Andrea Isoni

Every organisation wants to scale AI—but too many begin with technology instead of strategy. As enterprises move beyond experimentation, success increasingly depends on the foundations beneath the models: trusted data, robust platforms, organisational readiness and responsible governance. AI agents, copilots and automation promise transformational outcomes, but when are they genuinely the right solution, and when are organisations simply adding complexity? Bringing together leaders responsible for enterprise AI strategy, transformation and implementation, this panel explores the practical decisions that determine whether AI initiatives deliver sustainable business value or stall after the pilot phase. From preparing data and platforms for machine consumption, to deciding where AI agents fit, overcoming organisational barriers and balancing innovation with governance, attendees will gain practical insights into building AI strategies that scale with confidence. Key Takeaways Learn why data and platform foundations remain the biggest determinant of successful enterprise AI adoption. Understand how to identify where AI agents create genuine business value—and where simpler approaches may be more effective. Explore practical strategies for scaling AI across organisations while balancing transformation, governance and responsible AI.

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.