As retailers prepare for the year’s busiest season, Gap Inc. is turning to artificial intelligence to help process a surge of job applications. The company has introduced an AI-powered hiring assistant called Bridgette, designed to support high-volume seasonal recruitment. The tool arrives at a moment when many HR teams are being asked to hire faster without adding headcount, making automation both attractive and, for some, uncomfortable.

Why seasonal hiring pushes retailers toward automation

Holiday hiring is uniquely demanding. A single retailer may need to fill thousands of store, distribution center and customer service roles within a few weeks. Applications arrive in bursts, often from candidates who are applying to multiple employers at once. Screening those applications manually can create delays that cause good candidates to accept other offers before a recruiter even calls them.

AI tools like Bridgette are built for this environment. They can review applications around the clock, ask candidates a short set of qualifying questions, and move people into the next stage without waiting for a human recruiter. That speed can shrink time-to-hire from days to hours, which matters when a competitor is hiring from the same pool. For HR leaders, the promised benefit is not replacing recruiters but removing repetitive screening work so recruiters can focus on conversations, interviews and final decisions.

What Bridgette does differently

Gap’s tool is designed to act as a first point of contact for applicants. It can guide candidates through the application process, answer common questions about schedules and requirements, and help schedule interviews. In high-volume hiring, these are often the tasks that slow recruiters down most. By automating them, the company hopes to keep candidates engaged and reduce the number of people who drop out before speaking to a human.

The assistant also gives Gap a way to handle applicant volume that would be difficult to manage with a seasonal recruiting team alone. Instead of letting applications sit in an inbox, candidates receive an immediate response. That responsiveness can shape how applicants perceive the company, even when they are not ultimately hired. For a consumer brand like Gap, candidate experience can overlap with customer experience in ways that matter for reputation.

The questions HR teams still need to ask

For all the speed AI can provide, HR professionals are right to ask how these tools make decisions. If an assistant screens out candidates based on availability, language patterns or other signals, there is always a risk that qualified people are filtered out for reasons that have little to do with job performance. Bias can enter through training data or through the way questions are phrased, and it can be harder to spot when decisions happen inside a black box.

Transparency becomes especially important in seasonal hiring, where many applicants are students, older workers returning to the workforce or people balancing multiple jobs. A tool that unintentionally favors one type of candidate could quietly narrow the talent pool. HR teams using AI screening should regularly audit outcomes, test for disparate impact, and keep a clear path for candidates who want a human review of their application.

The broader lesson from Gap’s move is not that AI can replace judgment in hiring, but that it can change where human judgment is applied. Recruiters spend less time sorting and more time evaluating the people who reach a conversation. For that shift to work, the technology needs to be monitored as carefully as any human decision-making process. Used well, it can reduce routine workload during a season when every day counts. Used carelessly, it can create new risks that only become visible after the hiring rush is over.

For HR teams weighing similar tools, platforms like XMF can help connect flexible staffing workflows with the human oversight these systems still require. The goal is not to choose between speed and fairness, but to build a process where both can be measured and improved.

Originally published by XMF, inspired by publicly reported industry news.