Testing technology on ourselves first.
At Workday, AI starts in-house: we put our technology to work on our own operations long before it reaches a customer. This Customer Zero approach means testing features against actual data, security rules, and everyday workflows using Sana. Rather than just speeding up old, broken habits, teams are rethinking how they work from the ground up. This provides a clear path for other organizations looking to move safely from testing AI to running a smarter, more efficient business.
Building a secure data foundation.
To roll out these tools safely, technology leaders focused heavily on clean data and clear security boundaries. By setting up a central system for permissions and identity, employees can experiment without creating new risks for the company. This approach has driven speedy adoption, with 94% of employees actively using the custom assistant, Sana, week after week. Setting these strong guardrails upfront means AI agents can move past simply answering questions to safely assisting with complex tasks across the company.
We use these capabilities ourselves first. We learn from that experience, then bring those lessons directly to our customers so they can get the value they signed up for from day one.
Sheri Rhodes, Chief Customer Officer, Workday
Giving employees their time back.
In daily operations, these tools are giving employees time back for work that requires true human judgment. The recruiting and HR teams automated repetitive resume screening and interview scheduling, recovering 24,000 administrative hours a year. This allows teams to focus on candidate experiences and strategic talent planning rather than coordinating calendars. Similarly, the legal team used contract intelligence to compress a backlog of 1,500 contract reviews, slashing individual review cycles from six hours down to just 15 minutes and returning 45,000 hours quarterly to legal while keeping lawyers firmly in control of the final call.
Modernizing financial operations.
The finance team is using the same approach to modernize monthly planning and forecasting. Traditionally, pulling together a monthly headcount forecast took eight days of manual data reconciliation and back-and-forth conversations. By cleaning up their underlying planning data and letting AI run the initial baseline models in minutes, the team is shrinking that entire cycle down to a single day. Automating these routine rules frees finance professionals to stop spending their week assembling numbers and start focusing on strategic business trade-offs.
The goal of AI in recruiting isn’t to take human judgment out of the loop; it’s giving human judgment the time and space to thrive.
Allison Joyce, VP, Global Head of Talent Acquisition, Workday