Justin Langford

As AI adoption accelerates across industries, many organisations are realising that building a model is only the beginning. Real-world deployment of AI demands robust infrastructure, clean and connected data, and secure, scalable MLOps pipelines. In this panel, experts from across the AI ecosystem share lessons from the frontlines of operationalising AI at scale. We’ll dig into the tough questions: • What are the biggest blockers to AI adoption in large enterprises — and how can we overcome them? • Why does bad data still derail even the most advanced models, and how can we fix the data quality gap? • Where does synthetic data fit into real-world AI pipelines — and how do we define “real” data? • Is Agentic AI the next evolution, or just noise — and how should MLOps prepare? • What does a modern, secure AI stack look like when using external partners and APIs? Expect sharp perspectives on data integration, model lifecycle management, and the cyber-physical infrastructure needed to make AI more than just a POC.