In a job market that’s mostly candidate-driven, job adverts and traditional recruiting tactics just aren’t going to cut it. In fact, according to recruiting researchers from The Ladders, potential candidates only read job ads for around 50 to 75 seconds, depending on how much one matches their current skillset and interest right from the get-go. A minute is barely enough to convey a job’s full description, much less its perks and the company’s background. This is why recruiting strategies are being further developed every day — to keep up with the ever-changing recruitment landscape, and its equally dynamic list of applicants.
We’ve seen a lot of recruiting trends these past few years, but the thing that has stood out most recently has to be the growing use of artificial intelligence (AI) and machine learning. In this article, I want to introduce you to the basics of how recruiters are using this revolutionary piece of technology to find the right talent and improve.
Chatbots are much more than just flashy additions to a website or a social media profile, they are in many cases a by-product of AI and machine learning, which means they can work by analyzing data and making informed decisions out of it. In this respect, AI can be used to reduce the time it takes to hire potential candidates. Mya, for example, is a chatbot that actively converses with applicants while creating candidate profiles, shortlisting some of them, as well as scheduling interviews. In this way, AI is actively streamlining and automating big parts of the recruitment process.
Video interviews are also becoming an integral part of the recruiting and selection process. However, how do you determine that an applicant is genuine from the other side of the camera? Paññã is an AI-driven platform that can help detect “anomalies” during video interviews. For example, if the applicant regularly looks away from the screen a few times, it may indicate the use of cue cards. Paññã can even recognise other voices in the background, in case applicants have a friend on the phone for help in answering questions. Advances in facial recognition technology can also provide insights into the applicant when answering questions, based on confidence levels and indicating as to whether they are likely to be fabricating their answers.
In a time when competition for jobs is growing increasingly fierce, recruiters shouldn’t just leave the applicant hunt to chance. And more often than not, it starts with the kind of job postings you publish online. Fortunately, tools like Textio can help harness the power of data and predictive analytics to come up with the most effective language patterns when creating job posts. The software even provides alternative words that are statistically proven to appeal to the exact audience you’re looking for. True enough, Ayima Kickstart underlines the importance of SEO, using the right keywords, and utilising standard discovery processes. That way, not only do posts get the online traffic they need, but you’ll be able to come up with the most effective job advert in the process.
Grading and ranking of applications for relevance has been around for some time, in a crude type of format, using individual keyword matching algorithms, but advances in AI and Machine Learning in platforms such as Smart Recruit Online, are providing far more advanced automated screening than ever before, by cross referencing each word with dictionaries and thesauruses, looking at variants and combinations of words, semantics and predictive analytics are all combining to identify the best matched candidates automatically, based on their relevance to the information provided about the role.
The added advantage of such systems in addition to the time saving is in directing the recruiter to the best matched individuals more quickly, being devoid of human error, making real-time assessments and removing conscious or subconscious bias from the first phase of screening.
Most companies are already utilising “basic” CV filtering methods. That is, narrowing down the list of potential candidates based on skills and relevance. In fact, a previous article on Smart Recruit Online notes how recruiters often look for broad skill sets among potential employees; and this entire process can be made more efficient with AI. Specifically. It learns what an existing employee’s skills are and then applies this knowledge to new applicants or to CV’s in a database, to automatically shortlist and recommend potential candidates.
In this manner, AI can help sort through thousands of CVs faster than any human can, freeing up their time towards more productive activities as opposed to just doing repetitive, mechanical tasks.
As more and more tools and systems become centralized, we expect to see further developments in how AI and Machine learning will assist and influence recruiter behaviours. Hopefully, by removing repetitive and mechanical tasks, this will allow recruiters to deliver a better, more human candidate experience to those that are being considered and improve communication efficiencies when it comes to informing unsuitable applicants.
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