Answer extracted from The Josh Bersin Company podcast — listen to the full episode below.
What role does industrial-organizational psychology play in the future of AI recruiting?
Industrial-organizational psychology integrated into AI recruiting creates job-specific, culture-aligned assessments that filter candidates more effectively than generic algorithms alone. Before deploying AI to source candidates, companies work with a consulting firm or the AI provider to build realistic assessments tailored to the role and organizational culture — a process that takes more upfront time but produces significantly better candidate quality.
The traditional recruiting AI stack often automates at scale without understanding what actually predicts success in a specific role or company. This creates a mismatch: the AI floods recruiters with resumes, but many lack genuine fit. Smarter IO assessments flip this approach by embedding proven hiring science into the AI model itself.
Companies like Maki People and Paradox exemplify this model. Rather than letting AI cast the widest possible net, they work upfront with employers to define what job success actually looks like — technical skills, cultural values, communication style, and role-specific competencies — and then build assessments that filter for those attributes from the start.
The upfront investment that saves time downstream
This approach requires more initial effort than deploying an off-the-shelf resume parser. Teams must collaborate to answer hard questions: What does a high performer in this role actually do? Which soft skills matter most? How does this role interact with team dynamics and company culture? The payoff emerges in higher-quality candidate pipelines and fewer mismatches that lead to costly bad hires.
When IO psychology is absent, the result is what recruiters describe as a flood — high volume, low signal. As Bersin explains in the episode, the recruiting industry is caught in an arms race where AI creates resumes and AI consumes them, making it harder for hiring teams to surface genuine talent.
"We have AI creating resumes and producing and sending them out and AI consuming them — slop talking to slop — and the recruiters constantly tell us they're getting so flooded with fraudulent resumes that they really can't sort through it all."
Josh Bersin — Global Industry Analyst & CEO, The Josh Bersin Company. Bersin specializes in talent acquisition and organizational performance, drawing on decades of research and benchmarking data on hiring costs, time-to-fill, and candidate quality across industries.
In this context, IO psychology becomes a shield — a systematic way to define signal and filter out noise. The full episode explores how this plays out across different job types and skill levels, and why some companies are already seeing measurably better outcomes by investing in smarter assessments upfront.
See also
How should companies differentiate their AI recruiting approach by job type?
For entry-level or low-skilled roles, companies can automate heavily and move through a quick process focused on basic skills. Higher-skilled and senior roles demand more nuanced assessment and human judgment.
How does AI perform on skills assessment compared to holistic candidate evaluation?
AI tools like Galileo, loaded with a massive skills library from Lightcast, excel at skills-based assessment — identifying specific competencies. But holistic evaluation of culture fit and growth potential still requires human insight.
What is the North Korean fraudulent engineer threat to AI-powered hiring?
A Wall Street Journal article revealed that North Korea assembled a large operation of fraudulent software engineers who apply to jobs, conduct video interviews, and attempt to infiltrate companies — a stark example of why AI recruitment without robust vetting fails.
Key takeaways
- Industrial-organizational psychology in AI recruiting means defining job success criteria and culture fit upfront, then building assessments around those real factors.
- Companies like Maki People and Paradox invest time at the beginning to work with employers on tailored assessments, reducing low-quality candidate volume downstream.
- Without IO-informed assessments, AI recruiting devolves into high-volume noise — creating and processing thousands of resumes, many fraudulent or misaligned.
- The upfront design work pays dividends in hiring speed, candidate quality, and lower turnover from bad fits.