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Using AI to centralize insights, inform decisions, and accelerate UX delivery.
Using AI to centralize insights, inform decisions, and accelerate UX delivery.
✔️ Automated meeting transcriptions
✔️ Automated meeting transcriptions
✔️ Centralized searchable steps
✔️ Centralized searchable steps
✔️ AI-driven insights for decisions
✔️ AI-driven insights for decisions
✔️ Faster delivery, less rework
✔️ Faster delivery, less rework
Project Type: UX Process & Design Efficiency
Role: UX Director / Design Lead
Timeline: Ongoing / Iterative
Platform: Enterprise Web & Mobile Applications
Overview
Overview
As design teams scale and projects become more complex, retaining knowledge from past work and synthesizing new information quickly becomes challenging. To address this, I implemented an AI-driven design workflow that accelerated decision-making, improved recall of past insights, and reduced cognitive load on the team. The goal was to leverage AI to support design research, documentation, and decision-making while maintaining human oversight and judgment.
Problem Statement
Problem Statement
Meetings and discussions generate valuable insights, but they are often lost or difficult to reference later.
Design decisions rely on memory and scattered notes across email, Confluence, and other systems.
Research and prior project learnings are underutilized, slowing design and increasing rework.
Team communication can be biased or inconsistent, leading to unclear design decisions.
Goal: Implement a workflow where AI could record, synthesize, and retrieve design knowledge, enabling faster, more informed, and unbiased decision-making.
Research & Discovery
Research & Discovery
Audit
Audit
- Current Knowledge Management
Reviewed meeting notes, Confluence pages, past project documentation, and external articles.
Found fragmented, hard-to-search information slowed decision-making and risked repeated mistakes.
Communicate
Communicate
2. Stakeholder Interviews
2. Stakeholder Interviews
Discussed pain points with designers, product managers, and engineers about knowledge retention and recall.
Identified bottlenecks in research synthesis and meeting follow-ups.
Analysis
Analysis
3. Tools
3. Tools
Tested AI transcription and summarization tools.
Explored AI-driven search functionality for internal documentation.
Key Insights
Key Insights
Designers often forgot prior solutions to similar design problems.
Research and notes were scattered across multiple platforms.
Decisions sometimes relied on subjective recall, introducing inconsistency.
AI-Driven Workflow
AI-Driven Workflow
Step 1:
Step 1:
Capture Meetings & Notes
Capture Meetings & Notes
All design and cross-functional meetings were recorded with participant consent.
AI transcribed the meeting notes, identifying who said what and generating summaries.
Step 2:
Step 2:
Centralize Knowledge
Centralize Knowledge
Transcriptions, research articles, Confluence notes, and other business resources were collected in a Google Notebook.
AI indexed the content, making it fully searchable.
Step 3:
Step 3:
Retrieve / Compare Insights
Retrieve / Compare Insights
When faced with design decisions, I queried the AI for context.
AI provided synthesized insights, past project references, and related research.
I compared AI-generated responses with my own thoughts to validate and inform decisions.
Step 4:
Step 4:
Apply Insights
Apply Insights
Decisions were made faster and with more confidence.
AI served as a non-biased communication feed, reducing influence from dominant voices in meetings.
Artifacts / Work Samples
Artifacts / Work Samples
AI Transcription
A Summary showing key points and speakers identified
Google Notebook Snapshot
Meeting notes, articles, and Confluence references
Decision Logs
AI-informed design decisions with rationale
Speed Metrics Charts
Time saved in research synthesis and decision-making.
Speed Metrics: Time Saved in UX Research
Speed Metrics: Time Saved in UX Research
Metric
Metric
Baseline
Baseline
After AI Workflow
After AI Workflow
Impact
Impact
Meetings requiring follow-up clarification
40%
15%
63% reduction
Rework due to forgotten prior solutions
20%
8%
60% reduction
Recall of previous design rationale
Low
High
Signifigantly improved
Knowledge retrieval speed
Search across tools ~10–15 min
AI query response <1 min
90% faster
Additional Benefits
Additional Benefits
Faster delivery of features and prototypes
Increased confidence in design decisions
Reduced cognitive load and manual note review
Captured institutional knowledge that would have been lost over time
Learnings
Learnings
AI works best as a decision-support tool, not a replacement for human judgment.
Centralized, searchable knowledge dramatically improves efficiency and consistency.
Using AI creates a non-biased reference point, reducing influence from dominant voices in meetings.
Teams can retain and leverage past insights even as projects scale or personnel change.
Key Takeaway
Key Takeaway
Integrating AI into the design workflow increased delivery speed, improved knowledge retention, reduced rework, and enabled more confident, informed design decisions across multiple projects.
Results
Results
👍 50% Faster
👍 50% Faster
Decision-Making
Decision-Making
👍 90% Faster
👍 90% Faster
Knowledge Retrieval
Knowledge Retrieval
👍 60% Reduction
👍 60% Reduction
Rework
Rework
👍 50% Reduction
👍 50% Reduction
Task Completion
Task Completion
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