Highlights
- IMS People Possible look at the emergence of Artificial Intelligence (AI) in the recruitment sector.
- How AI has been implemented into recruitment processes across the industry and the benefits it has provided.
- The challenges that AI still needs to overcome in the staffing industry.
Artificial Intelligence (AI) has been present in our lives for a long time, whether we have noticed it or not. Basic versions of AI are used in a variety of ways, from Apple’s ‘Siri’ assistant to the suggestion queue on Hulu, but AI in recruitment is an emerging tool for the hiring world, impacting almost every stage of the recruitment process.
How could AI be utilized in recruitment?
AI in the recruitment sector is currently a hot topic and an area that many staffing tech companies are focussing their roadmap development plans. Many firms are already claiming the use of AI technology to offer the best service across the entire candidate journey – the question is how many are using AI and how many are just using the buzzword?
Many high-profile companies have tested AI for a broad scope of objectives, with varying amounts of success. Like any new technology, the ability to do something through AI doesn’t mean that it will deliver the best results or improved ROI, which is the key to getting industry buy-in. Top billers in the industry with years of service under their belt can be a little set in their ways and reluctant to change their proven methods, leading to a touch struggle with organizational adoption, making it all the more critical that AI tech for the staffing industry hits the mark from the get-go.
- Autonomous Candidate Sourcing
Finding the right person to fill a role starts with the sourcing of potential candidates from online social accounts and evaluating their capability. This process can be a tough, time-consuming job, and even with a large team, the reach will be small.
Innovations in AI have enabled recruiters to automate the process for sourcing candidates, with the software capable of faster deductions and the ability to search a larger pool of potential candidates across multiple platforms using the various search terms to discover the talent that would have remained hidden using conventional methods. Due to Machine Learning, the system will also begin to recognize what traits successful candidates held and start to hunt for those applicants with similar skills and experience, predicting the viability of those applicants and highlighting them to recruiters.
Tied in with the initial sourcing, AI keeps a record of previously reviewed candidates for recruiters to use when new roles emerge. The ability to screen the already vetted candidates to highlight high ranking applicants that are currently being overlooked across CRM’s. AI, usually using chatbots, can also contact dormant candidates to re-engage them with the recruitment process and update their profiles on CRM systems.
An AI-supported autonomous screening process is allowing recruiters to cast a considerably large net for available roles in a fraction of the time it would take to target a much smaller area.
While the positives are certainly attractive for autonomous screening, there are some difficulties to consider. With Machine Learning, you rely on the software to make a judgement on whether an applicant is a good fit for specific roles, if the software creates a rule that ignores a particular group from previous trends that may be coincidental, you could be missing out on a whole crop of talented individuals without knowing it.
- Assessment Stages
The use of screening assessments is prevalent among recruiters, allowing an applicant to demonstrate their skills as opposed to merely discussing them in an interview. It also allows you to see how they match up across various areas before committing to meeting with them.
The application of AI to these assessments have changed the concept of them entirely. Assessments used by recruiters are usually questionnaire-based personality or reasoning tests. These types of evaluations are often time-consuming and see quite a significant drop-out rate as they struggle to keep the applicant engaged. AI has been implemented to produce game-like assessments that work in the same way a psychometric test does. They can be completed in as little as 10 minutes and provide the same quantity of data that a more conventional test would. By providing your candidates with an alternative assessment completion rates are improving along with more consistent results, with applicants naturally answering the questions instead of trying to formulate what they perceive as the ‘correct’ answer required by the assessor.
Presently, these new concepts have received little scientific and scholarly testing due to their new status. They are available across multiple vendors, but until further analyzation is complete, the value is still unconfirmed officially.
- Chatbot’s
As we mentioned earlier, the use of chatbots can be utilized to screen candidates and maintain your applicant database for consideration in future available roles. However, they can provide benefits at stages further along the recruitment cycle during those periods of little contact while a decision is made. The use of chatbots in this instance is helping to keep candidates informed and engaged without the need for recruiters to contact them.
Chatbots can contact the candidate to let them know what stage their application is currently at, to gain any further information required, providing information for their commencement and providing interview feedback. This can lessen the load on recruiters, allowing them to pursue new candidates and clients, while the automatic chatbot keeps in touch with applicants currently involved in the recruitment process.
While chatbots can respond very naturally, it can soon become evident that you are not conversing with another person. Some people can find this unnerving or lack confidence in divulging information to a chatbot.
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- Diversity Driven Hiring
A massive topic in recruitment now is Diversity and its importance across the industry. AI across the board has improved almost all aspects of the recruitment process, but as discussed in previous points, there are certain drawbacks.
Issues have arisen with Diversity in mind with Machine Learning that has seen some unconscious bias during the screening and selection processes. An example is below:
- Online e-commerce titan Amazon, integrated AI into their recruitment process, to ultimately optimize their substantial recruitment efforts to generate the best candidates in an almost autonomous process. Amazon first needed to ‘train’ their software, to program into the AI, their requirements. They did this by inputting their data from the previous 10 years, allowing the AI to observe the patterns and trends in resumes submitted. However, due to the male dominance across the tech industry throughout the past decade, the AI software developed a bias toward male applicants and downgraded the resumes received from female applicants. Of course, Amazon pulled the software once it discovered the issue and the AI was sent back to the development stages.
In conclusion, the continuous development of different AI and Machine Learning, its application to the recruitment industry will be substantial and very promising. The ability to reach a vast pool of candidates will be quicker, easier and with a considerable improvement in cost-efficiency. There are many vendors currently making headway with AI from autonomous sourcing to chatbots and assessments are Plum, Wade & Wendy, Restless Bandit and many more. The potential impact is immeasurable once enough research and testing have been conducted, and the obstacles have been overcome.
