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We're challenging our Qlik Community: put agentic AI to work.
We saw lots of great posts in our recent Make Your Data Work for AI forum. We’re building on that momentum with a new board focused on the next step: driving real impact with agentic AI.
This is the place to get started with agentic capabilities, try them out, and share your experience with the rest of the Community.
Here's the challenge:
- Get started. Toggle on Qlik Answers, activate Qlik’s MCP Server, or get started with whichever capability fits your work, and see how easy it really is.
- Build your first workflow. Identify a use case and put the capabilities to work on a real problem or task.
- Share your experience. Tell the Community what you learned, what worked, and what surprised you.
Ask questions, share screenshots (obscuring any sensitive data), get ideas, and help your Qlik Community peers on their journeys.
You'll earn NEW Community badges along the way: Agentic AI Adopter when you share your first workflow or use case, and Agentic AI Expert when you share your learnings and results.
Plus, we’ll be awarding some fun surprises and new Qlik swag along the way!
A Qlik has been continuously innovating, and these innovations have had a significant impact on the way we develop and deliver solutions to our clients. We are seeing real gains in speed, productivity, and efficiency across our projects.
Here at our organization, we have been making extensive use of Qlik Answers and, especially, the MCP Server.
Regarding Qlik Answers, we have been actively introducing our clients to the benefits of this capability. Internally, we also use it to help our team identify insights and anticipate the actions that may be required.
For example, we can quickly analyze sales performance by salesperson over a specific period and identify the highest-performing individuals. What previously required manual analysis can now be explored much more quickly, allowing the team to move from data to insight, and from insight to action, in significantly less time.
Personally, I find the MCP Server particularly impressive. We currently use it together with Claude, and it has dramatically accelerated the documentation of our Qlik projects.
We are using it to document screens, objects, Set Analysis expressions, Master Items, and other components of our applications. In some cases, this has reduced the time required for documentation by up to 98%.
For script documentation, since the MCP Server cannot yet read QVS scripts directly, we simply provide the QVS code to the Claude project, which can then analyze and document the entire script. This has been another significant productivity gain for our team.
Beyond documentation, MCP has also helped us accelerate the dashboard development process itself, allowing our team to spend less time on repetitive activities and more time focusing on business requirements, user experience, and delivering value to our clients.
Overall, these capabilities are changing not only how quickly we can develop Qlik solutions, but also how we analyze data, document our work, and support our clients. The combination of Qlik’s innovations with AI is creating a much more efficient development workflow and helping us deliver faster, smarter, and with greater scalability.
The Qlik MCP Server has been a big help for me, not only to create analysis, but also to create documentation.
Recently, I needed to document a large project. Without the MCP, this would probably take many days because there was a lot of information to organize and explain. With the MCP, I was able to speed up this process and get help to structure the documentation.
My most recent use of the MCP was in a commercial project, where I used it to create statistical analysis, measures, and charts. I use the Qlik MCP Server with Claude to help me work with the data and create new analysis using simple text instructions.
For example, I asked the AI to:
Group customers by how often they buy and how much they spend, using the RFM method.
Create a Pareto chart (80/20) for customers and products, to understand where most of the sales come from.
Check if price and quantity make sense for each product group and find some unusual cases.
The AI also saved these as master items in Qlik, so I can use them again in other charts.
I also had an interesting situation with a running-total chart. The chart was showing the data in the wrong order because the AI used the wrong sort. I noticed this because I checked the numbers before using the chart. After that, I asked the AI to fix it. It used a number rank to sort the data and Dual() to show the name instead of only the number.
For me, the main learning was that AI is very fast, but I still need to check the results. It can make mistakes, so I don't just trust the chart without checking the numbers.
Before using AI, creating all these charts, measures, and rules manually could take many days. Now, I can do most of this in one day, but I still take some time to validate everything.
I think the Qlik MCP Server can be very useful for people who work with Qlik, especially when we need to do repetitive work, explore data, create analysis, or document a project. At the same time, I think it is important to understand what the AI is doing and always validate the results.
