From cramped chaos to clarity
Evolving Dell’s AI Support into a Purpose-Built Workspace, which cut ticket volume by 55% and boosted self-serve adoption to 92%
Dell employees faced fragmented tools and slow resolutions due to a cramped chat interface. IntelliAssist 2.0 replaced this with a full-screen AI platform unifying 12+ APIs and a centralized sales portal, streamlining support and empowering self-serve efficiency
Project snapshot
Problem
Sales teams struggle to efficiently assist customers today because they rely on a cramped, outdated chat interface and must toggle between multiple systems to access essential sales tools and information.
They needed to toggle between multiple systems to find resources like order status, sales guides, or troubleshooting tools while assisting customers.
This fragmented workflow slowed response times, frustrated employees, and delayed critical sales processes. The goal was to unify these tools into a single, modern interface to streamline support and improve efficiency
1
Incomplete Answer Context
The AI’s responses provided only surface-level details without critical supporting information like relevant information, or follow-up questions
Buried Features & Low Adoption
The sparse landing screen failed to highlight the AI’s full capabilities, resulting in low feature adoption as employees defaulted to peer-dependent learning
2
Business-Driven Scalability & Unified Experience Demands
The business required a stable, future-proof platform to integrate new functionalities (e.g., AI-driven sales analytics) and unify the sales portal’s tools; without integration, employees faced disjointed processes, risking errors and delaying enterprise-wide adoption of AI tools
What Users Were Saying?
“I just use bookmarks. Searching takes too long.”
“The chatbot gives generic answers — I can’t trust it.”
“I have to ping a teammate to get the right resource.”
These quotes reflected a pattern: employees lacked confidence in the tool, found it slow to navigate, and rarely discovered features on their own.
This led to low adoption, fragmented workflows, and delays in customer resolution.
What I Set Out to Solve
I reframed the challenge around three key principles
Speed: Help users get to the correct answer faster
Clarity: Provide context-rich, human-readable responses
Trust: Build confidence through transparency and personalization
Each design decision — from role-specific prompts to source-tagged answers — was designed to shift behavior, boost adoption, and reduce support dependency.
Solution
Unified Interface for Seamless Workflows
Transitioned from a cramped chat window to a full-screen AI platform with prioritized prompt suggestions (e.g., “Check order status”) and integrated self-serve tiles (e.g., Order Tracking Hub, Proposal Builder) directly below the chat
Unified AI and self-serve tools into a single workspace, directly tackling fragmented workflows and cutting resolution times
1
Smart Search Prompt
Affordance + Contextual Onboarding
Why: I added dynamic, role-specific example queries to guide first-time interaction and help users understand what BizWiz can do
The prompts are role-aware, driving perceived relevance and encouraging natural engagement with AI.
2
Familiar Cards from Legacy Design
Change Management + Perceived Control
Why: I retained the card-based layout from the previous experience to support mental model continuity and reduce cognitive friction.
This approach improves learnability and ensures a smoother transition for users adapting to a new AI-powered workflow.
🚨 Phased Transition Strategy
Self-serve tiles were prioritized at launch to ease adoption, with plans to phase them out as AI usage grows—balancing familiarity with long-term efficiency.
Full-Screen Interface for Contextual Efficiency
Transitioned to a full-screen interface to unify AI chat, self-serve tools, and contextual resources in one workspace—eliminating system juggling and retaining user focus through dynamic panel controls
Centralizing tools and AI simplifies workflows, cuts redundant tasks, and reduces reliance on multiple systems—lowering operational costs and supporting scalable growth
1
Collapsible Resource Sidebar
User Control & Flexibility
Why: I introduced a collapsible section so users could self-navigate at any point if they felt the AI output didn’t meet their needs.
It supports escape hatches and empowers users with familiar fallback paths — key for change management and confidence in the system
2
Follow-up / “Search Instead” Prompts
Supports Intent Refinement
Why: These prompts act as contextual suggestions that help users reformulate their questions or explore related topics
It reduces blank states and supports conversational UX patterns often used in AI interfaces.
3
Sources & Related Articles
Trust Through Transparency and Credibility
Why: I added visible sources and related content to increase credibility of the AI output
This reinforces AI explainability, helps users validate results independently, and encourages trust without dependence — especially critical in early-stage AI adoption.
🚀
Workflow Streamlining
Centralizing AI chat, self-serve tiles, and sales tools cut redundant tasks (e.g., order tracking) by 40%, letting employees focus on high-value work
✂️
Cut out unnecessary steps
Integrated sales portal resources eliminated daily platform-switching for 72% of pilot users, giving employees more time for high-impact tasks that drive outcomes
💡
Boosted AI Adoption
Tools are now easier to find and use, increased feature discovery by 55%, with 85% of users reporting “easier access to critical tools
What did I learn?
⚖️
Balancing Business Needs ≠ Compromising UX
Strict requirements from leadership often mirrored hidden user needs—like faster workflows—teaching me to reframe constraints as guardrails, not roadblocks
⏳
Limited Time? Prioritized Ruthlessly
With tight deadlines, I learned to prototype only critical flows first (e.g., order tracking), then iterate post-launch—proving agility can coexist with quality
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