内容来源：https://aibusiness.com/finance/start-small-scale-smart-the-ai-roadmap-for-financial-success

内容总结：

谷歌云赞助研究显示：生成式AI在金融服务业虽面临部署瓶颈，但通过"小步快跑"策略可实现突破性进展。根据《2025年CDO洞察报告》，欧洲65%的数据领导者因风控压力未能将超半数AI试点项目投入生产，35%因难以证明投资回报率而无法获得管理层支持。

尽管如此，AI每年可为全球银行业创造高达1万亿美元的附加价值。领先机构正通过三大实践路径稳妥推进：
一是部署执行代理实现可量化收益，从单一任务切入构建分层智能体系；
二是利用AI优先解决数据质量问题，77%的欧洲数据领导者计划今年增加数据管理投入；
三是通过AI简化合规报告流程，将BCBS 239等监管报告的起草工作自动化。

目前76%的金融机构计划在一年内部署代理型AI，采用小规模试点、验证价值后逐步扩展的模式，正成为兼顾创新与合规的有效路径。

中文翻译：

由谷歌云赞助
如何选择首个生成式AI应用场景
开展生成式AI应用时，应首先聚焦于能优化人类信息交互体验的领域。
金融服务机构可采用"落地推广"策略：既在早期验证价值又确保合规性，从而突破部署障碍
2025年8月28日
数据领导者正陷入两难境地：既要加速AI应用步伐，又需管控风险。根据我们《2025年CDO洞察报告》，欧洲65%的数据领导者将不到半数AI试点项目投入生产。另一个阻碍AI部署的关键因素是投资回报验证难题——高达35%的欧洲数据领导者因无法证明价值而难以获得管理层对AI投资的支持。

尽管存在这些障碍，据预估仅全球银行业每年就能通过AI创造高达1万亿美元的附加价值。

值得庆幸的是，部分金融机构正在安全部署代理型AI且不触发合规警报方面树立典范。这些企业在欺诈防控、信贷风险评估、客户服务路由等实时任务中取得进展时并未盲目扩张，而是采用"落地推广"策略：从小处着手、快速验证价值，待安全性与有效性得到证实后再扩大规模。

这种"落地推广"策略主要通过三种方式实施：

1) 部署执行代理实现可衡量的成效
从小规模起步并展现明确回报是关键。通过目标明确的单任务代理启动AI计划，能为更先进的代理系统铺平道路。这类工具的输出结果易于量化，成效展示也更为直观。

这种初级部署为后续扩展奠定基础，可逐步构建分层代理架构体系。待价值与安全性得到验证后，项目后期可扩展至规划型与协调型代理。

2) 优先利用AI修复数据
即使达到合规要求，AI部署的实际成效仍取决于数据质量。我们《2025年CDO洞察报告》显示，77%的欧洲数据领导者计划今年增加数据管理投入，其中近半数（45%）将AI部署前的数据准备列为主要动因。

这看似循环论证，但AI确实能改善数据质量。一些金融机构正使用AI模型解决应收账款中长期存在的数据质量问题，如记录不匹配和条目过时等。数据清理后，企业能清晰掌握债务明细，通过自动化跟进流程加速收款并改善现金流。这些经核验的准确数据还可放心投入更广泛的流程中。

3) 降低合规报告中的手工操作
合规领域本身正是开发AI应用的理想起点。合规报告耗时巨大，需要团队在数据海洋中艰难搜寻。

企业数据清理完成后，AI可大幅简化这项工作。常见的快速成效是实现监管报告（如BCBS 239）文档自动化——这类通常仅由一人完全掌握的任务本身存在风险。通过元数据映射与代理型AI结合，机构可自动生成报告初稿，同时通过人工监督确保合规性。

随着76%的金融服务机构计划在未来12个月内实施代理型AI，企业已拥有清晰的发展路径。无论金融机构在哪个领域部署AI，从小规模起步逐步扩展的策略都能促使数据团队与合规团队协同工作，共同开发既能服务组织、又能保持良好治理并全面提升绩效的模型。

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英文来源：

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Choosing Your First Generative AI Use Cases
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Overcome deployment barriers with a 'land and expand' approach that proves value early while maintaining compliance in financial services
August 28, 2025
Data leaders are stuck in limbo, caught between the pressure to accelerate AI adoption and the need to manage risk. As a result, 65% of data leaders in Europe have transitioned less than half of their AI pilots to production, according to our CDO Insights 2025 report. Another key blocker preventing AI deployment is the challenge of proving ROI. As many as a third (35%) of European data leaders are struggling to secure backing from leadership for AI investment because they can’t demonstrate value.
Yet despite these barriers, estimates suggest AI could deliver up to $1 trillion in additional value each year for global banking alone.
Fortunately, there are some financial services organisations that are showing what’s possible- deploying agentic AI safely, without triggering compliance alarms. From real-time tasks like fraud mitigation, credit risk evaluation, and customer service routing, the firms making progress aren’t betting big. Instead, they’re using a “land and expand” approach – starting small, proving value early, and scaling once safety and efficacy are demonstrated.
There are three practical ways this “land and expand” approach is being applied:
1) Deploying executor agents for measurable wins
Starting small and showing clear returns is key. But, beginning AI programmes with focused, single-task agents with clear goals can help beat a path for more advanced agentic systems. By nature, these tools have outputs that are simple to measure and results that are easy to present.
Starting at this level can help build a strong rationale to expand further, forming the foundation of a larger layered agentic architecture – which can be scaled to include planner and orchestrator agents later in the project, once value and safety have been proven.
2) Using AI to fix the data first
Even once compliance is achieved, the actual success of an AI deployment still depends on the data it’s being fed. With that in mind, it’s not surprising that our CDO Insights 2025 report found that 77% of European data leaders expect to invest an increasing amount in data management this year. Almost half (45%) cite getting their data ready for AI deployments as the primary driver behind this spend.
It might sound circular, but AI can help here. We’re seeing some financial institutions use AI models to overcome long-standing data quality issues in accounts receivable, such as mismatched records and outdated entries. Once the data has been cleaned up, they can gain a clearer view of who owes what and when, enabling automated follow-up processes that accelerate collections and improve cash flow. And that data can then be fed into wider processes with confidence that it’s accurate and up to date.
3) Reducing manual work in compliance reporting
Compliance itself can be a great place to begin developing AI deployments. For example, compliance reporting is heavily time consuming, requiring teams to trawl through a sea of data.
Once the company’s data has been cleaned up, AI can make light work of the task. A common quick win is automating the documentation required for regulatory reports like BCBS 239 – often a task that only one person is fully trained to complete, which brings risk in itself. By combining metadata mapping and agentic AI, institutions can automate first drafts of these reports, while still ensuring regulatory compliance through human oversight.
With 76% of financial services organisations planning to implement agentic AI within the next 12 months, businesses have a clear path forward. Wherever financial organisations are seeking to deploy AI, starting small and working up can enable data and compliance teams to work together – developing models that serve the organisation, maintain good governance, and improve performance across the board.
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