In this interview from Qlik Connect 2026, Christopher Powell, chief marketing officer of Qlik, joins theCUBE Research's Rebecca Knight and Rob Strechay to discuss how enterprises are moving past AI experimentation toward operational dependence — and what foundational work that shift demands. Powell argues the AI inflection point is less about whether the technology works and more about whether the data does. He outlines three prerequisites for enterprises ready to operationalize AI: a trusted data foundation, deep contextual understanding of proprietary environments and architectural flexibility to adapt as innovation accelerates. To address the trust dimension, Powell highlights Qlik's trust score for AI, which evaluates data lineage, provenance and access history to give AI systems confidence in the inputs they're acting on.

The conversation also explores how leading organizations are building human expertise into agentic systems before removing humans from the loop — a model demonstrated on stage with UPS, where domain knowledge defines the boundaries of autonomous action. Powell breaks down the evolution from standalone AI tools to agents to fully agentic workflows, noting how this progression is dissolving organizational silos and forcing companies to build shared data foundations across marketing, sales and customer success. He underscores cost management as a strategic imperative, warning that AI environments built without embedded cost controls will fail to scale. From emerging efficiency stories — including customers spending a few hundred thousand dollars to save $15 million annually — to the broader organizational rethinking required to lead in the AI era, Powell outlines why companies that ask not how AI can improve existing processes, but how it will fundamentally transform them, are the ones best positioned to win.

In this interview from Qlik Connect 2026 in Orlando, Kyle Jourdan, head of AI practice at Qlik, joins theCUBE Research's Rob Strechay to discuss how Qlik Answers and the Model Context Protocol are turning enterprise data into automated, real-time business decisions. Jourdan traces the evolution of Qlik Answers from a tool for extracting insights out of unstructured repositories to a full agentic experience bridging structured and unstructured data alike. He explains how two newly introduced agents, Predict and Automate, are now embedded in the Answers experience, moving organizations beyond dashboards and into automated action. Central to this evolution is explainability and trust: for every answer surfaced, Qlik traces the information to its source so users can verify accuracy before acting on it.

The conversation also explores how MCP extends Qlik's reach beyond the out-of-the-box experience. For organizations that prefer specific foundation models or LLM clients, Jourdan details how Qlik's trusted data context can be routed through MCP servers into whatever AI client a team prefers — solving the hallucination problem that surfaced as enterprises scaled their early AI deployments. He also highlights the unique advantage of the Qlik Associative Engine, which surfaces relationships across disparate data sources that traditional SQL-based joins simply cannot capture. Looking ahead, Jourdan underscores the platform's ultimate ambition: insights without downstream action have no business value. From knowledge-based access through MCP to predictive modeling and closed-loop automation via the Automate platform, Qlik is building the full stack for enterprises to not just see their data — but close the last mile back to the business systems that drive decisions.

In this interview from Qlik Connect 2026, Marcus Tannerfalk, head of product design at Qlik, joins theCUBE Research's Rob Strechay to discuss how AI is transforming the product design discipline and pushing analytics beyond the dashboard into an omnichannel future. Tannerfalk opens with a vivid real-world example: a professional hockey club using Qlik to run a Moneyball-style approach to squad optimization, analyzing players across multiple markets to maximize value within tight budget constraints. He then explains how AI is radically reshaping product design at Qlik — shifting the discipline from crafting native interfaces to enabling what he calls "omnichannel AI," where Qlik's capabilities surface wherever users already work, including inside tools like Claude through the Model Context Protocol (MCP).

The conversation also explores how Qlik is building what Tannerfalk describes as a "context layer" — enriching existing data models and applications with metadata and structure so customers extract more value from the LLMs they already use. He outlines the philosophy of meeting users where they are, adapting Qlik's presence to whichever AI tools a team relies on rather than requiring navigation of a fixed interface. Looking ahead to Qlik Connect 2027, Tannerfalk previews an initiative to unify and simplify Qlik's native experiences, with an early prototype set to debut ahead of a planned main-stage reveal — a signal of how the platform intends to become a core component across an expanding range of AI-powered workflows and channels.

In this interview from Qlik Connect 2026 in Orlando, Roberto Sigona, chief operating officer of Qlik, joins theCUBE's Rebecca Knight to discuss why the biggest obstacle to enterprise AI adoption is rarely the technology itself — and what it takes to move from data collection to real, measurable business impact. Speaking from Qlik's Sports Pavilion, where live dashboards track athlete performance in real time, Sigona argues that the harder challenge is process change and human habit. He notes that AI requires enterprises to fundamentally reinvent workflows, not simply accelerate existing ones, and that organizations stall when they try to move from zero to full scale overnight rather than delivering incremental value that builds momentum.

The conversation also explores how Sigona approaches ROI measurement, emphasizing the need to define outcomes in terms of genuine business impact rather than metrics that are merely easy to capture. He highlights the evolution from data literacy to AI literacy — the critical-thinking skills needed to interrogate AI outputs, verify sources and resist blindly accepting what generative models produce. Sigona underscores that this principled skepticism is becoming non-negotiable as AI systems grow more autonomous and more opaque. Looking ahead, he predicts that next year's conversations will center on agent-to-agent interaction and the deepening question of what "human in the loop" actually means as autonomous systems accelerate. From the sensor-laden golf swing to the agentic enterprise, he outlines why steady, incremental progress — guided by people who understand what good looks like — remains the most reliable path through rapid technological change.

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 informed actions and drive genuine organizational transformation.

The conversation explores how Qlik's platform supports users across the full analytics maturity spectrum — from PDF reports to agentic AI — rather than forcing a single consumption model on a diverse enterprise workforce. Fisher introduces the "go slower to go faster" principle, arguing that investing time in a governed data foundation delivers exponential long-term benefits for AI application performance and autonomous agent deployments alike. He also raises an urgent concern about the widening opportunity gap in data literacy, noting that democratizing access to AI tools must go hand in hand with democratizing the value those tools create. From helping cautious executives identify ROI in their existing Qlik applications to delivering new agentic capabilities for on-premise customers, Fisher outlines how organizations can build flexible, future-proof AI infrastructure without sacrificing governance or trust.