Edith Heroux

When I first heard about implementing AI into our talent acquisition workflow five years ago, I was skeptical. Our team had built a recruitment process that worked—we knew our ATS inside and out, our candidate engagement strategy was solid, and we prided ourselves on the personalized touch we brought to every interaction. The idea that algorithms could improve what we did felt threatening rather than promising. But after watching our time-to-fill metrics balloon to 47 days and our candidate drop-off rates climb above 60% during screening, I knew something had to change. What followed was a journey that completely transformed how we approach recruitment, revealing both unexpected pitfalls and remarkable wins that forever changed my perspective on technology in hiring. The decision to embrace AI in Talent Acquisition came after a particularly brutal quarter where we lost three critical engineering hires to competitors who moved faster. Our manual resume parsing process meant recruiters ...

The talent acquisition landscape in financial services has undergone a profound transformation over the past five years, driven by the integration of artificial intelligence into recruitment workflows. At firms like JPMorgan Chase and Goldman Sachs, AI-driven talent acquisition is no longer experimental—it is core infrastructure. Yet for many practitioners, the operational mechanics remain opaque. How do these systems actually function day-to-day? What happens between the moment a candidate submits an application and the point where a recruiter schedules an interview? This article pulls back the curtain on the technical and operational architecture that powers modern AI-driven talent acquisition in financial services, from initial candidate sourcing through compliance-integrated onboarding. Understanding AI-Driven Talent Acquisition requires distinguishing it from legacy applicant tracking systems. Traditional platforms collected resumes and enabled keyword searches. Modern AI-driven ...