Introduction
The way hearing technology is developed is changing profoundly. What once relied on engineering intuition and limited field feedback is now being transformed by real-world data, predictive intelligence, and deeply connected cross-functional teams. The question facing R&D leaders today is not whether to embrace this shift, but how to lead it while preserving the domain expertise
Anders Rosengren knows this challenge well. With more than two decades of international R&D leadership, including senior roles at Ericsson across strategy, software engineering, and architecture, he joined Sonova with a clear mandate: drive innovation at scale by integrating hardware, software, and digital capabilities into a cohesive, future-ready organisation. As Chief Research & Development Officer, he oversees Sonova's entire global R&D function, from early-stage research to product execution, with a sharp focus on AI, cloud, and data-driven development.
We sat down with Anders ahead of his appearance at the European Medical Device Summit to explore what it really means to build a data-centric R&D culture, how real-world evidence is changing the way hearing solutions are designed, and what it takes to turn continuous patient insight into faster, better innovation.
A data-driven approach introduces a powerful new dimension to the innovation process — allowing us to observe problems objectively and at scale, across diverse real-world conditions.
your responsibilities as Chief Research & Development Officer at Sonova?
As Chief Research & Development Officer at Sonova, I lead our global R&D organization across both hardware and software development. My role includes shaping our technology strategy, covering everything from long‑term research and innovation to product and platform execution. I bring more than 20 years of international R&D leadership experience. Prior to joining Sonova, I held several senior leadership roles at Ericsson, including Vice President positions in Strategy & Portfolio, Software Engineering, and Architecture & Technology.
In these roles, I focused on scaling global organizations, defining technology roadmaps, and delivering cloud‑based and AI data‑driven solutions, while also building strong expertise in mixed‑signal hardware and embedded systems.
At Sonova, my focus is on driving innovation at scale, integrating hardware, software, and digital capabilities, and leading large-scale organizational change. This ensures we continue to deliver high‑quality, market‑leading hearing solutions worldwide and help more people enjoy the gift of hearing.
How is a data-driven approach reshaping innovation and prototyping in hearing system R&D compared to traditional development models?
As Anders explains, a data‑driven approach introduces a powerful new dimension to the innovation process. For the first time, we can observe problems objectively and at scale, across diverse real‑world conditions. This allows us to uncover relationships and patterns that traditional development methods would rarely surface.
This shift fundamentally changes how engineers think about problem‑solving. Instead of relying solely on a model‑centric view of systems, development becomes increasingly data‑centric. In prototyping and product design, data‑driven functions are now built from and validated against large-scale real-world data, and tested against how products perform in the field.
The result is solutions that are more robust, more generalizable, and better aligned with real product usage. At the same time, the innovation cycle becomes much faster. Insights from real‑world data flow directly into the next iteration, keeping development closely connected to live signals and shortening the distance between learning and action.
How does Sonova leverage real-world evidence and user feedback from products to guide early-stage design and better address user needs?
Digital solutions such as myPhonak allow us to understand product quality and performance at an unprecedented scale. What fundamentally sets this apart from traditional feedback mechanisms is both scale and continuity. We are learning continuously from thousands of data sets across markets, and technology levels in real-world setting.
Because this data is always on, insights become available in near real‑time. That speed changes both how early and how confidently we can act in the design process. Rather than relying on assumptions or limited samples, we can adjust product requirements and specifications based on actual product usage ensuring we stay closely aligned with what patients truly need.
Data-driven development is not intended to replace expert judgment, but to complement it with a richer and more objective source of information.
Integrating clinical and market data provides a completely new observation point. By moving beyond anecdotal information to rich real‑world data, whether during product validation or at the go‑to‑market stage, we gain a much more holistic view of both opportunities and challenges.
This integration also fundamentally changes how decisions are made about what to build. When we can see the true impact of innovation on a patient’s daily life, prioritization becomes far more grounded. We are better equipped to invest in what demonstrably matters and quicker to rethink or deprioritize areas that show limited value.
In practice, this means clinical and market data are applied much earlier in the development process. They shape roadmap decisions and help challenge assumptions about user needs before those assumptions become embedded.
How are predictive analytics, simulation, and modeling used to forecast device performance and optimize patient outcomes?
