Robotaxis: Beyond Autonomy, It’s About Superior CX

I recently participated in an on-stage discussion about the state of autonomous mobility at the Ride AI Summit in Los Angeles. During the event, I engaged in several conversations with other participants about the robotaxi customer experience. The essence of these discussions reaffirmed what I’ve long maintained—customer experience, rather than just vehicle technology, will determine the winners in the next phase of mobility.

As I wrote in The Flagship Experience, the key to success in complex service ecosystems like mobility isn’t just the vehicle or the technology; it’s the intelligent orchestration of the entire customer journey, from initial awareness to long-term loyalty. In a service as personal and potentially stressful as daily transportation, a seamless, trustworthy experience isn’t just a ‘nice-to-have’; it’s fundamental to user adoption.

The new mobility customer experience must span the entire lifecycle of a customer journey. The lifecycle starts with the customer’s first interaction with a mobility brand and ends when the customer severs each such relationship. It covers every mobility-related activity between these two points. Ride-hailing as a mobility-related activity is part of that journey. To offer this comprehensive customer experience, the ride-hailing provider must consider the activities before, during, and after each trip. AI enables robotaxis to offer this end-to-end experience, removing friction and building trust.

When designed right, customer experience delivers value that customers can understand and appreciate—building loyalty and increasing the customer’s lifetime value. Yet, few companies, especially in transportation, truly grasp this. Incumbent automakers, for example, obsess over the in-cabin experience but outsource everything else to their dealer networks, missing the bigger picture.

Tesla was the first to understand that the automotive customer experience must span the entire lifecycle, from the mobile app used to customize a vehicle before purchase, to the service interaction after the sale. Tesla’s successes, and the reported missteps, offer powerful lessons in the competitive advantage that comes from controlling the end-to-end experience. It’s also no coincidence that this advantage stems in part from the company’s AI-first mindset.

Robotaxis offer us the opportunity to reimagine the mobility services customer experience. When we compare the customer experience that transportation network companies (TNCs) like Uber offer to the experience that autonomous vehicle fleet operators such as Waymo offer, the gap becomes clear not just in vehicle technology, but in AI-enabled consistency before, during, and after each trip.

Uber revolutionized the on-demand mobility experience through its mobile app. For the first time, travelers could summon a car from their phone, track its arrival, track their trip’s progress, view driver and vehicle details, and even rate the service. AI made many of these features possible, like predicting the estimated time of arrival, predicting where the traveler wants to go next, visualizing the route, or pricing transparency. Over time, Uber iterated on its business model, further improving the customer experience by offering customers multiple tiers of service and loyalty incentives.

Yet, the in-ride experience remained fundamentally inconsistent. Vehicle quality, cleanliness, driving style, cabin ambiance, and even the driver’s mood, could vary dramatically from one ride to the next and impact where the rider will be picked up and where will be dropped off. Riders faced inherent unpredictability, making it hard to deliver a consistently premium or even predictable experience.

Robotaxis change that.

Take Waymo. The service experience begins similarly, with a mobile app. But from there, the differences become apparent. The vehicle that arrives is always the same type and well-maintained. It picks up and drops off the rider at a spot that is safe for the rider and the vehicle. Its cabin is always configured the same way, consistently clean. Once a rider experiences it, the in-car interface offers predictable controls for climate and audio, real-time journey visualization, and direct access to remote support. The autonomous vehicle’s programming governs the ride itself, exhibiting a consistent, predictable driving style, noted for its smoothness and adherence to traffic laws, unlike the variability inherent in human drivers. User feedback frequently highlights the unique sense of privacy and calm, transforming the cabin into a reliable ‘third space‘ for work, relaxation, or private conversation.

This consistency is possible because AI orchestrates every layer of the customer experience. AI’s role extends beyond just the driving function; it’s performing hyper-personalized logistics in real time. It optimizes fleet deployment to anticipate demand surges, calculates dynamic ETAs factoring in dozens of variables, enables natural language interaction for support queries, and leverages sensor data not just for navigation but for proactive vehicle health monitoring, triggering maintenance before a rider is inconvenienced. Future iterations will likely see AI managing personalized in-cabin ambiances or proactively offering assistance based on perceived rider needs.

In Transportation Transformation, I discussed why fleet-based mobility enables better utilization, higher service density, and more control over the customer experience. I explored various models for deploying robotaxi fleets, emphasizing how centralized fleet management is the foundational enabler for controlling and continuously improving the end-to-end customer experience, as we now see with robotaxis. The operator dictates vehicle cleanliness protocols, software standardization across the fleet, the rider interaction framework, and the data feedback loops necessary for AI-driven optimization—advantages simply unattainable in distributed, peer-to-peer models.

But there is another, often overlooked benefit: robotaxis create a more consistent environment for everyone around them. Drivers, pedestrians, cyclists, and even city planners can learn how these vehicles behave because they operate within defined parameters, unlike idiosyncratic human drivers. This predictability is vital for building societal trust and facilitates safer interactions. Furthermore, this predictable behavior can eventually enable smoother integration with smart city infrastructure, potentially leading to optimized traffic flow and more efficient urban mobility ecosystems.

As AI continues to evolve, this will only become more profound. In several industries, we’re already seeing a need for AI to shift from backend optimization to frontline experience design. Mobility is no exception; in fact, it’s leading the way. The robotaxi experience represents one of the first large-scale, customer-facing, AI-first systems in the physical world. And because the vehicles themselves are software-defined, operators can roll out new features and experiences over the air, just like with smartphones. AI becomes the continuous layer of innovation, iteration, and insight.

Waymo shares its current challenge with competitors like Zoox, May Mobility, and others: It’s no longer solely about whether the technology works or the experience resonates. They do. The challenge now is scaling with the right business model. The company is experimenting with direct-to-consumer services, but also with partnerships. This is wise. Monetizing the AI-enabled experience at scale is a business design problem, not just an engineering one. Uber and Lyft are starting to respond by forming their own AV partnerships, but they remain constrained by their legacy models.

It is exciting and worth emphasizing to mobility leaders: robotaxis are not just another new mode of transport. They are a beachhead for a larger transformation, where AI defines the full-stack customer experience and mobility becomes an orchestrated, software-defined service.

This is the model for the future. Not just of how we move but how we build trust, loyalty, and brand equity in the AI age. Robotaxis embody the principles from The Flagship Experience in action: designing offerings not just around features but around intelligent, end-to-end journeys. As cities evolve into smart mobility infrastructure providers or full mobility orchestrators, the companies that thrive will be those that master AI-first customer experience design, not just autonomous driving.

Robotaxis are more than automated vehicles; they are sophisticated, AI-orchestrated service platforms. The companies mastering this fusion of AI, hardware, and human-centered service design aren’t just building the future of transportation; they are crafting the blueprint for customer engagement in an increasingly automated world. The future isn’t just self-driving; it’s self-orchestrating, deeply personalized, and built on a foundation of digital trust.

And that future is already on the road.