Product Design
Interview Guide & Scorecards
Interview Guide & Scorecards
Year :
2024
Industry :
Software
Client :
ServiceNow
Project Duration :
24 weeks
Enter password to view case study
🛑 THE PROBLEM:
At ServiceNow, the only structured interview resource available to hiring managers was a static 40-page PDF. It wasn’t embedded in workflow, wasn’t contextual, and wasn’t consistently used. As a result, interviews were subjective, misaligned, and inconsistent across teams. There was no structured way to connect role capabilities, interview questions, evaluation criteria, and feedback signals.
Beyond inconsistency, there was a deeper issue: hiring decisions lacked structured insight. Feedback was fragmented, hard to compare, and vulnerable to bias.
The opportunity wasn’t just to digitize a PDF — it was to design an AI-supported evaluation system that could guide structured decision-making across recruiters, hiring managers, and interviewers.
🎯 Objective / Goals:
Design an AI-enhanced, end-to-end hiring workflow within the NOW platform that:
Standardizes evaluation criteria
Reduces bias through structured signals
Integrates seamlessly with SmartRecruiters
Minimizes cognitive load during interviews
Surfaces clear, comparable decision insights
The goal was to combine structured human judgment with AI-supported guidance to create fairness, clarity, and speed.
📐 Program Scope
In Scope:
Integration with SmartRecruiters to allow for information to easily flow back and forth between tools
Align defined interview questions with our identified leadership capabilities & skills
An easy, objective way to capture, review and compare feedback and scoring, and clarity on the process for hiring managers and interview teams, while minimizing the “note taking” during the actual interview to maintain candidate experience
Out of Scope:
General interviewing best practices – compliance of what can vs can’t say, introductions, why ServiceNow, etc.
🔎 RESEARCH & FOUNDATIONS:
I led discovery using an AI-first approach.
AI was used to:
Synthesize recruiter and hiring manager interviews
Identify recurring friction patterns across roles
Cluster inconsistencies in evaluation methods
Surface gaps between job descriptions and interview questions
Map how bias entered through unstructured note-taking
Instead of manually mapping variations in process across dozens of teams, AI helped reveal structural breakdowns early.
Key insight:
Hiring teams didn’t share a clear definition of success. Questions were inconsistent. Feedback wasn’t structured enough to enable comparison.
This reframed the problem from “improving documentation” to “designing a structured evaluation system with embedded intelligence.”
What we heard from our Recruiters
Hiring teams don’t share a common vision of what makes a successful candidate
Questions need to be behavior-based and map to the leadership capabilities necessary for the role
Our process and questions aren’t optimized to help us mitigate bias and find diverse talent
Our lack of consistency, preparation, and coordination among interviewers lead to a poor candidate experience
Any solution needs to fit seamlessly into our ecosystem, which is becoming increasingly mobile
Design criteria for each of our key audiences
As a Hiring Manager > I want…
To find quality, diverse talent in less time.
Clarity on the process, my role in it, and how to get everyone on the same page.
Guidance on how to do the right thing and autonomy to ensure it meets my needs.
Consistency and efficiency and a process I can trust.
An easy, objective way to capture, review, and compare feedback and scoring
As a Recruiter> I want…
Myself, the hiring manager, and interview team to have a shared vision of success and steps to get there.
A solution that integrates with my existing tools, or replaces them
To reduce the amount of time and effort I spend collecting feedback
To have the resources and inputs necessary to run effective, focused feedback sessions
[To be able to recalibrate my searches based on feedback received]
As an interviewer> I want…
To understand what the whole process is and my role in it.
Clarity on what makes a good candidate and which capabilities I’m screening for.
A toolset that enables me to fulfill my role in the best, most efficient way.
Ease in entering my candidate feedback and score.
As a candidate> I want…
To feel like the team respects my time enough to prepare for my interview
To be treated and evaluated fairly and equitably
The interview experience and questions to match my expectations based on the role description
To feel like every contact with ServiceNow was a valuable use of my time [and have a better idea of what it would be like to work at ServiceNow]
🚀 THE SOLUTION:
We designed an end-to-end, AI-enhanced hiring system embedded in the workflow — not a static guide.
The system included:
AI-assisted interview plan creation aligned to leadership capabilities
Structured scorecards connected to defined skills
Smart feedback capture that minimized live note-taking
Aggregated insights for confident final decisions
SmartRecruiters integration for seamless data flow
The system guided behavior without being prescriptive.
