Afternoon Session: AI Agents in Real Workflows | 08.10.2026 | Zurich

Three live use cases with Claude, Qlik and n8n

The value of AI agents becomes clear when they take on work that is still handled manually today. At our After Work Session in Zurich on October 8, we will show three workflows we have built and tested ourselves using Claude, Qlik and n8n, from cleaning inconsistent data and assigning territories to interpreting APIs and creating trusted data products.

No polished concept demos. We will work with real data, show what the agents actually do and where human judgment is still needed.

Three AI Agents in Real Workflows

1. Automating Territory Assignments with Claude

A real customer challenge: assigning postal-code regions to the right territory manager. We show how Claude cleans inconsistent source data, makes the assignment, flags uncertain cases for review and feeds the result into Qlik Analytics.

You will see how Claude can:

• clean and validate source data
• assign the right territory manager
• flag uncertain matches
• prepare the result for Qlik Analytics

The example also shows how coding agents can increasingly support the building and maintenance of data pipelines.

2. Building a Trusted Data Product with Qlik

Clean data is only useful if teams know what it contains and why they can trust it. We show how the Qlik Data Product Agent combines different sources, applies business rules and makes data quality transparent.

You will see how Qlik can:

• combine different data sources
• apply business rules
• document the data product
• explain data quality and trust scores

3. Automating Interfaces and Data Workflows with n8n

Connecting systems still involves a lot of repetitive work. We show how n8n, REST APIs and AI can interpret API responses, suggest mappings, detect inconsistencies and automate the workflow around them.

You will see how the combination can:

• interpret API responses
• suggest field mappings
• flag inconsistencies
• automate data transfer and validation

The result: interfaces that are faster to build, adapt and maintain.

From Live Examples to Your Own Use Cases

After the live examples, we will look at what these approaches could mean for your own team: where AI agents and workflow automation make sense, which roles need to be involved, how much effort is required, and when automation creates real value. We will then open the discussion to your own use cases, whether that is manual data cleaning, territory assignment, data quality checks or interface maintenance, and explore where an AI agent or automated workflow could realistically help and where a conventional solution may still be the better fit.

Who Should Attend the After Work Session in Zurich?

The session is particularly relevant for people working with recurring manual data processes, data quality, system integration or assignment logic, including:

• Sales Operations
• BI, Data & Analytics
• IT and Data Engineering
• Business and department leads exploring practical AI applications

You do not need a deep technical background. The focus is on seeing AI agents in real workflows, understanding what works today and identifying where these approaches could make a difference in your own organisation.