What is recruiting automation?

It’s not easy to find and attract great candidates in an environment of widening skills gaps and competitive recruitment for talent in emerging fields. Recruiting teams that are able to get job openings in front of the biggest talent pool possible, and funnel top candidates across the recruitment process, will be at an advantage.

Maintaining a nimble and effective talent acquisition practice is a crucial competitive edge for any company. But recruiting involves some activities across the recruitment workflow such as reviewing resumes, crafting job postings and facilitating the integration of new hires, which can be repetitive, tedious, and time-consuming tasks. Many of these tasks can now be automated or augmented by artificial intelligence (AI) and machine learning, which allows human resources professionals to focus on higher-level tasks. Organizations that are able to capitalize on emerging AI tools can free up recruiters’ time. Allowing those workers to instead develop a more robust, more strategic recruitment function, and apply a more “human” touch to candidate engagements.

There are many benefits to HR automation across the recruitment workflow.

HR managers live in a world of information overload, and inbound data often exceeds teams’ ability to keep up. Automation streamlines repetitive tasks such as resume screening, candidate sourcing, and interview scheduling, reducing the time and effort that is required from HR personnel. Mitigating the costs of deploying workers who would otherwise complete those manual tasks.

Automation can enhance the candidate experience by providing quicker responses, personalized communications and a smoother application process. Leading to a faster time-to-hire, higher satisfaction levels among applicants and in turn, a higher-quality pool of potential candidates for the organization.

Automated systems follow rules and criteria consistently, reducing the likelihood of human error and encouraging fair and unbiased candidate evaluations. Automation allows organizations to scale their recruitment efforts efficiently, whether they are hiring for a few positions or managing high-volume recruitment campaigns. Automated systems can message hundreds of passive candidates every day, a potentially rewarding strategy that is difficult to manage manually.

Recruiting automation consolidates tools across the tech stack to give a complete view of the recruiting process. Recruitment systems collect and analyze vast amounts of data, providing valuable insights into recruitment metrics, candidate performance, and hiring trends, enabling organizations to make data-driven hiring decisions.

By automating routine tasks, HR professionals can allocate more time and resources to strategic activities such as employer branding, talent development, and workforce planning. This reallocation of resources transforms the HR function from an administrative role to a truly strategic one.

By introducing automated tools, HR departments can combat unconscious bias that might prevent them from meeting DEI goals or adhering to DEI standards and best practices. Recruiting automation can anonymize profiles, build diverse applicant pools and draw attention to potential human bias in decision-making.

Any process that uses automation to streamline workflows might be considered recruiting automation, but the use of AI in HR is currently where the most value is to be found. AI tools are intended not to replace human HR workers but to augment their ability to do their job. And free them from rote administrative tasks so they can provide a better service to both candidates and organizations. AI should not make hiring decisions, but it can help provide more information so that HR managers themselves can make better decisions.

AI can find hidden patterns and derive insights from mountains of unstructured data. However, because the quality of AI-based decision-making relies on the volume, quality, and accessibility of data, it’s essential for organizations to think critically about how data is treated across the organization. Where does data reside? Who has permission to access? Should potentially sensitive data be hidden from some users? What data sets can be brought together for analysis with machine learning? These are information architecture and data management and governance questions that organizations must answer before moving forward with AI-based recruiting automation.

Imagine a hypothetical candidate named Eda, a marketing professional who is interested in working for a fictional company called BizCorp. On her favorite career site—LinkedIn—Eda comes across a chatbot message sharing an intriguing job posting for a software development manager position at BizCorp. She submits her resume and cover letter.

Before Eda even sees the job posting, recruitment automation software is at work. The HR manager who advertised the open position used AI to create the ad with predictive analytics, which drew on historical hiring trends, employee turnover rates, business growth projections and workforce demographics. Generative AI tools helped her quickly develop a job description based on a short prompt. And AI was used to find and proactively message Eda on the job board so she wouldn’t have to hunt for it.

Shortly after submitting her application, Eda receives an automated email confirming that her application has been received. The email includes details about the next steps in the recruitment process.

The company's applicant tracking system automatically scans Eda’s resume for relevant keywords and qualifications that are outlined in the job description. Based on this analysis, her application is flagged for further review.

Eda receives another automated email inviting her to complete a brief pre-screening questionnaire with real-time facilitation by a chatbot. The questions assess her experience, skills, and qualifications related to the job requirements.

Impressed by Eda’s responses to the pre-screening questions, a chatbot schedules an automated follow-up video interview. Eda receives an email with a link to the interview platform, which records her responses to a series of interview questions.

After Eda completes the video interview, her responses are analyzed by the AI interview platform, which evaluates her communication skills, confidence and suitability for the role based on predetermined criteria.

Eda successfully passes the automated assessment and is invited for an in-person interview with the hiring team. During this stage, she meets with the hiring manager and other team members to discuss her experience, qualifications, and fit for the role.

Following the interview process, Eda performs a coding test, which is automatically evaluated with AI tools. And lastly, a chatbot facilitates a background check.

Eda receives an automated offer letter via email, outlining the terms and conditions of employment. She accepts the offer electronically through the company's HR portal, initiating the automated onboarding process, which includes filling out paperwork, completing training modules and scheduling her first day.

Throughout the entire recruitment process, automation has streamlined administrative tasks, provided timely communication, and enabled efficient candidate evaluation. Ultimately enhancing Eda’s experience as a job seeker and facilitating a smooth transition into her new role.

A significant question that many organizations have is when to transition into automated workflows. This often requires buying into new software ecosystems and training workers so they can effectively navigate them. Whether and when to adopt recruiting automation tools is a decision that requires careful consideration.

The obvious first reason to push toward automated systems is when there is a high volume of hiring needs that your HR team can’t sufficiently meet by using manual processes. When job openings aren’t quickly filled by quality candidates, that’s a significant opportunity cost. A related reason might be burnout from existing employees. If the HR function can’t hold on to good people, that’s a sign that they’re overworked, or spending too much time on tedious recruiting tasks rather than higher-level tasks that make better use of human intelligence.

Another metric to look out for is candidate attrition. If qualified candidates are dropping out of the hiring process, it might be a sign that the candidate experience is worse than it might be. Recruitment automation technologies can speed up and streamline the hiring process. They also allow organizations to provide more frequent touchpoints and other "human touch" service. As a result, these organizations are less likely to see candidates be snatched away by competing organizations with HR departments capable of faster time-to-hire. Similarly, if candidate engagement is low—internal benchmarking of candidate actions can be helpful here—that might signal that automation would add value to candidate relationship management.

Organizations can also survey department heads to try to get a better sense of whether staffing needs are being met at the ground level. If managers feel that the company is unable to acquire top talent, better hiring processes may be necessary. These processes can be facilitated through automation and help organizations find and secure the best candidates for the job.