Ask Your Data Anything, From Anywhere

Let people ask. Whether it's inside Qlik or from the AI assistant they already use, the foundation is the same: the governed data and business context you've already built.

In the first post in this series, From BI to Agentic AI: Why the Dashboards You Built Are the Foundation for Agents, we explored how the BI investment you've already built can become the foundation for what comes next. Now, let's look at what that can mean in practice.

One of the simplest places to start is changing how people access the insights already available in Qlik: let them ask.

A sales leader might want to know, “Why did margin fall in the Northeast last month?” Instead of finding the right dashboard, navigating filters, and interpreting several visualizations, they can simply ask the question.

And they can ask it in two ways: directly inside Qlik with Qlik Answers, or from an AI assistant they already use, such as ChatGPT, Claude, Gemini, Copilot, or Amazon Q, through Qlik MCP Server.

Ask more from your data inside Qlik

Answering all your questions, no matter how you ask them

You already have data and analytics inside your Qlik applications. Qlik Answers gives more people an accessible way to explore them using natural language, without requiring them to know how a dashboard was built or where to look for a particular insight.

Instead of searching through sheets, filters, and visualizations, you can ask a question such as “Why did margin fall in the Northeast last month?” and let Qlik determine the data and analysis needed to answer it.

And you don't have to stop with the first answer. You can ask follow-up questions to explore a finding further, request recommendations, and uncover insights in your data that could impact your business.

Behind the scenes, Qlik's analytics engine enables agents to perform advanced analysis and reasoning to determine what data and analysis are needed to respond. Agents can draw from Qlik data, internal unstructured content, and general knowledge. They can also break down compound questions and execute multiple queries to develop a more complete response.

Answers can include visualizations and supporting sources, and users can inspect the reasoning trace to better understand how a response was produced.

That means more people can move beyond simply seeing what happened and start exploring why it happened and what deserves their attention next.

And because Qlik Answers builds on your existing Qlik environment, you don't have to rebuild your analytics foundation to get started. You're extending the value of what you already have.

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Bring Qlik intelligence to the AI assistants you already use

But your users don't always start their questions inside an analytics application.

Increasingly, people are working from AI assistants such as ChatGPT, Claude, Gemini, and Copilot. Qlik MCP Server lets you bring Qlik's data and analytical capabilities into those experiences, so people can access trusted intelligence without leaving the tools where they're already working.

Take that same question: “Why did margin fall in the Northeast last month?”

Asked through a connected AI assistant, Qlik MCP Server can provide access to relevant governed data and Qlik's analytical capabilities to help answer it.

And this is about more than giving an AI assistant access to raw data. Qlik can also provide the business context captured in your analytics environment, helping the assistant work with the measures, dimensions, calculations, and terminology your organization already relies on.

That context matters. Rather than asking an AI model to interpret a table of data on its own, you can connect it to an analytics foundation designed to understand relationships in your data and perform the calculations needed to uncover meaningful insights.

No separate analytics experience to learn. No need to move between tools just to ask the next question.

Two doors. One trusted foundation.

Where should you start? It depends on your company's AI strategy and how your teams prefer to access insights from their data.

Your users can access trusted insights without needing to know how to build a dashboard or Qlik app. Or use both.

The goal isn't to force teams into a new way of working. It's to give them the flexibility to work in the tools that make sense for them while maintaining trust, governance, and business context.

You've already built the data and analytics foundation. Now you can extend that trusted intelligence across your organization, making it available wherever questions are asked and decisions are made.

And asking questions is only the beginning. In the next post, we'll look at what happens when agents don't wait for you to ask — when they continuously watch your data for meaningful changes and help you anticipate what's coming next.

I've been exploring Qlik's Agentic AI capabilities and recently tested the Discovery Agent using a retail scenario focused on a seasonal RainWear product category.

The use case was inspired by a real client engagement, with business labels anonymized to protect confidentiality.

Rather than waiting to search dashboards, the end users wanted to see whether AI could proactively monitor performance and surface business events that require attention.

Attached screenshots showcase how the Discovery Agent automatically identified:

• Record high sales during the monsoon season
• Record low sales during the winter period
• Higher-than-expected sales performance
• Regional sales downtrends requiring investigation

What stood out wasn't just anomaly detection. The Discovery Agent:

✅ Observed business activity continuously

✅ Detected meaningful changes automatically

✅ Explained insights in natural language

✅ Guided users toward further investigation

This felt less like traditional BI and more like having a digital business analyst continuously monitoring business performance and proactively surfacing insights that matter.

While the screenshots have been anonymized (RainWear, Store Cluster A, North Zone), the insight patterns are based on an actual client-inspired scenario and demonstrate how Agentic AI can proactively surface business-critical events.

Looking forward to hearing how others in the community are using Discovery Agent or other Qlik Agentic AI capabilities.