Title
AI Interview Insights
01.
Intro of case
Interview Insights is an AI-powered interview intelligence platform designed to help hiring teams extract actionable insights from interviews. My role in this project involved designing a seamless user experience that enables recruiters to make data-driven hiring decisions while minimizing bias and reducing manual effort.
02.
Problem Statement
User Problem
Recruiters and hiring managers spend hours conducting interviews, yet most of that valuable conversation data is lost. Notes are often inconsistent, insights are subjective, and hiring decisions rely on memory rather than data. Interviewers struggle with juggling conversations while taking notes, leading to missed details and inconsistent evaluations.
Business Problem
Companies invest significant time and money into hiring, yet many decisions are based on intuition rather than objective data. Without structured insights, businesses face challenges such as prolonged hiring cycles, poor candidate evaluations, and unconscious bias affecting diversity efforts. Traditional transcription tools like Fireflies.ai help capture meetings but lack the intelligence needed to analyze interview-specific data.
Why It Was Important to Solve
03.
Scope of Work and Timeline
04.
Research
Defining the Research Plan
To ensure our solution was truly user-centered, I first identified the key factors influencing research method selection:
With these factors in mind, I chose a mix of qualitative and quantitative research methods to gain a holistic view.
Research Methods Used
User Interviews (Qualitative & Attitudinal)
To understand recruiters’ workflows and frustrations, I conducted 10 user interviews with hiring managers and talent acquisition professionals. These interviews focused on:
Recruiters found it hard to extract structured insights from long interviews and often forgot key moments.
Competitive Analysis (Quantitative & Behavioral)
I studied Fireflies.ai and Metaview to analyze:
05.
Key Research Findings & Their Impact on Design
Through our research, we uncovered critical insights that shaped the design of Interview Insights:
1. Recruiters struggle with summarizing interviews manually.
2. Transcripts alone are not enough, insights need context.
3. Recruiters prefer structured feedback over long transcripts.
4. Trust in AI-generated insights is low without transparency.
5. Collaboration is key – hiring teams need shared insights.
These insights helped us design a solution that was not just another transcription tool but a true AI-powered decision-making assistant for hiring teams.
06.
The MVP
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08.
User Testing Metrics & Insights
09.
Next Steps
Since Interview Insights is still a concept project, the next steps would focus on refining the design, validating ideas with real users, and preparing for potential development.
1. Advanced AI Model Training
2. Expanded Usability Testing
3. Enhanced Collaboration & Workflow Features
4. Real-Time Interview Assistance
5. Business Viability & Market Fit
By iterating on these areas, Interview Insights could evolve into a fully functional, AI-powered interview intelligence platform that enhances hiring efficiency.