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Collinear AI, Robin AI, and Vody are just a few of the customers that have started simplifying model customization with SageMaker AI’s new capabilities. For example, Collinear AI, an AI improvement platform built for enterprise genAI, saved weeks using SageMaker AI. Soumyadeep Bakshi, co-founder, Collinear AI, said, “Fine-tuning AI models is critical to creating high-fidelity simulations, and it used to require stitching together different systems for training, evaluation, and deployment. Now with Amazon SageMaker AI’s new serverless model customization capability, we have a unified way that empowers us to cut our experimentation cycles from weeks to days. This end-to-end serverless tooling helps us focus on what matters: building better training data and simulations for our customers, not maintaining infrastructure or juggling disparate platforms.”

A Memorandum of Understanding between AI4LAM and the Europeana Foundation was signed in London during the Fantastic Futures conference on 3 December 2025 by our General Director, Harry Verwayen, who also proudly sits on the AI4LAM Board of Directors.

By pooling efforts and offering a platform for sharing knowledge and good practice, AI4LAM envisions a future where the collective strength of libraries, archives and museums drives innovation in responsible AI, supporting cultural development and societal growth.

AI4LAM brings together leading organisations in AI research, implementation, education and innovation, as well as those shaping technology strategies and policies. Members include long-standing Europeana partners and data providers such as the National Library of Norway, the Rijksmuseum, the British Library, the Bibliothèque nationale de France, the National Library of the Netherlands, Music Archive Finland, the Time Machine Organisation, the Netherlands Institute for Sound and Vision and Stanford University. We also look forward to establishing new connections with international institutions, including the Library of Congress and the Smithsonian, leveraging AI4LAM’s global reach.

This milestone builds on successful past cooperation, including the Alignment Assembly on Culture for AI, a collective intelligence and consultation process taking place within the common European data space for cultural heritage since last May. Through this Alignment Assembly, we have collectively identified points of consensus and agreement on the use, adoption and development of AI in our sector – but also important dilemmas, uncertainties and shared challenges. Addressing these effectively requires close collaboration across the entire ecosystem, and joining AI4LAM will mutually strengthen our collective capacity.

“Just as the Europeana community served as a blueprint for data sharing infrastructures in Europe, the data space for cultural heritage can serve as an example and a leading voice shaping critical adoption and engagement with AI. This is the vision we are working toward — but we cannot achieve it alone. We need to join forces with like-minded organisations and networks, both at European level and globally. AI4LAM will play an essential role in strengthening our collective intelligence and capacity to drive positive change. Together, we commit to accelerate and advocate for responsible AI in cultural heritage” said Harry Verwayen, General Director of the Europeana Foundation.

To mark this occasion, we are releasing an Impulse Paper: Publishing cultural heritage data in the Age of AI, commissioned by the Europeana Foundation to the Open Future Foundation to spark discussion on a key topic that emerged from our Alignment Assembly: generative AI and its implications for data sharing in the cultural heritage sector. The paper outlines the relevant technological and legal context, and proposes a differentiated access model for cultural heritage data. It was presented at the Fantastic Futures conference by our General Director, Harry Verwayen, who invited participants to reflect on the ideas put forward in this Impulse Paper, as we will be gathering feedback and insights in the coming months.