Qlik Unveils AI-Driven Data Lakehouse and Agentic Capabilities at Qlik Connect 2025

Qlik's clarity playbook for trusted, agentic AI - SiliconANGLE

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. The launch of a fully managed, open lakehouse within Qlik Cloud represents a bold step toward democratizing complex AI workloads, offering real-time interoperability, cost efficiency and the ability to query broad datasets without sacrificing control, according to Furrier and Laliberte. Meanwhile, Qlik's agent-powered cloud analytics reflect a deeper aim: closing the gap between insight and action. "These discovery agents really become the key because a lot of the stuff that saves time and money is getting those little answers," Furrier said. "A lot of the interactions that were once queries to databases in a warehouse are now agentic. Really, I think the cloud analytics with answers in the lakehouse with a broader data set, really is the magic here." During the event, Furrier and Laliberte talked with Qlik executives and industry voices from Truist Bank, the Q36.5 Pro Cycling Team and International Data Corp., among others. They discussed how Qlik's lakehouse architecture, AI agents and platform integration reflect a larger industry pivot -- from data complexity to agentic clarity. (* Disclosure below.) Qlik's AI strategy didn't happen overnight. Years before generative AI seized headlines, the company began shaping an end-to-end data platform to support the shift, according to Mike Capone (pictured), chief executive officer at Qlik. With 14 acquisitions and a foundation of governance, real-world integration and customer confidence, Qlik had positioned itself to meet enterprise AI demands before many competitors grasped what was coming. "A number of years ago, we set out to build this end-to-end platform," Capone said. "We saw this AI thing coming. We saw this wave of AI, but we knew the key to it was going to be data. Data, data quality, data governance [and] trust. We talk about trust a lot ... all the innovation that you're talking about was always in our head ... and not only did we get there, but I think we even got a little bit ahead." The company's foresight now supports customers struggling to make AI cost-effective. Capone's perspective also reflects his experience on the other side of the table. As a former chief information officer, he viewed vendor relationships through the lens of accountability, not promises. Today, as CEO, he carries that mindset into every partnership Qlik forges. "You've got to get in the bed with me, right?" Capone said. "You can't win and drive your Ferrari up because you sold me a lot of software if I didn't get my job done as CTO or CIO." Qlik's ability to execute on its AI strategy rests on more than platform hype or trend adoption. The company focuses on unifying data across environments and delivering results that users can understand and act on instantly, according to Martin Tombs, vice president, global go-to-market for analytics and field chief technology officer, EMEA, at Qlik. That mission includes handling everything from governance to visualization, designed around how different types of users consume information. "What Qlik is amazing at doing is getting all of that data and bringing it into one place," Tombs said. "Then, the next few steps that are one of the most important things we think of, and that is governing it and making sure the quality of that data is accurate." That data foundation supports a broader shift in Qlik's platform architecture, built to empower agents -- not just humans -- to drive decisions. Agentic AI isn't a future plan, but an embedded framework designed to act on the "so what" factor of modern analytics, according to Tombs. "We've just rolled out our agentic framework behind the scenes," Tombs said. "The agentic framework that we're rolling out with [Qlik] Answers -- structured and unstructured -- people can now get the outcome. We can now bring it together and get accurate answers, not the ChatGPT world of, 'Well, what it might come back with ... is that right or not?' This is accurate answers for organizations." Truist Bank's AI-forward mindset hinges on smart integration and eliminating friction. Created through the merger of two regional institutions, the bank faced the complex challenge of unifying disparate systems while upgrading its infrastructure, according to Harveer Singh, chief data officer at Truist. With Qlik and Talend at the core and workloads now live in Snowflake, Truist moved from what Singh calls "modern-legacy" to "modern-modern," shaving years off a transformation that once seemed daunting. "It was the speed at which we want to execute. It was the key," Singh said. "[If I were] trying to build this myself, it'll take three years, and by [that] time, something else will come in. This is where Qlik and Talend ... came in together. It's been a great nine-to-12 months ... and we've already made things live in production. Six months ago, it wasn't there." The bank's pursuit of agentic AI -- a model driven by autonomy, responsiveness and trust -- has prompted a laser focus on data access and simplification. Instead of chasing