proof of concepts. People are struggling to create value, to really operationalize AI into every aspect of their business and drive business success. I hear it over and over again. And the reason is that everybody wants to start at the end. They want to plug in a large language model, they just want to press the easy button, and that's not how this all works. There's foundational work that needs to get built before you can embark on a really successful at-scale AI journey. And that foundation has to be built on data, data governance, data trust, data quality. And those are obviously things that we can help our customers with. So I think that's why you see so much excitement here at the conference.
And another big theme is Agentic. It's clearly the unifying theme across the announcements from analytics to data engineering. But what does agentic actually mean in real enterprise workflow, beyond just the label and the words everyone's talking about this year?
You know, it's like AI. AI has been around for a long time. We've had sort of a revolution around generative AI and that's really exploded. But I was doing AI programming in college 40 years ago. And look, agentic's been around for a while too. It just wasn't called that. It was called automation. We've had the ability to get insights and automate them for quite some time now, for years. But you're right, agentic has now become a hot topic. And look, it's the same concept. It's the exact same thing, which is generating insights from data across the enterprise, getting that insight, making a decision, and automatically doing something with it. It's very powerful. It can generate a lot of productivity. It can generate a lot of business value if you get it right. But you can also create a lot of problems if you get it wrong because the foundation is wrong. So think about agents making decisions based on data that's incorrect or hasn't been governed or curated. That's a huge mistake. So yes, agentic is coming. Yes, it's going to be great value, but you have to get the foundation right.
Hundred percent. And there's also a strong message here that the real bottleneck is not the AI models, but the data that sits underneath them. And one of the things I've heard at a lot of tech conferences: no data, no AI. But where do you see most organizations breaking down today when it comes to delivering data that AI can actually rely on?
Well, there's a couple of things. First of all, agentic AI doesn't like walled gardens. So I think the big problem right now is that data is locked up in operational systems. And worse, to compound that issue, you've got some vendors, some really large vendors, who are actually pushing that agenda of, 'Give us, put all your data into this one system or this one platform, lock it in, and we'll help you get there.' And that's contradictory to the nature of agents. Agents want to run free. They want to access data from across your entire enterprise. I always tell customers, look, if you give all your data to somebody, you're not a customer, you're a hostage at that point. So the reality is what we encourage is freedom. And by freedom, I mean your data has to be accessible across the enterprise. Platforms have to be open. Your operational systems are going to do their thing, they're going to run your business for you, but they have to be able to let their data be open to AI and agentic. And that's what we do at Qlik. We harness data from across a disparate set of systems, everywhere from on-premise to the mainframes to the cloud, basically any source anywhere, and we curate that, we govern it, we move it, we integrate it, and we catalog it, and then we get it ready so customers can then have faith that the data they're putting into their AI models is going to be accurate and they can trust it.
And you're also introducing new concepts this year like trust score, data contracts, and service levels. How do you convince a business leader that might be listening that trust in data should be treated like any other operational metric, not just a technical concern? I would imagine it's quite a tricky balance sometimes.
Well, again, if you're going to make the leap to agentic, the data going into those models that then feed agentic has to be good. I used an example with our partner organization today. It's a funny one, but before I left for this conference, I was home in New Jersey and I asked Alexa, 'What is the weather today?' And she came back and said, 'It's 36 degrees today, will be partly sunny with a high of 58 and a low of 48.' But it was 36 degrees, so it was wrong. That data came from somewhere, there was no context, it was some weather report somewhere and it spit that out, and it didn't even have the wherewithal to come back and say, 'Yeah, I just gave you a contradictory answer.' Do you want that to be the thing that feeds your system? By the way, lots of businesses, the daily temperature is actually a really important metric. They make decisions on holding events, they make decisions on running certain machinery because under a certain temperature, certain machinery can fail based on the fluids they run on getting too thick. So these are important questions. So yes, having that trust in the data, and then this other word that's used a lot but is really, really important, is context. Data in and of itself without context is not meaningful in a world of AI. And so what Qlik has been doing for 30 years, our analytics platform, I know you've been coming to our conferences for a while now, is that context. We help customers not only harness data, not only analyze data, but provide context around that data. It is critical for AI to have that context.
It really is. And a lot of vendors are still focused on generating better answers. But what I love about what you're doing here is you're pushing towards connecting insight directly into action. So how hard is it in reality to move from insights to execution inside some of the largest organizations in the world?
There's work to be done. This is not something you snap your fingers and do, but it can be done. You have to wire it correctly from the start. You have to build your foundation understanding where you're taking it, which is ultimately to decision-making authority by autonomous agents. So now, businesses do this today. Businesses, they have supply chain systems that automatically order things, but what they do is they make sure that the data and underlying transactions that are feeding it are audited. They're in shape for this type of work. Now, just take that to a whole new level. And what we help our customers do, both with our technology but also we have advisory services where we can help customers kind of map out their AI journey. Where are you today? Where do you want to be? And you start with the answer. You don't start with the technology. You start with what business outcome are you trying to drive, and very often it is, yeah, faster decision-making, autonomous decision-making, taking humans out of the loop so that we can respond to customers faster. Excellent. Okay, what data do you rely on to do that? How reliable is that data? How can we make that data more reliable? How can we merge it with other sources of data to improve the context of it? That's what we do. But if you map it out correctly and you think about it right from the start, the rest of your life is going to be a lot easier.
