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[Blog post](/blogs)24 August 20265 mins

# How Insurance Leader Allianz Invests in Customer Relationships With AI

Learn how the COO at leading insurer Allianz invests in analyzing and automating repetitive tasks with AI to give teams more time to nurture customer relationships.

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### CONTENTS

-   How Allianz Is Streamlining the Happy Path to Customer Value With AI
-   Why insurance operations are harder to map than they appear
-   What Allianz discovered when they started observing
-   Why the human factor remains central to the Allianz approach
-   Using AI to understand where AI can help
-   Keeping competitive advantage where it belongs
-   What the Allianz story means for insurance leaders

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## How Allianz Is Streamlining the Happy Path to Customer Value With AI

There is a question at the heart of every large-scale transformation program that most organizations find surprisingly difficult to answer: how does work in your organization actually get done? In insurance specifically, that means going beyond how your procedures say work should happen and investigating how it really unfolds across every customer interaction, every distribution channel, and every operational touchpoint.

In March 2026, Skan AI CEO and co-founder Avinash Misra sat down on stage with Jan Malmendier, COO of Allianz Versicherung AG, the largest [property and casualty insurer](/blogs/top-7-agentic-ai-use-cases-for-pc-insurance-automation) in Germany, to explore what it takes to close that gap, and what Allianz has learned along the way.

It’s not every day that you get to hear about these challenges directly from a leader in Jan’s position, so we’ve recapped the discussion below.

## Why insurance operations are harder to map than they appear

Allianz handles more than 10 million customer interactions annually across digital channels, human distribution, and claims. At that scale, understanding how work really gets done is an operational imperative to ensure accuracy and identify opportunities for optimization and automation with AI.

Jan’s description of the traditional approach to building that understanding will be familiar to many people reading this blog: asking experienced employees, bringing in consultants, and assembling a picture of how processes run through interviews and observation. But the limitation, in his view, is clear. **"You will get a lot of great anecdotal evidence, the nice stories, and it's important to listen, but it's not sufficient."**

The gap between what those stories describe and what the data actually shows can be striking. Jan gave a specific example from Allianz's distribution operations: "You spend, whatever, three, four, five minutes to calculate a quote. Overall, the tied agents spend more than half an hour. What is doing that? It is nowhere described in any rule book."

Without the ability to measure that difference directly, organizations are making transformation decisions on assumptions rather than operational ground truth.

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## What Allianz discovered when they started observing

When Jan was asked what surprised him most during Allianz's observation work with Skan, his answer pointed to something the organization had not anticipated at the scale it actually occurred. "What surprised me personally most was indeed the big variety we see with our distribution partners. Right now in Germany, there's so much interaction done by WhatsApp, and that is fundamentally changing how messaging impacts sales processes."

That kind of discovery is only possible through direct observation of work as it happens because these behaviors don’t appear in procedure manuals or system event logs. **This  type of operational visibility determines whether an AI agent will perform reliably in the real world or struggle the moment it encounters something outside its training data.**

## Why the human factor remains central to the Allianz approach

A recurring theme throughout the conversation was the relationship between AI capability and human judgment, and Jan was consistent in his view of where each belongs. The goal of AI transformation at Allianz is not to automate the customer relationship. It is to **remove the [administrative and repetitive work](/blogs/workforce-intelligence-hidden-inefficiencies) that currently limits how much time people can spend on interactions that genuinely require empathy andexpertise** .

"Our target is to say we want to make every single customer interaction a perfect result, creating a perfect experience based on excellence and empathy," Jan said. Allianz currently operates at 4.75 stars across its customer touchpoints, measuring feedback at every stage of the customer journey. This framing shapes how the organization thinks about its "Happy Path" initiative: using operational data to identify every point where Allianz directly meets customer expectations, as well as where it falls short, and using that intelligence to drive continuous improvement at scale.

## Using AI to understand where AI can help

One of the more instructive threads in the conversation was the blueprint approach that Avinash outlined: using AI to [observe and analyze how an organization operates](/blogs/observation-based-process-discovery) before deciding where to deploy agents or models. Jan endorsed this and his framing of how Allianz prioritizes its transformation work was straightforwardly practical. "We have two simple dimensions: one is customer feedback, and the second is productivity and what is the actual waste we can take out."

That combination of **customer-facing impact and operational efficiency as the dual lens for prioritization is a useful framework for any insurer trying to sequence its [AI investments](/blogs/agentic-ai-enterprise-process-intelligence-roi)** in a way that generates demonstrable value early and builds toward more complex automation over time.

## Keeping competitive advantage where it belongs

The conversation also addressed a concern that is increasingly central to enterprise AI strategy in regulated industries: [data sovereignty](/blogs/privacy-first-process-intelligence-regulated-industries).

The operational intelligence that Skan captures for organizations like Allianz represents a genuine competitive differentiator: the lived execution patterns, the expert behaviors, the informal adaptations that define how the organization actually runs. It is the kind of institutional knowledge that takes years to accumulate and cannot be replicated simply by adopting the same AI tools.

For any insurer operating at scale in highly regulated markets, the operational data powering their AI capabilities they build within the organization's own infrastructure is a foundational requirement.

## What the Allianz story means for insurance leaders

When Jan was asked directly what he would say to insurers who are just beginning their AI transformation journey, his answer was unambiguous. "There's really no alternative to that. This time, AI is having a strong impact and will be far more fundamental than anything we have seen before. Everyone has to really put a lot of emphasis on this one."

That is a significant statement from the COO of the largest P&C insurer in Germany. The organizations that take it seriously, invest in understanding how work actually happens across their operations, and build their AI capability on that foundation rather than on documentation and assumptions will be the ones best positioned to lead through what comes next.

If you would like to explore what operational observability looks like in your own organization

[Request a Demo](/request-demo)

### CONTENTS

-   How Allianz Is Streamlining the Happy Path to Customer Value With AI
-   Why insurance operations are harder to map than they appear
-   What Allianz discovered when they started observing
-   Why the human factor remains central to the Allianz approach
-   Using AI to understand where AI can help
-   Keeping competitive advantage where it belongs
-   What the Allianz story means for insurance leaders

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