buzzwords such as "real-time" or "batch," Singh centers Truist's data strategy on "just-in-time" actionability. That mindset drives his platform philosophy, which emphasizes streamlined architecture and faster access over complexity for its own sake. "Platform is a key, and again, the more you have, [the] more moving parts," Singh said. "My goal is to eliminate as many moving parts as possible [and] keep a simplified architecture, because simplified architecture is easy to maintain, more cost-effective [for] total cost of ownership. And if the data doesn't move that much, it means that you're relatively able to access it and democratize it much faster." The Q36.5 Pro Cycling Team treats data as a competitive edge, using Qlik dashboards to blend biometric, equipment and logistical inputs into real-time decisions. The company's strategy revolves around three pillars: Athlete performance, equipment optimization and tactical decisioning, and each is powered by internal and external data, according to Adam Nunn, digital strategist at Q36.5. The team also feeds unstructured insights, such as scribbled notes and photos, into Qlik's platform to inform daily race strategies and streamline operations across a 200-day global schedule. "With sport these days, the marginal gains are just unheard of," Nunn said. "Everything is just getting smaller and smaller. We partner with Qlik, and we use a lot of the data that we gather from all places. Sometimes it's even public, and the other teams don't even know it. We allocate it onto a dashboard ... to minimize that marginal gain, it's just crucial." Qlik also underpins the team's recruiting and nutrition strategies through external data integration and custom scoring metrics. These allow Q36.5 to identify undervalued riders in junior leagues and optimize rider diets by syncing app-tracked nutrition data with coaching plans, according to Nunn. With agentic AI in its sights, the team hopes to evolve its model to automate decision support and expand individual empowerment across culturally diverse personnel. "We probably rank around 20th in terms of money earning, so we have to be smart on how to get the best riders," Nunn said. "We filter age [and] contracts' end, and then we start seeing which points they scored at these random smaller junior races ... and suddenly I can call my boss and say, 'Doug, these five riders are good riders. They're hungry for a contract, and they want to race in the biggest league.'" Qlik is strategically positioned for the rise of agentic AI thanks to its data readiness, unstructured data integration and analytics tooling -- capabilities that form a strong foundation for real-time, event-driven systems, according to Ritu Jyoti, general manager and group vice president for AI, automation, data and analytics at International Data Corp. Jyoti views agentic AI as the next maturity phase beyond generative AI, pushing past assistive tools toward autonomous, decision-shaping systems. "Gen AI could make [analysts] faster, but agentic AI is going to take it to the next level," Jyoti said. "AI is not going to work in silos. Some may be predictive AI, some may be agentic AI, and we are going to crawl, walk and run in the agentic AI maturity." Value realization is increasingly tied to business integration, where agentic AI must be embedded in core workflows, not bolted on, according to Jyoti. This reversal of the traditional data pipeline is key to long-term value realization. "[You've] ... got to start with the business outcome, 'What problem are you trying to solve?'" she said. "Then, you start asking the question, 'What data do I need? What infrastructure do I need to support that outcome?' That's essentially what we've been trying to do ... 'What infrastructure do you need to get data ready to support AI use cases, but then, how do you use AI to support data?'" While gen AI is moving steadily toward production in some sectors, adoption of agentic AI remains limited to early movers, according to Jyoti. Still, she added that the market shift shouldn't be underestimated, and leaders should focus on high-value use cases that align with corporate strategy. "This is the year when people are moving their experiments into production," Jyoti said. "But if you think about agentic AI, it's very, very low right now. 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 pricing strategy [should] I have so that I can actually drive my company revenue by 10%?' It's not here today, but it's coming." Agentic AI's advancement depends on infrastructure and how organizations reimagine collaboration, skill development and decision-making across teams. That's where the Qlik AI Advisory Council comes in. Working closely with the company to provide strategic guidance on responsible AI adoption, the Council focuses on establishing foundational safeguards, shaping governance best practices and helping Qlik's customers prepare for real-world deployment. "We cover ethics, geopolitics [and] science as well as how to interact with the business community and the applied applications of AI right now," said Nina Schick, founder and chief executive officer at Tamang Ventures Ltd. and AI Advisory