AI in Recruitment: Advantages, Disadvantages and Best Practises

The use of Artificial Intelligence (AI) and Machine Learning (ML) is slowly transforming the way we work. Every industry has the opportunity to utilise AI and ML to streamline their processes, especially when it comes to administrative tasks. Many employees are already comfortable using AI for administrative tasks (76%), analytical work (79%) and creative work (73%).

The worldwide AI marketing was valued at $95.9 billion in 2022 and is expected to expand at a CAGR of 32.8% during the forecast period, resulting in a market size of $276.6 billion by 2026, according to research conducted by Global Industry Analysts Inc..

It is no surprise that AI and ML are being more frequently used in the recruitment process; in fact, 88% of companies globally already use AI in some way for HR, according to SHRM.

In this blog, we will explore the advantages and disadvantages of AI and ML in recruitment, as well as highlight how best to use it.

Advantages of AI and Machine Learning in Recruitment

Disadvantages of AI and Machine Learning in Recruitment

How to best use AI and ML-based recruitment processes

Advantages of AI and Machine Learning in Recruitment

Automation of repetitive tasks 

Artificial intelligence and machine learning can be used to automate repetitive and time-consuming tasks in recruitment. This can include CV screening, interview scheduling and adding CVs into a CRM as well as sending follow-up emails and basic admin tasks. Admin tasks are increasingly being trusted by AI as employees feel more comfortable and trust the technology.

Most (70%) employees would delegate as much work as possible to AI, according to Microsoft’s global 2023 Work Trend Index Annual Report.

Increased automation of recruitment processes such as screening and assessment 

AI and ML can scan through vast databases of resumes and online profiles to identify potential candidates who match specific criteria. AI-powered applicant tracking systems (ATS) can also screen resumes and applications, automatically shortlisting candidates based on predefined qualifications and skills, ensuring that only the most relevant candidates proceed to the next stage.

This saves recruiters a significant amount of time and effort in manual candidate sourcing.

Enhanced accuracy of predictions regarding successful candidates

Machine learning can analyse historical hiring data to identify patterns of successful hires, enabling recruiters to make data-driven decisions and predict candidate suitability based on past performance indicators. 

Better candidate experience 

Shockingly, 75% of job seekers don’t hear back from employers after an interview, which can severely impact your employer brand. AI can engage with candidates through the automation of follow-up emails, updates and feedback, providing candidates with a better experience and helping employers and recruiters maintain a positive employer brand.  

Faster decision-making and cost reduction

By automating repetitive tasks, AI and ML can significantly reduce the time and resources spent on manual recruitment processes, enabling recruiters to focus on more strategic and value-added activities. While implementing AI may be costly at first, it will save the need to hire more recruiters or support employees providing a long-term return on investment.

Disadvantages of AI and Machine Learning in Recruitment

Risk of algorithmic bias

One potential risk of using AI and ML technologies in recruiting is the risk of bias. AI and ML systems are only as unbiased as the data they are trained on. If the training data contains biases, the AI will likely perpetuate them, leading to unfair recruiting. For example, if historical lending data has biases against certain demographic groups, AI algorithms may inadvertently perpetuate these biases when making credit decisions. To mitigate this issue, ensuring that the training data is diverse, representative, and free from biases is crucial.

Inability to assess qualitative traits

According to a 2019 report from LinkedIn, 91% of talent professionals say soft skills are very important to the future of recruiting and HR; however, this is something AI and ML cannot assess. Qualities such as communication, time management, organisation, problem-solving, critical thinking and interpersonal skills are human skills and can only be assessed by another human. 

Privacy concerns

AI and ML systems become more advanced the more they learn, meaning they collect data constantly. This includes data from every applicant and candidate, which could be misused by malicious third parties if not properly secured. 

Ensuring data privacy and compliance with relevant regulations (e.g., GDPR) can be challenging and requires robust data security measures.

Lack of human touch

The extensive use of AI and automation in recruitment can sometimes lead to a lack of personalisation and human interaction, potentially making candidates feel disconnected or undervalued.

Lack of diversity

Identifying patterns of successful hires can lead to the hiring of a certain type of person. This can lead to a lack of diversity in your hires in the way that people think and problem-solve.

Moreover, AI and ML algorithms are often “black boxes,” meaning that it is difficult to understand how they make decisions. This lack of transparency can make it difficult to detect and correct bias in the algorithm.

How to best use AI and ML-based recruitment processes

Define clear objectives

Clearly outline your recruitment goals and identify the specific areas where AI and ML can add value. Whether it’s candidate sourcing, screening, or assessment, having well-defined objectives will help you select the right tools and solutions. AI and ML are unlikely to replace recruiters or HR leaders in the recruitment process; however, they can definitely be leveraged to streamline the process.

Incorporate human evaluation into decision making

While AI can automate certain tasks, human expertise remains essential in the recruitment process. Use AI/ML as a tool to augment human decision-making rather than replace it entirely.

Assess potential risk

Continuously monitor the performance of AI/ML systems and conduct regular audits to identify and address any biases or errors that may arise. Stay proactive in resolving any issues to maintain fairness and accuracy in the recruitment process.

Utilise feedback loops

Continuous improvement should be at the forefront of everything your business does – this includes your use of AI/ML. Leveraging feedback loops to adjust parameters and improve performance over time can help ensure your systems and data are getting better at providing valuable and more accurate insights. Review your historical data to identify and address any biases. Ensure that your data sets are representative of your target candidate pool to avoid bias in the algorithmic decision-making process.