Enterprise software generative and evaluative research on AI-assisted hiring solution from concept to prototype: optimize hiring experience and find the right candidate for small and medium-sized business (SMB).
Faced with employers’ lack of trust in AI screening tools for large applicant pools, I led foundational research and design validation to deliver an AI-assisted, human-in-the-loop workflow with 4 core features, which achieved organization-wide visibility and cross-functional buy-in from 2 product teams and senior leadership at Indeed.
Finding the right candidates has always been a headache for employers. Even with all kinds of Application Tracking System (ATS) and algorithms available, employers struggle to translate their time and efforts spent on these tools into efficient hiring and decision-making.
In large applicant pools, employers (EMPs) struggle to identify qualified candidates even with AI toolsdesigned to facilitate resume screening. Without clarity, context, and control, EMPs lack trust to evaluate candidates confidently with AI, leading teams to abandon the tool.
We identified a gap in employer expectation and AI recommendations: despite internal data showing nearly 60% of applicants meet the listed qualifications, candidate quality remains the top employer complaint. The gap lies not in supply but in alignment: a disconnect between employer expectations and how the system surfaces matches.
How would we reimagine the current SMB employer experience to address these opportunities with GenAI at the core?
After stakeholder meeting with Indeed Employer Labs, I have identified the following constraints:
Considering the constraints and timeline, we decided to focus on the early stage of hiring before interviewing candidates.
Analyze 2000+ recent posts (posted within 6 months) from r/Indeedjobs and r/recruiting collected via Reddit API
Review 20 papers published within 5 years on AI use in hiring and job seeking
Compare current service and solutions offered by Indeed’s direct competitors and other companies
Conduct 60-minute sessions with 5 SMB employers, consisting of 3 recruiters and 3 hiring managers, with one participant serving in both roles
I collaborated with 1 researcher and 2 designers in the generative research phase. I led the Reddit analysis and in-depth user interviews and supported literature review and competitive analysis.
To answer our research questions and lay the groundwork for in-depth primary research on EMP pain points, we first conducted observational and secondary research to develop a broad understanding of the space.
I led the Reddit analysis, collecting 2,000+ posts via Reddit API to capture current discussions among job seekers and EMPs. I first used Latent Dirichlet Allocation (LDA) (an unsupervised machine learning technique) to rapidly identify groups of words that tend to appear together, which can then be interpreted as “themes." Then, I manually coded the posts based on the preliminary themes and derived insights based on the initial themes.
Marketing stakeholders initially questioned the value of this research and were concerned about biased voices from r/IndeedJobs. I emphasized that our objective was not to critique Indeed, but to surface job seekers’ concerns about employers’ use of AI technologies and invited them to think about the importance of incorporating perspectives from both sides of the hiring process, so that as a team, we could build a more equitable tool. Eventually, I gained their buy-in to proceed.
All 5 participants utilize Microsoft Excel as a critical touchpoint throughout the editing and updating process, particularly for specific data category management.
New Feature: Microsoft Excel Upload Integration – Incorporate seamless Excel upload capability within both creation and edit workflows to eliminate manual data transfer steps.
Annual spending activities are subject to mid-year guardrail modifications due to market volatility and policy adjustments, making complete automation unreliable.
New Feature: Flexible Configuration Framework – Develop an adaptable system allowing user-controlled adjustments within established guardrail parameters.
100% of users expressed no confidence in automated plan maintenance, requiring manual intervention for quality assurance and control.
Roadmap Adjustment: Deprioritize Full Automation – Shift focus from automation to user-assisted workflows until co-innovation partners demonstrate demand.
Users apply uniform editing approaches regardless of promotion duration, indicating opportunity for workflow standardization.
Design Strategy: Unified Workflow Architecture – Eliminate differentiated editing workflows in favor of a single, optimized editing experience.
