OVERVIEW
Finding Suppliers is Hard
I designed and shipped Sourcing Ally, an AI-powered sourcing assistant that helps enterprise teams find qualified suppliers by combining natural language with the accuracy and efficiency of artificial intelligence.
PROJECT DETAILS
Project Duration
My Role
My Contributions
Other stuff you might want to know.....
Project Duration
My Role
My Contributions
Other stuff you might want to know.....
SUCCESS METRICS
What Was Achieved
In under 3 months, I designed and shipped Sourcing Ally, a powerful AI-powered tool that helps enterprise teams find qualified suppliers without feeling overwhelmed. Our solution:
System delivered results with a 90% or above accuracy
Supplier discovery went from weeks to hours
Natural language prompt reduced user input by 40%
Fortune 1000 buyers now make sourcing decisions 3x faster
What used to take weeks, now takes hours…
…thanks to AI and a smarter way to ask.
An Overwhelming Search for Suppliers
PROBLEM
Enterprises face an overwhelming search for qualified suppliers. Endless databases, outdated information, and unreliable data lead to wasted time, costly setbacks, and missed opportunities.
An AI-Powered Sourcing Assistant
SOLUTION
Sourcing Ally simplifies supplier discovery with a natural language prompt - users describe their sourcing needs in plain language and the system interprets, enhances, and delivers high-quality matches.
Learn about some of the challenges we faced during ideation.
Learn More
Let's dive into how this solution came to life.
Full Case Study | 5-10 min read
CONTEXT
Product History
Sourcing Ally is the product of Scaleup, a B2B that originated at AWS (Amazon) and went independent in 2023.
Learn more about the product history, solutions that didn't work, and what we learned from them.
Learn More
My Design Approach
Learn & Discover
Industry Research
User Insights & Pain Points
Competitive Analysis
Persona Building
Journey Map
Define & Visualize
User Flows
Gather UI Inspiration
Define Brand Identity
Design System
UI Design
Test & Deliver
Prototypes
User Testing
Implement Feedback
Launch
Recap: Here's what we now know…..
Submitting a request is daunting
Users are often bombarded with too many options
They have no way of verifying supplier information/data.
USER INTERVIEWS
AI Usage
Identifying pain points…
I used ChatGPT to analyze my interview transcripts, cluster common themes, identify pain points, and summarize feedback. As a result, 3 core pain points emerged:
GOOD SUPPLIERS ARE HARD TO FIND:
Pain Point: On average, it takes 57 days just to go from posting a request to finding a supplier.
Impact: Delays operations, increases costs, and limits business agility.
OUTDATED SEARCH METHODS:
Pain Point: 63% search online and 59% rely on magazines, reports, conferences, and word-of-mouth.
Impact: Inconsistent results, wasted time, and limited visibility into qualified suppliers.
INCONSISTENT SUPPLIER DATA:
Pain Point: 60% report outdated or inaccurate supplier information.
Impact: 93% of supply chain leaders face setbacks due to inaccurate supplier data.
This shaped our approach: reduce search effort, surface qualified suppliers, and make key supplier details easy to verify.
And why did we decide to go with AI as our solution?
COMPETITIVE ANALYSIS
AI Usage
Gaps in Relevance, Transparency, and Usability
I used ChatGPT's 'Deep Research' tool to quickly summarize and reveal how competing platforms like Supplier.io, TealBook, and Thomasnet were positioned, what users loved, and where they struggled. What I found was that most platforms were strict and rigid in how requests could be submitted, offered too many results to sort through, and gave no clear explanation of why or how results were generated, leaving user to do all the work.
SUPPLIER.IO
Large supplier database
Trusted by Fortune 1000 companies
Integrates well with enterprise procurement workflows
Focuses heavily on certifications and not capability
Primarily a search directory
Rigid UI and form-based filters
No real-time guidance
PROS
CONS
TEALBOOK
Real-time supplier data enrichment using AI
Promotes data visibility across procurement systems
Less focus on matchmaking for specific sourcing requests
Can be data-heavy but not sourcing-decision friendly
Less intuitive UX for new sourcing professionals
PROS
CONS
THOMASNET
Huge volume of suppliers, especially in manufacturing
Has category-based filtering
Outdated user experience - clunky and dated UI
No vetting options or compatibility scoring
Lacks user guidance
PROS
CONS
As a result, I focused on making the sourcing experience more flexible, personalized, and compatibility-driven.
