AI-Driven Design, Faster, Smarter Decision-Making. 

Project Type: UX Process & Design Efficiency
Role: UX Director / Design Lead
Timeline: Ongoing / Iterative
Platform: Enterprise Web & Mobile Applications

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

• 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

Methods Used:

1. Audit of 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.

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.

3. Tool Analysis:

   • Tested AI transcription and summarization tools.

   • Explored AI-driven search functionality for internal documentation.

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.

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AI-Driven Workflow

Step 1: 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: 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: 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: 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.

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

Artifacts / Work Samples

1. AI Transcription Samples: Summaries showing key points and speakers identified.

2. Google Notebook Snapshots: Indexed meeting notes, articles, and Confluence references.

3. Decision Logs: Examples of AI-informed design decisions with rationale.

4. Speed Metrics Charts: Visualizing time saved in research synthesis and decision-making.

Reflection / 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:

Integrating AI into the design workflow increased delivery speed, improved knowledge retention, reduced rework, and enabled more confident, informed design decisions across multiple projects.

notes

Work