How to Use Automated Talent Acquisition Processes to Improve the Candidate Experience

There is some concern about using machine learning and/or artificial intelligence in talent acquisition (TA) technology because it may cause undesired effects. Specifically, there are concerns that hidden algorithms and computerized decision-making will replace humans altogether. However, very few organizations are preparing for a future where our TA technology makes hiring decisions for us, but rather a future where AL and ML enable TA technology to help us make more strategic hiring decisions that reduce bias and allow more human interaction in the hiring process. 

The use of automated TA processes has increased over the years and will increase in the years to come. Often much of the marketing from TA technology providers focuses on how a greater volume of candidates can be evaluated than ever before, but the real value in using these newer technologies is in reducing the amount of time recruiters need to spend on repetitive tasks, allowing them to spend more time on meeting and evaluating candidates.

  • 55% of organizations are using AI and ML in screening technologies
  • 48% of organizations are using AI and ML in sourcing technologies
  • 21% of organizations AI and ML in onboarding technologies

This is leaving a lot of organizations missing out on the capabilities of advanced TA technologies.
The low usage rate for AI and ML is creating a suboptimal TA process for many organizations.

To improve your organization’s ability to improve the candidate experience through the use of AI and ML-backed talent acquisition technology, you need to determine what people, processes, and technology you have in place to help talent acquisition professionals understand and make use of the technology they are given. To improve the TA process with AI and ML powered talent acquisition technology, you need to ask these questions: 

  • What data sources does your organization have access to in your current system, and what data sources are you missing? 
  • What should your TA process look like in the next few years and what is technology’s role in getting you there? 
  • What aspects of the TA process can benefit from AI, ML, and RPA capabilities? 

Audit Talent Acquisition Technology for Alignment with Organizational Goals 

Often talent acquisition technology is used to improve usage rates, time-to-fill, and cost-per-hire. These are all important metrics, but it is also necessary to align TA goals with organizational values and goals. Sometimes simply hiring as many people as fast as possible will hurt the candidate experience, which will cause the organization to miss its overall long-term goals.

Automate All TA Processes, Not Just the Early Stages 

Much of the current use of AI and ML-backed technology is in the early stages of the TA processes – sourcing and screening. This leaves post-hire TA processes sadly in need of more automation because the onboarding processes are just as ripe for improvement through the use of more modern systems. There is a great opportunity to leverage AI and ML for post-hire processes. Organizations should expand the use of AL and ML technologies into the post-hire process. 

Determine Which Processes Should and Which Should Require Human Interaction 

59% of organizations say they are not ready or only somewhat ready to have AI- and ML-driven technology replace human interactions and decision-making. AI and ML enabled technology can improve the TA process, but it is not a replacement for human decision making for many organizations. 

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