AI Is Training Staff Very Quickly, Says RBC's McKay

RBC CEO Dave McKay said AI is training staffers at an "unbelievable" pace and is making workers more efficient. The comments suggest productivity gains from artificial intelligence adoption in banking, but no financial metrics, guidance changes, or strategic announcements were provided. Market impact is limited and the report is primarily commentary on technology adoption.

Analysis

This is more important as an operating-margin signal than a headline about “AI enthusiasm.” If one of the best-capitalized North American banks is already reporting materially faster onboarding and task throughput, the first-order winner is not just RY but the entire crop of enterprise software and infrastructure vendors that can credibly show labor productivity gains inside regulated workflows. The second-order loser is any bank or fintech still treating AI as a pilot project: once a peer demonstrates measurable efficiency, procurement cycles tend to compress and management teams lose the ability to argue for slower adoption.

The key financial question is whether efficiency gains stay as soft-dollar cost avoidance or convert into hard expense compression over the next 2-4 quarters. In banking, even small productivity improvements can matter: if AI trims a low-single-digit percentage from non-interest expense run-rate, the incremental upside to pre-provision earnings can be meaningful because operating leverage compounds on a relatively fixed revenue base. The risk is that early gains are front-loaded in training and knowledge retrieval, while compliance, model risk, and workflow integration create friction that delays actual headcount reduction into 2025-2026.

The market may still be underappreciating how this shifts competitive dynamics within financials. Big banks with larger datasets and stricter process discipline are better positioned to capture AI ROI than smaller regional lenders, which suggests a widening gap in efficiency ratios and cost of funds resilience over time. The contrarian view is that the current reaction may overestimate near-term P&L translation: regulators are likely to slow the monetization of AI in customer-facing and decisioning processes, so the immediate winner is productivity optics, not an instant EPS step-up.

Key Decisions for Investors

• Long RY versus a regional-bank basket (e.g., KRE) over the next 3-6 months; thesis is that scale plus AI adoption will widen efficiency and operating-leverage gaps before smaller banks can respond.
• Add to quality enterprise software beneficiaries on pullbacks (MSFT, NOW) for a 6-12 month horizon; banks are early reference customers, and the spend should increasingly shift from experimentation to workflow deployment.
• Initiate a relative-value trade: long RY / short a labor-intensive bank with weaker scale economics over 1-2 quarters; risk/reward favors the bank that can convert productivity gains into expense discipline sooner.
• Buy medium-dated call spreads in RY into the next earnings season if management commentary starts quantifying cost saves; the catalyst is a re-rating on evidence, not the current optimism.
• Avoid extrapolating the signal into near-term headcount cuts; if regulatory or model-risk scrutiny intensifies, trim AI-exposed financial longs and rotate into vendors selling compliance-friendly automation.