Creative technologists, innovators, and curious minds meet in London for Creative Tech London 2027, a day dedicated to the ideas, tools, and talent shaping the next era of creative innovation.

This is where creativity meets code, where experimentation drives transformation, and where the boundaries between art, technology, and experience disappear.

In an age of AI acceleration, immersive worlds, and connected audiences, Creative Tech London is your space to explore what’s next - and how to make it real.

HS Creative Tech events provide me with the opportunity to learn about how other companies are blending technology and creativity to create products which deliver value, but also stand out in an age of information overload.

Over the past three years of the Semantic Data Conference, the community has shown itself to be comprised of strong, independent thinkers who are comfortable operating at the vanguard of this field.

We invite you to engage with keynote speaker Professor Viktor Mayer‑Schönberger directly in an open Q&A and dialogue. This is an opportunity to bring your toughest questions, challenge assumptions, and explore together how semantics, governance, and AI will shape the years ahead.

Context and challenge

As AI systems become the primary way people and applications access information, the semantic layer (ontologies, knowledge graphs, and contextual metadata) has moved from nice‑to‑have to critical infrastructure. Yet many organisations still struggle to decide when to invest in graphs, how to manage change, and how to align semantic work with data and AI teams.

Meeting the challenge

The first two years of AI hype centred around capabilities. Which AI reasoned best and hallucinated least? As the standard improved and solutions moved on from pilots and experiments to maturity, we’ve realised hallucination isn’t going away any time soon and the magic wands promised by vendors aren’t, you know, magic.

Current achievements and expanding opportunities

For many the transition from General Knowledge LLMs to RAG architectures has been revelationary with benefits including minimal hallucinations, greater accuracy and control over sources. The vast majority of RAG use cases do not use GraphRAG but there is huge interest that continues to grow in using Graphs alongside LLMs and embedding Semantic Guardrails into AI using Ontologies, Taxonomies and other approaches.