Uber Accelerates Customer Support and Development with Generative AI: How They Leverage Both Wheels

Which Company's Case Study?

This time, we look at the case study of Uber, which operates ride-sharing and food delivery services in over 70 countries and 10,000 cities worldwide. The company faces challenges unique to a global platform, such as managing a vast number of daily customer inquiries and improving the productivity of its large development organization.

Challenges They Wanted to Solve

Uber faced two major challenges. One was the difficulty for customer support agents to quickly and accurately find answers to inquiries from an enormous internal knowledge base. The other was the need to improve development speed, as software developers were spending significant time on coding, testing, and large-scale codebase migration tasks.

How They Used AI

Uber has implemented generative AI to solve multiple internal challenges. For customer support operations, they developed a tool that integrates their internal knowledge base with generative AI, allowing agents to instantly grasp summaries of past customer interactions and generate suitable answer drafts for inquiries. For developers, they introduced an AI assistant trained on internal code and documentation to support code generation, test automation, and large-scale refactoring (improving internal code structure). These solutions are built and operated on the company's AI platform, "Michelangelo".

Implementation Effects and Key Takeaways

• ▸Streamlined Customer Support: AI assistants enable agents to quickly understand the context of inquiries and shorten resolution times. This improves customer satisfaction and allows agents to focus on more complex issues.
• ▸Increased Developer Productivity: Through internal hackathons and other initiatives, the effectiveness of generative AI in areas such as code generation and test automation was validated. Developers are now expected to dedicate more time to creative tasks, leading to faster service improvements.
• ▸Investment in Employee Experience: Uber actively utilizes AI not only for customer-facing services but also for improving employee work efficiency. The company promotes AI utilization as a company-wide strategy, with the CEO himself stating that "engineers with AI are superhumans."

What Japanese Companies Can Learn

Uber's case demonstrates that AI utilization is not limited to developing customer-facing features. Specifically, the approach of combining "internal knowledge"—such as vast manuals, past inquiry histories, and specification documents accumulated within the company—with generative AI to improve employee productivity is applicable across many industries. A realistic first step would be to attempt small-scale AI implementation with the goal of streamlining operations in specific departments (e.g., customer support or IT departments).

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