Uber
Uber is one of the world's most widely used mobility platforms, connecting millions of riders and drivers across urban and suburban environments every day. The platform is built on real-time coordination, trust between strangers, and a seamless experience that helps riders get where they need to go quickly and safely.
While Uber optimizes routes, pricing, and pickup/dropoff logistics, it offers limited support for a subtle but critical part of the experience: understanding the human context behind each ride. Drivers often leave notes about past experiences with riders—such as preferred pickup locations, gate codes, accessibility needs, communication preferences, or important safety details. However, these notes are scattered, inconsistent, and generally hard for drivers to access or use effectively during future trips.
Uber's key personas tied to this need include:
Drivers are the frontline operators of the platform. They want clear, concise, and reliable information that helps them complete rides efficiently and safely. Today, driver notes are optional, unstructured, and often lengthy — making them difficult to parse quickly when starting a trip.
Riders expect smooth and consistent service: easy communication, correct pickup coordination, and personalized experience. While they don't interact with the notes directly, the quality and accuracy of driver preparedness shape the rider's overall perception of the platform.
Internal teams at Uber often rely on historical trip notes when resolving disputes, understanding incidents, and ensuring rider and driver safety. Unstructured or incomplete notes slow down this process and reduce context accuracy.
Uber's competitors — Lyft, Bolt, DiDi — all provide varying degrees of trip history and basic notes, but none offer a centralized, AI-powered system that helps drivers quickly interpret context from past interactions.
This creates an opportunity to introduce AI Driver Notes Summary, a feature that automatically summarizes driver-entered notes across past trips into a concise, actionable context card. This summary appears at the start of a new trip, enabling drivers to deliver smoother, more informed rides while reducing cognitive load and improving safety.
Over time, Uber drivers accumulate a significant amount of contextual knowledge about riders — ranging from preferred pickup spots to safety concerns, gate codes, communication preferences, and behavioral history. To capture this information, Uber provides a basic "Notes" feature where drivers can record details about past trips.
However, both qualitative discovery and quantitative signals surfaced major issues with how this feature supports drivers today:
1. Unstructured Notes Create Cognitive Overload
Driver-entered notes are often long, inconsistent, and filled with shorthand or fragmented details. Drivers report that notes are:
This reduces the usefulness of notes at the critical moment — when the driver is trying to prepare for or fulfill a trip safely.
2. Notes Are Not Easily Surfaceable Across Different Trips
Drivers frequently encounter repeat riders weeks or months apart. Today, they must:
This process is especially inconvenient during high-paced environments, like airport pickups or late-night rides.
3. Safety & Support Teams Lack Quick Context
Internal teams rely heavily on historical notes to:
Unstructured notes slow down case resolution and can create ambiguity around previous issues.
4. Notes Are Underused Because They Provide Low ROI
Many drivers choose not to write notes at all because:
This leads to inconsistent coverage and fragmented contextual data across the rider base.
There is a clear opportunity to build a system that transforms driver notes from raw text into actionable, structured, safety-enhancing intelligence.
AI can compress years of scattered notes into:
By placing this summary directly inside the trip preparation flow, Uber can empower drivers to deliver smoother, safer, and more personalized rides — without requiring them to manually read through old notes.
This aligns naturally with Uber's broader goals of:
Throughout exploration, several signals shed light on the scale of the opportunity:
Growing Volume of Driver Notes
Internal analytics showed a steady increase in note-taking among long-time drivers, especially in dense markets like New York, LA, and London — further highlighting the need for digestible context.
High Correlation Between Context Awareness & Trip Ratings
Drivers who frequently review or write notes tend to have:
This indicates that better access to meaningful context directly impacts platform performance.
Support Case Review Times Can Be Reduced
Many support tickets require agents to piece together historical driver notes. Structured summaries could reduce resolution time and improve consistency of decision-making.
Competitor Gap
No major rideshare platform offers AI-driven note summarization. This differentiates Uber in a meaningful way and strengthens the driver-side experience — a critical component of marketplace retention.
These insights collectively point to a strong, validated opportunity to help drivers work smarter and safer through structured intelligence embedded directly into the Uber trip flow.
We believe that transforming raw, unstructured driver notes into concise, AI-generated summaries will fundamentally improve the driver's ability to understand rider preferences, safety considerations, and important trip context. Today's notes require too much interpretation and offer limited value in time-sensitive environments. By introducing an AI-powered summarization layer, we can provide drivers with the right information at the right moment, improving both efficiency and overall ride experience.
Surfacing summarized insights directly in the trip acceptance and pre-pickup workflow is the safest and most strategic entry point for this feature. Giving drivers a brief, structured view—just as they begin a ride or reconnect with a repeat rider—helps minimize distraction while maximizing usefulness.