So, what was our goal?
Turn a messy, time-consuming workflow into a simple prompt and use AI to reduce workload, eliminate friction, and accelerate supplier discovery.
SOLUTION
Why AI Made Sense for This Problem
The problems we uncovered: unclear submissions, overwhelming match lists, and unverified data - these weren’t just UX issues. They pointed to a broken decision-making process. Traditional forms or filters wouldn’t be enough to fix it.
PRODUCT THINKING
We needed a system that could interpret user intent, surface quality over quantity, and validate supplier details in real time. AI gave us the power to do just that, transforming a manual, uncertain workflow into something more intuitive, efficient, and trustworthy.
To meet our 3-month timeline, I prioritized UI and early testing, helping us ship fast, reduce rework, and get critical feedback while the product was still flexible.
VISUAL DESIGN
UI Built for Decision-Making
The final UI was crafted to reduce hesitation and speed up decision-making. From minimal color to smart iconography, every visual element supports clarity, so users know what to look at, what it means, and what to do next.
NATURAL LANGUAGE PROMPT
Users describe what they need in plain language and the system does the heavy lifting. Through AI-powered filtering, only the best-fit suppliers are presented, saving users time and money.
Explore my design process
CURATED MATCHES
The systems extracts key requirements from the user's prompt and generates tailored matches, helping users focus only on options that meet their specific needs and standards.
Explore my design process
SUPPLIER PROFILES
Structured, scannable profiles highlight what matters most. Users have the transparency and confidence they need to make informed decisions.
Explore my design process
This project taught me how to be a Product Thinking Designer, thinking beyond features and focusing on impact.
EDGE CASES
AI Usage
Real-world sourcing isn't straightforward, and neither is user behavior
From vague prompts to not knowing where to start, we knew we had to design for the not-so-great experiences, not just the ideal ones. I leveraged ChatGPT to generate real-world scenarios, identifying areas where users would likely get stuck, confused, or discouraged. I used these insights to build thoughtful safeguards and flexible tools to support users throughout their platform experience.
AI-ENHANCEMENT FEATURE
To guide users in refining their results, we introduced an AI enhancement tool to help refine their prompt without forcing structure.
HELP & GUIDANCE
I added a ‘Help & Guidance’ section beneath the prompt to make things feel approachable from the start. It gives users prompt examples, explains how the AI-enhancement tool works, and sets clear expectations for what happens after they submit their request, so they feel supported, not unsure.
MY IMPACT
Defining Outcomes, Not Just UI
From day one, I was focused on impact. What problem were we solving? What would success look like ? As a product thinker, I helped shape the product's direction. I sat in on early conversations, helped reframe vague ideas into actionable hypotheses, and pushed for clarity around both user needs and business goals.
FRAMED THE PROBLEM, NOT JUST THE SOLUTION
I worked with leadership to clarify who we were designing for, what they actually needed, and how we'd define success.
ADVOCATED FOR SIMPLICITY AND INTENT
I reduced complexity in the supplier request workflow to lower cognitive load and increase usability.
USED UX AS A TOOL FOR BUSINESS ALIGNMENT
My designs weren’t just usable, they reflected key constraints such as limited resourcing, tight timelines, and scalability needs.
DESIGNED FOR OUTCOMES, NOT OUTPUT
Every design decision was tied back to user pain points and product goals, not just visual polish.
FOCUSED ON FLOWS, NOT SCREENS
I mapped full user journeys (including edge cases) to ensure the product worked as a cohesive experience, not a collection of isolated features.
USER TESTING
AI Usage
Integrating User Feedback
We listened closely to what mattered most - what information users needed, what could help them feel confident in making a decision, and what could potentially get in their way. I used ChatGPT to analyze transcripts from live testing sessions and used the results to incorporate the following improvements.
PRODUCT UPDATE #1
BRIDGING MENTAL GAPS
Improving visibility and clarity between extracted requirements and match results.
