ServiceNow — Employee Experience

Enterprise employee workflows — such as travel planning, workspace reservations, and approvals — often require navigating multiple systems, forms, and documents. At ServiceNow, I explored how conversational AI could serve as an orchestration layer for these workflows. Instead of requiring employees to navigate complex interfaces, the goal was to allow users to begin with intent while the system dynamically assembles the necessary steps. The result was a design exploration of AI-native employee experiences combining conversational interaction with structured UI components.

Enterprise workflows typically involve fragmented systems, repetitive data entry, multiple approval steps, and scattered documentation and policies. Even simple tasks — such as booking business travel — can require navigating several applications.

“How might AI capture user intent and guide employees through complex workflows while maintaining clarity and control?”

I explored a hybrid interaction model where conversation captures intent, AI gathers missing context, and structured UI modules appear when decisions are required.

Core Principles

Intent-first interaction — Users begin with goals rather than navigating menus. Progressive disclosure — Structured UI appears only when decision points require it. Human-in-the-loop control — Users review and confirm actions before completion. Conversation as orchestration — Dialogue coordinates multiple systems and workflow steps.

A prototype explored how employees could plan business travel through a conversational interface. 1. Intent — User begins: "Plan travel for next week's client meeting." 2. Context Gathering — The system asks clarifying questions: destination, travel dates, meeting location. 3. Structured UI Module — Once context is gathered, the conversation reveals a structured interface with flight options, itinerary preview, and travel policy checks. 4. Contextual Information — Relevant policy guidelines, approval requirements, and supporting documentation appear inline. 5. Review and Confirmation — The user reviews and confirms. The system completes the workflow while minimizing manual navigation.

To explore these ideas I developed prototypes using narrative storyboards, usability testing scripts, conversational UI flows, and chat-based design system components. I also experimented with AI-assisted prototyping workflows (including Claude Code) to rapidly explore conversational interaction patterns.

Early testing revealed several patterns: • Users prefer starting with intent rather than navigation • Structured UI within conversation improves clarity • Minimal input flows reduce cognitive load • Contextual information prevents system switching

This work demonstrates that conversational AI can significantly simplify complex enterprise workflows. The design patterns I developed — intent capture, progressive UI disclosure, and inline policy context — are now informing how ServiceNow thinks about AI-native experiences across the platform. Employee Works shipped as a conversational chat-based employee experience, and the patterns are being applied to the Move Works catalog experience currently in flight.