London is a hub for the world’s most well-known enterprises and organisations. Rev brings together data science and IT leaders at the cutting edge of AI innovation in industries such as financial services, life sciences, and media and entertainment.
Join us for direct conversations, success stories, and lessons learned on moving from AI experiments to production-grade systems with a lens on regulated industries.
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Enterprises have the models, the talent, and the budgets. What's missing is the operational foundation to turn AI investment into measurable business impact. Discover how a full enterprise AI application platform empowers teams to build, govern, and scale AI-powered decisioning systems.
AI regulation is accelerating—but not in unison. From the EU AI Act to diverging US and APAC frameworks, global enterprises face a fragmented compliance landscape. Hear senior leaders debate how to build AI governance that satisfies multiple regulators without sacrificing innovation velocity or operational coherence.
Leading life science companies have deeply embedded Domino across the organisation, from benchmarking foundation models in clinical development to automating QC workflows. Learn how Domino's compliant environment became the foundation for rapid, grassroots AI adoption at enterprise scale.
AI amplifies what's already there—good or bad. Hear honest lessons from building two contrasting systems: a deterministic pricing engine and an agentic financial planning tool. Discover where AI genuinely elevates, where it amplifies chaos, and why governance and observability make all the difference.
Modernising a statistical computing environment is more than a technology upgrade. Discover how pharma companies are evolving legacy SCEs in regulated contexts—consolidating technical debt, containing scope, and building the right foundations across people, process, and IT.
The real AI bottleneck isn't the model—it's infrastructure. Hear first-hand lessons from building an enterprise data science platform across decentralised teams in analytics, risk, and audit, and learn why organisations consistently underestimate the skills and investment required to scale.
Regulated enterprises don't have a model problem—they have an application problem. See live examples of AI applications driving real business outcomes, not just proof-of-concepts, and learn how enterprises can build and deploy production-ready AI apps in hours, not weeks.
Vibe coding lowered the barrier; agentic engineering raises the stakes. As AI takes on full dev lifecycles, the challenge isn't autonomy—it's knowing when humans must stay in the loop. Learn to design HITL checkpoints, build governance, and scale toward dynamic virtual engineering teams.
Excel is where your analysts, traders, and risk teams already live. See how Gen AI, ML models, and quant workflows can be surfaced directly in spreadsheets—bridging enterprise-grade ML with the tools your org already trusts, and driving AI adoption with zero friction.
What does enterprise-scale model risk transformation look like? Hear how one of the world’s largest banks unified quants, validators, and technologists—streamlining validation cycles, migrating risk models to cloud, and building a strategic AI/ML platform spanning compliance, investment banking, and beyond.
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