Cars24 operates in a market where buying or selling a car is a multi-day, multi-channel process involving calls, document checks, and follow-ups. Unlike traditional e-commerce, the majority of these interactions happen outside the app, creating a tension between delivering consistent, high-quality experiences and scaling operational teams. The article exposes the real challenge of maintaining a coherent customer journey across fragmented, manually-intensive touchpoints in a regulated and fragmented market.

To solve this, Cars24 built AI-powered voice and chat agents using OpenAI APIs that handle the entire buying and selling workflow: from initial inquiry and car recommendations to booking test drives, financing exploration, appointment confirmations, and post-purchase support. They also deployed Codex across their software development lifecycle, integrating it directly into Linear tickets, GitHub workflows, and bug reports to reduce standups and automate task tracking. The adoption rapidly spread beyond engineering—finance and investor relations teams now use Codex to pull data, run analysis, and prepare reports without manual chasing. The company reported handling over a million conversation minutes per month through AI agents and saw 85-90% daily active usage of ChatGPT Enterprise and Codex among roughly 600 central employees.

The serious takeaway is that Cars24 treated AI not as a standalone pilot but as an operating layer embedded into specific high-volume workflows from the start. They began with the most constrained part of the funnel—middle and bottom sales stages where conversation drives conversion—and then let AI adoption spread organically as teams built their own tools. For builders, the lesson is to focus on friction-heavy, conversation-driven processes rather than trying to automate the entire business at once, and to invest in making AI accessible across non-engineering teams so that operational pain points drive tool creation, not top-down mandates.