View Details
PRODUCT UPDATE #2
TAILORING TO OUR USERS
Personalizing the match experience by providing users with additional vetting options.
View Details
PRODUCT UPDATE #3
SHOWING PRODUCT VALUE
Improving UX by giving users a sneak-peek of the benefits of upgrading their subscription.
View Details
OUTCOMES
Post-Launch Outcomes
Sourcing Ally emerged from a fast-moving, resource-constrained environment. We went through two major product pivots, had limited funding, and just 3 months to design, build, and ship. Despite these challenges, we delivered a product designed to help enterprises operate more efficiently and productively, freeing up time to focus on more high-value work.
Team Impact
User Impact
Challenges
What I learned
Team Impact
User Impact
Challenges
What I learned
NEXT STEPS
Currently in Beta
Sourcing Ally is now in Beta, gathering real-world feedback from enterprise users, validating usability, refining key features, and continuing to align the experience with what procurement teams actually need.
WHAT'S BEING TESTED:
Are users finding matches they can trust?
Is the AI-enhancement tool improving prompt quality?
Are users engaging with supplier profiles and the added vetting option?
REFLECTION
What I'd do differently…
The product pivoted twice, and at times I felt a disconnect between what I was designing and why I was designing it. It became clear how easy it is to fall into execution mode when the problem space isn’t fully defined. Next time, I want to lean into product thinking even earlier to help clarify the “why,” define the solution space (as a team), and make sure every design decision is rooted in purpose, not just progress.
Next time, I'll apply product thinking earlier by…
Slowing down to define the core problem
Facilitating workshops to establish alignment on user needs and business goals
Building hypotheses and testing assumptions instead of baking them into the product
Defining success and using it as our North Star
Creating a simple problem statement so we don't lose sight of our mission
What used to take weeks, now takes hours…
…thanks to AI and a smarter way to ask.
What used to take weeks, now takes hours…
…thanks to AI and a smarter way to ask.
OVERVIEW
Finding suppliers shouldn't feel like detective work…
Finding suppliers shouldn't feel like detective work…
I designed and shipped Sourcing Ally, an AI-powered sourcing assistant that helps enterprise teams find qualified suppliers by combining natural language with the accuracy and efficiency of artificial intelligence.
I designed and shipped Sourcing Ally, an AI-powered sourcing assistant that helps enterprise teams find qualified suppliers by combining natural language with the accuracy and efficiency of artificial intelligence.
SUCCESS METRICS
What We Achieved
What We Achieved
In under 3 months we designed and shipped an AI-powered product that not only interprets plain language, but extracts key requirements to deliver only the best fit suppliers.
System delivered results with a 90% or above accuracy
Supplier discovery went from weeks to hours
Natural language prompt reduced user input by 40%
Fortune 1000 buyers now make sourcing decisions 3x faster
In under 3 months we designed and shipped an AI-powered product that not only interprets plain language, but extracts key requirements to deliver only the best fit suppliers.
System delivered results with a 90% or above accuracy
Supplier discovery went from weeks to hours
Natural language prompt reduced user input by 40%
Fortune 1000 buyers now make sourcing decisions 3x faster
OUTCOMES
Post-Launch Outcomes
Post-Launch Outcomes
This product emerged from a fast-moving, resource-constrained environment. Despite numerous challenges, we delivered a product designed to help enterprises operate more efficiently and productively, freeing up time to focus on more high-value work.
This product emerged from a fast-moving, resource-constrained environment. Despite numerous challenges, we delivered a product designed to help enterprises operate more efficiently and productively, freeing up time to focus on more high-value work.
Team Impact
User Impact
Challenges
What I learned
Team Impact
User Impact
Challenges
What I learned
Let's dive into how this solution came to life!
Let's dive into how this solution came to life!
Full Case Study | 5-10 min read
CONTEXT
Product History
Product History
Sourcing Ally is the product of Scaleup, a B2B that originated at AWS (Amazon) and went independent in 2023.
Sourcing Ally is the product of Scaleup, a B2B that originated at AWS (Amazon) and went independent in 2023.
Learn more about the product history, solutions that didn't work, and what we learned from them.
Learn More
Learn more about the product history, solutions that didn't work, and what we learned from them.
