Talent sourcing is arguably the most critical challenge facing organizations today. Filtering through hundreds, if not thousands of applicants in order to reduce the pool to a set of viable candidates is a task fraught with both system and human errors. Wouldn’t it be ideal if we could reduce the subjectivity in this process and find more time and cost-efficient ways to accurately match people with positions?

The art of staffing is gradually morphing into a science. Intelligent recruitment software platforms are beginning to incorporate Artificial Intelligence (AI), Machine Learning (ML), Neuro Linguistic Programming (NLP), and algorithms to help hiring managers and recruiters scientifically source the best-fitting applicants for each position. Today, we are in the nascent phase of what is likely to become a revolution in staffing. We are working towards sourcing talent more effectively and improving the hiring process enough to make accurate predictions about who will succeed in our positions, our companies, and our cultures.

We may soon look back on 2018 as a pivotal year for intelligent recruiting technologies. Here is what you need to understand about where we are now, and where we are likely headed.

How the technology works

There are waves of startup companies, along with one major player – Google, that have created AI recruitment platforms. Some are talent search platforms that enable one or more of three new and distinct types of employer and recruiter searches for candidates:

1. Conceptual Search: Using a few keywords, the system understands the user’s intent and identifies a basic concept or parameters for a type of candidate.

1. Semantic Search: The system tries to understand the intent and meaning of the searcher’s request, as well as the context, in order to bring up better matches.

3. Implicit Search: The system caters the output to the unique needs of the searcher, after gradually learning from previous searches.

Other intelligent recruiting platforms have a variety of purposes that serve staffing firms and HR departments. For example, Talent Sonar writes job descriptions specifically for the purpose of overcoming hiring biases in order to improve employee diversity. Entelo scours the internet and social profiles to proactively find candidates who will be receptive to a job opportunity, and would have a high probably of being retained for years. Another startup, Fama, looks for insights and clues into a candidate’s character, primarily through social media screening. Google has released a program called Cloud Jobs that major corporations are now beginning to use on their job listings in order to attract the best fitting candidates.

Expected improvements in candidate screening and job matching  

Fundamentally, AI, ML, and NLP will enable HR professionals to make sound decisions by screening huge amounts of candidate data. This will help them to solve for several hiring challenges, including:

1. Process speed: Traditional screening is highly labor intensive and expensive. Fully functional digital recruiting software (now in its infancy) will be able to narrow thousands of applicants down to five or ten, and possibly eliminate the need for human involvement in screening.

2. Refined applicant screening: AI software will provide more opportunities for employers to compare applicants and to develop data on talent pools by engaging viable candidates in testing, assessments, and even job scenario simulations. These will be used to narrow the field to candidates who will be granted interviews.

3. Workload management between recruiters: Intelligent recruiting platforms will enable staffing firms to balance workloads so that no recruiter gets a disproportionate share of the most difficult requisitions.

4. Intelligent matching to criteria: AI will dramatically reduce the time-consuming task of reading through resumes to match candidates with employer criteria using ML, NLP, and algorithms.

5. Work performance predictions: By plugging the success factors identified in current employees into an algorithm, talent sourcers will be able to make accurate predictions about which candidates will excel in which roles, and to what degree.

6. Resume language inconsistencies: For any given position, there can be a range of verbiage choices candidates make, which limits traditional screening methods. AI will uncover “dark matter” candidates, who use different language, but are highly qualified.

7. Legal challenges: Social media accounts can reveal a lot about candidates. However, when recruiters and hiring managers view social profiles, this can raise red flags to the Equal Employment Opportunity Commission about potential discriminatory practices. AI software can protect employers from this potential liability.

So what promise does the near future of AI hold for staffing? At Imprimis Group, we expect to see significant strides in the development of software with human capabilities for analyzing applicants and presenting ideal sets of candidates for any given position. As the functionality of the software improves, recruiters and hiring managers will become increasingly stronger talent sourcers, which will have great value for their respective organizations.