Across the world, businesses are adopting AI to orchestrate supply chains, engage with customers, analyse operational efficiency, and more. Data is therefore more crucial than ever: if the data foundations underpinning technology are not AI-ready, there can be severe, negative, invisible downstream effects on AI implementations.
In partnership with SAP, our team has developed a framework for AI-ready enterprise data, exploring the specific ways datasets, metadata, infrastructure, and governance protocols should be optimised for successful, responsible AI adoption.
Join our Director of Research Prof. Elena Simperl as she presents this new framework, followed by an in-depth panel discussion with her, and an expert panel, chaired by our Director of Learning and Consultancy, Stuart Coleman. You’ll have the chance to ask your questions and hear about the overall landscape of data and AI in the global private sector.
Speakers
Paul Groth, Professor of Algorithmic Data Science, the University of Amsterdam
Paul Groth is Professor of Algorithmic Data Science at the University of Amsterdam where he leads the Intelligent Data Engineering Lab (INDElab) and is scientific director of the UvA’s Data Science Center. He holds a Ph.D. in Computer Science from the University of Southampton (2007) and has done research at the University of Southern California, the Vrije Universiteit Amsterdam and Elsevier Labs. His research focuses on intelligent systems for dealing with large amounts of diverse contextualized data with a particular focus on web and science applications. This includes research in data provenance, data integration and knowledge sharing.
Previously, Paul led the design of a number of large scale data integration and knowledge graph construction efforts in the biomedical domain. Paul was co-chair of the W3C Provenance Working Group that created a standard for data provenance interchange. He has also contributed to the emergence of community initiatives to build a better scholarly data ecosystem including altmetrics and the FAIR data principles.
Paul is co-author of “Provenance: an Introduction to PROV” and “The Semantic Web Primer: 3rd Edition” as well as over 200 academic articles. More information at pgroth.com.
Katryn Cheng, Vice President of Product Marketing for SAP Data and Analytics
Katryn Cheng is a Vice President of Product Marketing for SAP Data and Analytics. A seasoned business and technology leader, she helps shape go-to-market strategy and drive the adoption of SAP Business Data Cloud, as well as leading strategic technology partnerships. Since joining SAP in 2001, Katryn has held roles across solution advisory, field operations, solution management, and product marketing, bringing a deep, end-to-end perspective on data and analytics.
Elena Simperl, Director of Research, ODI
Elena Simperl is one of the UK’s leading advocates for AI that is transparent, trustworthy, and truly human-centred.
As the ODI’s Director of Research and co-Director of King’s College London’s Institute for Artificial Intelligence (AI), she is in the AMiner top 2000 most influential scholars in AI in the world, and is the UK’s expert on building AI that works ‘for’ people, rather than just ‘on’ them. She is on Stanford University's list of "World's Top 2%" scientists.
Elena is the UK’s technical compass for trustworthy AI, building AI systems that are technically robust and grounded in openness and collaboration. Her work tackles a fundamental issue: most AI systems are opaque ‘black boxes’, making decisions and generating content based on data the public can’t see, understand, or challenge.
As a technical leader, building data foundations such as knowledge graphs, metadata systems, and open data flows, Elena can explain how these systems work in simple terms and why AI systems must be explainable, accountable, and participatory.
Together, Elena and the ODI are working to build a world where data works for everyone. Quality and volume of high-quality data are vital for training accurate AI systems, unlocking business innovation, and delivering benefits to society. Open data - freely available for anyone to access, use, and share - is central to this vision. Put simply, without data, there is no AI. And without accountability and participation, there’s no trust.
Elena currently leads the ODI’s programme of data-centric AI research, which studies and designs the data infrastructure of AI models and applications. In 2025, she co-authored a Wellcome Collection paper, outlining a blueprint for an AI-ready UK National Data Library (NDL) and ran the NDL Symposium at King’s College London. To date, Elena has written more than 300 academic papers.
In her 18-year career, Elena has led dozens of UKRI, EU, and industry-funded projects across trustworthy AI, data governance, and civic tech, advised multiple governments and global organisations, and chaired conferences in AI, social computing, and data innovation. Elena is also a Fellow of the British Computer Society, the Royal Society of Arts, and the Association for Computing Machinery.
Chair: Stuart Coleman, Director of Learning and Consultancy
Stuart is Director of Learning and Consultancy at the ODI. He is passionate about opportunities for people across society to use technology, data and machines to learn new skills and shape a better future.
Stuart has a diverse entrepreneurial and executive leadership background, with experience leading and developing teams in venture-backed, publicly listed and mission-led high-growth technology, training and consulting organisations. Stuart is proud to have been a founding Director at the Open Data Institute in 2012 and delighted to have returned in 2020. Stuart recently graduated from the University of Oxford with a postgraduate diploma in Artificial Intelligence. In March 2025, Stuart was appointed to the Department for Science, Innovation and Technology Skills Council to help tackle the UK skills gap.