As banks shift toward more predictive and clientcentric engagement models, traditional lead management systems (LMS) face growing pressure to evolve. The study provides a comprehensive assessment of current AIenabled LMS and orchestration platforms, including Salesforce Financial Services Cloud, Pega Customer Decision Hub, and Personetics, evaluating their capabilities in lead scoring, real-time decisioning, and hybrid advisory support.

Drawing on a qualitative, scenario-based methodology, the research integrates platform documentation, regulatory frameworks, and industry analyses to examine the technological, ethical, and compliance dimensions of AI adoption in banking. Particular attention is given to the evolving role of client advisors and the operational implications of embedding GenAI into advisory workflows. The thesis proposes a maturity model and strategic roadmap for AI-enabled orchestration, identifying key stages of technological integration and human-AI collaboration.

Findings indicate that while GenAI offers significant potential for improving lead prioritization, customer engagement, and advisor productivity, its implementation also introduces complex challenges around explainability, bias mitigation, and regulatory compliance. Emerging legal frameworks, such as the EU AI Act and global data protection laws, underscore the need for robust AI governance and human oversight. The thesis concludes with actionable recommendations for financial institutions aiming to implement AI responsibly and effectively, aligning innovation with compliance and trust. By bridging technological capabilities with human-centric advisory models, this research contributes to the academic discourse on AI adoption in financial services and offers strategic insights for institutions preparing for the client engagement landscape of 2030.