Fostering breakthrough AI innovation through customer-back engineering

通过“以客户为中心”的工程理念推动 AI 突破性创新

Despite years of digitization, organizations capture less than one-third of the value expected from digital investments, according to McKinsey research. That’s because most big companies begin with technological capabilities and bolt applications onto them, rather than starting with customer needs and working backward to technology solutions. 根据麦肯锡的研究，尽管经历了多年的数字化转型，企业从数字投资中获得的价值却不到预期价值的三分之一。这是因为大多数大公司往往从技术能力出发，再在上面附加应用程序，而不是从客户需求出发，反向推导技术解决方案。

Not prioritizing the customer can create fragmented solutions; disjointed customer experiences; and ultimately, failed transformations. Organizations that achieve outsized results from AI flip the script. They adopt a “customer-back engineering” mindset, putting customers at the heart of technology transformation. 不优先考虑客户可能会导致解决方案碎片化、客户体验脱节，最终导致转型失败。那些从人工智能中获得超额成果的组织则反其道而行之。他们采取了“以客户为中心”的工程思维，将客户置于技术转型的核心位置。

It’s a strategy in which products and services are developed with the customer experience first in mind, including the customers’ challenges, needs, and expectations. Product development teams then work backward in a nimble and agile way to find the steps necessary to design and build solutions that achieve the desired experience. 这是一种将客户体验放在首位的开发策略，涵盖了客户面临的挑战、需求和期望。产品开发团队随后以灵活敏捷的方式进行反向推导，寻找设计和构建解决方案所需的步骤，以实现预期的体验。

“When you get your engineers closer to customers, you get a lot more sideways innovation,” says Ashish Agrawal, managing vice president of business cards and payments tech at Capital One. “That leads to a multiplier effect, because engineers can approach a problem from a different dimension that can be unique to the sales or product perspective.” “当你让工程师更贴近客户时，你会获得更多的横向创新，”第一资本（Capital One）商业卡与支付技术部门执行副总裁 Ashish Agrawal 表示。“这会产生乘数效应，因为工程师可以从不同于销售或产品视角的维度来处理问题。”

The case for customer-centricity in engineering

工程领域中“以客户为中心”的必要性

Engineers are problem-solvers by nature, says Agrawal. When they hear about challenges customers are experiencing, or how they are using products and services in the real world, they can devise ways to efficiently address customer needs, since they are naturally closer to systems and data than many other teams across the company. Agrawal 说，工程师天生就是解决问题的人。当他们了解到客户所面临的挑战，或者客户在现实世界中如何使用产品和服务时，他们就能想出有效满足客户需求的方法，因为他们比公司内其他许多团队更接近系统和数据。

“Fostering a customer-centric culture has a motivational effect on engineers when they actually start seeing how the core changes they’re making, or the features they’re adding, are having a direct impact on the lives of customers,” says Agrawal. “当工程师真正看到他们所做的核心变革或添加的功能如何对客户的生活产生直接影响时，培养以客户为中心的文化会对他们产生激励作用，”Agrawal 说道。

It also takes discipline. Agrawal explains that Capital One has set a goal for every engineer in his organization to establish several touchpoints with customers throughout the year in different forms, including: 这也需要纪律。Agrawal 解释说，第一资本为其组织内的每位工程师设定了一个目标，即全年通过不同形式与客户建立多个接触点，包括：

• Digital empathy sessions to observe user journeys and identify where users hit friction
• 数字共情会议：观察用户旅程并识别用户在何处遇到阻碍
• Embedded customer support for periods of time to deepen understanding of servicing needs
• 嵌入式客户支持：在一段时间内参与客户支持工作，以加深对服务需求的理解
• Engineering ride-alongs, in which engineers join customer success, sales, and support staff on calls or on-site visits
• 工程随行：工程师加入客户成功、销售和支持人员的通话或实地考察中
• Hackathon competitions to build solutions around real customer problems
• 黑客马拉松竞赛：围绕真实的客户问题构建解决方案

The AI opportunities with customer-centricity

以客户为中心的 AI 机遇

“The biggest challenge engineers within large companies face is a lack of direct access to customers,” says Agrawal. “This can make it harder for technologists to work with customers to identify problems and innovate solutions.” “大公司内部工程师面临的最大挑战是缺乏与客户的直接接触，”Agrawal 说。“这使得技术人员更难与客户合作来识别问题并创新解决方案。”

AI has accelerated the challenges as well as the opportunities. The lifecycle of launching products has become significantly faster. But the good news is that engineers are closer to the data that feeds into AI, so they can more rapidly apply AI-informed data techniques to solve customer problems. 人工智能既加速了挑战，也带来了机遇。产品发布的生命周期已显著加快。但好消息是，工程师更接近为人工智能提供支持的数据，因此他们可以更迅速地应用基于人工智能的数据技术来解决客户问题。