Learn More
PROJECT DETAILS
Project Duration
My Role
My Contributions
Other stuff you might want to know.....
Project Duration
My Role
My Contributions
Other stuff you might want to know.....
PROBLEM
An Overwhelming Search for Suppliers
Enterprises face an overwhelming search for qualified suppliers. Endless databases, outdated information, and unreliable data lead to wasted time, costly setbacks, and missed opportunities.
SOLUTION
An AI-Powered Sourcing Assistant
Sourcing Ally simplifies supplier discovery with a natural language prompt - users describe their sourcing needs in plain language and the system interprets, enhances, and delivers high-quality matches.
Learn about some of the challenges we faced during ideation.
Learn More
An Overwhelming Search for Suppliers
PROBLEM
Enterprises face an overwhelming search for qualified suppliers. Endless databases, outdated information, and unreliable data lead to wasted time, costly setbacks, and missed opportunities.
An AI-Powered Sourcing Assistant
SOLUTION
Sourcing Ally simplifies supplier discovery with a natural language prompt - users describe their sourcing needs in plain language and the system interprets, enhances, and delivers high-quality matches.
Learn about some of the challenges we faced during ideation.
Learn More
My Design Approach
Learn & Discover
Industry Research
User Insights & Pain Points
Competitive Analysis
Persona Building
Journey Map
Define & Visualize
User Flows
Gather UI Inspiration
Define Brand Identity
Design System
UI Design
USER INTERVIEWS
AI Usage
Identifying pain points…
I used ChatGPT to analyze my interview transcripts, cluster common themes, identify pain points, and summarize feedback. As a result, 3 core pain points emerged:
GOOD SUPPLIERS ARE HARD TO FIND:
Pain Point: On average, it takes 57 days just to go from posting a request to finding a supplier.
Impact: Delays operations, increases costs, and limits business agility.
OUTDATED SEARCH METHODS:
Pain Point: 63% search online and 59% rely on magazines, reports, conferences, and word-of-mouth.
Impact: Inconsistent results, wasted time, and limited visibility into qualified suppliers.
INCONSISTENT SUPPLIER DATA:
Pain Point: 60% report outdated or inaccurate supplier information.
Impact: 93% of supply chain leaders face setbacks due to inaccurate supplier data.
This shaped our approach: reduce search effort, surface qualified suppliers, and make key supplier details easy to verify.
My Design Approach
Learn & Discover
Industry Research
User Insights & Pain Points
Competitive Analysis
Persona Building
Journey Map
Define & Visualize
User Flows
Gather UI Inspiration
Define Brand Identity
Design System
UI Design
Test & Deliver
Prototypes
User Testing
Implement Feedback
Launch
USER INTERVIEWS
Synthesized Using AI
Identifying pain points…
I used ChatGPT to analyze my interview transcripts, cluster common themes, identify pain points, and summarize feedback. As a result, 3 core pain points emerged:
PAIN POINT #1 - Good Suppliers Are Hard to Find
Pain Point: On average, it takes 57 days just to go from posting a request to finding a supplier.
Impact: Delays operations, increases costs, and limits business agility.
PAIN POINT #2 - Outdated Search Methods
Pain Point: 63% search online and 59% rely on magazines, reports, conferences, and word-of-mouth.
Impact: Inconsistent results, wasted time, and limited visibility into qualified suppliers.
PAIN POINT #3 - Inconsistent Supplier Data
Pain Point: 60% report outdated or inaccurate supplier information.
Impact: 93% of supply chain leaders face setbacks due to inaccurate supplier data.
This shaped our approach: reduce search effort, surface qualified suppliers, and make key supplier details easy to verify.
