At Uber, the adoption of generative AI isn’t just a matter of technique or innovation—it’s a mirrored image of tradition. For Andrea Monllau, Senior Supervisor of Americas Technique for Uber, the shift to embracing Gen AI has been as a lot about folks because it has been about platforms. Talking from her perspective as each a strategist and a champion for transformation, Monllau in her interview with me describes a office the place curiosity is inspired, experience is shared, and experimentation is the norm.
Uber’s relative youth and deep tech roots have made it fertile floor for early Gen AI adoption. “We have been born within the period of information and expertise,” Monllau notes. That offers Uber an edge over legacy organizations nonetheless wrestling with digital transformation. Throughout the corporate, groups usually are not simply exploring Gen AI—they’re actively embedding it into their workflows. Throughout the Locations group, which oversees office technique and company actual property, Gen AI isn’t only a shiny new software. It’s a foundational part of how work is evolving.
Prompting Events and Peer Assist
The story of Gen AI at Uber isn’t one in every of top-down mandates or inflexible applications. As a substitute, it’s one in every of grassroots studying and community-driven innovation. Group members usually are not solely utilizing instruments like ChatGPT and Gemini—they’re teaching one another in how you can use them higher.
“We name them prompting events,” says Monllau, with a smile. These casual periods are designed for collective problem-solving. Somebody may share a immediate that didn’t ship the specified consequence, and others will soar in to tweak and enhance it. This shared iteration helps folks transfer from frustration to fluency.
Because the group’s expertise have grown, so has the ambition of their initiatives. Colleagues are constructing their very own chatbots, then sharing them with others. “I created this chatbot, right here’s what it does—if it’s helpful, be happy to make use of it,” Monllau explains. The neighborhood has moved past sharing successes to co-creating options. It’s not about doing the work for another person—it’s about exhibiting them how, and empowering them to develop.
Peer mentorship is central to this ethos. Monllau describes turning to colleagues with deeper technical information and intentionally inviting others into these conversations. “If I don’t know one thing, I don’t ask privately. I ask publicly. As a result of if I’ve the query, another person most likely does too.” This intentional vulnerability fosters psychological security and speeds collective studying.
Making Adoption Inevitable
When it got here to rolling out Gen AI extra broadly, Monllau and her group leaned into two messages: the chance and the inevitability. The chance is evident—higher effectivity, quicker evaluation, and extra time for artistic, strategic pondering. However the inevitability is simply as compelling. “When you have been an accountant when Excel got here out and also you didn’t use it, you bought left behind,” Monllau says. Gen AI, she believes, is that degree of disruption.
To get everybody on board, Uber deployed enterprise-wide trainings that combined technical demos with sensible use instances. “We blocked time on calendars and mentioned, this coaching is extremely inspired—be sure to take it.” These periods supplied the groundwork for particular person groups to start experimenting and sharing on their very own.
The objective wasn’t perfection—it was participation. Individuals have been inspired to talk overtly about their experiments, regardless of how small. When somebody used Gen AI in a brand new approach, they have been celebrated. These tales helped normalize the expertise and constructed momentum throughout the group.
Navigating Dangers and Constructing Belief
After all, Gen AI isn’t with out its dangers. Bias, hallucinations, and knowledge safety are actual considerations. Uber addresses these via a mix of coverage and observe. The corporate classifies knowledge into completely different ranges, with strict guidelines about what could be shared with AI platforms.
However the greatest safeguard, Monllau says, is human judgment. “AI is a help software—it helps you speed up, nevertheless it doesn’t exchange you.” Groups use Gen AI to generate preliminary drafts or compile situation knowledge, however they nonetheless come collectively to research and determine. “We imagine there isn’t one reply to something. AI gives a perspective, not a conclusion.”
That collaborative mindset applies to managing bias as effectively. By sharing outputs, difficult assumptions, and reviewing outcomes collectively, Uber’s groups hold human perception on the heart of AI use. It’s a mannequin of augmented intelligence quite than synthetic substitute.
The Street Forward for Gen AI at Uber
Trying ahead, Monllau sees Gen AI accelerating the tempo of labor and elevating the kind of work people do. “We’ll automate repetitive duties and use AI to get us to the primary draft quicker. Then we come collectively to do the pondering, the enjoyable half.” For company actual property—a historically conservative sector—this shift may very well be transformative.
She’s notably enthusiastic about how Gen AI is attracting new gamers to the area. “We’re seeing entrants from exterior the normal actual property world—folks with tech backgrounds, new concepts. That’s wholesome. It challenges us to maneuver past the old-school approach of doing issues.”
Even because the instruments evolve, Monllau emphasizes that the center of Uber’s success with Gen AI lies in its tradition. A tradition the place asking questions is protected, sharing information is predicted, and nobody is left behind. “That is new for everybody,” she says. “We must be serving to one another.”
In a world racing towards digital transformation, Uber’s method stands out. It’s not nearly deploying expertise. It’s about constructing communities of studying round that expertise—the place each chatbot, each immediate, and each query is a part of a shared journey ahead.
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