Allianz Direct improves contact center agent-assist accuracy by 10–15% with Databricks Mosaic AI
Allianz Direct's contact center agents were spending too much time on mundane back-office tasks, leaving less time for direct customer engagement. Policy questions required agents to search multiple systems and second-guess answers rather than focusing on building personal customer relationships.
Allianz Direct had an existing GenAI-powered tool for contact center agents, but it was less accurate and required a more complex development and implementation process than the Databricks-based solution.
The RAG-based agent-assist tool demonstrated a 10 to 15% accuracy uplift over the previous solution, earned agent trust, increased agent usage, and is being rolled out across all Allianz Direct contact centers.
Frequently asked questions
What did this team achieve with this AI workflow?
The RAG-based agent-assist tool demonstrated a 10 to 15% accuracy uplift over the previous solution, earned agent trust, increased agent usage, and is being rolled out across all Allianz Direct contact centers.
What tools did this team use?
Databricks Mosaic AI, Databricks Data Intelligence Platform, Databricks Notebook, Agent Bricks Custom Agents, Unity Catalog, RAG, Slack.
What results were reported?
Accuracy uplift vs previous solution: 10 to 15%; Agent tool adoption: use it more often; Agent time with customers: spend more time talking to customers (source-reported, not independently verified).
What failed first in this deployment?
Allianz Direct had an existing GenAI-powered tool for contact center agents, but it was less accurate and required a more complex development and implementation process than the Databricks-based solution.
How is this call center ai AI workflow structured?
Customer policy question received → RAG retrieves T&C information → Human agent reviews AI answer → Agent delivers accurate answer.