Our AI Journey

From Idea to Impact: Springer Nature's AI Innovation Journey

As the Vice President AI at Springer Nature, Thomas’ role is to lead the AI team and oversee AI Governance and AI Innovation Management across the publishing group. His primary contribution to innovation is through the AI Innovation Framework he has established together with the colleagues of Springer Nature’s AI Board. This framework ensures that their innovation aligns with ethical and legal guidelines, avoids duplication, and most importantly, creates value for users and customers.


Thomas Suetterlin

Vice President AI

Springer Nature Group

Fuel for AI Innovation: Passion and Potential

I'm driven to work on AI-related projects at Springer Nature by my passion for emerging technologies and the potential they hold to simplify professional life. I'm particularly excited by the possibilities of Generative AI, which can help to not only make our employees’ lives easier but also eliminate pain points for authors and bottlenecks in the research cycle and accelerate the generation of knowledge.

From an Idea to Impact: How the AI Innovation Funnel Leverages AI for Customers’ Benefit

Springer Nature encourages innovation and experimentation with AI, particularly Generative AI. We have an AI Innovation Funnel where new ideas can be submitted and the most impactful ones are selected for prototyping. This approach encourages creativity and ensures that we are always exploring new ways to leverage AI for the benefit of our customers.

The potential of Generative AI to transform the way we approach scientific publishing is truly inspiring. As a former scientist, I understand the challenges faced by researchers and am excited by the potential of AI to alleviate these. With AI, we can automate tasks such as quality checks, fact checks, early feedback for researchers based on research data, automatic copy editing, and translation in both language and style. This not only improves the quality of their work but also allows researchers to focus on their research.

Current Endeavor: Tailored, Safe and Ethical Access to Generative AI

Currently, I am deeply involved in evaluating technologies and approaches to harness the power of Generative AI for our Springer Nature employees in a customisable, safe and user-friendly way. Within scope are time consuming day-to-day tasks every employee has such as drafting of minutes and discovery of relevant internal information in an efficient way. A focus is on open-source technologies on all levels including the used AI models.

This project aims to address several challenges. Primarily, it ensures that Springer Nature employees have safe, ethical, legal, and efficient access to Generative AI capabilities that are specifically tailored to their needs. Many Generative AI tools available today are not suitable for commercial organisations, with only a few offering business licences or an truly enterprise-ready platform. Additionally, the integration of internal information and connection of associated systems often present significant challenges. We try to find answers for these questions.

On a personal level, this initiative is incredibly fascinating. It gave me the opportunity to get back to coding, take my Python skills to the next level and actively engage with the latest tech. It forced me to rethink user experience, as the interaction is primarily chat-focused. It's an exciting journey of discovery and innovation in a global landscape that constantly pushes the boundaries of what is possible with AI at a rapid pace one has to keep up with.

Key Steps in Deploying the AI Approach

Implementing this approach involved several key steps. It begins with in-depth research into suitable libraries and models, considering their usage conditions. This was followed by identifying the tech stack which is most future-proof given the short technology update cycles. I then explored how to incorporate emerging technologies into our existing Springer Nature cloud landscape in a scalable way. Finally, a concept to test prototypes with a truly representative sample of participants had to be developed to make sure that we can measure success as in value add for the users reliably.

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