内容来源：https://aibusiness.com/finance/the-hidden-risks-of-following-big-banks-into-ai

内容总结：

谷歌云赞助内容：生成式AI在银行业的应用与挑战

当前，银行业正加速拥抱人工智能技术。美国银行、高盛等大型机构与谷歌、OpenAI合作推出AI工具，以提升运营效率和客户服务。但Hapax首席营销官Kevin Green指出，中小型银行在AI应用上面临独特挑战。

大型银行拥有充足资源组建工程师团队，能有效控制AI应用风险并快速推进规模化部署。然而，这种"军备竞赛"可能给行业带来误导性认知——认为AI部署简单且能立竿见影。事实上，缺乏持续培训和支持的AI工具往往难以发挥真正价值，甚至可能陷入投入产出失衡的困境。

Green特别强调两个关键风险：一是对AI能力的短视认知，目前行业多聚焦流程自动化，却忽视了AI在未来三到五年将带来的根本性变革；二是数据安全隐患，特别是涉及核心金融数据的访问权限问题，这直接制约中小银行从AI中获得价值的能力。

针对中小银行的发展路径，专家建议采取双轨策略：既要关注当前可通过AI实现的流程优化，更要制定长期发展规划。真正的竞争优势将来自对行业未来格局的前瞻性布局，而非简单地效仿大银行的现成方案。

（注：本文根据谷歌云赞助的行业访谈内容整理，观点来自Hapax公司首席营销官Kevin Green）

中文翻译：

由谷歌云赞助
选择您的首个生成式AI应用场景
开启生成式AI之旅，首先应关注能够提升人类信息交互体验的领域。

Hapax首席营销官Kevin Green揭示：为何中小型银行需要定制化AI解决方案，以及如何避免仓促应用带来的风险。随着美国银行、高盛等大型机构与谷歌、OpenAI合作推出AI工具以优化运营和客户服务，AI正在银行业加速渗透。

然而，大型银行拥有快速试验和扩展的资源，中小型机构却难以企及。专为金融领域打造AI平台的Hapax公司CMO Kevin Green指出：对于缺乏基础设施或战略支撑的机构而言，行业向AI的急速转型暗藏风险。

在接受《AI商业》独家专访时，Green阐述了缺乏商业计划就匆忙部署AI的隐患、行业定制化AI解决方案的必要性，以及金融企业如何在快速变革中保障自身安全。

《AI商业》：目前银行业AI应用呈现哪些趋势？
Kevin Green：所有银行都在探索AI如何融入其生态体系。应用速度非常快，当前趋势主要体现在银行试图在现有运营框架内快速部署这些工具。但大型银行与中小型银行之间出现了明显的断层。

我们目睹了富国银行、摩根大通、美国银行等巨头与OpenAI、谷歌达成大规模合作，将AI工具引入运营。这合乎逻辑——这些机构拥有大批能评估风险、构建防护体系和管理基础设施的工程师。但排名50开外的银行根本不具备这种资源实力。

这种断层会引发什么问题？
对大机构的关注营造了某种误导性叙事，让人以为AI易于部署且能立竿见影。但对中小型银行而言，实际情况复杂得多。大银行的行动虽验证了AI潜力，却给行业其他参与者设定了不切实际的预期。首要风险在于误以为AI是即插即用的解决方案。

富国银行向全员部署AI工具，并不代表员工能有效使用或创造实质价值。若缺乏持续培训、实际案例和支持，这些部署可能纸上谈兵，最终难以证明其成本合理性。富国与谷歌的合作年耗资很可能超过千万美元，终会有人质疑这笔投入的实际效果——而"优化搜索"远不足以成为有力答案。

第二大风险是对AI能力的短视。目前即便大银行也主要聚焦效率提升和流程自动化，这虽有价值，却只是第一步。真正的差异化将取决于机构如何为未来三到五年AI赋能的时代做准备。重点不仅是优化现有业务，更是重塑未来运营模式。

