With the Great Resignation showing no signs of letting up, recruiters are looking for all the help they can get to replenish their headcounts with qualified talent. The human resource management (HRM) market \u2013 including talent acquisition software and services \u2013 is currently valued at nearly $20 billion.\n\nIt is expected to grow at a rate of over 12% annually until 2028 on the back of continued digitization and automation of recruiting and HR operations.\n\nAcross the world, enterprises are putting an emphasis on creating and retaining the best, brightest, and most diverse employee pool. Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises \u2013 as well as small\/medium-sized businesses \u2013 a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.\n\nIn fact, four out of every five recruiters surveyed in an Entelo study believe productivity would increase if they could automate candidate sourcing altogether. They were unanimously of the opinion that having more data would assist them in qualifying candidates, evaluating candidate pools, improving outreach, and perfecting hiring workflows. Despite this, 42% didn\u2019t have the data or the time to implement or dig into analytics, let alone turn the data into insights.\n\nEnter recruiting automation solutions.\n\nWhat is recruiting automation and how can it help?\n\nHuman resource or people management as a function begins with hiring. Every day an open role remains unfulfilled costs companies profit and productivity. Intelligent tools based on AI can gather relevant data on candidates, make it available to recruiters, and then process it accurately to speed up and streamline multiple sub-processes, including candidate sourcing, screening, diversity and inclusion, interviews, and applicant tracking.\n\n\u201cThe days of physically sorting through hundreds of resumes and posting your job descriptions on each individual board are over,\u201d notes Ilit Raz, CEO of Joonko, a talent feed solution for surfacing candidates from underrepresented backgrounds. \u201cWithout some form of automation or HR tech, you\u2019re always going to be a step behind your competitors, especially when it comes to recruitment.\u201d\n\nRecruiting automation is a category of technology \u2013 delivered as software-as-a-service (SaaS) apps and increasingly powered by AI \u2013 that an organization can use to manage all aspects of its workforce. Its central aims include:\n\nHow does a typical AI-based recruiting automation technology help you go about achieving these goals? Here are the different functions where it can play a key role:\n\nWhen can recruiting automation go wrong?\n\nDespite the advances in recruitment automation software, it is not a panacea for hiring challenges. There is no technology cure for broken recruiting processes. Data overload is one critical problem. Recruiters have so much data (on candidates as well as job roles) these days that they have neither the time nor the skills to analyze it and arrive at the right decisions. Many times, the cost and complexity of accessing and verifying this data turns out to be prohibitive.\n\nAnother long-standing problem is bias. While the recruiting process itself is frequently biased (owing in no small part to companies\u2019 propensity to rely on employee referrals), the use of AI and automation in hiring can sometimes compound the problem.\n\n\u201cIf you don\u2019t have a representative data set for any number of characteristics that you decide on, then of course you\u2019re not going to be properly finding and evaluating applicants,\u201d says Jelena Kova\u010devi\u0107, IEEE Fellow and Dean of the NYU Tandon School of Engineering.\n\n\u201cFor example,\u201d she continues, \u201cif Black people were systematically excluded in the past, or if you had no women in the pipeline, and you create an algorithm based on that, there is no way the future will be properly predicted. If you hire only from Ivy League schools, then you really don\u2019t know how an applicant from a lesser-known school will perform, so there are several layers of bias.\u201d\n\nIn an infamous instance, Amazon developed an AI-based recruiting tool that analyzed patterns in resumes received over a ten-year period and ended up discriminating against women. Needless to say, they scrapped it.\n\nThe biggest area where data and AI have failed is Diversity, Equity, and Inclusion (DEI). Some of the biggest diversity-related mistakes in recruiting that are amplified by automation and machine learning are:\n\nThe last one deserves special attention.\n\nAI as the problem, analytics as the cure\n\nWhile AI is certainly not a silver bullet for recruiting, it has come a far way since the Amazon fiasco. The Entelo study found that data-driven recruiting teams are already outperforming their peers. Further, 84% of recruiters are fairly confident in their ability to use AI and machine learning in their day-to-day workflow.\n\nThe million-dollar question is: How can recruiting automation technology use AI algorithms in the hiring process without adding (and amplifying) human bias into the mix?\n\nThe answer lies in establishing company-specific performance benchmarks, identifying key metrics to objectively measure the competency of candidates, and using talent analytics to measure the success and efficiency of your recruitment efforts.\n\nAlgorithms that fulfill the purpose they\u2019re built for frequently do so because the largest and widest datasets are available for them. It is your responsibility to collect these data points and feed them into your talent pipeline or recruiting automation software. The process is reversed on implementation \u2013 it is always a good idea to test the algorithm on a small (but diverse) pool of candidates and manually review its output before adopting it as the de-facto hiring solution for your organization.