The text explores the strategic adoption of generative AI in financial services, emphasizing that the sector is shifting from debating AI's adoption to determining effective implementation while managing risks. As financial firms operate under strict regulatory scrutiny, security remains a top priority, particularly concerning customer data and transaction records, leading to a preference for private AI deployments to ensure compliance. The text highlights the importance of starting with high-impact internal use cases that provide immediate efficiency gains, such as using AI to enhance customer service by quickly retrieving policy information, thereby building trust and proving GenAI's value. It discusses the competitive advantage gained by leveraging proprietary data for customized applications, such as improved fraud detection through large language models, citing Mastercard's success in reducing false positives. The emphasis is on planning for enterprise-wide AI adoption from the outset, considering infrastructure, data governance, employee training, and regulatory compliance. Ultimately, the transformation will favor firms that balance innovation with compliance, focus on internal efficiencies, and strategically plan for scaling AI adoption.