Speaker

Dimitris Vlitas

CEO of Sovereign AI Lab ( SAIL ), Visiting Faculty at University of Toronto

Reply

Dr. Dimitris Vlitas is an AI strategist, advisor and mathematician specialising in AI-powered enterprise transformation and the application of advanced machine learning to complex business challenges. He is currently Partner at Sail Reply, where he works at the intersection of AI strategy, bespoke Large Language Models and enterprise-scale transformation.Dimitris has also consulting background, being for four years the AI lead of Accenture UK and Ireland. Over the course of his career, he has advised organisations across industries on how to translate emerging AI capabilities into scalable, commercially relevant solutions, and previously served for four years as Artificial Intelligence & Data Director at Accenture. His academic background spans mathematics, computer science and machine learning, with studies at the University of Toronto, a PhD from Paris Diderot University, and research experience at Cornell University, the Fields Institute and the University of Toronto. His current work also includes the development of next-generation AI models in collaboration with Mistral AI and the Austrian Academy of Sciences, including Apollo, an advanced AI model for Ancient Greek.

Thursday 8 | 10

Domus - ASTERIA GlyfadaThu 8 | 10

 Mellon Technologies and Mistral AI announce a strategic collaboration focused on bringing bespoke, enterprise-grade AI solutions to organisations across Greece and Southeast Europe. The session will explore how banks and enterprises can move beyond generic AI tools and isolated pilots to redesign core business processes around AI, while retaining control over their data, intellectual property and operating environment. The discussion will also address scalability, technological sovereignty, deployment speed and the measurable business value of enterprise AI. 

    09.40 - 11.10 | BREAK

    Discussion Framework: AI is rapidly becoming a new decision-making layer across banking, reshaping how institutions understand customers, manage risk, allocate resources, automate workflows and drive growth. The challenge is no longer simply whether banks should adopt AI, but how they can embed it sustainably across the organisation and translate technological capability into measurable business value. The discussion will focus on practical conditions required to move from experimentation to scaled, enterprise-wide adoption across three core dimensions: Where to apply AI, How to build the AI operating model, and How to scale effectively.

    Central Question: What does it take to move AI from isolated pilots to an enterprise capability that delivers measurable value at speed, without compromising control?