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TL;DR

Led the design, strategy, and implementation of an AI-powered knowledge assistant ("Excel Wiki Agent") that transforms how 10+ designers access institutional knowledge—reducing onboarding time by ~40% and saving an estimated 3 hours per designer per week through instant, cited answers to common workflow questions.

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Key Outcomes

| ~40% Onboarding Time Reduction | 90% Team Adoption Rate | 2,147 Sessions in 4-Week Pilot | 87% Query Resolution Rate | | --- | --- | --- | --- | </aside>

https://drive.google.com/file/d/1WPimlHRxh8MflH67Pm797asvuCDF-LUe/view?usp=drive_link

Context

The Copilot EXCEL Wiki agent is a purpose-built AI assistant designed to streamline the daily workflow of Excel designers. It centralizes knowledge, automates information retrieval, and proactively guides designers with actionable insights, references, and next steps—making them more efficient and up to date with Excel features and best practices.

The Excel Design team at Microsoft operates across multiple geographies (Redmond, IDC) with 10+ designers working on one of the most complex productivity applications in the world. Design knowledge was scattered across:

• SharePoint portals and OneDrive folders
• Loop workspaces with guidelines and how-tos
• Figma files, libraries, and comment threads
• Teams channels and meeting recordings
• PowerPoint decks and Word documents
• Azure DevOps work items

Pain Point

💬 Designer Friction Points (from 1:1 interviews)

"I spend 20+ minutes just finding the right Figma library for my feature."

"Every time there's a new hire, I become the human wiki for a month."

"I know we've solved this problem before, but I can't find where we documented it."

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Problem Statement

How might we enable Excel designers to instantly access the right knowledge, resources, and guidance—without disrupting colleagues or searching through scattered systems?

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Background & Motivation

The journey began with a simple directory of links—portals, files, presentations, Loop pages, Figma files—intended to help Excel designers quickly find resources. However, this approach was static, required heavy manual maintenance, and was dependent on others for updates. Recognizing these limitations, the vision evolved:

• Initial Attempts:
  • Aggregated existing Wiki links and guidelines.
  • Experimented with AI Notebook in Copilot (limited by reference cap).
  • Tried grounding the agent on Loop workspace (blocked by compliance/features).
• Key Insight:
  • Designers needed more than a directory—they needed an intelligent, context-aware agent that could answer nuanced questions, surface the latest resources, and reduce manual searching.

Brainstorming & Iterative Development

• Research:
  • Conducted 1:1 and whiteboarding sessions with designers to gather real-world questions and expectations.
  • Collected and categorized prompts into five main buckets (e.g., design craft, process, handoff, accessibility, onboarding).
• Pain Points Identified:
  • Existing Wiki was hard to maintain, lacked excitement, and didn’t scale.
  • Manual updates to SharePoint and Loop were tedious.
• Solution Evolution:
  • Rebuilt the Wiki structure in SharePoint for compliance and scalability.
  • Hard-coded tables, links, and authored how-to guides for key workflows (icon creation, bug bash, research, etc.).
  • Iteratively fine-tuned the agent’s responses for clarity, relevance, and actionable output.

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💡 Insight #1: Presentations Were a Bigger Pain Point Than Expected

Designers spent significant time preparing for reviews and LT presentations. The need wasn't just "where are templates" but "which past presentations landed well with stakeholders like Venkat?"

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💡 Insight #2: "Find Prior Art" Was the Hidden Superpower

Senior designers wanted to reference how problems were solved before. This shifted the agent from a "link finder" to a "design memory" system.

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💡 Insight #3: Trust Requires Citations

Designers would only trust agent answers if they could verify the source. This informed the TL;DR + Citations response format.

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Development Phases