When we can see the true impact of innovation on a patient's daily life, prioritization becomes far more grounded.
Predictive maintenance provides a concrete example of what advanced analytics, simulation, and modeling make possible. Through the use of machine learning, we are transforming hearing‑aid reliability by continuously screening system performance and identifying reliability indicators in real time.
This allows potential issues to be flagged before a patient ever notices that something is wrong. It represents a fundamental shift from reactive service to predictive maintenance. Instead of waiting for a device to fail, we can intervene earlier, reducing interruptions, extending device life, and helping patients maintain a consistent, high‑quality hearing experience.
At the same time, this level of insight enables a new level of functionality. When device performance can be monitored and predicted so precisely, it creates opportunities to continuously improve the product reliability, not just maintain it.
From an organizational perspective, how do shared data platforms enable stronger cross-functional collaboration and faster innovation across R&D, clinical, and commercial teams?
All functions within the company ultimately serve the same goal: creating solutions so that everyone can enjoy the delight of hearing. While different teams focus on their own data sets - each capturing a distinct part of the hearing solution journey - it is only when those perspectives are combined that a holistic picture emerges.
Shared data platforms create a single source of truth across the organization. This shifts discussions toward aligning around best solutions, rather than working on fragmented problems.
As a result, innovation opportunities open up across the entire customer and patient journey. When everyone is working from the same picture, collaboration becomes faster, more effective, and ultimately delivers better results.
What are the biggest challenges in implementing a truly data-driven R&D approach, and how can organizations overcome them to unlock its full potential?
The most significant challenge is cultural rather than technical. R&D organizations are built on deep domain expertise, which forms the foundation of their competitive advantage. Data‑driven development is not intended to replace expert judgment, but to complement it with a richer and more objective source of information—one that can surface patterns, validate intuition, and challenge assumptions in ways that accelerate discovery.
Overcoming this challenge requires bringing the organization along on the journey. That means investing in data literacy, demonstrating data quality, creating early use cases that show tangible value, and ensuring that domain experts are actively involved throughout the process.
Another challenge that is often underestimated is data quality itself. Getting the fundamentals right—from how data is captured to how it is structured and governed—requires upfront investment. A strong initial use case and clear prioritization are critical here, as they help demonstrate what becomes possible, build confidence, and prove return on investment early enough to sustain momentum.
Looking ahead, how do you see CMC functions further evolving to support innovation, agility, and excellence in external supply networks?
Greater digital maturity, streamlined governance, and more integrated quality‑technical collaboration will enhance speed and consistency across partners. As data becomes more connected and processes more harmonized, CMC teams will be able to anticipate issues earlier, adapt faster, and guide innovation with clearer insight. This evolution will support not only agility and reliability but also a more proactive, partnership‑driven model of external supply excellence.
Which aspect of the European Medical Device Summit are you most looking forward to?
One of my key motivations is continuous learning and staying closely connected to how our industry is evolving. I actively seek opportunities to understand the latest technology developments and broader trends, particularly at the intersection of hardware, software, cloud, and AI. This allows me to anticipate what is coming next rather than simply react to change.
I am also motivated by exploring new areas that may not yet be fully addressed today, whether that involves emerging technologies, new business models, or shifting user expectations. Engaging with these topics early helps inform better strategies and more robust long‑term decisions.
Finally, I place significant value on networking with outstanding industry peers and thought leaders. Exchanging perspectives with others who face similar challenges in different contexts is one of the most effective ways to broaden thinking, challenge assumptions, and continuously raise the bar—both personally and for the organizations I lead.
Conclusion
What comes through clearly in Anders Rosengren's perspective is that the future of hearing innovation will not be built on instinct alone. Data collected in real-world use, interpreted rigorously, and shared across functions is becoming the foundation on which better products, faster decisions, and stronger patient outcomes are built.
But technology is only part of the story. As Anders notes, the deeper challenge is cultural: helping expert-driven organisations embrace data not as a replacement for their knowledge, but as a way of sharpening it. Getting that balance right, between domain expertise and data intelligence, between speed and quality, between innovation and reliability, will define which R&D organisations lead and which ones follow. His session at the European Medical Device Summit is a timely opportunity to explore exactly how that balance can be struck.
Register now: emdsummit.com