Making employee feedback actionable for business leaders

The Challenge

At Workday, I led discovery research on the Peakon platform to address a growing challenge: Business Leaders were overwhelmed by the volume of employee feedback comments and struggled to extract meaningful insights. While the platform offered topic analysis and trend detection, the existing solution failed to connect qualitative and quantitative data in a way that was digestible or actionable. As a result, many leaders turned to external tools, and lack of comment analysis capabilities became a contributor to customer churn.

The goal was to understand what actionable insights look like for Business Leaders, and how to deliver them in a way that supports confident decision-making and improves retention.

My Role

I worked closely with product leadership to align research goals with product strategy and long-term business outcomes. As the lead researcher on the initiative, I collaborated with a cross-functional team (product, design, engineering, and data science) to plan and execute discovery, guide solution development, and define how we’d measure success.

What I Did

  • Facilitated discovery planning workshops to align on business goals, surface assumptions, and frame research questions using frameworks like the Opportunity Solution Tree.

  • Conducted desk research and stakeholder interviews to surface existing pain points and identify key decision moments and requirements.

  • Led qualitative research with Business Leaders and HR professionals to define what “actionable insight” really means in their context.

  • Developed criteria for actionable insight (e.g., relevance to strategic goals, reliability, clarity, contextualization), which became a shared framework for product decision-making.

  • Designed and conducted a Kano survey to evaluate and prioritize insight types based on perceived value to users.

  • Partnered with designers and PMs to support concept development and testing. Helped prepare research plans, recruited participants, set up sessions, joined debriefs, and helped iterate concepts.

  • Co-defined measurable success metrics that linked product performance to both user needs and business goals. This included tracking behavior signals like whether Business Leaders copied summaries to share with peers.

Outcomes

  • Shaped two AI-powered features: Research findings directly informed the development of two AI-powered features that synthesize qualitative and quantitative feedback into digestible, contextual insights.

  • Positioned Workday as an innovator: The launch of these features played a key role in showcasing Workday’s leadership in applying AI to real-world enterprise challenges and was highlighted in Bloomberg’s coverage of Workday’s AI strategy.

  • Improved key metrics: Success metrics confirmed the features were moving the needle on key outcomes such as increasing satisfaction and supporting strategic decision-making.

  • Increased UX maturity: The project strengthened the partnership between UX research and product leadership, ML engineering and data science.

  • Enabled long-term strategy: Discovery laid the roadmap and foundation for future GenAI exploration across the platform.