AI Assistant
Problem Snapshot
Today users encounter search experience in three different places, each with its own interface, capabilities, and limitations.
Search bar
Limitations
Limited to navigation, return links, not answers. Users get a list of pages, then have to figure out which one helps. No conversational follow up
DD: AI Search
Limitations
Hidden and contextual: Only available in DD. No access from other parts of the platform and no conversational follow up.
Chatbot
Limitations
Support only perception. Users see it as help desk, not assistant. Limited to FAQs. No task completion or cross context awareness.
The Experience
The assistant is designed to feel fast, clear, and reliable inside the existing TriNet service experience. Conversations emphasize clarity, confirmation, and seamless escalation when human support is needed.
Core Flows
The assistant is designed to feel fast, clear, and reliable inside the existing TriNet service experience. Conversations prioritize clarity, confirmation, and smooth escalation when a human is needed.
Quick answers with supporting context
Guided task completion in chat
Clear boundaries and graceful escalation to live agents
Knowledge Base:
Users can ask questions about payroll, benefits, and HR policies and receive clear answers with supporting resources.
Automate Tasks:
Common HR tasks can be completed directly in chat, reducing back-and-forth and time to resolution.
Connect to a live agent:
When a request needs personalized guidance, the assistant helps route users to a specialist without breaking the conversational flow.
Handling out of scope questions:
When a question is not supported, the assistant responds transparently and redirects users toward the best next step.
Design Principles and Outcomes
The TriNet Assistant was designed for an enterprise environment where reliability, clarity, and trust matter more than open-ended conversation. Design reviews and stakeholder walkthroughs helped validate these decisions.
Predictability over novelty: Positioning the assistant between rule-based chatbots and open-ended AI helped establish trust in payroll and benefits use cases.
Task-first by default: Moving users toward resolution, not just answers, was consistently seen as more valuable than link-based search experiences.
Trust-driven tone: Calm, transparent responses were favored over conversational personality, especially for sensitive information.
Escalation as part of the experience: Clear boundaries and seamless handoffs to live agents ensured users never felt blocked.
Room to extend task completion: Feedback highlighted opportunities to deepen self-service through richer task integrations while maintaining guardrails.