User's flow (Made with Miro)
✏️ WIREFRAMES:
I led the UX design process from scratch, starting with low-fidelity wireframes that established the core structure and flow of the experience. These early prototypes allowed us to align the team on functionality before moving into visual refinement.
📊 MASTER SCORECARD DESIGN:
The Master Scorecard standardized evaluation around leadership capabilities and role-specific skills.
AI supported the experience by:
Suggesting behavior-based questions aligned to job requirements
Highlighting inconsistencies between feedback entries
Surfacing missing evaluation inputs
Flagging potential bias patterns (e.g., overly vague qualitative feedback)
The design focused on:
Clarity of criteria
Visible scoring structure
Reduced cognitive load
Transparent reasoning
Through usability testing, we validated that trust increased when AI guidance was contextual and explainable — not opaque or overly automated.
🧩 INDIVIDUAL SCORECARD & INTERVIEW ASSIGNMENT FLOW:
We redesigned interview planning and assignment to:
Clarify ownership
Reduce coordination friction
Improve task visibility
Prevent stalled steps
AI-supported prioritization helped identify:
Urgent interview stages
Delayed feedback cycles
Misalignment between planned capabilities and assigned interviewers
The system acted as a coordination assistant rather than a rigid controller.
📑 CONSOLIDATED SCORECARD:
The final experience aggregated:
Structured human scores
Qualitative notes
AI-identified signal patterns
Comparative candidate views
Instead of presenting a single “AI recommendation,” the system made reasoning visible:
Where signals aligned
Where they conflicted
Confidence indicators
Capability-level breakdowns
This empowered hiring managers to make faster, more defensible decisions with reduced cognitive load.
⚠️ CHALLENGES:
Introducing structure — and especially AI support — required careful trust design.
Key challenges:
Behavior change
Hiring managers were used to autonomy. We designed AI as assistive guidance, not enforcement.Transparency
AI outputs were always explainable. Users could see how suggestions mapped to leadership capabilities.Adoption
We embedded the system within tools already used (SmartRecruiters integration) to reduce friction.Feedback speed
We designed lightweight, structured feedback capture to encourage timely submission and reduce drop-off.
📄 SUMMARY:
We transformed a static, inconsistent interview guide into an AI-supported, structured hiring system embedded directly into operational workflow.
By combining AI-accelerated discovery, structured evaluation design, and iterative usability validation, we created:
A consistent evaluation framework
Reduced bias through standardized criteria
Faster feedback cycles
Clearer, more confident hiring decisions
🚀 Impact:
Increased collaboration efficiency
Reduced subjectivity and bias
Improved evaluation consistency
Accelerated decision cycles
Reduced feedback delays
👤 My Role
Lead UX Designer
📦 Key Deliverables
AI-enhanced workflow architecture
Master & Consolidated Scorecard design
SmartRecruiters integration flows
Interactive prototypes (Figma)
Usability testing synthesis
Design specifications
More Projects
Product Design
Interview Guide & Scorecards
Interview Guide & Scorecards
Year :
2024
Industry :
Software
Client :
ServiceNow
Project Duration :
24 weeks
Enter password to view case study
🛑 THE PROBLEM:
At ServiceNow, the only structured interview resource available to hiring managers was a static 40-page PDF. It wasn’t embedded in workflow, wasn’t contextual, and wasn’t consistently used. As a result, interviews were subjective, misaligned, and inconsistent across teams. There was no structured way to connect role capabilities, interview questions, evaluation criteria, and feedback signals.
Beyond inconsistency, there was a deeper issue: hiring decisions lacked structured insight. Feedback was fragmented, hard to compare, and vulnerable to bias.
The opportunity wasn’t just to digitize a PDF — it was to design an AI-supported evaluation system that could guide structured decision-making across recruiters, hiring managers, and interviewers.
🎯 Objective / Goals:
Design an AI-enhanced, end-to-end hiring workflow within the NOW platform that:
Standardizes evaluation criteria
Reduces bias through structured signals
Integrates seamlessly with SmartRecruiters
Minimizes cognitive load during interviews
Surfaces clear, comparable decision insights
The goal was to combine structured human judgment with AI-supported guidance to create fairness, clarity, and speed.
