Workday, Inc. (NASDAQ: WDAY) announced the formation of Workday AI Research, a dedicated technical research team focused on enterprise AI, along with a PhD fellowship program offering $50,000 in annual funding to doctoral students.

The research team has published peer-reviewed work accepted at conferences including the International Conference on Machine Learning, the International Conference on Learning Representations, the ACM Web Conference, and the Association for Computational Linguistics. Research areas include persistent agent memory, explainability, multi-agent orchestration, reward overoptimization in AI training, recommendation systems, and adaptive resource control.

Three recent findings were highlighted in the announcement. In agent memory, a selective filtering method delivered 12% higher precision and approximately 8% better overall memory quality while retaining 97% of relevant memories and running about 31% faster than a leading AI-driven comparison. In multi-agent systems, splitting tasks among specialized agents improved accuracy by 5.8%, with all outputs meeting defined constraints. In data deletion, researchers found that information deleted from an agent's memory remained recoverable from older summaries roughly one in five times, requiring deletion of every summary containing the information for full removal.

"As AI agents evolve to remember context and take action on behalf of employees, enterprises are facing complex challenges around privacy, auditability, efficiency, and enterprise-grade accuracy that off-the-shelf models simply cannot solve," said Gerrit Kazmaier, president of product and technology at Workday.

The Workday AI Research PhD Fellowship provides $50,000 in annual research funding as an unrestricted gift to a fellow's university, mentorship from a Workday AI researcher, and early access to career opportunities at the company.