90% of firms use AI in hiring, but fewer than 5% see transformational gains
More than 90% of organizations have deployed AI in talent acquisition, yet fewer than 5% report transformational outcomes, according to a report by ManpowerGroup Talent Solutions and Everest Group. The research identifies fragmented workflows, governance gaps, and AI-assisted candidate behavior as primary barriers to realizing value. While 39% of organizations report significant operational efficiency gains, improvements in decision quality and workforce agility remain limited.

*this image is generated using AI for illustrative purposes only.
More than 90% of organizations have deployed AI in talent acquisition, yet fewer than 5% report transformational outcomes, according to a report commissioned by ManpowerGroup Talent Solutions and developed by Everest Group. The research highlights a significant gap between widespread AI adoption and its realized business value, with fragmented workflows and governance gaps blocking transformation. The findings were featured at VivaTech 2026 in Paris.
The report, titled "The New Talent Equation: Building Better Talent Decisions," draws on a survey of 80 C-suite, CHRO, and senior talent acquisition leaders across the United States and the United Kingdom. It examines why AI adoption in hiring has scaled rapidly while its impact on talent decision-making continues to lag. The study spans healthcare, life sciences, manufacturing, and technology sectors.
Caroline Pfeiffer Marinho, Global Senior Vice President at ManpowerGroup Talent Solutions, noted that AI is exposing talent operations rather than transforming them evenly. She emphasized that the constraint is no longer access to AI tools but how talent operations are designed around them. Sailesh Hota, Vice President at Everest Group, added that adapting workforce models and operating structures is proving as important as technology adoption.
The research documents that while 39% of organizations report significant impact on operational efficiency, improvements in decision quality and workforce agility remain limited. Most organizations are layering AI onto workflows built for a pre-AI environment, relying on isolated tools and siloed data. This prevents AI from generating cumulative value across the full hiring lifecycle.
Key Barriers to AI Impact
Organizations cite several top barriers to scaling AI in hiring:
| Barrier | Percentage of Organizations |
|---|---|
| Change management and adoption challenges | 58% |
| Governance and compliance concerns | 55% |
| Data readiness limitations | 55% |
Nearly 54% of organizations report that AI-assisted candidate behavior, such as AI-generated resumes and interview preparation, is making it harder to accurately assess true candidate capability. Additionally, 72% of organizations report achieving expected AI outcomes within two years, with 26% realizing value in under a year. However, the research suggests this speed comes at the cost of prioritizing near-term gains over deeper workflow redesign.
The report outlines a four-stage roadmap from rationalization through adoption, enablement, and transformation. It identifies foundational investments in data integration, governance, and operating model alignment as necessary to move organizations toward lasting impact.
How will organizations need to restructure their operating models to move beyond isolated AI tools and achieve the transformational outcomes currently seen in less than 5% of companies?
As AI-generated resumes and interview prep become more sophisticated, what new verification technologies or assessment methods will emerge to accurately gauge true candidate capability?
Will the pressure to demonstrate ROI within two years force companies to deprioritize the necessary long-term workflow redesigns required for sustainable AI integration?























