Artificial Intelligence (AI) has become a revolutionary part of industry 4.0. With the power to link data and generate actionable insights, this tool offers incredible benefits to the companies that utilise it. Even the recruitment process is strengthened through AI.
But most hiring professionals don’t understand the full extent of AI recruitment. These systems can help staff a workforce with ideal talent in an unbiased manner. Additionally, AI recruitment supports successful employee referral programmes.
While this might seem unlikely, the truth is that AI recruitment helps create and maintain networks of dedicated employees that fit company needs. Digital smart tools assist in everything from finding candidates to crafting incentives. The results can mean a smarter hiring system, so explore these details regarding AI recruitment in employee referral programmes.
The importance of employee referral programmes
When it comes to hiring, employee referral programmes are a great way to attract new and qualified candidates. In fact, almost a third of new hires in various industries come from employee referrals, according to SilkRoad Research. That’s because no one understands how good of a fit a person’s skill set might be better than their friends and acquaintances.
Meanwhile, employee referrals play an important role in team success and morale. Everyone wants to work with personalities they get along with. By referring people you already know will work well with a position, you experience benefits to working productivity and satisfaction. This makes for a happier, more productive workplace.
But optimising referrals with company goals and integrated recruitment practices isn’t always an easy task. From getting the word out to employees to creating a shortlist of qualified candidates, there’s a lot that determines the success of employee referral programmes.
That’s where AI recruitment tools come in.
The role of AI recruitment in successful referrals
AI has been a feature among hiring departments for some time. These tools already help streamline the hiring process, automating notifications as well as preliminary candidate reviews. With these benefits, hiring managers of any industry can adopt more efficient methods of recruitment that extend into the referral process.
Building a successful referral programme isn’t easy. From narrowing down candidates to getting the word out, these programmes require a lot of groundwork to make the impact you’re striving for. The role of AI recruitment in referrals is all about making this process feasible while freeing up time for HR and recruitment professionals.
AI achieves these benefits in recruitment through the following functions:
Automating referral programmes
One of the biggest obstacles to success in referral programmes is a lack of awareness across the company. AI is great at solving this problem. That’s because an AI solution can send out automatic notifications for job postings through an Applicant Tracking System (ATS). From here, all employees can receive instant notifications, reminding them to consider the opening and if anyone they might know would be good for it.
These automated referrals work best when they remind employees of associated referral incentives and how to qualify for them. AI has the power to streamline all of this, instantly applying rewards to employees who have completed a referral. From here, hiring managers can build more value into their referral incentives through efficiency.
Then, AI even has the power to pre-qualify candidates, assessing their resumes for content and quality within an ATS. This automation potential enables HR and recruitment workers to focus on how best to support employees by freeing up time they would otherwise spend coordinating these programmes. As a result, the implications of these tools in HR are more about assisting than displacing recruiters.
Combatting biases
Additionally, AI recruitment tools in employee referral programmes help combat human biases. This enables recruiters to assess referrals based on merit rather than just a network connection.
Biases, unfortunately, slip into all of our actions, even when we think we’ve adequately prepared against them. Even AI tools can perpetuate bias if designed with that bias unintentionally built into the system. However, AI is typically great at combatting biases by evaluating candidate information beyond demographic information.
There are a lot of traits recruiters will look for in any referral candidate. Increasingly, these traits include leadership and communication skills like cultural sensitivity and technological literacy. AI can help ensure that these traits aren’t overshadowed by other factors like employee friendship or implicit ethnic bias on the part of the recruiter. In addition, AI won’t be affected by gender bias, which is especially relevant in the tech industry where women are typically overlooked.
AI in referral programmes can mask Personal Identifiable Information (PII). From here, skill and relevant experience can be compared to job needs. By focusing candidate review through these categories first, AI helps to keep referral programmes equitable and diverse.
Downsides of referral programmes and how to mitigate them
Despite all of the benefits of using AI within an employee referral programme, there are some downsides. Here are just a couple that has occurred for other businesses in the past:
- Bias: Even though AI itself does eliminate areas for bias within the recruitment bias, there have been several instances in which AI adopts whatever bias exists within the developers themselves. However, this can easily be fixed by carefully examining data that AI collects now and then or opting for more unbiased versions of AI.
- Impersonal: From the candidate’s perspective, AI, particularly chatbots, can be impersonal within the hiring process. Candidates want to talk to actual humans to understand more about the position instead of a robotic interaction. However, this can also be improved with other methods of technology, such as VR, which can help candidates visualise their position and help put a name to a face for your hiring team.
- Lack of human understanding of nuance: Despite its accuracy, AI can also lack the human understanding of nuance for certain candidates. It may even approve of the same type of candidate over and over again. However, this can easily be fixed by screening candidates after the fact and providing more feedback for the AI to improve upon in the future.
Even though there are plenty of benefits, AI still has room to grow. However, the more aware you are of these downsides, the more you can prepare to counteract the kinks. By doing so, you’ll be that much more closer to enjoying only the benefits of AI within your employee referral programme.
Implementing AI for recruitment success
Getting the most out of AI recruitment tools takes dedication and know-how. As you implement this technology into your employee referral programme, there are a few things you’ll need to get right from the start.
To support a successful referral programme, AI recruitment has to be clearly defined and optimised. These are some tips for implementing it successfully:
- Set clear objectives and key results. Your OKRs will determine the outcome of the hiring process and the candidates the AI will filter. Set the right ones to maximise success. For example, DP Electric exceeded 100% of its hiring goal through the use of Olivia, an AI recruitment tool. One of DP Electric’s main OKRs was decreasing the time it takes to fill positions, and Olivia helped the company save 56 hours per week.
- Ensure that your AI tools aren’t creating biased results. Sometimes, unintended outcomes of an algorithm can lead to inequitable ATS processes. Conduct reviews of the system to prevent this from occurring. Pymetrics, a gamified AI hiring platform, had experts from Northeastern University audit the software to eliminate inadvertent discrimination. As a result, the company reports a 62% increase in female representation.
- Be transparent. Referral candidates analysed by the AI system should be notified automatically as they advance or fail to advance through the hiring process. This should include a clear and tactful explanation. Unilever conducted a transparent hiring process with HireVue, an AI interview assessment tool, in which candidates could provide and were given feedback on the process at each stage, regardless of whether they were selected. As a result, 80% of candidates were positive about the experience.
- Implement as a team. Whenever you integrate new tech in employee processes, you’ll run into user challenges. Ensure your team has the resources to succeed with a new AI-driven referral programme. By involving employees in testing Gloat’s Open Talent Market AI software before adopting it, Schneider Electric was able to assess employees’ experience and enthusiasm for the tool.
After running a pilot programme and a gradual roll-out, Schneider implemented Open Talent Market across its huge enterprise with a workforce of 135,000 people. The results: 81% of employees would recommend Gloat, and the AI saved Schneider $15,000,000 in enhanced productivity and recruitment expenses.
- Diversify your hiring. Finally, make sure your AI-aided system isn’t producing harmfully homogenous results. A diverse team representing many backgrounds and professions will better serve any team. With AI recruiting tools, Hilton was able to increase the diversity of its talent pool while simultaneously increasing speed to hire by 85%.
To Conclude..
With these tips, you can implement AI recruitment tools that effectively supports an employee referral programme. From here, employees can enjoy the benefits of bringing friends and former colleagues into the workplace through a fair and inclusive process. Assisted by automation, your referral programme can be a breeze to manage as well. This gives recruiters the time and power to establish greater employee success across a business.
Author: Charlie Fletcher – Freelance Writer, and Journalist at Charlie Fletcher
Photo credit: Tara Winstead