📐 Program Scope
In Scope:
Integration with SmartRecruiters to allow for information to easily flow back and forth between tools
Align defined interview questions with our identified leadership capabilities & skills
An easy, objective way to capture, review and compare feedback and scoring, and clarity on the process for hiring managers and interview teams, while minimizing the “note taking” during the actual interview to maintain candidate experience
Out of Scope:
General interviewing best practices – compliance of what can vs can’t say, introductions, why ServiceNow, etc.
🔎 RESEARCH & FOUNDATIONS:
I led discovery using an AI-first approach.
AI was used to:
Synthesize recruiter and hiring manager interviews
Identify recurring friction patterns across roles
Cluster inconsistencies in evaluation methods
Surface gaps between job descriptions and interview questions
Map how bias entered through unstructured note-taking
Instead of manually mapping variations in process across dozens of teams, AI helped reveal structural breakdowns early.
Key insight:
Hiring teams didn’t share a clear definition of success. Questions were inconsistent. Feedback wasn’t structured enough to enable comparison.
This reframed the problem from “improving documentation” to “designing a structured evaluation system with embedded intelligence.”
What we heard from our Recruiters
Hiring teams don’t share a common vision of what makes a successful candidate
Questions need to be behavior-based and map to the leadership capabilities necessary for the role
Our process and questions aren’t optimized to help us mitigate bias and find diverse talent
Our lack of consistency, preparation, and coordination among interviewers lead to a poor candidate experience
Any solution needs to fit seamlessly into our ecosystem, which is becoming increasingly mobile
Design criteria for each of our key audiences
As a Hiring Manager > I want…
To find quality, diverse talent in less time.
Clarity on the process, my role in it, and how to get everyone on the same page.
Guidance on how to do the right thing and autonomy to ensure it meets my needs.
Consistency and efficiency and a process I can trust.
An easy, objective way to capture, review, and compare feedback and scoring
As a Recruiter> I want…
Myself, the hiring manager, and interview team to have a shared vision of success and steps to get there.
A solution that integrates with my existing tools, or replaces them
To reduce the amount of time and effort I spend collecting feedback
To have the resources and inputs necessary to run effective, focused feedback sessions
[To be able to recalibrate my searches based on feedback received]
As an interviewer> I want…
To understand what the whole process is and my role in it.
Clarity on what makes a good candidate and which capabilities I’m screening for.
A toolset that enables me to fulfill my role in the best, most efficient way.
Ease in entering my candidate feedback and score.
As a candidate> I want…
To feel like the team respects my time enough to prepare for my interview
To be treated and evaluated fairly and equitably
The interview experience and questions to match my expectations based on the role description
To feel like every contact with ServiceNow was a valuable use of my time [and have a better idea of what it would be like to work at ServiceNow]
🚀 THE SOLUTION:
We designed an end-to-end, AI-enhanced hiring system embedded in the workflow — not a static guide.
The system included:
AI-assisted interview plan creation aligned to leadership capabilities
Structured scorecards connected to defined skills
Smart feedback capture that minimized live note-taking
Aggregated insights for confident final decisions
SmartRecruiters integration for seamless data flow
The system guided behavior without being prescriptive.
User's flow (Made with Miro)
✏️ WIREFRAMES:
I led the UX design process from scratch, starting with low-fidelity wireframes that established the core structure and flow of the experience. These early prototypes allowed us to align the team on functionality before moving into visual refinement.
📊 MASTER SCORECARD DESIGN:
The Master Scorecard standardized evaluation around leadership capabilities and role-specific skills.
AI supported the experience by:
Suggesting behavior-based questions aligned to job requirements
Highlighting inconsistencies between feedback entries
Surfacing missing evaluation inputs
Flagging potential bias patterns (e.g., overly vague qualitative feedback)
The design focused on:
Clarity of criteria
Visible scoring structure
Reduced cognitive load
Transparent reasoning
Through usability testing, we validated that trust increased when AI guidance was contextual and explainable — not opaque or overly automated.
🧩 INDIVIDUAL SCORECARD & INTERVIEW ASSIGNMENT FLOW:
We redesigned interview planning and assignment to:
Clarify ownership
Reduce coordination friction
Improve task visibility
Prevent stalled steps
AI-supported prioritization helped identify:
Urgent interview stages
Delayed feedback cycles
Misalignment between planned capabilities and assigned interviewers
The system acted as a coordination assistant rather than a rigid controller.
