The future belongs to those who master both data and process. In the age of AI, process excellence is emerging as the true driver of competitive advantage in the sector. This keynote panel brings together industry leaders to discuss why organizations are shifting focus from proprietorial data to proprietorial process, how they are mapping and improving critical workflows, and the ways they align process improvement with data and AI initiatives. Panellists will share insights on measuring and communicating process-driven outcomes, and why the future belongs to those who master both data and process.
As AI moves from proof-of-concept to mission-critical in the sector, the real differentiator isn’t the model—it’s the data foundation beneath it. This fireside chat explores how leading BFSI organizations are re-engineering their data stacks to be built-for-purpose with AI in mind, embedding governance, semantic layers, and real-time pipelines from the start. Join us to hear how AI-native architectures can unlock trusted insights, faster innovation, and regulatory confidence.
• Discover how AI-first data architectures reduce risk and accelerate value creation in regulated BFSI environments.
• Learn how to embed governance, semantic layers, and feature reuse into your stack to support both analytics and AI.
• Understand the role of real-time pipelines, unstructured data, and vector search in enabling next-generation customer experiences
As AI becomes embedded across operations, from credit risk modeling and fraud detection to customer experience and regulatory reporting, data governance structures are under new scrutiny. Should data and AI be governed together under a unified framework, or separately to reflect their distinct risks, expertise requirements, and regulatory oversight?
• Discuss the advantages and limitations of unified versus separate governance models, and what each means for accountability and oversight.
• Explore examples of new committee structures and role definitions piloted by leading BFSI organisations to manage AI responsibly.
• Share experiences and lessons learned, including failures, to help shape adaptable governance frameworks that balance compliance, trust, and innovation.
Data culture and literacy are seen as long-term journeys, not quick fixes. Leaders have always stated that the biggest hurdles are often people and mindset, not technology. Investing time in communication, training, and gradual team growth (rather than rapid hiring) is critical. Modern data literacy goes beyond tool training—it’s about creating a culture where decisions are backed by evidence and accountability is shared. This panel will examine approaches for embedding data competence across the organisation.
• Hear actionable advice for successfully promoting data literacy within organizations.
• Learn how to increase stakeholder’s exposure to data to foster familiarity and encourage ownership and accountability.
• Explore ways to break down data silos and empower employees at all levels to access and utilize data effectively.
In 2026, the competitive edge lies in embedding insights directly into the workflows where frontline employees, risk analysts, and customer-facing teams make decisions. Whether it’s real-time credit scoring at point-of-sale, fraud detection in transaction flows, or personalised product offers during digital banking journeys, embedded analytics moves intelligence out of static dashboards and into the business moments that matter most.
This session explores the journey of building and scaling data products within a global financial services organization. Drawing on recent initiatives, it highlights the shift to data mesh architectures and the adoption of a client-centric platform approach to reduce time to market. The presentation will feature practical examples while candidly addressing the organizational and cultural challenges faced along the way. While showcasing few successes, the talk will touch also on the lessons learned, pitfalls encountered, and concrete steps to drive efficiency and value in complex data environments.