当前是否存在盲目追逐AI的急迫心态？
正是如此。双重动力在推动：AI技术快速演进，企业又面临彰显领先优势的压力。但若缺乏正确战略、资源或认知，仓促应用会适得其反。这并非新错误——软件时代已有前车之鉴。问题在于AI不仅是软件，更是基础设施，其构建、使用和维护方式存在根本差异。

想想多少企业曾重金引入大型软件平台，最终只使用了30%的功能。现在历史重演：企业采购通用AI工具，期望完美适配业务，却发现它们并非为特定需求设计。

中小银行或细分场景会出现更多定制化AI工具吗？
绝对如此，且已在发生。每个行业运作模式不同，甚至同行业企业也各有特点。因此我们需要更多定制化AI解决方案。科技巨头通过无代码代理构建器和拖拽界面降低了试验门槛，这有利于学习，但若未经大量额外工作，这些工具永远无法完全契合特定行业或机构。

网络安全如何影响行业格局？
当所有人使用相同通用工具时，风险随之而来。即使模型未用您的数据训练，数据仍存于系统中形成暴露风险。对处理高度敏感财务和个人数据的银行而言，这是重大漏洞。金融机构制定AI战略时首先应自问："是否放心让AI工具完全访问核心系统和数据？"

大银行可投入整个工程师团队规避这些风险，但数千家中小银行无此条件。若小银行因安全顾虑限制访问权限，其获取AI价值的能力从起点就已受限。对社区银行和中等规模银行而言，AI的最大价值将来自个性化服务——这些银行的核心竞争力在于了解客户并提供关系驱动型服务。但如果无法安全地将AI与核心数据连接，就无法提升这些体验，从而陷入两难：要么承担数据风险，要么错失发展机遇。

企业如何既跟上AI发展又保障安全与标准？
最明智的是制定双轨战略。首先关注AI能带来的即时效益，通常是通过自动化实现流程优化和效率提升——这也是当前大多数银行的焦点，这没有问题。但与此同时，必须具备长远视野。

目前多数战略周期偏短，极少组织真正思考"五年或十年后我们希望成为什么样"。虽然无法精准预测AI演进路径，但仍需要指引愿景的北极星。AI战略规划不能止步于改善现状，更需考量技术如何根本性重塑行业。

