No data, no AI

Why data matters more than ever for the next wave of AI

Two and a half years ago, I launched the ODI’s data-centric AI programme. The core message was simple: without data, there is no AI. At the time, there was a clear sense this needed to be said more loudly, because the conversation was so dominated by foundational models and compute that data risked being treated as an afterthought.

The programme set out to tackle three interlinked challenges (could I ever use bullet points without wondering if I sound like an AI?):

· Getting data AI-ready: for AI to be reliable and fair, the data it uses must be well governed, clearly structured, and accompanied by information about its provenance and content.

· Making AI-ready data available: AI can only deliver on its promise if access to data in an AI-ready format is not restricted to a few organisations. Open data remains a driver of AI progress and innovation.

· Ensuring responsible data practices: transparency about training and other forms of data is essential for oversight and public trust. Governments and organisations need clearer information about which datasets AI systems use and how well those systems perform.

Today, I think the same message is not only still valid but considerably more urgent than it was then. The AI landscape is changing and there’s specific data-adjacent points that are overlooked and could have grave consequences for the next wave of AI innovation. As I a techno-optimist, I see them creating a renewed opportunity to build data ecosystems that serve broad public interests rather than narrow commercial ones as it’s the case for consumer-facing AI.

Enterprise AI needs data foundations, not just foundation models

AI has matured rapidly from a research curiosity into something closer to a general-purpose technology. This means it now needs to deliver in specific, often messy contexts: enterprise processes, regulatory environments, legacy systems, real business constraints. Companies that try to adopt AI without first getting their data in order tend to find this out the hard way, and spend lots of money to do so.

This must sound familiar! In the early 2000s, the vision of semantic web services promised that software components would discover each other, negotiate terms, and compose themselves into workflows automatically, provided the right metadata, ontologies and reasoning/planning capabilities were in place. I co-authored a book on this topic with my mentor, the late Dieter Fensel, whom I still thoroughly miss.

Business process management, workflow languages, and enterprise application integration essentially say the same thing: describe your data and services richly enough, and machines, today called AI agents, can do the rest. Those ideas were ahead of their time and adoption was limited, partly because the investment in data foundations that would have been needed to make it work never materialised at scale.

Now, as AI is increasingly being adopted in enterprise settings, there is a genuine chance that organisations may finally build the semantic data layer they have needed for decades: rich metadata, transparent provenance, proper governance. Not because these things are nice to have, but because without them, AI will fail. The models will hallucinate, the agents will make poor decisions, and the compliance risks will be unmanageable. Atanas Kiryakov gave a fabulous talk about this at the European Semantic Web Conference.

This is also the focus of IDEA, a programme of research, community building, and peer learning that the ODI is delivering in partnership with SAP. IDEA stands for “Interchange for Data and Enterprise AI” and it is designed to help organisations of all sizes make their data infrastructure ready for AI. It is vendor neutral and community led, and we are actively looking for more partners who want to help shape the standards and frameworks that will define what AI-ready enterprise data looks like in practice.

Embodied AI needs new kinds of data (and new kinds of care)

This one has been on my mind a lot recently. AI is entering the physical world through robotics and embodied intelligence, and the data requirements for this are acute. There’s no public web to create foundational models for embodied AI for everyone, including smaller players, to use and build on.

China has understood this at an extraordinary scale. Over the past year, a series of government-backed humanoid robot training centres have opened across the country, in Suzhou, Wuhan, Sichuan, and elsewhere, where human trainers wearing VR equipment physically teach robots to fold clothes, clear tables, pour tea, and sort materials. These centres are generating millions of data points per year and sharing them across the robotics industry. In parallel, Chinese companies are paying thousands of residents and factory workers to film themselves doing everyday tasks at home to create first person training data for robot learning. MIT Technology Review recently described data infrastructure as the defining story of 2026 in robotics.