Council Member at Qlik. "I think that [it's] something that all business leaders are starting to understand: You cannot view AI and its development in isolation, just as ... a business transformation process. It's something far more profound than that." The Council highlights a workforce readiness gap that mirrors the slow progression from experimentation to production. This lag underscores the need for companies to align internal culture and talent development with the accelerating demands of intelligent automation. Agentic AI is not just a technical shift, but a strategic imperative that must be integrated across ecosystems and global workflows. "The organizations that are really thriving are the ones that are implementing the right programs so that they prepare their workforce to really integrate the technology within their workstreams," said Kelly Forbes, president and executive director of the AI Asia Pacific Institute and AI advisory council member at Qlik. "I think that only bringing in technology without doing the preparation is probably not going to see the full benefit here." Interdisciplinary collaboration also plays a critical role in architecting truly agentic systems that orchestrate meaningful results across business units. Successful AI adoption depends on context-specific literacy and breaking down silos between technologists, policymakers and domain experts, according to Council member Dr. Rumman Chowdhury, chief executive officer and co-founder at Humane Intelligence. "We are, in a sense, looking for ways to make it meaningful in use," she said. "What we haven't really thought through, and this is really the human in the loop part of it, is how to architect and how to mastermind this massive amount of digital transformation. To get that, we can't just have programmers in the room." From cloud-native analytics to real-time agent collaboration, Qlik Connect 2025 delivered a sweeping view of enterprise AI evolution. TheCUBE's coverage featured a diverse lineup of technologists and thought leaders helping shape Qlik's journey into the agentic AI era. Here are a few other insightful, engaging conversations to check out:

Qlik Launches Cloud Data Lakehouse, AI Agentic Capabilities

At this week's Qlik Connect 2025 event the analytics, data integration and AI tech company expanded its product portfolio with an eye to providing partners and customers with more effective ways to prepare data for AI tasks and build analytical insights and AI outcomes into business workflows. Qlik has expanded its portfolio of data integration, management and analytics offerings, unveiling this week a fully managed data lakehouse service based on the Apache Iceberg standard that the company says offers faster query performance and lower infrastructure costs to meet the demands of enterprise-scale AI and data analytics tasks. The Qlik Open Lakehouse topped the list of new products and technologies unveiled at the company's Qlik Connect 2025 conference this week in Orlando. The company also debuted new advanced agentic AI capabilities for the Qlik Cloud platform and new embedded AI functionality in its Qlik Cloud Analytics service. The new software and services are intended to help bridge what Qlik CEO Mike Capone (pictured) calls "the AI activation gap" by helping businesses and organizations more effectively collect and prepare data for AI and data analytics workloads and build workflows that act on AI results and analytical insights. [Related: Meeting The Data Needs Of The AI World: The 2025 CRN Big Data 100] "There's a lot of hype around AI. A year and a half ago everybody was talking about this sea change that was going to happen," Capone said in an interview with CRN prior to Qlik Connect. "But look, let's face it, most companies haven't really realized much of a return on their AI investments to date. A lot of companies are experiencing significant costs associated with their AI investments. And there's actually a reset going on right now." "And so our view is, we're here to help you get the value out of AI. And our technology does that by really getting the data harnessed and ready for AI with our data integration platform. Then our technology allows you to get insights from analytics and AI. But then, more importantly, take action, activate your insights, put them into the workflow, into the day-to-day lives of everybody who's using it, and do that all at scale in real time," Capone said. Qlik Open Lakehouse is a fully managed data lakehouse system, built into the Qlik Talend Cloud, that delivers real-time data ingestion at enterprise scale (millions of records per second) from hundreds of sources. Qlik says the lakehouse provides 2.5x-to-5x faster query performance and up to 50 percent lower data storage infrastructure costs. Open Lakehouse is based on Apache Iceberg - an open-source, high-performance data table format designed for large-scale datasets within data lakes. That provides the data lakehouse with fully automated optimization capabilities including data compaction, clustering and pruning. The lakehouse also supports a number of Iceberg-compatible data processing engines including Snowflake, Amazon Athena, Apache