📑 CONSOLIDATED SCORECARD:
The final experience aggregated:
Structured human scores
Qualitative notes
AI-identified signal patterns
Comparative candidate views
Instead of presenting a single “AI recommendation,” the system made reasoning visible:
Where signals aligned
Where they conflicted
Confidence indicators
Capability-level breakdowns
This empowered hiring managers to make faster, more defensible decisions with reduced cognitive load.
⚠️ CHALLENGES:
Introducing structure — and especially AI support — required careful trust design.
Key challenges:
Behavior change
Hiring managers were used to autonomy. We designed AI as assistive guidance, not enforcement.Transparency
AI outputs were always explainable. Users could see how suggestions mapped to leadership capabilities.Adoption
We embedded the system within tools already used (SmartRecruiters integration) to reduce friction.Feedback speed
We designed lightweight, structured feedback capture to encourage timely submission and reduce drop-off.
📄 SUMMARY:
We transformed a static, inconsistent interview guide into an AI-supported, structured hiring system embedded directly into operational workflow.
By combining AI-accelerated discovery, structured evaluation design, and iterative usability validation, we created:
A consistent evaluation framework
Reduced bias through standardized criteria
Faster feedback cycles
Clearer, more confident hiring decisions
🚀 Impact:
Increased collaboration efficiency
Reduced subjectivity and bias
Improved evaluation consistency
Accelerated decision cycles
Reduced feedback delays
👤 My Role
Lead UX Designer
📦 Key Deliverables
AI-enhanced workflow architecture
Master & Consolidated Scorecard design
SmartRecruiters integration flows
Interactive prototypes (Figma)
Usability testing synthesis
Design specifications
More Projects
Product Design
Interview Guide & Scorecards
Interview Guide & Scorecards
Year :
2024
Industry :
Software
Client :
ServiceNow
Project Duration :
24 weeks
Enter password to view case study
🛑 THE PROBLEM:
At ServiceNow, the only structured interview resource available to hiring managers was a static 40-page PDF. It wasn’t embedded in workflow, wasn’t contextual, and wasn’t consistently used. As a result, interviews were subjective, misaligned, and inconsistent across teams. There was no structured way to connect role capabilities, interview questions, evaluation criteria, and feedback signals.
Beyond inconsistency, there was a deeper issue: hiring decisions lacked structured insight. Feedback was fragmented, hard to compare, and vulnerable to bias.
The opportunity wasn’t just to digitize a PDF — it was to design an AI-supported evaluation system that could guide structured decision-making across recruiters, hiring managers, and interviewers.
🎯 Objective / Goals:
Design an AI-enhanced, end-to-end hiring workflow within the NOW platform that:
Standardizes evaluation criteria
Reduces bias through structured signals
Integrates seamlessly with SmartRecruiters
Minimizes cognitive load during interviews
Surfaces clear, comparable decision insights
The goal was to combine structured human judgment with AI-supported guidance to create fairness, clarity, and speed.
📐 Program Scope
In Scope:
Integration with SmartRecruiters to allow for information to easily flow back and forth between tools
Align defined interview questions with our identified leadership capabilities & skills
An easy, objective way to capture, review and compare feedback and scoring, and clarity on the process for hiring managers and interview teams, while minimizing the “note taking” during the actual interview to maintain candidate experience
Out of Scope:
General interviewing best practices – compliance of what can vs can’t say, introductions, why ServiceNow, etc.
🔎 RESEARCH & FOUNDATIONS:
I led discovery using an AI-first approach.
AI was used to:
Synthesize recruiter and hiring manager interviews
Identify recurring friction patterns across roles
Cluster inconsistencies in evaluation methods
Surface gaps between job descriptions and interview questions
Map how bias entered through unstructured note-taking
Instead of manually mapping variations in process across dozens of teams, AI helped reveal structural breakdowns early.
Key insight:
Hiring teams didn’t share a clear definition of success. Questions were inconsistent. Feedback wasn’t structured enough to enable comparison.
This reframed the problem from “improving documentation” to “designing a structured evaluation system with embedded intelligence.”
What we heard from our Recruiters
Hiring teams don’t share a common vision of what makes a successful candidate
Questions need to be behavior-based and map to the leadership capabilities necessary for the role
Our process and questions aren’t optimized to help us mitigate bias and find diverse talent
Our lack of consistency, preparation, and coordination among interviewers lead to a poor candidate experience
Any solution needs to fit seamlessly into our ecosystem, which is becoming increasingly mobile
Design criteria for each of our key audiences
As a Hiring Manager > I want…
To find quality, diverse talent in less time.
