Taofeek Lawal on Why Hiring Is Still Broken in the Age of Automation

Artificial intelligence was expected to usher in a fairer and more efficient era of recruitment. Yet, despite the widespread use of Applicant Tracking Systems and automated screening tools, hiring remains opaque and frustrating for many job seekers and employers alike.

Taofeek Lawal, co-technical founder of UseHirable and a front-end developer who builds digital products with business KPIs in mind, argues that the problem is not automation itself but how it has been applied. According to him, most hiring technologies are designed to manage scale rather than to understand real capability, context or potential.

Many systems rely heavily on keyword matching, a method Lawal describes as technically limited. While it speeds up screening, it often filters out candidates with non-linear career paths or transferable skills, favouring keyword optimisation over genuine competence. As a result, organisations risk missing capable professionals whose experience does not fit neatly into predefined categories. “Efficiency has come at the cost of insight,” he notes.

Lawal also highlights candidate experience as a major blind spot in automated hiring. Applications frequently disappear into systems that offer no feedback, no timelines and little indication that a human review ever took place. “We’ve automated judgment but left communication behind,” he says, adding that this lack of transparency erodes trust between employers and talent.

Looking ahead, Lawal believes automation should be repositioned as a support tool rather than a gatekeeper. AI, he argues, should help recruiters surface meaningful insights, summarise candidate strengths and manage administrative tasks, while humans retain responsibility for evaluation and decision-making. Until hiring technology is built with transparency, context and empathy at its core, he warns, automation will continue to magnify existing flaws rather than fix them.