This case study documents a real implementation with specific challenge context, approach, and measurable results.

The Challenge

A retained executive search firm handling C-suite and VP-level placements was struggling with research efficiency. Each search required 40 to 60 hours of manual research before a qualified longlist of 20 candidates was ready for client presentation. Researchers were spending 70% of their time reading profiles and writing summaries rather than building relationships and sourcing passive talent.

The Approach

The firm implemented AI resume parsing configured for executive-level profiles, with scoring criteria built around the specific leadership competencies each client required. AI-generated candidate summaries replaced manual profile writing for initial research. Structured scoring against client criteria reduced the time from longlist to shortlist by removing candidates who did not meet threshold scores before researcher review.

The Results

Research time per search dropped from 40 to 60 hours to 16 to 24 hours. Shortlist quality scores from clients improved by 28% based on post-search surveys. The firm increased active search capacity from 8 to 13 concurrent searches without adding research staff. Researcher time shifted from documentation to relationship development and candidate engagement.

Apply This to Your Organization

The framework behind these results: HR automation guide.