AI Recruitment: Top 6 Operational & Ethical Benefits in 2024
Great Resignation has made talent acquisition an intricate game of speed and precision, as millions of employees are leaving their jobs in search of:
- Better opportunities
- Work-life balance
- Improved working conditions
But while hiring managers should sign top talent quickly, impulsively hiring passive candidates can incur opportunity costs: According to the US Bureau of Labor Statistics, hiring a wrong candidate costs 30% of the income of the employee in their first year.
The utilization of AI recruiting tools in the recruitment process, known as AI recruitment, can expedite the hiring process while ensuring the best candidates are hired.
In this article, we will discuss the top 6 operational and ethical benefits of using AI in recruiting process. To simplify the outline, we’ve divided the benefits into 2 brackets, ethical benefits and operational benefits.
1. Faster resume screening
- Candidate experience
- Skills and qualifications, etc.
For example, Unilever receives 1.8M applications annually. They wanted to use artificial intelligence and machine learning to streamline recruitment process for hiring managers and job seekers.
2. Efficient shortlisting
After data extraction, the tools within the AI recruiting software, like RPA (robotic process automation), can compare job seekers’ info against their knowledge base to eliminate those who don’t meet the job descriptions.
For example, a position might require the employee to speak Portuguese. So the bot can shortlist only the qualified candidates with “Portuguese” or “Brazilian Portuguese” mentioned on their resumes.
AI recruiting tools can make the initial shortlisting more efficient by ranking candidates based on their ability. So once candidates who speak Portuguese are selected, the AI tool can rank them based on their proficiency level.
3. Reduced time-to-hire
Employee onboarding is a step within the recruitment process with many time-consuming, bureaucratic processes, including:
- Paperwork and documentation
- Orientation and company overview
- Training and skills development
- HR policy and benefits overview
Artificial intelligence and machine learning tools such as:
- Applicant tracking system (ATS) for interview scheduling and candidate communication
- Electronic document management system (EDMS) for digitizing and automating document-related tasks
- Learning management system (LMS) for delivering and tracking online training modules, assessments, and certifications
- Chatbots and virtual assistants for providing automated responses to FAQs
- Workflow automation tools for triggering actions and connecting various tasks together
Can fasten the recruitment processes and ensure candidates are set quicker.
4. Faster offboarding
AI tools can speed up the offboarding process through:
- Automating administrative tasks: These include deactivating access to systems and databases, revoking physical access privileges, scheduling exit interviews, or generating final paychecks.
- Knowledge transfer: These include systematically capturing and transferring the departing employee’s knowledge to their successor or team.
- Exit interview analysis: NLP can be used to process interview transcripts or written responses to identify common themes and issues.
- Forecasting: AI-powered recruiting software can use predictive analytics to recognize patterns in employees’ departure to predict future attrition. This allows for proactive planning and smoother transitions
5. Data-driven skill testing
Skill testing within the talent acquisition process is when hiring managers assess candidates’ suitability for a specific job through skill tests.
Generative AI, for example, can create and grade these tests. By training their AI powered tools on a vast dataset of relevant professional skills, the AI will generate a set of custom assessment questions or tasks.
Furthermore, upon completion, the AI evaluates the responses based on the benchmark data to score and find the most suitable candidates. For recruiting teams, a major benefit of using AI in recruitment is that if the model training is unbiased, the tool will assess the relevant candidates without any biases.
6. Reduced bias & increased diversity
Recruiting bias is real1:
- Automated resume screening: Having artificial intelligence robots read and extract resume data
- Blind hiring: AI can anonymize candidate information like names, photographs, addresses, or educational institutions
- Standardized interviews: By embracing AI, HR teams can give all job applicants standardized set of questions
- Data-driven decision making: Machine learning tools can make recommendations based on objective data. For example, it can help the hiring team identify performance patterns of successful employees, and seek those traits in new candidates.
- Unbiased job descriptions: AI recruitment tools can create natural job postings free of unconscious bias or gendered language which might discourage diverse candidates from applying.
- 43%2 of 1,500 of American employees quit their jobs within 90 days of hiring because they felt the job requirements weren’t matching the info given on the job ads. Using recruiting technology can create ads that reflect the true nature of the jobs.
Can reduce bias and give all qualified candidates a fair opportunity of having their applications be viewed by the hiring managers.
Unilever receives 1.8M applications annually. They used artificial intelligence and machine learning to enhance the recruitment process for hiring managers and job seekers.
Through partnership with companies that offer recruiting tools, they were able to:
- Create online games (i.e. skill tests) that tested applicants’ reasoning, test appetite, etc.
- Conduct online interviews, with candidates being interviewed by machine learning algorithms instead of a human recruiter with human intelligence.
- Voice recognition software, for instance, can identify tension, introversion, extroversion, and other characteristics in an applicant’s voice and responses to particular questions. These could reveal insights into their personality traits that would otherwise go unnoticed by the human interviewer.
- Eliminate potential biases where the online games and video interviews are unbiased and give every individual applicants the chance to have their applications assessed and receive a feedback
By automating repetitive tasks, talent acquisition professionals at Unilever was able to cut reduce the recruiting process by 70,000 hours.
For more on HR automation
To learn more about automation in HR, read:
- Top 5 Use Cases of Intelligent Automation in HR
- Top AI use cases in HR
- The Ultimate Guide to Web Scraping for Recruiters
And if you believe you would benefit from automating your HR processes, head over to our HR hub where we have data-driven vendors prepared for different use cases.
We can help you navigate the process of choosing a vendor:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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