The role of AI and machine learning in talent acquisition
- Posted on May 28, 2019
- Estimated reading time 4 minutes
This article was originally published in Forbes.
As the head of talent acquisition for a high-growth technology company with lofty ambitions, I know firsthand that recruiting top-flight candidates is challenging. In an environment where there is a scarcity of talent and ferocious competition, it’s downright difficult.
Do we rely on the impact of personal connections developed during our hiring process? Do we power up the efficiency driven by artificial intelligence (AI) and machine learning tools and risk losing that person-to-person advantage? The debate is over. We do both.
Recent AI research by Avanade and Forbes Insights found that 87% of senior executives believe that artificial intelligence is important to achieving overall business objectives. Now it is up to us to determine what role AI and machine learning will have within our organizations. We have the ability to create systems that include the best of human experience and AI capabilities. We can use the efficiency of AI and machine learning to give our recruiters the bandwidth they need to turn candidates into hires.
For me, a balance of two elements is key.
- Improving our candidate experience
We know time is money. Being more efficient, reducing both our time to hire and the amount of manual effort required during a hire is imperative. AI and machine learning are already freeing recruiters from repetitive tasks, allowing them to spend more time engaging a candidate. In the future, it will do much more.
Sure, AI can create a simple candidate sort. It also can look at millions of data points, compare similar roles in similar locations within similar organizations and determine which candidates have a combination of skills and attributes that will best position him or her for success in a particular role. Internally, it can analyze the skills we typically hire for this and similar roles, track how successful those hires are at our company and then prioritize that list of skills and success attributes according to how much they contribute to an employee’s performance in that particular role. Prioritization is key. With clear priorities defined for each role, AI and machine learning can analyze our candidates against the specific requirements of a role faster and more accurately than a human can.
However, we know that a team of even the most talented employees who all think alike does not benefit us. We are looking for success factors, not uniformity. The power of our organizations relies on the diversity of our workforce. We need the range of creativity, insight and inspiration that comes from combining our diverse experiences and perspectives. That’s why my company, for example, is working with an AI writing company to make sure our job profiles are gender neutral and worded to be attractive to as many qualified applicants in the marketplace as possible.
We also are using AI to help us determine the most inclusive wording to use as we engage with candidates on job boards and in social networks, and we are looking at AI tools that mask CVs to make sure candidates are screened in ways that eliminate gender, class and other attributes so that everyone has a level chance. By prioritizing critical success factors and attracting and engaging with a broad range of candidates from the very beginning, our chances of hiring the best talent increase.
When our business is growing fast, typically so are our competitors’, and we are all after the same talent. A candidate’s experience as they go through a company’s hiring process can rule a company in or out. AI and machine learning can improve that experience and add value for job seekers, as well.
For example, even though younger workers, in particular, are more used to constant change, there is an element of risk associated with any job switch, so the more we can make a person feel comfortable, the better the outcome. In addition to using AI-influenced interactions to attract and engage diverse candidates early, AI technologies can give candidates confidence as they go through our hiring experience and even help candidates identify opportunities they may not otherwise have considered.
For example, studies show that typically men feel comfortable putting themselves forward for a role even when they may not have all the skills required. Women, on the other hand, typically want to be sure they tick all the boxes. AI matching tools identify a good fit and encourage a candidate to apply for a role even though a requirement or two is missing. Or it may recommend a similar job that is an even better fit.
In the coming years, I expect to see broader and more innovative use of AI and machine learning to identify, recruit and successfully hire candidates. For example, we already use video conferencing software to conduct interviews. I suspect that in five years’ time, a lot of first-round interviews will actually be pre-recorded. Think about it: If you are hiring at the rate of my company, over the course of a year, thousands of interviews have to be scheduled, conducted and reviewed, even to get to the next step. In the future, candidates could record themselves answering a consistent set of questions on a schedule convenient for them, then upload their video to be sorted according to customized and prioritized criteria established for that particular role.
Similarly, a greater use of online bots and more customized, user-friendly recruiting web pages and social media sites can let our candidates know immediately that we care about them and even can accompany them from start to finish.
To be sure, there are still complexities to be figured out. While deployment of more advanced AI and machine learning practices in recruiting is happening globally, adoption is at a different pace in different areas. More mature tech markets will be first. And the handling of language and cultural differences must improve overall. However, in the end, I predict we will no longer see AI and machine learning as an option. It will be a must.