SUPPLIER.IO
Large supplier database
Trusted by Fortune 1000 companies
Integrates well with enterprise procurement workflows
Focuses heavily on certifications and not capability
Primarily a search directory
Rigid UI and form-based filters
No real-time guidance
PROS
CONS
TEALBOOK
Real-time supplier data enrichment using AI
Promotes data visibility across procurement systems
Less focus on matchmaking for specific sourcing requests
Can be data-heavy but not sourcing-decision friendly
Less intuitive UX for new sourcing professionals
PROS
CONS
THOMASNET
Huge volume of suppliers, especially in manufacturing
Has category-based filtering
Outdated user experience - clunky and dated UI
No vetting options or compatibility scoring
Lacks user guidance
PROS
CONS
COMPETITIVE ANALYSIS
Summarized Using AI
Gaps in Relevance, Transparency, and Usability
Gaps in Relevance, Transparency, and Usability
I used ChatGPT's 'Deep Research' tool to quickly summarize and reveal how competing platforms like Supplier.io, TealBook, and Thomasnet were positioned, what users loved, and where they struggled. What I found was that most platforms were strict and rigid in how requests could be submitted, offered too many results to sort through, and gave no clear explanation of why or how results were generated, leaving user to do all the work.
I used ChatGPT's 'Deep Research' tool to quickly summarize and reveal how competing platforms like Supplier.io, TealBook, and Thomasnet were positioned, what users loved, and where they struggled. What I found was that most platforms were strict and rigid in how requests could be submitted, offered too many results to sort through, and gave no clear explanation of why or how results were generated, leaving user to do all the work.
SUPPLIER.IO
Large supplier database
Trusted by Fortune 1000 companies
Integrates well with enterprise procurement workflows
Focuses heavily on certifications and not capability
Primarily a search directory
Rigid UI and form-based filters
No real-time guidance
PROS
CONS
TEALBOOK
Real-time supplier data enrichment using AI
Promotes data visibility across procurement systems
Less focus on matchmaking for specific sourcing requests
Can be data-heavy but not sourcing-decision friendly
Less intuitive UX for new sourcing professionals
PROS
CONS
THOMASNET
Huge volume of suppliers, especially in manufacturing
Has category-based filtering
Outdated user experience - clunky and dated UI
No vetting options or compatibility scoring
Lacks user guidance
PROS
CONS
As a result, I focused on making the sourcing experience more flexible, personalized, and compatibility-driven.
Recap: Here's what we now know…..
Recap: Here's what we now know…..
Submitting a request is daunting
Submitting a request is daunting
Users are often bombarded with too many options
Users are often bombarded with too many options
They have no way of verifying supplier information/data.
They have no way of verifying supplier information/data.
SOLUTION
So, what was our goal?
So, what was our goal?
Turn a messy, time-consuming workflow into a simple prompt and use AI to reduce workload, eliminate friction, and accelerate supplier discovery.
Turn a messy, time-consuming workflow into a simple prompt and use AI to reduce workload, eliminate friction, and accelerate supplier discovery.
And why did we decide to go with AI as our solution?
And why did we decide to go with AI as our solution?
PRODUCT THINKING
Why AI Made Sense for This Problem
Why AI Made Sense for This Problem
The problems we uncovered: unclear submissions, overwhelming match lists, and unverified data - these weren’t just UX issues. They pointed to a broken decision-making process. Traditional forms or filters wouldn’t be enough to fix it.
We needed a system that could interpret user intent, surface quality over quantity, and validate supplier details in real time. AI gave us the power to do just that, transforming a manual, uncertain workflow into something more intuitive, efficient, and trustworthy.
The problems we uncovered: unclear submissions, overwhelming match lists, and unverified data - these weren’t just UX issues. They pointed to a broken decision-making process. Traditional forms or filters wouldn’t be enough to fix it.
We needed a system that could interpret user intent, surface quality over quantity, and validate supplier details in real time. AI gave us the power to do just that, transforming a manual, uncertain workflow into something more intuitive, efficient, and trustworthy.
To meet our 3-month timeline, I prioritized UI and early testing, helping us ship fast, reduce rework, and get critical feedback while the product was still flexible.
To meet our 3-month timeline, I prioritized UI and early testing, helping us ship fast, reduce rework, and get critical feedback while the product was still flexible.
VISUAL DESIGN
UI Built for Decision-Making
The final UI was crafted to reduce hesitation and speed up decision-making. From minimal color to smart iconography, every visual element supports clarity, so users know what to look at, what it means, and what to do next.
NATURAL LANGUAGE PROMPT
Users describe what they need in plain language and the system does the heavy lifting. Through AI-powered filtering, only the best-fit suppliers are presented, saving users time and money.
