Uber CEO says its AI push is aimed at boosting conversion and improving the customer experience

Uber CEO Dara Khosrowshahi said the company’s investments in artificial intelligence are intended to deliver practical business results, not just experimentation. In comments reported by Customer Experience Dive, Khosrowshahi said Uber is “early in its AI journey,” but expects its AI efforts to produce measurable improvements over time.

Khosrowshahi said the goal is for AI-related work to do two things at once: drive “conversion” and enhance the “fundamental experience of the consumer.” In ride-hailing and delivery markets, conversion generally refers to whether a customer who engages with the app ends up placing an order, rather than abandoning the process.

He suggested that rather than treating AI as a standalone initiative, Uber is evaluating whether specific uses either increase the likelihood that customers complete a trip request or improve the day-to-day experience of using the service. That framing places emphasis on consumer-facing outcomes, such as the smoothness and reliability of getting a ride or completing an order, even though the reported remarks did not enumerate which AI projects are expected to produce the biggest lift.

The remarks also highlight an approach that many large consumer platforms are increasingly taking with AI: linking machine-learning systems to operational and commercial metrics. For Uber, that could mean testing whether AI can affect customer decisions at key moments in the funnel, such as choosing to open the app, selecting a pickup time, confirming a trip, or completing a purchase.

Still, Uber did not provide specific details in the reported comments about the scope, timeline, or expected magnitude of returns from AI. The post did not identify particular models, products, or internal programs, and it did not offer any quantified forecast, such as revenue impact, conversion-rate changes, or time-to-deployment targets.

Industry context matters because ride-hailing businesses compete on friction and reliability. Customers typically expect fast matching, accurate pricing, and fewer failures or delays. AI systems can be used to help forecast demand, support route and dispatch decisions, and manage risk, but the reported coverage did not specify what Uber is deploying or how those systems are being measured.

What remains unclear from the public remarks is how Uber is prioritizing between customer experience improvements and conversion gains, or how it is balancing those goals against cost, performance, and privacy considerations. Without additional disclosure, the company’s definition of “incremental” benefit and the boundaries of what is included in that assessment are not visible.

Investors and customers will likely be watching for follow-on disclosures, such as commentary around product changes in app experience, operational performance, or any metrics Uber chooses to share on how AI affects ordering behavior. The next announcement will likely come from earnings communications or engineering and product updates that connect AI work to customer-level outcomes. Until then, Uber’s stated expectation remains directional, supported by a broad goal rather than detailed, attributable results.