Core Viewpoint - The competition for generative AI in regional banks has shifted from technological exploration to value realization, making it essential for these banks to capture AI value and implement applications effectively [1]. Group 1: Current State of Generative AI in Banking - Generative AI applications are expanding from internal use to client-facing services, transforming operational models and customer service methods within banks [2]. - The emergence of multi-agent systems is providing comprehensive solutions that can cover complex processes, allowing generative AI agents to act as virtual colleagues [3]. Group 2: Impact on Profitability - Generative AI is expected to significantly enhance productivity across industries, with banking projected to see a potential productivity increase of $200 billion to $340 billion, translating to a 14%-24% potential profit increase, which could rise to 60%-80% over the next three years [4]. Group 3: Challenges in AI Adoption - Despite the apparent technological benefits, regional banks face significant barriers to large-scale AI application, including data silos and a shortage of hybrid talent, with an estimated talent gap of 5 million in China by 2030 [7]. - Regional banks must address three core questions: how to focus on high-value scenarios with limited resources, how to balance short-term wins with long-term strategies, and how to manage innovation and ecosystem collaboration [7]. Group 4: High-Value AI Application Scenarios - Six high-value AI application scenarios are emerging as key areas for regional banks to leverage AI capabilities, transitioning from experimental phases to growth drivers [8]. - These scenarios include credit risk management, customer relationship management, software development efficiency, intelligent customer service, hyper-personalized services, and knowledge management [10]. Group 5: Strategic Pathways for Regional Banks - Regional banks must choose between three strategic models: "builders" who deeply reconstruct core business, "innovators" who enhance middle and back-office processes, and "adopters" who focus on efficiency improvements [14]. - A comprehensive AI transformation framework is necessary, integrating AI with overall business strategy and ensuring that AI investments are directly linked to financial metrics [15][16]. Group 6: Collaboration and Ecosystem Development - Finding suitable ecosystem partners is crucial for regional banks to quickly develop strategies and implement use cases, allowing them to leverage existing solutions and accelerate their AI adoption [17]. - The future of banking will see AI not just as a tool for efficiency but as a core competitive advantage for enhancing customer service, optimizing risk management, and improving operational resilience [18].