早期采用者将塑造未来。他们设定方向，其他企业要么跟随，要么掉队。因此行动确需紧迫感，但若没有清晰的长期路线图，就会暴露于不必要的风险之中。

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英文来源：

Sponsored by Google Cloud
Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
Hapax CMO Kevin Green reveals why smaller banks need tailored AI solutions and how to avoid the pitfalls of rushed adoption
AI is gaining traction in banking, with major institutions like Bank of America and Goldman Sachs launching their own AI-powered tools alongside Google and OpenAI to streamline operations and enhance customer service.
However, while large banks have the resources to experiment and scale quickly, the same can't be said for smaller institutions. According to Kevin Green, CMO of Hapax, an AI platform purpose-built for the financial sector, the industry's rapid shift toward AI comes with hidden risks for those without the infrastructure or strategy to support it.
In an exclusive interview with AI Business, Green discussed the pitfalls of rapidly deploying AI without a proper business plan, the need for bespoke AI solutions tailored to specific industries, and how financial companies can protect themselves in the rapidly changing landscape.
AI Business: What are you seeing in terms of AI adoption in banking right now?
Kevin Green: Right now, every bank is looking at how AI fits into their ecosystem. Adoption is happening fast, and the trend we're seeing is really around banks trying to rapidly implement these tools within their existing operational structure.
However, a problem we're seeing is a disconnect between the larger and smaller-scale banks.
We hear a lot about major banks — Wells Fargo, Chase, Bank of America — signing large-scale deals with the likes of OpenAI and Google to bring AI tools to operations. And that makes sense. These institutions have armies of engineers who can understand the risks, build guardrails, and manage infrastructure. But when you look beyond the top 50 banks, that level of resourcing simply doesn't exist.
What sort of problem does this disconnect cause?
The attention on the big players creates a somewhat misleading narrative. It suggests that AI is easy to implement and the value is immediate. However, the reality is much more complex for smaller or mid-size banks.
Moves by large banks are helping to validate AI's potential, but they also set unrealistic expectations for the rest of the industry. That's one big risk: Assuming this is plug-and-play when it's not.
Just because Wells Fargo rolled out AI tools to all their employees doesn't mean they're using them effectively or getting meaningful value. Without continuous training, real-world examples, and support, these rollouts might look impressive on paper but end up struggling to justify their cost. That Wells Fargo–Google deal is likely well over $10 million annually, and eventually, someone's going to ask what impact that spend is actually having—and "better search" won't be a strong enough answer.
The second major risk is a short-sighted view of what AI can do. Right now, even large banks are largely focused on efficiency gains and process automation, which is valuable, but that's just step one. The real differentiation will come from how institutions prepare for what AI will enable in the next three to five years. It's not just about doing what we do today better; it's about redefining how we'll operate in the future.
There's clearly pressure to adopt AI quickly. Is that urgency causing some companies to jump in without the right foundation?
Exactly. There's this dual momentum: AI is evolving rapidly, and companies feel pressure to show they're ahead of the curve. But if you adopt without the right strategy, resources, or understanding, it can backfire.
This isn't a new mistake; we saw the same thing in the software era. The problem is that AI isn't just software; it's infrastructure. It's fundamentally different in how it's built, used and maintained.
Think about how many companies adopted major software platforms only to end up using 30% of the features. They paid full price for a solution they barely used. We're seeing the same thing now: companies are buying general-purpose AI tools, expecting them to slot perfectly into their business, only to realize they're not designed for their specific needs.
Will we see more bespoke AI tools for smaller banks or niche use cases?
Absolutely, and that's already happening. Every industry operates differently, and even within industries, each company has its own nuance. So we'll need more customized AI solutions.
Big tech has made it easy to experiment with no-code agent builders and drag-and-drop interfaces, and that's great for learning. But they'll never be fully tailored to specific industries or institutions without a lot of extra work.
How do concerns around cybersecurity fit into this industry landscape?
There's a risk when everyone's using the same generalized tools. Even if the models aren't being trained on your data, the data still exists in those systems, creating exposure risk. For banks, where you're dealing with highly sensitive financial and personal data, that's a serious vulnerability.
One of the first questions a financial institution should ask when forming an AI strategy is: "Am I comfortable giving an AI tool full access to our core systems and data?"
Larger banks can afford to put entire engineering teams on mitigating those risks. But thousands of other banks don't have that luxury. And if a smaller bank decides it's not comfortable with that level of access, its ability to derive value from AI is limited right from the start.
The biggest value from AI, especially for community and mid-sized banks, will come from personalization. These banks compete on knowing their customers and delivering relationship-driven service. But if they can't safely connect AI to their core data, they can't use it to enhance those experiences. So they're stuck; either they take on the data risk or miss out on the upside.
What should companies do to stay ahead of AI advancements without compromising safety and standards?
The smartest approach is to develop a two-pronged strategy. First, look at the immediate benefits AI can bring; typically, that's around process optimization and efficiency through automation. That's what most banks are focusing on today, and that's fine. However, in parallel, there also needs to be a long-term view.
Right now, most strategy cycles are short. Few organizations are truly asking: "What do we want to look like in five or ten years?" And while it's impossible to predict exactly how AI will evolve, there still needs to be a North Star guiding that vision. Strategic planning around AI can't just be about improving what you do today; it should also consider how the technology will fundamentally redefine your industry.
Early adopters will help shape the future. They'll set the direction, and everyone else will either follow or get left behind. So yes, there's urgency to move, but without a clearly defined long-term trajectory, you're exposing yourself to unnecessary risk.
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