Most recruiters are busy with their day-to-day work. So, some fail to realize that many recruiting processes and tools currently in use will soon improve significantly by the continual learning provided by Artificial Intelligence (AI). In addition, not only will AI and its advanced cousin Machine Learning (ML) make recruiting processes faster and cheaper, soon and in many cases are already adding significant new capabilities that were simply not possible with legacy systems. However, relax, this isn’t a job security issue, it’s an opportunity to improve performance with little effort on the recruiter’s part.
The Top 10 Recruiting Areas That Will Be Most Impacted By AI And Machine Learning
Recruiting areas related to finding and attracting prospects
- Finding individual prospects – during sourcing will become much more automated and accurate when augmented with machine learning capabilities. Automated sourcing programs will be able to find many more and better matches, based on the continually updated target profile that you develop as a result of feedback. There are already vendor packages that allow you to identify currently employed individuals (e., passives) that are likely to quit soon and prospects that are likely to be diverse.
- Enhancing prospect profiles – can make the existing candidate profiles found on sites (like LinkedIn) more complete by supplementing them with additional information that a machine learning program will find on the Internet. Machine learning driven programs can sort through a prospects search histories, cookies and social media sharing. The additional information on a prospects interest, capabilities and behaviors might indicate that a candidate can do things that they haven’t done in the past. Once they apply, chatbots can contact an applicant directly to clarify unclear elements in their resume or profile.
- Improving job descriptions and postings – Recent research data has revealed that job descriptions and job postings can be dramatically improved so that the content better attracts your target audience. So, rewriting them can reduce terms that create a bias. Software can now help you reduce those biases and add content that draws initial attention and that attracts more qualified applicants.
Recruiting areas after candidates apply
- Resume sorting – with machine learning software uses the resumes of successful hires at your firm to find patterns and then it can use these past success patterns as a basis for predicting which resumes and candidates are most likely also to be successful when hired. If programmed correctly, resume sorting software can also help to eliminate a great deal of unconscious bias in resume screening and candidate slate selection. Machine learning assisted search programs can also help you find hidden or lost talent within your ATS database.
- Matching people and jobs – Using matching programs supplemented by machine learning can help a firm determine if there are any, less obvious, jobs that an applicant would also qualify. Matching people with jobs will also be improved by looking not just at an applicant’s past job titles and degrees, but also at their skills and capabilities.
- Interview scheduling – is time-consuming and dramatically reduces your speed of hiring. Fortunately, there is existing software that allows a candidate to self-schedule their own interviews depending on their availability.
- Interviews – can be time-consuming, so it makes sense to automate the initial ones with a chatbot that provides personalized questions based on your job profile. Also, there already exists technology that allows the use of neuroscience tools like voice and facial recognition to assess aspects of video recorded interviews that no humans could detect. There are even voice modulation programs that can help you obscure the voice of telephone interviewees so that it’s harder to identify their gender and national origin.
- Supplemental candidate assessment – in addition to traditional interviews. Natural language processing can check language skills and online technical tests [add utm codes] and challenges can help to assess the skills of applicants. There are automated programs that can more consistently determine cultural fit. Eventually, virtual reality simulations will be able to supplement interviews by giving candidates actual problems from the job to solve.
- Learning from hiring failures – By definition, machine learning processes continually identify mistakes and errors. Recruiting will have an ongoing failure analysis process that continually and automatically finds hiring and bias errors and their root causes, allowing recruiting processes to improve at a much faster rate.
- Other technologies – in addition to AI/ML technologies. Block Chain may eventually make checking educational and employment credentials easier and more accurate. Skype and video technologies already make it much easier to interview remote candidates without requiring them to travel. Machine learning will make predictive analytics in the area of projecting the future trajectory of finalists (in the areas of performance, retention and promotions) much more accurate.
Want to see how machine learning can help you find better technical matches for your open roles? Check out CodeFights Recruiter or attend an upcoming webinar.
Attribution: This article is excerpted from: AI Will Dominate Recruiting – So Prepare For Major Changes In These Areas, originally written by John Sullivan. Click here to read the original article.