Recruitment has been one of the key focus areas for any organization since decades. Let us understand how recruiting era has changes over this period. If we talk about 1970’s, recruiting as a process was more Employer centric which involved a lot of individualized touch, phone and networks, high agency reliance and a lot of cost. Moving out of 1970’s, the world moved in to a Candidate Centric era (1990’s) which was more based on the internet. There was a rise in the job boards, and applicant tracking systems came into being. The Agency reliance got lowered down with free job boards coming over and taking over the market place. Looking at the situation now (2010’s), we have moved to a world which is more relationship centric and is known as the digitization era wherein you have the rise of automation and artificial intelligence in recruiting solutions. A lot of focus is on communication and candidate relationship management.
Statistics show that more than 85% of applicants don’t hear back after applying and 71% of employers claim that they can’t find a candidate with the right skill set. These percentages held true 20 years ago and not much has changed even today. Even today recruiters spend a lot of time in activities like sourcing, matching candidates to jobs and screening. Automating these tasks in the recruitment software would assist in bringing a lot more process efficiencies and ensure that the recruiter becomes much more effective in the work assigned.
Applications for AI
Let us review the five most promising applications for AI for RPO Solutions –
Candidate sourcing is still a major recruiting challenge. A recent survey found 46% of talent acquisition leaders (LinkedIn Global Recruitment Trends 2017) say their recruiting teams struggle with attracting qualified candidates.
AI for candidate sourcing would help in searching for data that people leave online (e.g., resumes, professional portfolios, or social media profiles) to find passive candidates that match the job requirements.
Sourcing in-turn gets more effective and efficient process because it can simultaneously search through multiple sources of candidates for the recruiter. This replaces the need to manually search the profiles and thereby saves a lot of hours per requisition. The time one saves in sourcing can be spent in becoming a talent advisor for the business.
AI for screening in Recruitment software can use post-hire data such as performance and turnover to make hiring recommendations for new applicants. It can make these recommendations by applying the information it learnt from the earlier screened candidates i.e. employees’ experience, skills, and other qualifications to automatically screen new candidates. This can also enrich resumes by using public data sources about previous employers and candidates’ social media profiles.
It helps automates a low-value, repetitive task and allows recruiters to re-focus their time on higher value priorities such as talking and engaging with candidates to assess their fit.
Match making goes beyond keywords and Boolean searches by enriching candidate resume data with information from the candidate’s public digital footprint.
Using natural language processing in your recruitment software, a resume enricher can scrape a candidate’s public social media profiles and online work portfolio to create a preliminary profile of his or her skills set and personality traits and match them against the job requirements.
Today’s candidates want and expect continuous feedback, often immediately. More than 50% of job seekers have a negative impression of a company if they don’t hear back after applying. AI in the form of chatbots promises to improve the candidate experience.
Recruiting chatbots (Jinie) use natural language processing to interact with candidates in real time by asking qualification questions related to job requirements and providing feedback and next-step suggestions. By automating repetitive tasks such as answering FAQs about a job, a chatbot can ensure the candidate experience isn’t suffering from a lack of communication on the recruiter’s end.
Reduce unconscious bias
Research has found that resumes with white-sounding names receive 30% more interviews than identical resumes with the names changed to black-sounding ones (www.ideal.com). AI can reduce unconscious bias during the recruitment process by ignoring demographic information on a candidate’s resume or profile such as age, gender, and race.
AI is trained to find patterns in previous behavior; however, so any human bias that may already be in your recruiting process – even if it’s unconscious – can be learned by AI.
To succeed in the new work environment, companies must build strategic recruiters capable of managing complex talent pipelines and advising the business on talent needs and labor market conditions. By automating low-level administrative tasks with smart recruitment software, AI will enable recruiting to pivot into a more strategic talent advisor role. These high impact recruiters will be the voice of talent strategy. They will help align recruitment efforts with organizational strategy to build talent pipelines to meet both short and long-term business needs. They would use a lot of market knowledge and insights to challenge stakeholder thinking and influence decision-making.