What is machine learning?
Machine learning (ML) is a type of AI that identifies patterns using algorithms to make predictions based on previous behaviour. You’ve probably come across algorithms when using YouTube, Facebook or Netflix. These platforms make recommendations based on the content you’ve viewed in the past.
In recruitment, machine learning can train computers to complete tasks that were previously done manually, such as comparing CVs and selecting top candidates. It uses data-driven insights to help you make informed decisions and recruit people who can add to your company culture. You can also use it to streamline the onboarding process and improve employee training, which enhances the recruitment process for your company and its future team members.
How managers and HR professionals can use machine learning in recruitment
Machine learning offers an effective way for your company to improve the recruitment process while also potentially reducing costs. Below are five ways machine learning and AI in recruitment could help you improve the quality of new recruits.
1. Marketing job roles
Writing job descriptions and outlining roles and responsibilities can be time-consuming. Machine learning could help your company discover which locations and job boards are best to invest in, the best times of day to post jobs and how to optimise job descriptions to get the most out of the recruitment process. Using a tool such as ChatGPT can help you craft high-performing job posts and onboarding materials.
2. Sourcing candidates
Machine learning can potentially help you source candidates for your roles by analysing online platforms, social media profiles and professional networks. Machine learning can also analyse existing employee data and performance records to identify characteristics or attributes that correlate with success in a particular role.
Some AI tools might help you connect with candidates in real time when you need to, while others could use chatbots to screen candidates before moving to the next stage of the recruitment process. The chatbot can guide them through inputting their details and save you time and money compared with collating and waiting for paperwork to be filled out manually.
To secure candidates who are an excellent match for your company, it’s important to have the right talent pool to draw from. Most recruiters spend a lot of time on sourcing alone, and machine learning can potentially help you find the right candidates faster.
3. Engaging candidates
Automated messaging systems and AI assistants could completely eliminate the need for a person to carry out repetitive conversations regarding scheduling and next steps. Chatbots are available at any time of day and can easily book interviews and send reminders about upcoming appointments. Machine learning tools can even use market data to work out salaries and generate job offers.
4. Screening CVs
Machine learning can be used to quickly screen CVs and present you with what it determines are the most well-suited candidates. This CV scanning feature is often included with applicant tracking systems (ATSs). How does it work? Put simply, machine learning models can be trained on data to learn patterns and make predictions about the suitability of a CV for a particular job position.
It’s important to note that machine learning-based CV screening can be a great assistant in the recruitment process, but human review is still essential for final decision-making.
5. Personalised outreach
Employees often have a lot of choices when it comes to job opportunities in the modern marketplace, and machine learning can help you create personalised messages that make an impact. Sending out a customised message to every new recruit is an excellent way to show prospective recruits that you take them seriously, but it’s almost impossible for today’s busy HR and recruitment managers. Machine learning can help you send out messages that make an impact without needing a person to write them.
Benefits of machine learning in recruitment
Let’s look at the main advantages of using machine learning in HR and recruitment.
Onboarding
Using ML and AI during the onboarding of new recruits can make the process more cost-effective, time-efficient and personal. Algorithms can potentially take into account a new recruit’s job role, strengths and past experience to create and deliver customised onboarding programmes. This can increase engagement and help the new employee adjust to their role faster.
Efficiency and accuracy
Machine learning could speed up every element of the recruitment process, including the creation of job descriptions, screening CVs, analysing candidates’ work histories, delivering skills tests, scheduling interviews and informing recruitment decisions.
Diversity and inclusivity
You can potentially use machine learning to identify any biases that influenced previous recruitment decisions and determine the best solutions for avoiding them in the future. Having a diverse and inclusive company culture helps to ensure that all employees can thrive and grow within your organisation.
Employee retention
While not explicitly part of the recruitment process, retaining employees is essential for keeping turnover and attrition low. Recruiting new employees can be very expensive, so employers should strive to retain staff wherever possible. Machine learning uses existing data to analyse trends and patterns in the reasons people leave your company, enabling your leadership team to address those issues.
Training and business planning
Machine learning enables you to customise training programmes for your business and for each employee within it. You can use it to identify knowledge gaps and recommend training to fill them. It can also sort through training data to determine which team members need refresher courses.
AI and ML can potentially go even further by analysing historical and current data on job roles, employee performance and training to help decision-makers allocate roles and responsibilities more effectively.
Potential risks of using machine learning in recruitment
Machine learning and AI are excellent tools, but you need to carry out the necessary training to ensure you make the most of them. Some risks to be aware of include:
- Machine learning errors: since models are trained based on available data, their performance can be influenced by the quality, quantity and representativeness of that data. For example, you could miss out on excellent candidates who are eliminated early in the recruitment process. One way to reduce errors is by combining machine learning with human judgement.
- Implementation and adoption: introducing new technology to an existing team requires patience as they progress through the learning curve. It’s crucial that you use change management best practices to support recruiters as they adjust to new ways of working and fully engage with the use of new tools.
- Unconscious bias: while machine learning can be used to identify biases in your recruitment process, it’s important to remember that ML tools may still be subject to bias from the people who programmed them. Assess ML tools carefully to ensure they haven’t incorporated real-world biases, and take steps to avoid this.
- Drop-off: it’s important to balance AI communications with human interaction to ensure qualified candidates remain engaged with the recruitment process.
Machine learning and AI in recruitment are set to change the way every business finds candidates, analyses CVs and recruits new employees. To get the most out of new technology, be sure to conduct thorough research before selecting a machine learning tool, and provide comprehensive training to the entire team before rolling it out.
