Across the realm of business technology, there may be no hotter avenue of innovation than artificial intelligence. Between the relative hype -- from AI replacing up to 40% of US jobs to robots conquering humans -- and its actual benefits, AI represents the vast potential of technology in 2020 and the new decade ahead. Today, nearly 26% of businesses (according to upcoming Ardent Partners research) are actively using artificial intelligence in some capacity. Some use true, standalone AI tools (for example, scikit-learn, a machine learning library) while others use AI-led functionality via larger, more comprehensive enterprise software solutions. In fact, Ardent research finds that enterprises understand the value of AI in talent acquisition, a new key area.
Talent is the average organization’s top competitive differentiator, and in an age when a tight labor market defines staffing and recruitment efforts, businesses are finding that AI in talent acquisition might be an important edge required to enhance overall talent management efforts. According to a 2019 report from iCIMS, a recruitment software provider, companies spent an average of 55 days to fill a tech role in 2016. By 2019, that number had jumped to 66 days. These unfilled roles can cost about $680 in lost revenue per day per vacancy.
Over the next three years, 61% of enterprise organizations expect to integrate AI into their talent acquisition strategies, according to Ardent Partners research. In the past, the idea of trusting AI to do this type of work has been a hurdle for the average business, as there are many enterprise leaders who have viewed artificial intelligence as a threat to human-led thinking and, of course, traditional jobs. Today, however, 70% of businesses state they trust AI-led functionality to effectively match candidates with open positions and projects. It seems the tide is turning in favor of artificial intelligence truly making an impact on talent acquisition in 2020 and beyond.
“AI will automate away the inefficiencies and unnecessary manual labor presently associated with sourcing, screening, and onboarding talent. For instance, AI will be able to intelligently read and evaluate thousands of CVs before recommending the ones that are most compatible with a job opening or project opportunity,” says Faris Mersi, co-founder and CEO of Jarvis, a digital staffing provider that specializes in providing access to top-tier AI talent. “Using chatbots and natural language processing, we can even extract other personal info that is critical to the compatibility score: for example, relocation preferences and compensation expectations. Most recruiters today tend to match keywords, which isn’t really AI, as many would like to believe, and results in an unhealthy number of false positives and negatives.”
Here’s what AI in talent acquisition -- specifically recruiting -- might look like, according to Anthony Patricio, CEO of Spero:
- Using an algorithm, software integrates into your applicant tracking system (ATS) and the job boards you have licenses with (for example, CB, Monster, Dice).
- The algorithm develops the correct search strings (Boolean strings) based on the job descriptions in your ATS. These strings search the licensed job boards and social media sites, including LinkedIn, for candidates who might be a match.
- It collects these candidates, as well as those who have already applied to your job postings.
- Using the information about each candidate it collects, it then ranks them. Many AI recruiting programs can also predict how likely each candidate is to accept your offer, including salary expectations and geographic requirements.
The Need for Human Touch
AI can’t operate alone, however. There are some caveats to how well AI will work for talent acquisition, including:
- Businesses can’t expect a 100% plug-and-play approach. In other words, humans still need to be active participants in the selection of talent. Artificial intelligence, at its core, mimics human thinking, and to that effect, human thinking is nowhere near perfect (sorry, folks). AI isn’t, and will never be, a cure-all for talent acquisition woes. Businesses must ensure the data is good in vendor management systems, human resources information systems, applicant tracking systems, and CRM tools. AI can then augment human capabilities and improve overall results in these systems by building future workforce scenarios and providing predictive measures as to future talent needs. While the power of data and intelligence is astounding, the very realms of recruitment, staffing, and talent acquisition will always require some human elements. “Until an AI recruiter becomes indistinguishable from a human recruiter, a longer-term prospect I won’t rule out, I do believe that human involvement will still be necessary since we’re not dealing with casual shoppers, but rather emotionally invested individuals making important life decisions,” says Mersi.
- Different human-AI strategies are required for passive candidates and active candidates. Although businesses will always maintain a consistent outreach to active candidates, an innovative, nurture-based approach to passive candidates is required to tap into top-tier skills and expertise. According to a recent LinkedIn report, 70% of the global workforce is made up of passive talent who aren’t actively job searching. According to the report, the most effective talent branding tools are company websites (68%), online professional networks (i.e., LinkedIn), and social media (i.e., Facebook, Twitter). AI can use algorithms and data science to determine where to place recruitment ads within social media to target these passive job seekers. AI-led functionality that analyzes talent retention, job movement, market trends, and other key sources of data can assist talent acquisition leaders in developing enhanced messaging, while also helping them identify the ideal time to engage with these candidates.
- The concept of unconscious bias is an issue with AI for staffing and talent acquisition. Forbes reports that nearly half (44%) of applicants have experienced discrimination in the hiring process. Unfortunately, even AI can be subject to unconscious bias. Artificial intelligence must be trained in order to execute effectively. Training AI models based purely on enterprise history is a recipe for bias that essentially eliminates the core benefits of the technology. The idea of similarity attraction is a true problem for AI-led talent acquisition tools, so it is critical for businesses that are trusting these solutions. “Bias in AI-powered decisions is a real concern,” says Joe Hanna, chief strategy officer at Workforce Logiq. “Machines learn from history; we make a lot of biased decisions on a daily basis and we need to take methodical steps to ensure that our prejudice doesn’t carry over to AI models. Implemented correctly, AI can be a tremendous tool in eliminating or reducing human bias, but organizations need to ask tough questions on how the models’ developers are handling bias in all aspects of data collection and model development.”
Luckily, AI can be audited, and biases can be removed. For example, “the California State Assembly passed a resolution to use unbiased technology to promote diversity in hiring, and the San Francisco DA is using ‘blind sentencing’ AI in criminal justice proceedings,” according to Forbes.
Artificial intelligence reflects an idyllic means for enterprises to harness pure automation that mimics the power of the human mind. It seems to be an incredible technological fit for any business that desires not just a competitive edge in the new year but strives for true organizational agility.
For all of the frequent, future of work-oriented discussions around AI, however, the hardline adoption figures for this type of technology (26%, according to Ardent research) prove that businesses are still mired in an education phase in understanding its implications and impact. Incorporating AI into talent acquisition is a step forward.