A new study from Taggd finds that 80% of organizations use artificial intelligence in recruitment, but only 10% see measurable improvements in hiring outcomes. The findings highlight a persistent disconnect between HR technology investments and tangible recruitment results.
Taggd, an AI-powered digital recruitment company, released the study alongside its new AI-native recruitment delivery model. The model combines its agentic AI recruitment assistant, TARA, with expert recruiters the company calls Forward-Deployed Recruiters.
According to Taggd, the hybrid model is designed to improve recruitment efficiency, shorten hiring timelines, and deliver more predictable outcomes. The study suggests that AI alone is not sufficient to solve complex hiring challenges.
Study findings
"Data, agents, and domain put together gives an entirely different dimension in solving complex recruitment challenges, elevating our Forward-Deployed Recruiters to direct execution at a scale that simply wasn't possible before," said Kiran Kumar, President, Platform, Taggd.
Devashish Sharma, Co-Founder and CEO of Taggd, said organizations using the platform have already seen improvements in hiring speed, talent fit, real-time market intelligence, and stakeholder satisfaction.
"Agentic AI architecture will also allow the company to expand its recruitment process outsourcing (RPO) services to mid-sized, high-growth businesses, enabling them to access enterprise-grade hiring capabilities," Sharma said.
The study indicates that enterprises may need to combine technology with domain expertise and human intervention to translate AI investments into measurable hiring outcomes.
Why this matters for HR
For HR leaders, the practical takeaway is to audit existing AI recruitment tools against specific metrics like time-to-hire, quality of hire, and cost-per-hire. The data suggests that simply purchasing AI tools does not guarantee better outcomes. Building a workflow where AI handles high-volume tasks and experienced recruiters focus on strategic decisions is more likely to close the gap between adoption and results.
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