Agrawal outlines a recent scenario: In the customer servicing space, conversations can instantly be summarized and give a customer agent context on the member’s original request and remaining action points. Agentic AI can also be enabled to ask pointed follow-up questions about the interaction that would otherwise take human agents time to read through the entire thread. Agrawal 概述了一个最近的场景：在客户服务领域，对话可以被即时总结，并为客服人员提供关于会员原始请求和剩余待办事项的背景信息。智能体 AI（Agentic AI）还可以被启用，针对互动提出针对性的后续问题，而这些问题如果由人工客服处理，则需要花费时间阅读整个对话记录。

“A solution would have been a lot harder in an ecosystem without a lot of high-quality data,” says Agrawal. “But when you combine a rich data ecosystem with agentic tools, you move from incremental fixes to high-velocity transformation.” “在一个缺乏高质量数据的生态系统中，这样的解决方案会困难得多，”Agrawal 说。“但当你将丰富的生态数据与智能体工具结合时，你就从渐进式修复转向了高速转型。”

By investing in AI data and tools and focusing on rapid experimentation, Agrawal says the cycle of deploying solutions can be accelerated. Teams learn that if they meet customer needs and iterate on a wider range of solutions much faster, then the entire innovation cycle speeds up. Agrawal 表示，通过投资 AI 数据和工具并专注于快速实验，部署解决方案的周期可以被加速。团队了解到，如果他们能更快地满足客户需求并迭代更广泛的解决方案，那么整个创新周期就会加快。

For example, Capital One used customer insights to build a state-of-the-art, multi-agent AI framework called Chat Concierge to enhance the customer experience for car buyers and dealers. In a single conversation, Chat Concierge can perform tasks like comparing vehicles to help car buyers decide on the best choice and scheduling test drives or appointments with salespeople. 例如，第一资本利用客户洞察构建了一个名为“Chat Concierge”的最先进的多智能体 AI 框架，以提升购车者和经销商的客户体验。在单次对话中，Chat Concierge 可以执行诸如比较车辆以帮助购车者做出最佳选择，以及安排试驾或与销售人员预约等任务。

Agrawal explains that car buyers can engage with Chat Concierge directly through participating dealer websites. Dealers can access and can take over the chat through Navigator Platform. The AI assistant consists of multiple logical agents that work together to mimic human reasoning, allowing it to provide information and take action based on the customer’s requests. Agrawal 解释说，购车者可以直接通过参与活动的经销商网站与 Chat Concierge 互动。经销商可以通过 Navigator 平台访问并接管聊天。该 AI 助手由多个逻辑智能体组成，它们协同工作以模拟人类推理，从而能够根据客户的请求提供信息并采取行动。

The elements of an AI-first mindset

“AI 优先”思维的要素

According to a recent MIT Technology Review Insights survey, 70% of leaders say their firm uses agentic AI to some degree. Roughly half of executives say agentic AI systems are highly capable of improving fraud detection (56%) and security (51%), reducing cost and increasing efficiency (41%), and improving the customer experience (41%). 根据《麻省理工科技评论》洞察（MIT Technology Review Insights）最近的一项调查，70% 的领导者表示他们的公司在一定程度上使用了智能体 AI。大约一半的高管表示，智能体 AI 系统在改善欺诈检测（56%）和安全性（51%）、降低成本和提高效率（41%）以及改善客户体验（41%）方面能力极强。

Looking into the future, achieving these outcomes looks even more likely. More than half of the banking executives surveyed say they expect to continue to improve fraud detection (75%), security (64%), and the customer experience (51%). 展望未来，实现这些成果的可能性看起来更大。超过一半受访的银行高管表示，他们预计将继续改善欺诈检测（75%）、安全性（64%）和客户体验（51%）。

Agentic AI use cases that show strong potential to transform the customer experience in financial services include responding to customer services requests, adjusting bill payments to align with regular paychecks, or extracting key terms and conditions from financial agreements. 在金融服务领域，展现出巨大转型潜力的智能体 AI 用例包括：响应客户服务请求、调整账单支付以匹配定期工资，或从金融协议中提取关键条款和条件。

Placing the customer at the center of a transformation requires an AI-first mindset. Companies must shift from simply augmenting an existing product to fundamentally reimagining the problem and the user’s needs through the lens of AI’s capabilities. A few best practices that Agrawal recommends include: 将客户置于转型的中心需要一种“AI 优先”的思维方式。企业必须从单纯地增强现有产品，转变为通过 AI 能力的视角，从根本上重新构想问题和用户需求。Agrawal 推荐的一些最佳实践包括：