As far as I know, we do not have anything comparable in Europe or the UK. This matters not only for competitiveness but also for tech sovereignty (not my favourite term, but that’s a story for another post!). Robotics training data is deeply contextual. It captures how people move, live, and work in specific cultural and home settings. Collecting it at scale raises questions about consent, working conditions, representation, and who benefits from the resulting systems.

These are questions I’d love to work more on again. I spent years researching microtask crowdsourcing and the people who do data work for AI. For instance, in a paper I co-authored in Data & Policy, we explored whether data workers could be included as stakeholders in data trust design, giving them a say in how the datasets they help create are governed and used. The conclusion was that current governance models almost entirely ignore the people who produce the data, even though they are directly affected by how it is exploited. Others have gone beyond abstract frameworks to drive change in the sector e.g. Mark Graham’s Fairwork project that rates companies across 39 countries, slowly and steadily driving policy changes. They have recently extended their methodology to AI data supply chains, examining the conditions under which the data powering AI systems is actually produced. As new forms of physical, embodied training data become central to the next wave of AI, we could do things better from the start. When I’m asked about this topic, I always say that if AI were a sandwich or a t-shirt, people would not touch it if they knew its supply chain.

What does good data look like for agentic AI?

The third development is of course agentic AI. The original pitch for data-centric AI was: we really need to pay attention to well-governed AI data to train or fine-tune machine-learning models. In that context, the data was read and maybe occasionally updated. Agentic data flows are more dynamic, as agents need to discover data, assess its quality and provenance, combine it with other sources, and increasingly, write back to it, update it, and act on the basis of what it says.

Hence data needs to be well structured, richly described, and transparently governed. At the ODI, we have been working on what this means in practice through our Framework for AI-ready Data, which I co-authored with Neil Majithia and Thomas Carey-Wilson. The framework, now in its second version, sets out actionable recommendations across four areas: the design of datasets, metadata, infrastructure, and governance. It was published as an academic paper in the AI Magazine earlier this year. What I find most interesting about the agentic use case is that it exposes the inadequacy of data that was designed only for human consumption. Agents operating autonomously in the world need machine-readable provenance, clear access protocols, and reliable quality signals. If the data is not there, the agent cannot be trusted, and rightly so.

We need to build new data foundations fast, but can we also build better?

Each of these developments is generating new and distinctive forms of data: robotics training data, business ontologies, agent interaction logs, multimodal datasets that capture movement, context, and intent. These are not existing data repurposed for AI. They are new data being created right now, and that gives us something valuable: a chance to get the foundations right from the start.

For these new ecosystems, governments, the private sector, and civic society have a real opportunity to come together and establish governance and stewardship mechanisms that create genuine public data utilities, supporting broad innovation rather than concentrating it among a few large players. The Data Labs established in Europe need to make this a priority, building on the common data spaces. In the UK we don’t have anything equivalent that I’m aware of, as the National Data Library is growing closer to data.gov.uk. There are some encouraging models to draw on. Wikimedia Enterprise, for instance, offers a way for AI companies that depend on Wikipedia’s human curated knowledge to access it through a sustainable, structured arrangement rather than simply scraping it. It is a practical example of how commons based resources can be maintained and fairly financed in the generative AI age, ensuring that the communities who create the knowledge continue to benefit from it.

No data, no AI does sound like a slogan and yet it’s a call to take the data side of AI seriously and, crucially, to do so with an eye on the entire ecosystem, not just a handful of organisations.

What else has been happening

· I finally managed to visit V&A East Storehouse. Amazing experience, the way they are clustering their artefacts was refreshing for a classically trained knowledge engineer. Almost no temporal or categorical element, which made me think about how to design serendipitous knowledge organisation. No big ideas yet, but I’ll keep thinking.

· Most speaking and writing I’ve been doing lately has been on AI and the workforce, which made me wonder about how my and other data and computational professions are changing. More about this hopefully soon.

· I was honoured to be invited on Friday to the Wikipedia’s 25th anniversary, which also marked 15 years of Wikimedia UK being a charity. Such a rich, diverse community. Looking forward to Wikimania later this month.