International Data Group
Designed an AI-powered enterprise software selection service to improve buyer confidence
The Problem: Incomplete MVP with fragmented UX and unclear user flows
What I Did: Led UX to restructure the platform, define key flows, and design RFP & admin experiences
What Changed: Delivered a coherent, AI-powered sourcing tool that launched on time and cut procurement timelines dramatically. Addressed the #1 concern of 44% of CIOs: high cost and complexity
Summary
I developed a self-service tool to assist CTOs, CIOs, and procurement teams in purchasing their next enterprise software. To increase buyers’ confidence, we incorporated our historical data insights and artificial intelligence.
My Deliverables
Information Architecture, User Flow, Wireframing, Prototyping, Design QA
Role
Product Design Consultant
Tools
Figma, FigJam, Jira
When
2024
Team
Myself
1 Product Management, VP
1 Product Manager
1 AI & Scientist, SVP
The Challenge
Improving buyer confidence in procurement
When I joined, TechMatch was in its early MVP stage. A design agency had just exited, and two contract designers had created an initial component library and design system. My job was to lead UX and bring coherence and user-centered thinking to a fragmented product.
TechMatch’s concept was ambitious: create a self-service tool to help CTOs, CIOs, and procurement teams confidently select enterprise software using AI and IDC’s proprietary research. 44% of CIOs reported complexity and cost as top challenges in sourcing. TechMatch needed to deliver clarity and trust.
Key challenges:
Aligning UX with a data-rich, AI-powered product
Clarifying and completing fragmented user flows
Supporting multi-stakeholder collaboration
Visualizing AI and analyst insights for confident decision-making
The Approach
Framing the problem and prioritizing work
I partnered with product leadership to clarify which tasks would unblock progress and align with business goals. The team needed clarity around structure and flow, so I began by assessing what had been built and where gaps remained.
Discovery
Stakeholder syncs to understand product vision and design backlog
Review of existing Figma files and design artifacts
Analysis of target user types and decision-making processes
Strategy
Developed an Information Architecture diagram to map out what existed vs. what still needed to be designed
Separated platform into functional zones: vendor shortlisting, RFP creation, team collaboration
Reframed the “Teams” section as a true Admin Portal to meet real user needs
Mapped out the RFP flow using a service blueprint to align tech, UX, and business goals
Planning
Built alignment documents and artifacts to facilitate scoping with engineering
Prioritized user flows based on business value and user impact
Defined a plan to tackle high-risk features (like AI-powered recommendations and RFPs) early
Onboarding (Before)
Onboarding (After)
The Work
Discovery & Insights
Identified friction in incomplete flows and AI explainability
Confirmed the platform needed to support multi-user collaboration and RFP document generation
Understood executive expectations and launch pressure
Experience Mapping & IA
Created and presented a new Information Architecture, organizing features by functional area
Replaced vague “Teams” page with structured Admin Portal for role-based access
Developed Service Blueprint for RFP flow: mapping user actions, backend interactions, and recipient experience
Design Execution
Redesigned several high-traffic, high-impact areas of the product:
Onboarding
Before: Generic screens with unclear user value
After: Personalized, goal-oriented onboarding tailored to user role
Home
Before: Sparse dashboard with minimal guidance
After: Focused entry point showing current tasks, vendor matches, and RFP status
Requirements
Before: Confusing forms with no logic
After: Dynamic, guided flow that adapts based on product category and business size
Iteration & Feedback
Collaborated closely with PM and engineers for Design QA
Used Figma prototypes to walk through flows with stakeholders
Integrated feedback quickly into final designs to maintain delivery pace
Home (Before)
Home (After)
The Results
Shaped the MVP foundation by proactively defining structure and completing missing flows
Enabled confident decisions with AI scoring and IDC data visualizations
Improved collaboration through role-based access and guided RFP building
Accelerated MVP launch, helping the team hit key Q1 2025 deadlines
Outcomes:
TechMatch launched Q1 2025 as the first AI-powered sourcing platform backed by IDC market intelligence
Addressed the #1 concern of 44% of CIOs: high cost and complexity
Cut sourcing timelines from months to weeks
Positioned IDC as a leader in enterprise AI tooling
Project gained executive visibility and became a model for future research-led products
Requirements (Before)
Requirements (After)
Reflection
This project reminded me how important structural clarity is when a product is in flux. By delivering foundational artifacts early — like IA maps and service blueprints — I helped the team move with confidence and reduce rework later on.
If I were to do it again, I’d advocate for earlier access to user research to further validate design assumptions before building. Even without direct user interviews, we could’ve leveraged proxy personas or early beta testers to validate flows.