This placement allows Uber to:
Engineering guidance confirms that existing Notes infrastructure, combined with anonymized historical trip metadata, provides a technically feasible foundation for this first version.
We hypothesize that once accurate AI summaries are established for drivers, they can power a broader set of intelligence across the Uber ecosystem.
This includes:
However, replacing or automating any existing note-taking or safety workflows before validating the accuracy and reliability of the summarization model would be risky. The safest approach is to first introduce AI summaries as a support tool, then gradually expand their capabilities once trust and performance are established.
Together, these hypotheses anchor the initial scope, guide technical implementation decisions, and shape a roadmap that balances immediate value with long-term scalability.
Below are the four key use cases that consistently emerged from driver interviews, support insights, and observational research. These job stories helped define the functional requirements for both the first release and future iterations, and ensured that Product, Design, and Engineering stayed aligned on what problems the solution must meaningfully solve.
I want to... quickly understand any important rider preferences or behavioral patterns
So that I can... personalize the experience and avoid issues that have occurred in past trips.
Example: "Summarize if this rider usually prefers silent rides, specific pickup spots, or has shown previous communication challenges."
I want to... see critical pickup or access instructions immediately before a ride starts
So that I can... avoid delays, wrong turns, or confusion that frustrates both me and the rider.
Example: "Highlight if this rider always waits at the side entrance, requires a gate code, or has accessibility needs."
I want to... identify any safety-related notes from previous drivers
So that I can... be aware of potential risks and take precautions ahead of time.
Example: "Surface if prior notes mentioned aggressive behavior, intoxication risks, or repeated disputes."
I want to... review the most relevant details from multiple past notes without reading full text each time
So that I can... quickly orient myself without distraction while navigating or preparing for pickup.
Example: "Combine all past notes about this rider into a 2–3 sentence summary I can read within seconds."
What it does: Automatically condenses multiple historical driver notes into a 2–3 sentence summary displayed at the start of a trip with a repeat rider.
What it includes:
Why it's valuable: Gives drivers immediate clarity without requiring them to scroll through lengthy note entries, reducing cognitive load at pickup time.
What it does: Identifies recurring rider preferences from past notes and displays them in a structured, quick-to-read format.
Examples include:
Why it's valuable: Helps drivers prepare for smoother trips and reduces rider frustration caused by repeated explanation of preferences.
What it does: When past notes indicate potential safety concerns, the system surfaces a discreet, standardized callout to alert the driver.
Possible insights include:
Why it's valuable: Improves driver situational awareness and supports safer ride operations without overwhelming them with raw text.
What it does: Offers a "View All Notes" option that expands into the complete history of driver-written notes, organized chronologically.
Capabilities:
Why it's valuable: Supports drivers and internal teams who may need deeper detail, while ensuring the summary remains the primary surface.
What it does: AI automatically groups notes into semantic categories such as:
Why it's valuable: Makes historical context easier to navigate, both for drivers on the road and for Safety/Support agents during issue resolution.
What it does: If the rider recently took a trip with another driver, Uber displays a lightweight snapshot highlighting what changed or what was newly noted.
Includes:
Why it's valuable: Keeps drivers informed with the most recent, high-signal information, even when the rider's habits or needs change over time.
"Driver Preparation Efficiency Per Trip"
This measures how effectively drivers can understand rider context without manual searching or interpreting long notes.
It reflects reductions in cognitive load, improved pickup accuracy, and better overall trip readiness.
Feature Adoption
Driver Behavioral Metrics
Trip Experience Metrics
Operational & Safety Metrics
Control Group
Variant Group
Key Metrics to Measure:
The experiment runs across multiple cities to ensure diversity in driver experience, rider density, and trip patterns.
Goal: Establish a reliable foundation for summarizing and structuring driver notes.
Key Deliverables:
Goal: Strengthen safety, improve personalization, and support operational teams.
Key Deliverables:
Goal: Transition from passive summaries to predictive, proactive assistance.
Key Deliverables:
Drivers operate in demanding contexts. Even the best AI is ineffective if it introduces distraction or requires too much attention. This project reinforced the importance of designing summaries that are short, scannable, and safe to consume.
Driver notes represent years of contextual insight, yet most of that data is inaccessible in real time. Seeing how AI can convert fragmented entries into meaningful, actionable guidance highlighted the strategic value of surfacing intelligence from existing behavior, rather than forcing new workflows.
Any feature that touches behavioral patterns or incident context must be reliable and unbiased. Designing safe, trusted experiences required careful thought about where summaries appear, how much detail they expose, and how to validate accuracy over time.