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Data Quality Is the Guardrail for Agentic AI

Gartner has named Qlik a Leader in the 2026 Gartner Magic Quadrant for Augmented Data Quality Solutions, our seventh time being recognized as a Leader in this Magic Quadrant. As AI becomes operational, data quality matters more than ever. We’re past the phase where AI just produces outputs. AI is starting to initiate, route, and act across real workflows.

Supercharge Retail Growth: Get Qlik & Snowflake's Expert Guide

According to a recent report, 90% of retail leaders say they began experimenting with gen AI solutions and scaling priority use cases. Retailers are looking to update data systems and boost AI for risk, cost reduction, and growth need an all-encompassing approach — and that means efficiently blending AI and analytics into operations. Learn how to use data and AI to manage risk and drive growth for your organization, see how Qlik and Snowflake are making this happen today.

How to Make Data Work for Agentic AI

For decades, organizations have worked to use data to make better decisions and drive better outcomes. Data has become the lifeblood of the business, and AI now has the power to unlock it in new ways. The paradigm is shifting, from dashboards and visual interfaces to AI driven experiences. But too much data is still stuck in silos, incomplete, and inaccurate. Many analytics workflows remain manual, which slows time to value, limits insight quality, and raises cost.

Meet Qlik Answers

Discover how Qlik Answers transforms the way teams work with data. As part of Qlik’s newly released agentic experience in Qlik Cloud, Qlik Answers brings together natural language interaction, the power of the Qlik Analytics Engine, and governed, trusted data, all supported with transparent reasoning and citations.

Why Every AI Deployment Needs a Pre-Flight Data Checklist

You’re in the cockpit of a small plane, cruising a few thousand feet in the air. Then, out of nowhere, the airspeed dips and an alarm rings out. The nose drops, and you're in a full-out stall by the time instinct kicks in. You pull back on the yoke, trying to steady the plane, stop the descent and patch things up midair. But that’s exactly the move that seals your fate, sending you into a deeper spiral.

Qlik for Snowflake - From Ingestion to Insights

In partnership with Snowflake, Qlik helps customers modernize data estates end to end—from ingestion to insight—using Qlik Talend Cloud Data Integration and an internal data marketplace. With no‑code/SQL pipelines, thousands of secure connectors, and built‑in governance and lineage, Qlik automates trusted data movement into Snowflake and turns it into certified, reusable data products—an “app store for insight”—that accelerate time to value and power advanced analytics and AI.

Qlik: Making Data Work for AI

AI is moving fast, but outcomes still depend on one thing: trusted data, in the right place, at the right time, with the right controls. In this short Qlik story video, you’ll see how we help teams accelerate AI with confidence, turning data into answers you can explain, and actions you can stand behind. From strengthening supply chain decisions, to building a campaign plan in seconds, to spotting changes as they happen, Qlik connects analytics, automation, and governed AI experiences, so AI becomes operational, not experimental.

Making Data Work for AI

AI is not a pilot anymore. In 2026, it is the operating agenda. And if you’re leading a business or an IT project right now, you’re probably getting the same two questions. First: “When do we see real outcomes?” Second: “Can we trust what we’re getting?” Those are fair questions. They’re the right questions. Because the truth is, the model is rarely the problem. The hard part is everything around it. The data. The access. The silos. The controls.

Building for Agentic AI

Our customers’ worlds are complex, and for good reason. It’s multi-cloud. It’s SaaS plus on-prem. It’s Snowflake, Databricks, AWS, Azure, Salesforce, and more. Underneath every one of those choices is the same constraint: data must be accessible, stay current, and stay controlled. The hard part is getting trusted data where it needs to be, when it needs to be there, with the controls to use it responsibly.