The 5 biggest AI-related trends talent acquisition leaders need to know in 2023

As we move further into the digital age, artificial intelligence (AI) is becoming increasingly pervasive in every aspect of our lives. Talent acquisition is no exception. In 2023, TA leaders need to be aware of the latest AI-related trends to stay competitive. In this article, we’ll explore the top 5 AI-related trends that talent acquisition leaders need to know.

Jasper Spanjaart on May 17, 2023 Average reading time: 3 min
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The 5 biggest AI-related trends talent acquisition leaders need to know in 2023

Recruiters have always tried to make the hiring process feel more personal, but sometimes they end up overdoing it. Having your recruiters ask about a favourite time of ice-cream, or asking them which company they’d ask for, beyond the one their interviewing for, is a massive waste of time. Personalisation, among other things, is exactly what Artificial Intelligence (AI) aims to solve in the hiring process. From ChatGPT to customised onboarding experiences, AI is changing the way we recruit. We’ve amassed five trends

1. Personalisation

The concept of personalisation has been around for a while, but only recently has AI enabled us to deliver it at scale. In talent acquisition, personalisation means tailoring the recruitment process to the individual candidate based on their unique characteristics and preferences. This can range from personalised job recommendations to customised onboarding experiences. An excellent example of personalised recruitment is the use of chatbots to engage with candidates. By asking candidates questions to understand their preferences, chatbots can provide relevant job recommendations that improve candidate engagement while reducing the time and effort required to find the right candidate.

Case study: Stanford Healthcare

US-based Stanford Healthcare, major player in the healthcare industry aimed to enhance the hiring process by leveraging AI-powered chatbot Phenom, that offers candidates personalised experiences. The chatbot has effectively simplified and optimized the journey for job seekers, enabling them to complete the application process at their convenience, including via mobile devices. Over a period of six months, the AI chatbot generated an impressive quarter-million interactions, resulting in over 11,000 candidate leads, close to 35,000 distinct visits, and more than 12,000 clicks to apply.

2. Automation

AI is also enabling greater automation in the recruitment process, from automated job posting to automated candidate screening and scheduling. This can help recruiters save time and focus on higher-value activities. For instance, AI-powered candidate screening can analyse resumes and identify the best candidates for a particular role based on their skills and experience. This, in turn, can reduce the time and effort required to screen candidates, allowing recruiters to focus on more strategic activities.

Case study: Interdent

Interdent is a rapidly expanding Dental Service Organisation. Due to the COVID-19 pandemic, InterDent faced challenges in optimising their hiring processes while working with a limited budget to fill over 1,000 positions annually. The company faced an immediate 50% decline in candidate flow when their candidate application process broke down. Interdent turned to Appcast to help them restructure their hiring processes and optimise their recruitment budget. The results showed a 70% increase in candidate flow, a 2.5x increase in qualified candidates, and consistent volume of qualified applicants for Interdent’s hardest-to-fill and priority positions.

3. Bias detection and mitigation

Bias in recruitment can lead to unfair hiring practices and limit diversity in the workplace. To tackle this issue, AI can be used to detect and mitigate bias in the recruitment process. Job descriptions can be analysed to identify language that may be biased, while resumes can be anonymised to reduce bias in the screening process. By detecting and mitigating bias, AI can improve diversity in the workplace and create a fairer hiring process for all.

4. Predictive analytics

AI can also provide insights into the recruitment process by using predictive analytics to identify trends and patterns in recruitment data. Predictive analytics can help talent acquisition leaders make more informed decisions about the recruitment process based on data-driven insights. This can improve the quality of hires and reduce the time and effort required to find the right candidate.

Case study: Data Hire

Data Hire is a staffing and recruiting company which faced challenges with slow candidate sourcing and email campaigns. AI-powered outbound recruitment platform hireEZ was the solution that allowed for the creation of dynamic and customisable talent pools, serving multiple clients simultaneously. With hireEZ’s automated email campaigns, Data Hire was able to quickly and easily nurture top candidates. Before it, Daren had to manually copy, paste and send emails, leading to a time-consuming and inefficient process.

5. Skills mapping

Finally, AI can map skills and identify skill gaps in the workforce. By identifying which skills are in high demand and where there may be shortages, talent acquisition leaders can develop targeted recruitment strategies to address the organisation’s needs. Skills mapping can also help to identify candidates with the required skills for a particular role.


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Jasper Spanjaart

Jasper Spanjaart

Editor-in-Chief and Writer at
Editor-in-Chief and writer for European Total Talent Acquisition platform
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