Data Management

Governance in Motion — A Peek into RBC’s Adaptive Real-Time Edge in the AI Era

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Written by: CDO Magazine Bureau

Updated 3:00 PM UTC, Fri September 5, 2025

Royal Bank of Canada (RBC), one of the largest financial institutions in North America with over 17 million clients across 29 countries, has consistently been at the forefront of innovation in banking and financial services. Known for its early investments in digital transformation and AI-driven solutions, RBC continues to build scalable, intelligent systems that enhance decision-making and deliver measurable value for clients and the enterprise alike.

In this first installment of a two-part series, Farid Sheikhi, Senior Manager of Analytics Innovation and Data Enablement at RBC, sits down with Clive Smith, CRO at Datavid, to discuss how the bank is architecting its data and AI foundations for the future. From evolving data platforms and adaptive governance models to embedding intelligence directly into business workflows, Sheikhi offers a candid look at how RBC is positioning itself to thrive in an increasingly AI-powered landscape.

Edited Excerpts

Q: To start with, could you explain your role at RBC?

I lead initiatives at the intersection of data strategy, AI innovation, and scalable analytics platforms. My focus is on transforming complex data environments into intelligent systems that deliver real business outcomes. From automating data discovery to enabling AI-powered decision-making — whether I’m working hands-on with data architecture or shaping governance frameworks for emerging technologies like GenAI — my goal is to accelerate insight and create measurable value across the enterprise.

Q: What are the key architectural decisions that prepared RBC for the shift toward AI-driven decision-making?

We’ve evolved our architecture around four pillars: Cloud-native scalability, Reusable data products, Event-driven pipelines, and Embedded governance. Moving away from monolithic platforms toward modular components allows us to deploy AI capabilities rapidly, securely, and in a composable way across different parts of the business.

Q: How has RBC adapted its governance to balance innovation speed with risk management in a heavily regulated environment?

There’s a myth that governance must be rigid. The reality is that with GenAI, governance has to be adaptive. We’re integrating real-time controls — such as policy-driven access, automated lineage, and bias detection — directly into the data lifecycle. AI-specific risk assessments are also embedded early in the development lifecycle, enabling us to collaborate closely with compliance and model risk teams to design governance rather than enforce it after the fact.

Q: What capabilities are you building now that you believe will differentiate RBC in the next three to five years?

We’re focused on three key areas. First, model observability to ensure transparency, explainability, and drift management. Second, synthetic data to enhance privacy and simulate edge cases in banking scenarios. And third, composable intelligence, enabling business teams to configure and tailor AI components without having to rebuild from scratch.

Q: How is RBC embedding intelligence into business processes to drive more autonomous decision-making?

Our philosophy is to augment first. In an enterprise of our size, there’s a significant opportunity for automation. In commercial banking, for example, we’ve embedded intelligent nudges into CRM tools to help relationship managers spot cross-sell opportunities or flag potential client risk. These recommendations are contextual and explainable, seamlessly integrated into their daily workflows instead of requiring them to reference separate dashboards.

Q: What’s your perspective on agentic AI systems that reason, plan, and act with minimal human intervention?

A: In banking, agentic AI is promising for closed-loop systems like internal automation or data reconciliation, but we’re cautious. We focus on building systems with well-defined guardrails, ensuring that agents escalate decisions when uncertainty is high. It’s not about removing humans; it’s about knowing when the AI should ask for help.

CDO Magazine appreciates Farid Sheikhi for sharing his insights with our global community.

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