Tech • AI • Robotics • Game
Amplifon: AI Boost Bites: Fast Prototyping Workflow
6/10TL;DR
Amplifon is using Google AI tools including Gemini and NotebookLM to turn scattered hearing-care knowledge into structured insights, automate manual analysis, and scale those insights across the company.
KEY POINTS
AI strategy at scale
Amplifon, a hearing-care company founded in 1950, operates in 25 countries and employs more than 20,000 people. It has launched AmplifAI, a companywide program designed to move artificial intelligence from isolated experiments to governed, large-scale use across the business.
A knowledge problem in hearing care
A central challenge was not a lack of information but a lack of usable context. The company held complex office data, contact-center information, store-system records, procedures, training guidelines, and large volumes of notes captured by audiologists, but connecting those sources required heavy manual effort and time.
NotebookLM used for synthesis
The first step in the workflow is synthesis with NotebookLM. Teams upload knowledge from multiple sources and use the tool to detect patterns that would be difficult to find manually, including trends across thousands of customer interactions.
Customer behavior analysis
One practical use case has been the analysis of recurring customer behaviors. By synthesizing large volumes of interactions rather than reviewing them one by one, teams can identify where customers most often need support and use that insight to improve service design.
Reducing manual work for audiologists
The company says automation is helping remove repetitive information-processing tasks from frontline specialists. That allows audiologists to spend more time on the quality of customer interactions and less time sorting through fragmented records and operational material.
Gemini as the refinement layer
After initial synthesis, the output is moved into Gemini for refinement. Teams use it as an interaction layer to ask targeted questions, sharpen logic, and generate structured summaries that can be used by business units.
From raw patterns to business language
In practice, teams ask Gemini to convert customer patterns into clear business summaries and to define data points consistently. One example is support for a global KPI glossary, helping standardize how performance information is described and understood across markets.
Industrialization through Google Cloud
Once a use case is validated, the company scales it through an internal agent platform built on Google Cloud architecture. This step is intended to turn early AI insights into governed, repeatable solutions rather than one-off pilots.
Global KPI data catalog
A key result has been the creation of a global KPI data catalog, now described as a core component of the company’s knowledge platform. That catalog supports AI agents across the organization by giving them structured, standardized business definitions to work from.
Operational and strategic impact
The reported impact goes beyond efficiency. Business teams can spend less time processing information and more time applying insights to decisions, while the broader organization can innovate through a cycle of experimentation, learning, refinement, and then scaling proven ideas.
CONCLUSION
Amplifon is using AI to formalize frontline and operational knowledge that was previously dispersed across systems and notes. The company’s approach shows how Gemini, NotebookLM, and cloud-based agents can support both customer service quality and enterprise decision-making when expertise is translated into structured, reusable intelligence.
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