Clarity on the process, my role in it, and how to get everyone on the same page.
Guidance on how to do the right thing and autonomy to ensure it meets my needs.
Consistency and efficiency and a process I can trust.
An easy, objective way to capture, review, and compare feedback and scoring
As a Recruiter> I want…
Myself, the hiring manager, and interview team to have a shared vision of success and steps to get there.
A solution that integrates with my existing tools, or replaces them
To reduce the amount of time and effort I spend collecting feedback
To have the resources and inputs necessary to run effective, focused feedback sessions
[To be able to recalibrate my searches based on feedback received]
As an interviewer> I want…
To understand what the whole process is and my role in it.
Clarity on what makes a good candidate and which capabilities I’m screening for.
A toolset that enables me to fulfill my role in the best, most efficient way.
Ease in entering my candidate feedback and score.
As a candidate> I want…
To feel like the team respects my time enough to prepare for my interview
To be treated and evaluated fairly and equitably
The interview experience and questions to match my expectations based on the role description
To feel like every contact with ServiceNow was a valuable use of my time [and have a better idea of what it would be like to work at ServiceNow]
🚀 THE SOLUTION:
We designed an end-to-end, AI-enhanced hiring system embedded in the workflow — not a static guide.
The system included:
AI-assisted interview plan creation aligned to leadership capabilities
Structured scorecards connected to defined skills
Smart feedback capture that minimized live note-taking
Aggregated insights for confident final decisions
SmartRecruiters integration for seamless data flow
The system guided behavior without being prescriptive.
User's flow (Made with Miro)
✏️ WIREFRAMES:
I led the UX design process from scratch, starting with low-fidelity wireframes that established the core structure and flow of the experience. These early prototypes allowed us to align the team on functionality before moving into visual refinement.
📊 MASTER SCORECARD DESIGN:
The Master Scorecard standardized evaluation around leadership capabilities and role-specific skills.
AI supported the experience by:
Suggesting behavior-based questions aligned to job requirements
Highlighting inconsistencies between feedback entries
Surfacing missing evaluation inputs
Flagging potential bias patterns (e.g., overly vague qualitative feedback)
The design focused on:
Clarity of criteria
Visible scoring structure
Reduced cognitive load
Transparent reasoning
Through usability testing, we validated that trust increased when AI guidance was contextual and explainable — not opaque or overly automated.
🧩 INDIVIDUAL SCORECARD & INTERVIEW ASSIGNMENT FLOW:
We redesigned interview planning and assignment to:
Clarify ownership
Reduce coordination friction
Improve task visibility
Prevent stalled steps
AI-supported prioritization helped identify:
Urgent interview stages
Delayed feedback cycles
Misalignment between planned capabilities and assigned interviewers
The system acted as a coordination assistant rather than a rigid controller.
📑 CONSOLIDATED SCORECARD:
The final experience aggregated:
Structured human scores
Qualitative notes
AI-identified signal patterns
Comparative candidate views
Instead of presenting a single “AI recommendation,” the system made reasoning visible:
Where signals aligned
Where they conflicted
Confidence indicators
Capability-level breakdowns
This empowered hiring managers to make faster, more defensible decisions with reduced cognitive load.
⚠️ CHALLENGES:
Introducing structure — and especially AI support — required careful trust design.
Key challenges:
Behavior change
Hiring managers were used to autonomy. We designed AI as assistive guidance, not enforcement.Transparency
AI outputs were always explainable. Users could see how suggestions mapped to leadership capabilities.Adoption
We embedded the system within tools already used (SmartRecruiters integration) to reduce friction.Feedback speed
We designed lightweight, structured feedback capture to encourage timely submission and reduce drop-off.
📄 SUMMARY:
We transformed a static, inconsistent interview guide into an AI-supported, structured hiring system embedded directly into operational workflow.
By combining AI-accelerated discovery, structured evaluation design, and iterative usability validation, we created:
A consistent evaluation framework
Reduced bias through standardized criteria
Faster feedback cycles
Clearer, more confident hiring decisions
🚀 Impact:
Increased collaboration efficiency
Reduced subjectivity and bias
Improved evaluation consistency
Accelerated decision cycles
Reduced feedback delays
👤 My Role
Lead UX Designer
📦 Key Deliverables
AI-enhanced workflow architecture
Master & Consolidated Scorecard design
SmartRecruiters integration flows
Interactive prototypes (Figma)
Usability testing synthesis
Design specifications