Many of us can recall the excitement generated by the first Applicant Tracking Systems or ATS’s hitting the market in the late 1990s and early 2000s. After all, activities related to sourcing, screening, selecting and offering jobs to candidates was perennially a very manually intensive endeavor that also produced many false positives (unsuccessful hires) as well as false negatives (potentially great hires that were never brought into the recruiting process). The first wave of ATS’s proved to be extremely successful in the market due to the impact of their automation capabilities, with virtually all of the ATS market leaders back then either getting acquired and folded into larger HCM platforms, or continuing their path to amassing very large, typically global, customer bases today.
Early capabilities that attracted corporate buyers included role-based experiences designed for candidates (internal and external), hiring managers, recruiters and staffing agencies; automated job posting to source candidates; search, match and ranking functionality; interview process collaboration and scheduling; job offer management; and the passing of new hire details for payroll, benefits and compliance-related processing. And fast forwarding to today’s recruiting technology landscape, we see advances that are every bit as impactful, if not game-changing, largely as a result of digital innovations such as artificial intelligence and machine learning (AI/ML). This market shift was highlighted in the assertion from my 2021 HCM Market Agenda that “over one-third of organizations using HR technology will adopt platforms utilizing machine learning to enable real-time alerts and personalized experiences in the flow of work.”
The new crop of personalization capabilities, for example, has been a huge factor in the explosion of features designed specifically for effective Candidate Relationship Management (CRM). This is because an organization must be proficient at CRM in order to be “first in line” when otherwise passive candidates (those not actively seeking a new opportunity) decide to engage. These dynamics were previously detailed in a pair of Ventana Research analyst perspectives: The New World of Engaging Candidates and Candidate Engagement Best Practices Aren’t Always Best.
More recent entrants in the recruiting and talent acquisition technology market have also been generating market share by specializing in areas such as video interviewing and assessments underpinned by predictive science, recruitment marketing (focused on promoting the employer brand), proactive internal mobility support, removing bias from the entire process, and a newer capability known as programmatic recruiting (where the system guides toward the optimal sourcing strategy and related channels based on the job to be filled.
By most industry observer’s accounts, however, many of these recruiting technology advances could be considered incremental when compared to the potentially transformational capabilities we are seeing today as a result of products being infused with AI/ML. Aside from the previously highlighted personalization capabilities being a major boon to the CRM-focused solutions in the recruiting technology market, principally due to outreach efforts being much more tailored and personally relevant, this previously published analyst perspective features an “AI in HCM” framework and recruiting-related use cases.
The framework aligns with five functions that, in general, more HCM systems will be performing in the near future, if not already manifested in the product: Predict, Personalize, Prescribe, Understand and Curate. Most importantly, these functions will be performed at scale. In the world of recruiting, though, an example of a use case across each of these “AI in HCM” pillars would be:
- Predict job, team or culture fit
- Personalize the job pitch for each candidate
- Prescribe a process change that would elevate conversion rates with top-tier candidates
- Understand why not enough quality candidates are interested
- Curate information that would better inform a hiring decision
Moving to some other impressive AI/ML-enabled recruiting technology capabilities we expect to see in the next 12 to 24 months, we foresee products that offer the ability to determine the best time to reach out to a candidate about a job opportunity, as well as the best medium and approach to maximize receptivity; guide career site visitors to the appropriate open jobs using a range of job, skills and candidate profile data deemed to be predictive; use performance management system data to continually improve candidate screening and hiring in general; and highlight candidates who were previously passed over who might be worth reconsidering because either the required job spec or skills changed, or the candidate amassed highly relevant experiences.
Finally, one of the most compelling capabilities we are starting to hear vendors talk about is the ability to “hire for potential” based on considering a candidate’s “adjacent skills” or skills that have proven to be learnable fairly quickly based on other skills demonstrated.
There are two points of caution to consider, however, for the many organizations excited by such high-impact innovations in recruiting technology. First, various types of bias can permeate any or all of data collection, algorithm creation, validation and certification, results interpretation, and the eventual application of the model. Second, the age-old Achilles’ heel of so many HCM system deployments, data distrust - either justified or not. For these reasons, we highly recommend that buying organizations expect if not demand transparency from potential vendors in exactly how AI/ML is being leveraged and the care taken to ensure ethical and non-biased processes are used throughout product design and deployment.