How is AI used in Applicant Tracking Systems?

AI is changing the way Applicant Tracking Systems (ATSs) operate by embedding smart algorithms and automation into the recruitment process. From sifting through vast numbers of resumes to delivering actionable insights, AI improves the efficiency, accuracy, and fairness of hiring practices. 

Let’s now explore how AI is leveraged in ATS platforms:

1. Resume Screening and Parsing:

AI in ATS solutions is primarily used to automate the screening and parsing of resumes. Traditionally, recruiters had to manually sift through hundreds or even thousands of resumes, a process that was both time-consuming and prone to error. 

With AI, machine learning algorithms are trained on large datasets of resumes to understand the structure and identify key elements such as skills, education, and work experience. These algorithms can handle various resume formats and extract relevant information then convert it into a standardized format that the ATS can easily process. 

The AI then ranks candidates based on predefined criteria, such as specific skills or years of experience. As the AI system is exposed to more data over time, it refines its ability to screen resumes more accurately, reducing the likelihood of overlooking qualified candidates.

2. Candidate Matching:

Candidate Matching is a relatively new feature that came into existence when AI was introduced in ATS platforms. AI-driven candidate matching in ATSs does a lot more than basic keyword searches. Instead of looking for matches between job description keywords and resume content, AI understands the context and relevance of a candidate’s experience and skills. 

For example, if a job description requires experience in “project management,” the AI doesn’t just look for the phrase “project management” in a resume. It also considers related terms, past roles, and the industry context in which the experience was gained. 

Additionally, AI can analyze past hires to predict which candidates are likely to succeed in a specific role or company culture. This allows AI to match candidates based on their potential fit, not just their experience. 

3. Predictive Analytics for Hiring Decisions:

AI is increasingly being used in ATSs to provide predictive analytics to offer you data-driven insights into candidate selection. Predictive analytics involves analyzing historical data such as previous hiring decisions and the subsequent performance of those hires to forecast future outcomes. 

For example, an ATS might use AI to analyze patterns in the backgrounds of employees who performed well in a particular role, then apply those patterns to new candidates to predict who is most likely to excel. This could include factors such as education, prior job performance, or even soft skills that are often difficult to quantify. 

Using AI to predict candidate success helps organizations make smarter hiring decisions, reducing turnover and improving job fit. This makes the hiring process more strategic and aligned with business goals.

4. Candidate Engagement

To nurture your talent pipeline and sustain an impressive talent pool AI can help a lot. An AI ATS can automate candidate interactions, scheduling interviews, and more. For example, you may find an AI chatbot that can communicate with candidates using natural language processing. AI Chatbots can quickly handle tasks like:

• Answering basic queries about the role 
• Notifying candidates about their application status
• Delivering information like culture, values, and benefits

This ensures a pleasing experience for candidates and can improve engagement with them.

5. Bias Reduction:

AI in ATSs plays a critical role in promoting diversity and inclusion by mitigating possible biases in the hiring process. Bias can occur at various stages, from resume screening to final interviews, mostly due to unconscious human prejudices. 

Integrated AI tools address this by making resumes anonymous. This means AI hides details such as name, gender, or even the name of educational institutions to ensure that they are evaluated solely on their qualifications and experience. 

Furthermore, AI can be trained to detect and flag potentially biased language in job descriptions or evaluations. For example, certain adjectives or phrases might unconsciously favor one gender over another. The AI can suggest more neutral alternatives, thus broadening the appeal of job postings to a more diverse range of candidates.