What this demonstrates

Designing AI-assisted enterprise features that respect human judgment, explainability, and trust in high-impact domains.

The problem space

Workday Career Hub is used by enterprises to support employee development, internal mobility, and long-term workforce planning. Career exploration is a sensitive domain where decisions carry real consequences, which raises the bar for how AI-assisted insights are introduced.

Any system suggesting career paths must be interpretable, contextual, and clearly positioned as support for exploration rather than direction or instruction.

The design problem

How might we help employees explore potential career paths using AI-assisted insights while preserving agency, trust, and interpretability in an enterprise setting?

Key constraints included:

• Career decisions have long-term personal and professional impact
• AI suggestions can be misinterpreted as directives
• Users need context, not confidence
• Enterprise products require consistency, restraint, and explainability

My role

I led UX design for career exploration features within Workday Career Hub, partnering closely with product, engineering, and data science teams.

My focus was on shaping how AI-assisted insights were framed, explained, and integrated into existing workflows so users could interpret them appropriately rather than follow them blindly. I worked within enterprise constraints around reliability, governance, and trust.

Who I designed for

THE CAREER NAVIGATOR

Workday Explore was built for employees navigating career growth inside a complex organization.

Primary focus
Early to mid career employee inside a large enterprise who wants to understand where they can grow and how to get there.

Responsibilities

• Tracking skills and competencies
• Identifying internal mobility opportunities
• Aligning development goals with business needs
• Making informed career decisions
  

Pain points

• Limited visibility into adjacent roles
• Unclear skill gaps between current and target roles
• Static job descriptions that lack context
• Career planning that feels abstract or disconnected

Needs

• Clear skill-to-role mapping
• Visibility into realistic next steps
• Personalized recommendations
• Confidence that their effort leads somewhere tangible

Key design principles

DECISION SUPPORT, NOT DECISION MAKING

AI-assisted insights were positioned as inputs for exploration, not recommendations to act on.

• Language avoided prescriptive phrasing
• Multiple potential paths were shown rather than a single “best” outcome
• Users retained control over interpretation and next steps

EXPLAINABILITY BEFORE CONFIDENCE

The experience prioritized helping users understand why certain paths appeared, rather than presenting results as authoritative.

• Clear explanations of contributing factors
• Context surfaced alongside suggestions
• No opaque scoring or unexplained rankings
  

Outcomes

The resulting experience allowed employees to explore career possibilities with additional context while preserving trust and agency. AI-assisted insights complemented existing Career Hub tools rather than replacing them, aligning with enterprise expectations for responsible automation.

Reflection

Designing career exploration features within Workday reinforced the importance of restraint when applying AI to human-centered decisions. In enterprise environments, success is measured not by how confident a system appears, but by how well it supports thoughtful, informed decision-making.

This project shaped my approach to AI design as augmentation, not authority.