Edith Heroux

Multi-asset broker-dealers operating in today's capital markets face operational complexity that has outpaced traditional automation capabilities. A typical firm like Fidelity Investments or Charles Schwab manages millions of client accounts across equities, fixed income, options, futures, and alternative investments, executes billions in daily trading volume, maintains custody relationships with hundreds of counterparties, and navigates regulatory obligations spanning multiple jurisdictions. Each of these functions involves decision-making that requires contextual understanding, pattern recognition across unstructured data, and adaptive responses to novel situations—capabilities where rules-based automation fails but where generative AI excels. Understanding how Generative AI for Investment and Brokerage transforms specific operational workflows reveals not just efficiency gains but fundamental reimagining of how broker-dealers create value in the modern financial ecosystem. The a...

Investment banks are moving beyond experimental AI pilots into full-scale production environments where generative models handle everything from equity research synthesis to regulatory compliance documentation. The mechanics of this transformation reveal a far more complex undertaking than simply purchasing software licenses and flipping a switch. Enterprise GenAI Deployment in the investment banking context requires orchestrating infrastructure across trading floors, risk management systems, and client-facing platforms while maintaining the stringent controls that regulators and internal audit teams demand. The operational reality of Enterprise GenAI Deployment begins with architecture decisions that most banks confront within their first planning quarter. Leading investment banks architect their GenAI infrastructure around three distinct layers: a foundational model layer hosting either proprietary or commercial large language models, a middle orchestration layer managing prompt eng...