Empowering teams with AI agents: Learn from the real-world success of Uber

Once the global content leaders at Uber saw AI agents in action, it was easy to imagine the impact on critical processes. Then came the hard part — getting buy-in from the team.

Sound familiar? Our enterprise AI adoption survey finds that more than one out of three executives say generative AI adoption has been a “massive disappointment.” But Uber turned this challenge into a step-by-step playbook on exactly how to build an AI-ready culture with a people-first mindset.

WRITER’s CMO, Diego Lomanto, sat down with Uber’s Director of Global Community Operations, Hadley Ferguson, and the Program Lead of Global Content Innovation, Michael Kenney. Learn firsthand what worked, where they faced challenges, and the things they’d do differently.

Summarized by WRITER

• Uber built an AI-ready culture by embedding AI directly into existing workflows, fostering a growth mindset, and turning support agents into AI champions.
• They reinvented their content request system with WRITER AI agents, creating intelligent forms that provide real-time feedback and automate triaging, significantly reducing processing time.
• Their approach emphasizes trust and transparency in AI systems with sophisticated governance that aligns with diverse regulatory environments.
• Future-proofing efforts include rotational programs, learning sessions, and structured knowledge management.

Three ways AI is revolutionizing work at Uber

Uber identified three key areas where AI creates transformative value:

• Scaling manual tasks: AI now handles heavy lifting that previously required human intervention.
• Preparing for a generative AI future: High-quality knowledge powers all AI initiatives. They’ve built a multi-step AI process to clean up their data.
• Elevating team performance: According to Ferguson, AI tools give teams confidence that they can “quickly do a task and come up with a really clear and impactful output.”

But to properly embed AI into their organization, they had to build an AI-ready culture. This involved more than just technology — it required a shift in mindset and a commitment to continuous learning and improvement.

Building an AI-ready culture

The cultural mindset at Uber was ready for innovation. “Everyone is always looking for what’s the next big tech thing, how can we accelerate something we’re doing?” Ferguson says.

The hard part is addressing the adoption challenges that accompany such a big change.

Embedding AI in the Flow of Work

The first rule of AI adoption at Uber? Don’t make people leave their workflow. Uber removes the barrier to entry and reduces friction by embedding AI directly into existing workflows (like Google Docs) instead of forcing people to switch contexts.

Setting a growth mindset as the foundation

Rather than focusing only on technical training, Uber’s teams emphasize cultivating a growth mindset that empowers people to explore new possibilities with AI.

Uber emphasizes cultivating a growth mindset that empowers people to explore new possibilities with AI.

“A little bit of upskilling, a little bit of investment in your people goes a long way,” Kenney adds. “And I think a lot of it has to do with that growth mindset and fostering that within your organization.”

Turning support agents into AI champions

Instead of hunting for technical experts, they invest in complementary skills like systems thinking, user experience research, service design, and journey mapping.

The best AI champions often come from customer support backgrounds due to their deep knowledge of business processes. By blending these existing skills with targeted AI training through platforms like WRITER Academy, Uber creates internal champions who reinvent processes rather than just operating them.

Automating operational processes

At Uber, AI isn’t just about flashy consumer-facing applications — it’s about fixing the behind-the-scenes tasks that keep the business running. Kenney shares how they pinpointed the ideal use cases, describing how they identified a sweet spot between:

• Personal productivity tools where ROI is hard to measure
• Big bets on major customer-support AI initiatives (like virtual assistants) that are less accessible to most employees

Their focus, as Kenney puts it, is on “highly manual operational processes that are business critical.” These are the routine but essential tasks that keep everything running smoothly but take up a lot of employee time.

WRITER’s AI HQ allows them to automate multi-system, multi-step processes by blending deterministic steps with generative AI where it makes sense. This approach frees up employees from mundane tasks and redirects them to more strategic projects.

Building trust through supervision

Transparent systems you can trust are non-negotiable, especially at Uber. Supervising what AI is doing, setting clear guardrails, and keeping everything transparent are must-haves — not nice-to-haves.

The beauty of this transparency? It keeps everyone in the loop — legal folks, product teams, engineers, and business units all stay aligned with a clear understanding of how things work. This shared visibility creates a foundation of trust that’s good for both the company and its customers.

How Uber reinvented their content request system with AI agents

When Uber’s content workflows started causing delays, they retired their outdated ticketing system and reinvented it with WRITER AI agents.

The headaches of traditional request systems

Previously, Uber faced several content request challenges:

• Static forms that couldn’t adapt to specific needs
• No real-time feedback mechanism for submission quality
• Excessive back-and-forth communication

Reimagining the end-to-end request process

When you bring AI into the mix, you can’t just slap it onto existing processes and expect magic. You’ve got to rebuild from the ground up.

Uber uses WRITER’s AI HQ to create an improved workflow that maintains human oversight while eliminating unnecessary manual work. Now, their system has:

• Intelligent intake forms that adapt based on content type and provide real-time feedback.
• Automated triaging that routes requests to the right team members without back-and-forth.
• Auto-drafted content that adheres to legal and compliance requirements.
• System integration that connects form outputs directly to Jira and Google Suite.

Finding the right balance between human and AI

They give routine tasks to AI while saving creative and strategic decisions for humans.

The payoff has been huge — processing time was reduced from weeks to days, allowing content specialists to focus on work that deserves their expertise.

Future-proofing through adaptability

Uber balances strategic vision with acknowledging future uncertainties.

Uber has implemented:

• Rotational programs exposing team members to cutting-edge technologies
• “Feed your mind Fridays” to cultivate growth mindset
• Skills mapping to identify gaps and address them through training

Taking knowledge management from chaos to clarity

The double-edged sword of this era of AI is that you need well-structured, clean knowledge to extract maximum ROI out of those systems you put in place. But most industries haven’t kept their content clean or structured, so they can’t take full advantage of AI.

“We want to be moving towards this idea of graphing out our knowledge,” Kenney explains. “We want to have structured content — create once published everywhere. We just essentially want to have a really clean data ecosystem. So whether gen AI or anything else comes knocking at the door, our house is clean, and we can take advantage of that stuff.”

Advice for others on their AI journeys

Sure, Uber’s setting the pace in AI adoption, but don’t let that intimidate you. Forrester’s Total Economic Impact™ study found WRITER customers recouped their investment in under six months. Apply Ferguson and Kenney’s advice, choose the right AI partner, and you’ll be measuring ROI while your competitors are still mulling their options.

Invest in people

Uber’s success stems from early investment in their team’s capabilities, creating a culture of continuous learning.

Choose high-impact, high-visibility use cases

When selecting your first AI use cases, aim for projects that solve problems across multiple departments. As Ferguson notes, the most compelling use cases speak to diverse stakeholders throughout your organization.

Take smart risks

Take the time to identify where the potential benefits clearly outweigh the costs. Ferguson refers to these as “no regrets” decisions — “choices that make so much sense because you have clear challenges and a need for a solution.”

Show and tell

Uber’s team acknowledges they could have done more to broadcast their early wins.

When you achieve success with AI, make it visible. Document the journey, quantify the impact, and share compelling stories that bring the transformation to life. This visibility validates your current investments while inspiring others to explore how AI might transform their own work.

AI is an organizational superpower

Uber’s AI revolution is grounded in transforming the way their teams think, work, and innovate. They’ve turned what could have been a daunting shift into a smooth, efficient upgrade — thanks to the emphasis they’ve put on a people-first approach.