Talent acquisition is at an inflection point. Urgency is rising, investment is increasing, and expectations are higher than ever before. At the same time, execution remains uneven. Most organizations are still operating with fragmented workflows, manual coordination, and point solutions layered on top of outdated processes. The result is a hiring function that feels rife with activity, but lacking in real productivity.

What stands out most in this report isn’t a lack of technology at the organizations we audited, but a lack of orchestration. Teams have tools for sourcing, screening, scheduling, and assessment, yet human effort remains concentrated on coordination rather than decision making. Interview scheduling, candidate follow ups, and handoffs across systems continue to absorb time that should be spent evaluating talent, advising hiring managers, and improving quality.

This report examines hiring as a connected system. It looks at how automation shows up inside the workflow, how the candidate experience is seamless, and how intelligence and automation work together to reduce friction and manual effort for recruiters while enhancing human judgment. 

The data we analyzed also reflects a meaningful shift in priorities. Quality of hire has overtaken speed of hire as the top challenge for many organizations. We believe this change signals maturity in the hiring function. It suggests that HR teams recognize the limits of purely transactional hiring automation, and are searching for better signals earlier in the process. Automation is no longer just about moving faster, but rather making better decisions at scale.

The report also makes clear where hiring needs to go next. End-to-end automation remains rare. Measurement is improving, but still inconsistent. AI agent-based workflows are emerging, yet many organizations are unsure how to deploy them beyond scheduling. 

The opportunity ahead isn’t simply incremental automation, but the intentional design of hiring systems that balance efficiency, quality, and experience.

The purpose of this study is to provide a clear and practical view of how hiring automation is being used today and where it delivers the most value. This is not a report about future promises or experimental use cases. It is an audit of real hiring workflows across more than 100 organizations, spanning high-volume and high-value hiring models.

This study was designed to accomplish four objectives: 

• First, to quantify current adoption of automation across key stages of the hiring process, including pre-hire assessments, screening, scheduling, and candidate communication. 
• Second, to understand how adoption varies by industry, hiring model, and role type.
• Third, to identify where automation is improving outcomes and where gaps remain.
• Fourth, to establish a baseline for future benchmark studies.

A central focus of this year’s study is the inline candidate experience. Historically, hiring technology has focused heavily on job matching and sourcing. This report broadens the aperture of that lens to examine what happens when a candidate applies. Inline experiences provide guidance, assessment, scheduling, and communication without forcing candidates to leave the workflow. When done well, these experiences reduce drop off, improve completion rates, and create a more consistent hiring process.

The study also introduces a clearer framework for understanding automation maturity. Organizations range from Getting Started, where processes remain largely manual, to Leading the Pack, where automation is orchestrated end-to-end and continuously optimized. Understanding where an organization sits on this spectrum is critical to making realistic decisions about investment, change management, and expected outcomes.

Ultimately, this study is intended to help talent leaders make better decisions. It provides evidence-based guidance on where to start, how to prioritize, and how automation and applied intelligence can work together to improve speed, quality, and experience without forcing tradeoffs.