Today I launched my own tiny experiment in AI job search.
Between asking Google to check tomorrow’s weather and to turn off the kitchen light, I decided to test how my friendly digital home assistant would fare when I asked her to help me find a job in sales. “Hey Google, can you help me find a job in sales?”
Even though my intent was to search for available jobs in sales, the AI assistant replied, “Here are five ways to find a job in sales.” Although still a relevant response to my question, we may have to wait a while longer before we can search and apply for a job via voice-activated AI.
Still, I am pretty excited about the substantial improvement to job-search technologies over the last few years. Thanks to multiple AI and machine learning models and powerful, trusted AI job matching, we’ve gone from a basic keyword and location-based job search to skills-based recommendation engines.
Here’s an example from iCIMS’ Talent Cloud platform aided by the iCIMS Digital Assistant. If a jobseeker types “I am a Server Admin” in a chat, the digital assistant would not return a result for a restaurant server since the AI can recognise context cues.
Recognizing acronyms is another significant advancement in search powered by AI. Suppose a job seeker enters a search for “CDL” (Commercial Driver’s License). In that case, AI will return results for relevant roles for drivers, including those gigs that may fit into a typical driver lifestyle or work preference.
Last year iCIMS AI aided in the hiring of four million people. Through our customers’ Talent Cloud platforms, iCIMS AI captured CV and relevant skills data from ~ 64M applicants. Then, it helped hiring teams compare and prioritise candidates based on skills, qualifications, and career interests and matched those candidates to current openings.
If you’re not personally familiar with how AI works, you likely have questions about how it improves job search and skills-based hiring. Here are three ways you can use AI to improve the job seeker experience.
The traditional approach to finding work depends on candidates searching through job sites or going to their network of connections for recommendations. Traditional search is naturally limited.
Here’s an example of how AI can expand candidates’ opportunities beyond their network or limited time.
If you need a recommendation on something, you may ask a friend for their thoughts. While that’s helpful, it may vary by the person you ask. You may also look to your family members for their opinion, which will increase the validity of the recommendation, but still may waiver depending on personal opinion. What if, instead, you asked a 20-person focus group who had all the data on the subject? The result would be a well-informed decision backed by unbiased insights. Multiple perspectives fuel stronger decisions – and that is the power of ensemble learning and AI.
AI developments have created a shift from narrow search to open-ended suggestions, offering possible roles that job seekers would have considered or thought possible. Consider the concept from another industry. Netflix can make relevant entertainment recommendations for you because its AI has learned from the profiles of billions of other people and calculated the probability that likes, search, and views are all related. Here’s a throwback on how AI and a $1M contest changed binge-watching forever.
Take away: Expand what’s possible and make recommendations that job seekers would never have seen or considered. That’s the power of AI.
AI can help you compare and prioritise best-fit candidates. For example, iCIMS’ Talent Cloud AI provides you with a visual explanation of why an applicant might be a good match so that you understand the alignment without extensive guesswork. We call this explainability. We cover deeper insights on how speeding discovery and how explainability of AI works in this recent blog.
Take away: Rethink job descriptions. Be transparent about the job’s actual requirements and replace experience for skills. Doing so will reduce the guesswork and enable job seekers to apply with confidence.
According to the recent Exceptional Experience in Talent Acquisition report from Aptitude Research and Talent Board 58% of screened out applicants never receive a response and 61% of applicants are not hearing back from employers even after two months.
Fortunately, hiring teams are increasingly turning to AI for their talent acquisition needs. In the same way that AI provides transparency on talent fit, it can give the recruiters valuable insight in at least two ways.
First, recruiters can identify the candidates with aligned skills and experiences without wading through dense resumes.
Second, companies may elect to anonymize the names of candidates to minimise the potential of name-related bias.
Conversational AI digital assistants have been effective at gathering data to return relevant skills and job matches AND helping to nurture job seekers through the application process. Recruiters will enjoy the ability to task these AI-enabled assistants to answer FAQs, create applicant profiles, and instantly pre-screen and schedule interviews with hiring teams. Every job seeker and applicant gets a personalised understanding of where they stand in the hiring process.
Take away: “Companies using AI matching are 2x more likely to improve diversity of source and 3x more likely to improve time to fill.” – Aptitude Research 2021 Talent Acquisition Tech Key Findings
Want to learn more about how AI helps candidates and recruiters? Check out our Collaborative Guide for Talent and IT Leaders.