The relevance of artificial intelligence (AI) is nothing new. But if you had any doubts about its usefulness pre-pandemic, it's easier to see how the technology is solidifying its position as a game-changer in today's increasingly virtual world. According to an Oracle future workplace report, 50% of HR professionals today use some form of AI on the job compared to 32% in 2018.
For some, however, AI may still feel a bit overwhelming. Even those comfortable with the concept may not be familiar with how AI-driven tools can help them do their jobs better.
On the latest episode of Talent Experience Live, we explored how AI can help recruiters and HR leaders more confidently navigate this evolving talent landscape efficiently and effectively with Phenom’s Kumar Ananthanarayana, Director of Product Management.
What exactly is artificial intelligence, and how does it apply to HR?
Fortunately, AI in the HR space does not involve a robot logging in to do your job. In its simplest terms, AI is the science of training machines to perform human-like tasks so that they can be automated.
The broad-brush benefit of AI-driven HR applications is creating efficiencies by automating repetitive tasks, which frees teams up to focus more on strategic, big-picture goals. AI tools also have the potential to help HR departments make more data-driven decisions.
What’s the difference between artificial intelligence and machine learning?
Machine learning (ML) is a subset of artificial intelligence, Kumar explains. While AI is automation-focused, ML is the process of making machines “intelligent” by training them on large data sets and specific examples. From these, the machine can learn to make informed decisions.
What impact can artificial intelligence have in HR?
The effectiveness of an AI-powered HR platform really depends on the quality of data flowing into it, says Kumar. Encouraging user collaboration on a platform driven by centralized AI is going to increase benefits. Having a system with robust bias detection frameworks built in is important as well, he reveals.
Kumar and his team advise taking a “crawl-walk-run” approach when adopting AI. For example, start by integrating a bot, then move on to candidate fit scoring and matching technologies, and evolve usage from there as you grow more comfortable.
How is artificial intelligence changing the recruiting process?
Recruiters are always pressed to accomplish more with less, Kumar points out. Tools such as chatbots that leverage AI and ML can alleviate some of this pressure by enabling greater efficiency around some of the most important recruitment functions:
Sourcing: Identify best-fit candidates through matching and scoring technologies
Screening: Score and rank candidates based on defined criteria
Scheduling: Use chatbots and calendar integrations to set up optimal times for interviews and other meetings
What are the other benefits of AI in recruitment?
Personalization. Gone are the days of “spray and pray” email tactics. We’re in the age of personalization, where candidates are used to being served up targeted, relevant content. AI provides data-driven insights so that recruiters can better match appropriate content and messages to specific audiences.
Video analytics. With many more interviews taking place by video, this is a promising area. Applications include converting interview video into text and scanning that text to determine how well a candidate’s skill sets and proficiency level match the role. Feature recognition and expression reading applications are also in play for recruiters.
Fraudulent application detection. Similar to detecting spam or suspicious bank account activity, AI applications used to scan resumes can use data points and algorithms to differentiate fraudulent applications from genuine ones.
Internal candidate sourcing. According to Kumar, there are some very powerful ways to use AI in internal talent programs, including career path management, performance review management, skills gap analysis, and internal candidate sourcing. Notably, organizations have a lot of information on internal talent, which means a robust amount of data points are available with which to optimize internal AI applications.
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