AI Workforce Assistant
Designing an AI-powered workforce assistant that helps frontline managers schedule, staff, and manage their teams through natural language interactions.
As AI Product Lead, I partnered with Product and Engineering to define the vision for conversational AI across workforce management. Through customer research, rapid prototyping, and iterative validation, we explored how generative AI could simplify scheduling, attendance, and staffing workflows while keeping managers in control of every decision.
My Role
Challenge
Frontline managers spend much of their day switching between scheduling, attendance, staffing, and employee management tools. Existing workflows required navigating multiple systems and making repetitive decisions, creating unnecessary cognitive load and slowing daily operations.
Solution
We reimagined workforce management around conversational AI. Instead of navigating complex interfaces, managers could ask questions, receive intelligent recommendations, and complete everyday workforce tasks through natural language while maintaining full visibility and decision-making control.
Design Process
- 1
Defined the Product Vision
Established a long-term vision for conversational AI in workforce management by identifying high-value manager workflows where AI could reduce effort and improve decision making.
- 2
Researched Customer Needs
Interviewed frontline managers to understand scheduling challenges, staffing decisions, and daily operational pain points that represented the greatest opportunities for AI assistance.
- 3
Designed AI Workflows
Created conversational experiences for scheduling, attendance, staffing recommendations, and employee support using natural language interactions rather than traditional navigation.
- 4
Rapidly Prototyped Concepts
Built interactive prototypes to evaluate multiple AI interaction models, helping Product and Engineering validate ideas before committing development resources.
- 5
Validated with Customers
Conducted usability testing to refine recommendations, conversation flows, and interaction patterns while increasing confidence in AI-generated responses.
- 6
Applied Responsible AI Principles
Designed AI experiences that emphasized transparency, explainability, and human oversight by ensuring managers always retained final decision-making authority.
Design highlights
AI Workforce Assistant
Designed a conversational assistant that unified scheduling, attendance, and workforce management into a single natural language experience, reducing navigation and simplifying complex daily tasks.
Intelligent Scheduling
Created AI-powered scheduling recommendations that balanced staffing needs, labor rules, and manager preferences while keeping users in control of final scheduling decisions.
Human-in-the-Loop AI
Designed every recommendation to support—not replace—manager decision making by providing clear reasoning, editable suggestions, and transparent AI interactions.
Rapid AI Prototyping
Used rapid prototyping and customer feedback to evaluate multiple conversational interaction models before engineering investment, accelerating product learning and reducing delivery risk.
My Role
Leadership Contributions
AI Product Vision
Defined the long-term AI product strategy that aligned Product, Engineering, and Design around a shared roadmap for conversational workforce management.
Cross-Functional Leadership
Facilitated collaboration across Product, Engineering, AI specialists, and UX teams to transform emerging AI capabilities into validated customer experiences.
Customer Research
Grounded every product decision in customer interviews, workflow analysis, and usability testing to ensure AI solved meaningful operational problems.
Responsible AI Design
Established experience principles centered on transparency, explainability, human oversight, and user trust across all AI-assisted workflows.
Key Results
AI Strategy Established
Created a validated product vision that aligned Product, Engineering, and Design around future investments in conversational AI.
Customer-Validated Concepts
Customer research confirmed strong demand for AI-assisted workforce management and validated conversational workflows before engineering investment.
Reduced Cognitive Load
Simplified complex workforce management tasks by replacing multi-step workflows with guided conversational interactions.
Foundation for Future AI
Established reusable AI interaction patterns and experience principles that informed future enterprise AI initiatives across the platform.
Reflection
This project reinforced that successful AI products begin with customer problems—not technology. By grounding every design decision in user research and keeping humans in control, we demonstrated how conversational AI can simplify complex work while building trust, confidence, and better operational outcomes.