How Uber Is Using Shibumi to Save Millions of Dollars
With Shibumi Managing Automation Opportunities, the Rideshare Company Is Saving Time and Money
Uber is the preeminent rideshare company in the world. Across more than 70 countries, more than 130 million people use the app every month, with 23 million trips happening each day.
Enhance Business Value and Cost Efficiency Through Intelligent Automation
Established in 2018, Uber’s Intelligent Automation COE reports up to the CFO. The company wanted to create a digital workforce with tools to support numerous lines of business but knew they had to take a “crawl, walk, run” approach.
They started with implementing RPA for the accounting function, with a focus on automating the Close process, and then expanded RPA into other lines of business, such as freight. Next they added OCR and then chatbots and other AI/ML solutions—leading toward the north star of becoming an automation-first enterprise.
But since they were using various tools to manage and track opportunities, it became harder and messier to stay organized as they scaled automations across different business functions. Without a single source of truth for that data, the team found it challenging to scale their impact.
Implement Shibumi to Manage and Track Automation Opportunities
By implementing Shibumi’s Automation Accelerator, Uber gained a robust tracking mechanism to help manage their pipeline and monitor potential opportunities from ideation to deployment and production.
“Before Shibumi, we were using Google Docs and Forms to manage our intake process,” said Chad Aronson, Global Head of Uber’s Intelligent Automation COE. “It was messy, cumbersome, and inefficient. Now, there’s no more searching for files or problems finding data. We can quickly pivot and, with the click of a button, have our pipeline sliced and diced by various statuses, such as ‘in progress,’ ‘on hold,’ ‘in assessment,’ and even ‘canceled.’”
With Shibumi, Uber is able to systematically capture key fields and generate visualizations in order to prioritize automations. For example, one visualization the team often refers to shows estimated annual savings on one axis and automation complexity on the other. “Since we of course like to have higher value with lower complexity, this is useful for helping us figure out how to switch gears,” Aronson said.
“With the click of a button we can have our pipeline sliced and diced by various statuses. There’s no more searching for files or problems finding data.”
GLOBAL HEAD OF INTELLIGENT AUTOMATION COE
Through an integration with Kibana, Shibumi also enables Uber to track savings and exceptions. Previously, this would have required significant manual work, but now it can be done with the click of a button to inform real-time decision making.
Aronson’s team has also rolled out a dashboard built for upper management and leadership teams to provide visibility and insights into key metrics, such as transaction volumes, estimated time savings, actual savings, and headcount. These can be filtered by program and line of business.
>$10 Million in Annual Savings and 200,000 Hours of Manual Effort Saved
As of 2021, Uber’s Intelligent Automation COE had delivered 100+ live automations across 11 lines of business. Over the cover of three years, the team projected savings would grow to between $22 million and $35 million.
Aronson noted that Shibumi was critical for securing executive buy-in, since it let his team showcase real-time data that persuaded upper management of intelligent automation’s benefits.
“To get where we are, we needed complete alignment between intake, development, support, and the business,” Aronson said. “If they didn’t work in synergy, we would be delivering automations that don’t work. We would never be at this scale without Shibumi.”