CURATED MATCHES
The systems extracts key requirements from the user's prompt and generates tailored matches, helping users focus only on options that meet their specific needs and standards.
SUPPLIER PROFILES
Structured, scannable profiles highlight what matters most. Users have the transparency and confidence they need to make informed decisions.
VISUAL DESIGN
UI Built for Decision-Making
The final UI was crafted to reduce hesitation and speed up decision-making. From minimal color to smart iconography, every visual element supports clarity, so users know what to look at, what it means, and what to do next.
NATURAL LANGUAGE PROMPT
Users describe what they need in plain language and the system does the heavy lifting. Through AI-powered filtering, only the best-fit suppliers are presented, saving users time and money.
CURATED MATCHES
The systems extracts key requirements from the user's prompt and generates tailored matches, helping users focus only on options that meet their specific needs and standards.
SUPPLIER PROFILES
Structured, scannable profiles highlight what matters most. Users have the transparency and confidence they need to make informed decisions.
USER TESTING
Summarized Using AI
USER TESTING
Synthesized Using AI
Integrating User Feedback
Integrating User Feedback
We listened closely to what mattered most - what information users needed, what could help them feel confident in making a decision, and what could potentially get in their way. I used ChatGPT to analyze transcripts from live testing sessions and used the results to incorporate the following improvements.
We listened closely to what mattered most - what information users needed, what could help them feel confident in making a decision, and what could potentially get in their way. I used ChatGPT to analyze transcripts from live testing sessions and used the results to incorporate the following improvements.
PRODUCT UPDATE #1
BRIDGING MENTAL GAPS
Improving visibility and clarity between extracted requirements and match results.
View Details
PRODUCT UPDATE #2
TAILORING TO OUR USERS
Personalizing the match experience by providing users with additional vetting options.
View Details
PRODUCT UPDATE #3
SHOWING PRODUCT VALUE
Improving UX by giving users a sneak-peek of the benefits of upgrading their subscription.
View Details
PRODUCT UPDATE #1
BRIDGING MENTAL GAPS
Improving visibility and clarity between extracted requirements and match results.
View Details
PRODUCT UPDATE #2
TAILORING TO OUR USERS
Personalizing the match experience by providing users with additional vetting options.
View Details
PRODUCT UPDATE #3
SHOWING PRODUCT VALUE
Improving UX by giving users a sneak-peek of the benefits of upgrading their subscription.
View Details
EDGE CASES
Enhanced Using AI
EDGE CASES
Enhanced Using AI
Real-world sourcing isn't straightforward, and neither is user behavior
Real-world sourcing isn't straightforward, and neither is user behavior
From vague prompts to not knowing where to start, we knew we had to design for the not-so-great experiences, not just the ideal ones. I leveraged ChatGPT to generate real-world scenarios, identifying areas where users would likely get stuck, confused, or discouraged. I used these insights to build thoughtful safeguards and flexible tools to support users throughout their platform experience.
From vague prompts to not knowing where to start, we knew we had to design for the not-so-great experiences, not just the ideal ones. I leveraged ChatGPT to generate real-world scenarios, identifying areas where users would likely get stuck, confused, or discouraged. I used these insights to build thoughtful safeguards and flexible tools to support users throughout their platform experience.
AI-ENHANCEMENT FEATURE
AI-ENHANCEMENT FEATURE
To guide users in refining their results, we introduced an AI enhancement tool to help refine their prompt without forcing structure.
To guide users in refining their results, we introduced an AI enhancement tool to help refine their prompt without forcing structure.
HELP & GUIDANCE
HELP & GUIDANCE
I added a ‘Help & Guidance’ section beneath the prompt to make things feel approachable from the start. It gives users prompt examples, explains how the AI-enhancement tool works, and sets clear expectations for what happens after they submit their request, so they feel supported, not unsure.
I added a ‘Help & Guidance’ section beneath the prompt to make things feel approachable from the start. It gives users prompt examples, explains how the AI-enhancement tool works, and sets clear expectations for what happens after they submit their request, so they feel supported, not unsure.
MY IMPACT
Defining Outcomes, Not Just Interfaces
From day one, I was focused on impact. What problem were we solving? What would success look like ? As a product thinker, I helped shape the product's direction. I sat in on early conversations, helped reframe vague ideas into actionable hypotheses, and pushed for clarity around both user needs and business goals.
