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Reverse Engineering “Perfect Fits”, Intuitive Candidate Search Shorter Hiring Windows and Balancing Recruitment Risk. Find out what new is happening with AI in the recruitment world.
Artificial intelligence (AI) has emerged as a game-changer in the recruiting process, transforming the way organisations discover and hire top talent.
With the power of AI and machine learning, recruiters and sourcing professionals can analyse vast amounts of data to make data-driven decisions and predictions.
This article will explore how AI is revolutionising the art of hiring, from shortening the hiring window to reverse-engineering the “perfect fit” and surfacing stronger candidates.
Shortening the Hiring Window
Unfilled job postings can significantly impact an organisation’s productivity. By leveraging survival analysis, a machine learning technique used in healthcare, sourcers can identify the time it takes to fill a job opening.
By analysing past job data, market information, and other relevant factors, sourcing professionals can set reasonable expectations for clients and allocate appropriate resources to harder-to-fill roles.
This approach helps ensure that the right recruiting resources are applied to each job opening, reducing the time it takes to fill positions.
“We take data on jobs we’ve filled for clients in the past, how long those took, how many candidates, open roles, information about the company, as well as job market data from sources like the BLS and CareerBuilder, to find out how all of those things impact the ‘survival rate’ of our open jobs.”– Summer Husband, Senior Director of Data Science at Randstad Sourceright
Reverse-Engineering the “Perfect Fit”
Forward-thinking recruiters and hiring managers are using AI and machine learning to reverse-engineer candidate “fit” and predict their performance in a role.
By gathering resumes, performance reviews, work products, and other relevant information about highly successful employees, organisations can create algorithms to identify the ideal candidate.
These algorithms can be customised to the specific needs and definitions of success within each organisation, ensuring a better match between candidates and job requirements.
“The question everyone’s trying to answer through all the interviews, screenings, tech and coding challenges is, ‘How can I predict someone’s performance?’ So, the smartest recruiters and hiring managers would start gathering resumes, performance reviews, work product, any information about highly successful people that already work for them and plug that into an algorithm to figure out what you are looking for.” – Chris Nicholson, CEO of Skymind
Balancing Recruiting Risk
AI and machine learning technology can help determine when sourcers and recruiters need additional support.
By analysing the risk associated with different job requisitions, organisations can identify which roles are more likely to take extra time or resources to fill.
This information allows them to allocate resources effectively, ensuring that high-risk requisitions receive the necessary attention and support.
“When we call something a high-risk req, we see that 85 per cent of the time, those miss their target time-to-fill. So, we can see who has a heavier load of these kinds of reqs and then make decisions about what to do. Do we need to shift these workloads around? Do we need extra sourcers and recruiters working on these?” – Summer Husband, Senior Director of Data Science at Randstad Sourceright
Intuitive Search for Stronger Candidates
Finding the right candidates amidst a sea of resumes and job descriptions can be challenging. AI can help by enhancing the search process and surfacing candidates who strongly match the desired criteria. Semantic search, conceptual search, and implicit search are AI-powered techniques that improve the relevance of search results.
Semantic search understands the intent and context of a search, while conceptual search forms concepts based on a few keywords. Implicit search pushes information and results based on assumptions and gathered data.
These techniques enable recruiters to find candidates who possess the required skills, experience, and cultural fit, even if they use different terminology to describe themselves.
“What does ‘the best people’ look like? What does ‘the right fit’ look like? As sourcers and recruiters, what problem are we trying to solve? We’re trying to find ‘the best people.’ That’s easy to say, but it doesn’t really translate into a traditional Boolean search! What wording can I use to identify them?” – Glen Cathey, Senior Vice President of Global Digital Strategy and Innovation at Randstad
Peering into “Dark Matter” Results
Traditional search methods often lead to “dark matter” results, where potentially suitable candidates are overlooked. AI and machine learning can help uncover these hidden candidates and provide recruiters with a broader pool of talent.
By analysing data beyond traditional screening methods, recruiters can focus on the human element of the hiring process.
Even if a candidate’s resume doesn’t fit the specific criteria, they may possess valuable soft skills, leadership experience, or other qualities that align with an organisation’s needs.
AI tools enable recruiters to discover the potential in candidates that might have been missed otherwise.
“If we’re really trying to find the best candidate, then you’re excluding people with those searches. Doing it this way means you’re actually looking only for the best of the easiest candidates to find. But that’s what is happening here.” – Glen Cathey, Senior Vice President of Global Digital Strategy and Innovation at Randstad
AI and machine learning technologies continue to evolve rapidly, providing recruiters and sourcing professional with powerful tools to streamline the hiring process.
By automating mundane tasks and delivering more relevant results, AI allows professionals to focus on connecting with candidates, engaging with them, and ultimately making successful hires.
Embracing AI in the art of hiring can lead to more efficient and effective recruitment practices.
