Finextra 与 Cloudera 联合发布报告显示，混合 AI 已成金融服务领域新标准，91% 的受访者认为其价值极高

Core Insights - 97% of enterprises face data silos, with data security identified as the biggest barrier to successful AI implementation, indicating a persistent "implementation gap" [1] - Hybrid AI deployment has become a crucial strategy in the financial services industry, with 91% of institutions recognizing its significant value [1][2] - Despite widespread AI adoption, major obstacles related to data and security hinder comprehensive integration within organizations [1][3] Hybrid AI Strategy - Currently, 62% of financial services institutions are adopting a hybrid AI strategy that integrates public cloud, data centers, and edge environments to deploy AI across any data environment [2] - This model is deemed essential for managing traditional systems, achieving efficient scaling, and addressing resource-intensive tasks during AI model training and deployment [2] - The widespread adoption of hybrid strategies indicates that flexible and data-unified infrastructure is now a core requirement for maintaining competitive advantage [2] Bridging the AI Implementation Gap - Data security is the primary obstacle in bridging the AI implementation gap, with 97% of financial services institutions stating that data silos impede their ability to build and deploy effective AI models [3] - Nearly half of the enterprises (48%) have completed initial AI pilot phases but have not fully integrated the technology into their core operations, putting them at a disadvantage compared to the 26% of companies that have achieved full-scale AI integration [3] - A unified data and AI platform is necessary for financial institutions to ensure consistent governance and security across all environments, thereby building trust and managing risks [3] Importance of Data Governance - As hybrid deployments rise, 84% of organizations consider implementing a unified data governance and security framework as "critical" or "very important" [6] - One-quarter of enterprises (25%) prioritize security when evaluating AI platform vendors, highlighting the importance of security capabilities in driving AI investments [6]