FRAMED THE PROBLEM, NOT JUST THE SOLUTION
I worked with leadership to clarify who we were designing for, what they actually needed, and how we'd define success.
ADVOCATED FOR SIMPLICITY AND INTENT
I reduced complexity in the supplier request workflow to lower cognitive load and increase usability.
USED UX AS A TOOL FOR BUSINESS ALIGNMENT
My designs weren’t just usable, they reflected key constraints such as limited resourcing, tight timelines, and scalability needs.
DESIGNED FOR OUTCOMES, NOT OUTPUT
Every design decision was tied back to user pain points and product goals, not just visual polish.
FOCUSED ON FLOWS, NOT SCREENS
I mapped full user journeys (including edge cases) to ensure the product worked as a cohesive experience, not a collection of isolated features.
This project taught me how to be a Product Thinking Designer, thinking beyond features and focusing on impact.
This project taught me how to be a Product Thinking Designer, thinking beyond features and focusing on impact.
MY IMPACT
Defining Outcomes, Not Just Interfaces
From day one, I was focused on impact. What problem were we solving? What would success look like ? As a product thinker, I helped shape the product's direction. I sat in on early conversations, helped reframe vague ideas into actionable hypotheses, and pushed for clarity around both user needs and business goals.
FRAMED THE PROBLEM, NOT JUST THE SOLUTION
I worked with leadership to clarify who we were designing for, what they actually needed, and how we'd define success.
ADVOCATED FOR SIMPLICITY AND INTENT
I reduced complexity in the supplier request workflow to lower cognitive load and increase usability.
USED UX AS A TOOL FOR BUSINESS ALIGNMENT
My designs weren’t just usable, they reflected key constraints such as limited resourcing, tight timelines, and scalability needs.
DESIGNED FOR OUTCOMES, NOT OUTPUT
Every design decision was tied back to user pain points and product goals, not just visual polish.
FOCUSED ON FLOWS, NOT SCREENS
I mapped full user journeys (including edge cases) to ensure the product worked as a cohesive experience, not a collection of isolated features.
REFLECTION
What I'd do differently…
What I'd do differently…
The product pivoted twice, and at times I felt a disconnect between what I was designing and why I was designing it. It became clear how easy it is to fall into execution mode when the problem space isn’t fully defined. Next time, I want to lean into product thinking even earlier to help clarify the “why,” define the solution space (as a team), and make sure every design decision is rooted in purpose, not just progress.
The product pivoted twice, and at times I felt a disconnect between what I was designing and why I was designing it. It became clear how easy it is to fall into execution mode when the problem space isn’t fully defined. Next time, I want to lean into product thinking even earlier to help clarify the “why,” define the solution space (as a team), and make sure every design decision is rooted in purpose, not just progress.
Next time, I'll apply product thinking earlier by…
Next time, I'll apply product thinking earlier by…
Slowing down to define the core problem
Facilitating workshops to establish alignment on user needs and business goals
Building hypotheses and testing assumptions instead of baking them into the product
Defining success and using it as our North Star
Creating a simple problem statement so we don't lose sight of our mission
Slowing down to define the core problem
Facilitating workshops to establish alignment on user needs and business goals
Building hypotheses and testing assumptions instead of baking them into the product
Defining success and using it as our North Star
Creating a simple problem statement so we don't lose sight of our mission
NEXT STEPS
Currently in Beta
Currently in Beta
Sourcing Ally is now in Beta, gathering real-world feedback from enterprise users, validating usability, refining key features, and continuing to align the experience with what procurement teams actually need.
Sourcing Ally is now in Beta, gathering real-world feedback from enterprise users, validating usability, refining key features, and continuing to align the experience with what procurement teams actually need.
WHAT'S BEING TESTED:
WHAT'S BEING TESTED:
Are users finding matches they can trust?
Is the AI-enhancement tool improving prompt quality?
Are users engaging with supplier profiles and the added vetting option?
Are users finding matches they can trust?
Is the AI-enhancement tool improving prompt quality?
Are users engaging with supplier profiles and the added vetting option?