all case studies
boosting employment through AI-directed job search
Algorithmic, skills-based assessments can improve hiring rates for entry level job seekers while reducing search time.
project type
measurement & evaluation
impact
12%
increase in employment outcomes
30%
reduction in time spent seeking work
1,117
participants across seven U.S. metro areas
How might we help low-income jobseekers access high-opportunity, good-fit jobs where they are likely to succeed?
Many entry-level frontline roles build highly transferable skills, yet jobseekers often search narrowly within familiar occupations because it’s hard to judge how their skills (especially soft skills) translate to roles in new sectors, and job search is time-consuming and costly.
Our experiment tests whether AI-based (predicts performance) and/or preference-based vacancy rankings help jobseekers find jobs faster and move across occupations, without sacrificing wages or satisfaction.
Safe, speedy and effective communication is vital for firms, more so for those that employ migrant women. Many existing grievance redressal channels do not allow for smooth communication, and a lack of voice affects worker motivation and leads to attrition. Based on rigorous research, GBL developed Inache, a homegrown, anonymized worker voice tool that resolved 90% of concerns, while having a dramatic impact on productivity, retention and attendance.
