Tech Show & Tell

A room full of recruiters and sourcers trades real, working examples of how they use AI in daily practice, starting from the problems they're actually trying to solve rather than the tools being pitched to them. The conversation moves person to person, each sharing a tactic pulled straight from their own workflow.

• Building custom GPTs on a team plan so an entire recruiting staff can pull from the same standardized prompts for candidate submittals, resume screening notes, and call summaries, keeping output consistent no matter who handles a requisition.
• Feeding a custom GPT raw inputs like a job description, an intake call transcript, notes, and a client's website to generate a full sourcing and research profile in minutes.
• Using a recruitment marketing GPT to turn a job description into ready social posts.
• Running biweekly peer-only team meetings to surface recurring problems, such as recruiters who lack strong Boolean skills, and solving them with tools like a custom Google search engine that pulls competitor staff listings for a field where most professionals aren't on LinkedIn.
• Using AI to handle low-dopamine writing tasks like candidate outreach emails as a practical accommodation for attention challenges.
• Building a competitor-mapping tool that replaces manual spreadsheets hiring managers ask for, showing nearby competitor staff within a set radius automatically.
• Running deep research queries to prepare for intake calls on unfamiliar roles, producing a market report on candidate population size, new graduate numbers, regional concentration, and top employers so a recruiter can walk in knowing more than the hiring manager.
• Setting up automated stage-based redirects inside an applicant tracking system so candidates moving through a pipeline automatically receive role-specific content, like a relevant case study or product page, with almost no added effort once the workflow is built.

Across every example, the emphasis stays on identifying a concrete, repeated pain point first and only then deciding whether an off-the-shelf tool, a custom-built GPT, or a simple internal process change solves it.