Royal Bank of Canada has taken an unusual step in the AI era: creating a dedicated AI Group with a leader reporting directly to the CEO, signaling that artificial intelligence is no longer just a side project but a core business strategy. The move comes as the bank, which launched its Borealis Research Institute in 2016, looks to scale AI across banking, wealth management, and capital markets while managing the risks that come with deploying powerful new technologies across a global workforce of more than 100,000 employees .

The creation of this standalone team reflects a shift in how large financial institutions are approaching AI. Rather than embedding AI capabilities within existing business units, RBC decided that the scale of the opportunity required dedicated leadership and resources. The bank has already ranked in the top three among 50 global banks in the Evident AI ranking for four consecutive years, suggesting that early investment in AI infrastructure is paying off .

What AI Products Is RBC Already Running in Production?

RBC isn't starting from scratch. The bank has several AI-powered products already serving clients and employees. These include NOMI Insights for online banking, Aiden for Capital Markets, the Lumina data platform, and ATOM, a large transaction model used across 15 RBC products and processes. The bank is also seeing real adoption: more than 35,000 employees are using RBC Assist, the bank's in-house generative AI tool; 9,000 Capital Markets employees are using Aiden; and 4,000 developers are using generative AI-enabled development tools .

These aren't experimental projects. ATOM, for example, is improving credit performance with early warning signals and enabling advisors to deliver personalized advice at scale. Aiden is enhancing trading through deep reinforcement learning, a machine learning technique that allows systems to learn by trial and error. The bank is also using AI to transform procedural processes like code development, fraud detection, and cybersecurity .

How Will RBC Balance Speed With Safety and Compliance?

One of the thorniest challenges in deploying AI at scale is moving fast without creating regulatory or reputational problems. RBC has invested heavily in governance frameworks designed to let the bank explore and build generative AI tools while meeting regulatory expectations. The bank has formalized a set of responsible AI principles focused on ensuring accountability, fairness, privacy, security, and transparency across all AI efforts .

This governance-first approach reflects a broader shift in how regulators are thinking about AI in finance. The Securities and Exchange Commission (SEC) has signaled that it will evaluate advisers' use of AI through the lens of fiduciary duty of care, meaning regulators will ask whether advisers understood, tested, and supervised AI tools and ensured that AI-influenced decisions remained in clients' best interests .

Steps for Financial Firms Implementing AI in Investment Decisions

• Explainability: Investment personnel should be able to explain in plain terms what an AI tool is designed to do, what information it relies on, its material limitations, and how its output is weighed against other analysis. Personnel should avoid treating tools as a black box and understand when outputs should be discounted or overridden.
• Documentation: Maintain clear records of intended use cases, material features, and any changes to AI tools. This is critical because AI tools can evolve quickly, and documentation prevents models from being repurposed beyond their original scope, which is often where operational and regulatory problems emerge.
• Model Transparency and Validation: Understand the types of patterns a model is designed to detect, the controls around training, and circumstances in which output has historically failed. For commercially developed models, this may require due diligence on the provider's training data to ensure material non-public information has not been ingested.
• Governance: AI policies should sit within an adviser's existing compliance framework with a clear line of authority for use of AI tools and a framework for monitoring their use and implementation.
• Privacy and Data Security: Understand how customer information flows into and through AI tools, how information is transformed, and whether any client data is disclosed or used for further training.

These considerations matter because as AI tools move closer to making investment decisions autonomously, additional oversight becomes necessary. The SEC's Division of Investment Management has explicitly raised questions about potential benefits and risks from advisers' use of AI, whether an AI agent itself might need to be registered, and who bears liability when AI-driven outputs are wrong or misleading .

What Does Workforce Transformation Look Like in Practice?

RBC's approach to workforce transformation reveals how large banks are thinking about the human side of AI adoption. The bank recognizes that AI is a generational change and that, like all major technology shifts, current jobs will change and new jobs will be created. Rather than viewing AI as a replacement for human workers, RBC sees it as a tool that can enhance and augment the work of people, liberate them from mundane tasks, and create new roles and mandates .

"Our employees are our greatest asset, and we believe that AI can help us achieve our global ambition to serve millions more clients than we do today with the team and resources we already have," said Bruce Ross, Group Head of AI at RBC.

Bruce Ross, Group Head of AI, Royal Bank of Canada

The bank is taking concrete steps to prepare its workforce. This means equipping employees with AI tools suitable for their roles, boosting AI training capabilities so they can learn, and helping managers and leaders assist employees through the transition. The bank also recognizes the unique and special contributions of human relationships in many business areas, viewing AI as a complement rather than a replacement .

Where Is the Broader AI Finance Market Heading?

RBC's investment in AI reflects a massive market opportunity. The global applied AI in finance market was valued at $14.82 billion in 2025 and is predicted to grow to $17.80 billion in 2026, reaching approximately $92.53 billion by 2035, expanding at a compound annual growth rate of 20.10% . This explosive growth is being driven by increasing adoption of automation solutions in the banking, financial services, and insurance sectors, coupled with the rise in fintech startups worldwide .

The market is being shaped by several key trends. Fraud detection and prevention currently leads applications with a 32% market share in 2025, but risk management is expected to grow at the highest rate of 18.1% annually. Machine learning dominates the technology segment with a 43% share, though robotic process automation (RPA), which automates repetitive tasks, is expected to grow fastest at 17.9% annually. Banking currently leads by end-use industry with a 48% share, but insurance is expected to expand fastest at 18.3% annually .

North America currently leads the applied AI in finance market with a 39% share, but Asia Pacific is expected to grow with the highest compound annual growth rate of 20.5% during the forecast period. This geographic shift reflects the rapid expansion of fintech and digital banking in emerging markets .

RBC's creation of a dedicated AI Group, combined with its track record of early investment and responsible governance, positions the bank to capture significant value from this market expansion. The bank's approach, balancing speed-to-market with careful risk management and workforce preparation, offers a template for how large financial institutions can navigate the AI era without sacrificing stability or employee wellbeing.