Spark, Trino and SageMaker. Qlik Open Lakehouse is currently available in private preview and is scheduled to be generally available in July. Qlik also announced what it called an AI "agentic experience" across the Qlik Cloud - including the company's data integration, data quality and analytics software - that will use AI agents to provide a single, conversational interface for business users to interact with data, uncover insights and take action based on analytical results. At Qlik Connect the company debuted a discovery agent that the company said proactively identifies critical risks and opportunities across applications and datasets, presenting insights and recommended actions. A new pipeline agent, demonstrated as a concept, will allow users to describe desired business outcomes conversationally, prompting automated recommendations and the design of necessary pipelines. Qlik initially introduced AI agents last year with Qlik Answers, a generative AI knowledge assistant tool for discovering and finding useful insights within unstructured data. The company plans to begin rolling out the new agentic AI capabilities this summer, starting with private previews. "What we are doing is we're building out a much more robust framework to configure these agents and to make sure that once customers decide on what actions they want to take, based on insights from AI, they'll be able to execute those workflows very, very easily. So it'll really be a next generation of agentic AI for us," CEO Capone said. Qlik also announced plans to embed new AI capabilities within its Qlik Cloud Analytics service. A move the company said will provide a new layer of intelligence across the platform. The company said the new functionality will help prepare data faster, forecast complex trends, detect anomalies, and help take immediate action through embedded decision workflows. The AI capabilities will include the new discovery agent as well as Qlik Predict (the renamed Qlik AutoML) and Qlik Automate (previously Qlik Application Automation). Capone said the Qlik platform, including the new offerings unveiled at Qlik Connect, position the company to meet the needs of today's AI and data analysis systems. "When I got here in 2018, this is what we wanted to build. We saw this AI revolution coming, but we knew that without quality data and without an integrated platform, it was going to be really hard to achieve the value of AI. So the 14 acquisitions we've done, the close to a billion dollars in R&D [research and development] that we put into this, is really to get us to this moment - a full data analytics platform, from data integration [and] quality governance, all the way through analytics, AI and automation. That's what we built." Capone said all this presents opportunities for Qlik's channel partners. "Customers are overwhelmed with the promise of AI [and their] inability to actually deliver value with AI. And more and more, they're looking to their partners and our partners to give them an outcome," he said. Capone said that at Qlik Connect the company focused on demonstrating to partners "how our platform and our capabilities can actually be a real enabler for them to actually provide outcomes for their customers." Qlik also aimed to show partners how the company has heavily invested in partner enablement and incentives, "to get [partners] up to speed on our technology and bring that to the market faster, because partners always have been and will continue to be a force multiplier for us." In an interview with CRN, David Zember, Qlik senior vice president of worldwide channels and alliances, echoed Capone's comments about what Qlik's latest technology offerings mean for partners. "There's a ton of opportunity to help customers get their hands around their data and build a great data strategy," Zember said. He specifically cited the opportunities for systems integrators and implementors around large-scale IT modernization projects - especially for partners with vertical industry domain expertise. They can help customers extract data from out of these big systems of record [such as SAP applications] and get value out of them and get ready to use that data to build great AI experiences for their customers and employees," Zember said. "The value comes from the business outcome." Qlik acquired data integration platform developer Talend in May 2023 and the company has worked since them to unite the two companies' partner programs, Zember said. "We took that opportunity to basically transform our partner experience, which included a new learning portal and the new partner hub ... making sure we've got a program that's simple and predictable and profitable for the partners," Zember said. The work also included introducing Qlik partners to the Talend product portfolio and Talend partners to Qlik's products. "We're seeing the fruit of all of that work now, as partners adopt the platform end to end, they're able now to deliver more services. They derive more services revenue from a platform that starts with data, all that ingestion, transformation and quality and trust and data products, all the way through to AI agentic experiences," Zember said.