LISE PILOT | UX
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AI Workforce Assistant

Designed an AI-powered workforce assistant that enabled frontline managers to schedule, staff, and manage their teams through natural language interactions. Partnering with Product and Engineering, I defined the product vision, interaction model, and responsible AI principles, demonstrating how generative AI could simplify complex workforce management while keeping humans in control.

My Role

AI Product StrategyProduct VisionUX ResearchAI Experience DesignCross-Functional LeadershipHuman-Centered AI

Challenge

Frontline managers spent much of their day switching between scheduling, attendance, staffing, and employee management tools. Existing workflows required navigating multiple systems and completing repetitive tasks, creating unnecessary cognitive load and slowing operational decision-making.

Solution

Reimagined workforce management around an AI-assisted experience. Instead of navigating complex interfaces, managers could complete everyday workforce tasks using natural language while maintaining full visibility, transparency, and decision-making control.

Design Process

  1. 1

    Defined the Product Vision

    Defined a long-term vision for AI-assisted workforce management by identifying high-value workflows where AI could reduce effort while keeping managers in control.

  2. 2

    Researched Customer Needs

    Interviewed frontline managers to understand scheduling challenges, staffing decisions, and operational pain points best suited for AI assistance.

  3. 3

    Designed AI-Assisted Workflows

    Designed natural language workflows for scheduling, attendance, staffing recommendations, and employee support that simplified complex workforce management tasks.

  4. 4

    Rapidly Prototyped Concepts

    Built interactive prototypes to evaluate multiple AI interaction models, helping Product and Engineering validate concepts before development.

  5. 5

    Validated with Customers

    Conducted usability testing to refine recommendations, conversation flows, and interaction patterns while building trust in AI-assisted experiences.

  6. 6

    Applied Responsible AI Principles

    Designed AI experiences centered on transparency, explainability, and human oversight to ensure managers always retained final decision-making authority.

Highlights

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Embedded AI Workforce Assistant

Integrated a contextual AI assistant directly into the workforce management experience, enabling managers to identify exceptions, prioritize actions, and complete common workforce tasks through natural language without leaving their workflow.

AI Workforce Planning

Empowered managers with an AI assistant that answered workforce planning questions, surfaced scheduling insights, and recommended next actions using natural language while keeping managers in control of every decision.

Transparent AI Reasoning

Designed AI experiences that exposed reasoning, supporting evidence, and source attribution so managers could understand, verify, and confidently act on AI recommendations.

Interactive AI Prototyping

Created high-fidelity interactive prototypes to validate AI workflows with users, gather stakeholder feedback, and rapidly iterate on conversational experiences before development.

Key Results

AI Strategy Established

Created a validated product vision that aligned Product, Engineering, and Design around future investments in enterprise AI.

Customer-Validated Concepts

Validated AI interaction models and workflow concepts before engineering investment, reducing delivery risk and improving product direction.

Reduced Cognitive Load

Simplified complex workforce management by replacing multi-step workflows with guided natural language interactions.

Foundation for Future AI

Established reusable AI interaction patterns and responsible design principles that informed future enterprise AI initiatives.