Amazon's Hiring Transformation: 90% Cost Savings with 95% Hiring Accuracy

Amazon hires hundreds of thousands of hourly employees and tens of thousands of corporate staff each year. Any hiring miss risks broken customer promises and millions in overtime. This session dissects how Amazon embedded AI and ML throughout the hiring funnel to simultaneously improve cost, quality and speed. See how precision systems reduced hiring cost by 90%, saving over a billion dollars annually, how applicant-to-hire ratios shrank from 11:1 to 3:1, and how a closed-loop model achieved 95% hiring accuracy while meeting rigorous compliance requirements. Lessons will highlight both technical design and organisational adoption.

• Scale of the hiring challenge in hourly and corporate contexts, and why accuracy drives cost avoidance.
• Hourly hiring transformation across three years: data foundations, rule-based optimisation, ML models, and resilience improvements.
• Corporate hiring innovations: success profiles, richer evaluation signals, integrated decision data, and AI-driven guardrails for human judgment.
• Outcomes achieved: 90% cost reduction, 95% hiring accuracy, 11:1 to 3:1 applicant-to-hire ratio.
• Key lessons: solving the right problem at the right stage, building in intelligence not bolting it on, and cultural adoption of AI systems.

• Understand how to design a phased roadmap for AI adoption in hiring, balancing rule-based and ML-based systems.
• Learn techniques for improving hiring accuracy and speed without sacrificing compliance or human oversight.
• See how to quantify and communicate the financial impact of AI-driven hiring systems to senior stakeholders.
• Gain insight into change management practices that enabled adoption at global scale.
• Take away practical lessons on aligning talent, culture and technology when embedding AI in core HR processes.

Ashish Parulekar

Global Head of Talent Acquisition Science and Analytics · Amazon

Hiring at global scale is one of the costliest and most critical challenges in business today. Organisations face pressure to increase quality of hire, cut cost per hire, and accelerate time-to-fill—while remaining compliant across jurisdictions. For large enterprises, inefficient hiring systems can result in billions in wasted labour spend and missed revenue opportunities. With AI and ML capabilities maturing, the opportunity is to re-engineer hiring for accuracy, speed and cost simultaneously.