Built an AI knowledge agent on SharePoint giving Excel designers instant, cited answers on craft, process, accessibility, and handoffs. Cut onboarding by 40%, saved 3hrs/week per designer, and hit 87% query resolution with 90% team adoption in a 4-week pilot.
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Introduction
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.
Key Outcomes
Onboarding Time Reduction
Team Adoption Rate
Sessions in 4-Week Pilot
Query Resolution Rate
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:
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."
How might we enable Excel designers to instantly access the right knowledge, resources, and guidance—without disrupting colleagues or searching through scattered systems?
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:
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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
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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).
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Pain Points Identified:
Existing Wiki was hard to maintain, lacked excitement, and didn’t scale.
Manual updates to SharePoint and Loop were tedious.
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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.
Key Insights
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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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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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Designers would only trust agent answers if they could verify the source. This informed the TL;DR + Citations response format.
Development Phases
| What | Excel spreadsheet with categorized links to resources |
| Why Failed | High maintenance burden; dependency on others to update; not searchable |
| Learning | Designers need answers, not links. The cognitive load of navigating links was the original problem. |
| What | Copilot's AI Notebook feature with ~20 reference documents |
| Why Limited | 20-reference ceiling was too restrictive for our knowledge base; couldn't cover all 6 theme areas |
| Learning | Need a more powerful grounding solution—Copilot Studio with enterprise connectors. |
| What | Created structured Wiki in Loop workspace, hoping to ground agent on it |
| Why Blocked | Copilot Studio did not support Loop workspace as a grounding corpus due to compliance/feature limitations—only SharePoint accepted |
| Learning | Platform constraints matter. Had to migrate everything to SharePoint—a week of re-work. |
Agent Capabilities & Instructions
The Copilot EXCEL Wiki agent is designed to be:
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Response Formatting:
- TL;DR summary.
- Numbered action steps.
- References table (Title, Owner, Last Updated, Link).
- Platform-specific notes (Win/Web/Mac/Mobile).
- Inline citations and proactive next steps.
- **Tone & Style:**
- Friendly, concise, and designer-centric.
- Uses bullets, tables, and short paragraphs for clarity.| Feedback | Action Taken | Result |
| Responses too verbose | Refined prompt instructions; added TL;DR requirement | Response time ↓34% |
| Missing accessibility docs | Authored 5 new a11y guides; added to knowledge bank | A11y queries resolved ↑ to 91% |
| Can't verify citations | Added clickable hyperlinks in references table | Trust score ↑ (qualitative) |
| Need Figma file search | Explored MCP/API integration (see Section 7) | Parked for future (technical constraints) |
Figma Integration: MCP or API?
Designer feedback consistently requested the ability to search Figma files and access comment threads directly through the Wiki agent. This would eliminate the manual labor of updating the Wiki when designs change and enable queries like:
- "Find the Checkout flow file and summarize recent comments"
- "What components are used in the Dashboard redesign?"
- "Show me files updated in the last week in the Charts project"
I explored two technical paths to integrate Figma with the Copilot agent:
| What It Is | Figma's official MCP server (Dev Mode) for design-to-code workflows |
| Primary Goal | Extract CSS/React props from a user-provided file link |
| Tools Exposed | get_design_context, get_variable_defs, get_code_connect_map |
| Critical Limitation | Link-based only. Cannot browse team files or search projects. User must provide specific file URL first. |
| Missing Capabilities | No list_team_projects, search_files, or get_file_comments tools |
| What It Is | Power Platform custom connector wrapping Figma REST API with OAuth 2.0 |
| Available Endpoints | GET /v1/teams/:team_id/projects, GET /v1/files/:file_key/comments, etc. |
| Advantages | Can browse team files, search projects, read/post comments, list components |
| Blocking Constraints | Requires HTTPS-hosted MCP server with valid certificate; rate limiting concerns for team-wide queries; OAuth app registration with MS org compliance review |
| Capability | Official MCP | Custom Connector |
| Find files by name/search | ❌ No | ✅ Yes |
| Browse team projects | ❌ No | ✅ Yes |
| Read/post comments | ❌ No | ✅ Yes |
| Design-to-code context | ✅ Yes | ⚠️ Limited |
| Setup complexity | Low | High |
| Meets our use case | ❌ No | ⚠️ Possible but complex |
Neither option met our immediate needs with acceptable effort. The Official MCP lacks the required capabilities entirely. The Custom Connector requires infrastructure (HTTPS-hosted MCP server, OAuth app compliance review, rate limit management) that would take 4-6 weeks to implement properly. Given the strong baseline value already delivered by the SharePoint-grounded agent, we chose to park Figma integration for the "Run" phase.
Impact & Value
| Metric | Before Agent | After Agent |
| Onboarding time (new designer) | ~5 weeks | ~3 weeks (↓40%) |
| Weekly active users | N/A | 9/10 designers (90%) |
| Total pilot sessions | N/A | 2,147 sessions |
| Query resolution rate | N/A | 87% |
| Est. time saved per designer/week | N/A | ~3 hours |
| Avg. response time | N/A | <40 seconds |
Challenges & Lessons Learned
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⭐
Future Vision
Near-Term
Medium-Term
Long-Term Vision
"How is my day looking?"
The ultimate vision: a designer asks this one question and receives a synthesized brief including:
Conclusion
The Copilot EXCEL Wiki agent represents a leap forward in how Excel designers access, use, and contribute to organizational knowledge. By combining user-centered design, enterprise-grade knowledge management, and AI-powered assistance, it sets a new standard for efficiency and collaboration in design teams.