Talent Acquisition: Smart Technology, Efficient Processes, Enabled People Allegis Group,  September 19, 2017

Talent Acquisition: Smart Technology, Efficient Processes, Enabled People

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(Editor’s Note: The following is an excerpt from “AI and the World of Work: Embracing the Promises and Realities,” a free white paper available on the Allegis Group website that tackles the rise of AI and its influence on workforce strategy and innovation.)

Today, the sheer volume of activity involved in talent acquisition overwhelms many organizations. Historically, most organizations judged recruiting performance based on the idea that the faster and more efficiently a company could funnel a large number of initial candidates through a sifting process to reach a finalist and make the hire, the better the outcome. This approach was the general “candidate funnel” mentality. Toward that end, advances in technology have helped to boost speed and efficiency for tasks such as sourcing talent, reviewing résumés, screening, and scheduling.

That said organizations recognize that speed and efficiency do not guarantee success, and moving candidates quickly through a funnel can still yield poor hires or leave many candidates in the dark. Companies cannot afford a reputation for poor treatment of current or potential employees, so the candidate experience is important. Likewise, the cost of a poor hire, from an inability to perform the job to the negative impact on other employees, is seen as a significant liability. With that in mind, recruiting must also be smart. That’s where artificial intelligence (AI) comes in.

At its core, smart talent acquisition requires data intelligence and human communication capability. Nearly every interaction in the recruiting process involves communication with people or the use of data that is either unstructured or inconsistently structured. AI is suited to address these issues, offering the potential for near-human communication combined with the broad analysis function that can automate and boost intelligence throughout the talent acquisition function. As a result, AI developers are beginning to address many challenges and needs.

Sourcing: Replicating the Rock Star?

The ability to consistently find strong candidates remains elusive. For roles with high-demand skills, sourcers have few active candidates to attract through traditional job boards. The majority of the talent pool often consists of the much desired “passive candidates,” workers who are employed but would consider opportunities if approached in the right way.

Great sourcers can find and analyze data, particularly information generated online and through social media, and pinpoint passive candidates who may be right for the job. They can also dig up details to help “sell” the opportunity to passive candidates. For example, did a candidate complain about moving to a location for their current job on Facebook? A good sourcer may dig that information up and use his or her company’s location as a selling point against the current job. All of this sleuthing takes time, hard work, and intuition. Even today, a good sourcer enjoys “rock star” status.

The challenge for a talent organization is that sourcing rock stars are difficult to replicate. Great training and a strong learning culture across the profession — combined with sophisticated tools and access to data — are raising the bar on sourcing excellence. Nevertheless, research takes time and intuition. AI addresses this challenge with new advances in sourcing, including services that combine a human interface with a powerful search capability that leads sourcers to strong candidates.

One notable innovation is HiringSolved and its recruiting artificial intelligence (RAI) tool. The solution aggregates billions of social profiles and data from the web, and it applies advanced machine learning algorithms to search that data based on the sourcer “conversation.” The application asks questions about the job and the requirements, and, in many ways, resembles the same conversation that a sourcer would have with a hiring manager. The goal of the technology is to become the “Siri for recruiting.”

By taking on some of the research aspects of sourcing, new AI tools will free sourcers to identify quality talent quickly and effectively. AI may not fully replace the rock star sourcer, but it will raise the overall quality and consistency of results, giving adopters a critical advantage in competing for talent.

Candidate Matching and Résumé Screening: Speed, Accuracy, Bias-Avoidance

Screening résumés, and the effort to identify and match potential candidates to job requirements, remains one of the most daunting, low-value activities in talent acquisition, and it is ripe for automation. In fact, one study found that recruiters spend an average of six seconds of screening time per résumé, and more than 75% of received résumés are unqualified.

Much of the problem stems from the fact that most candidate data is unstructured. Machine learning-based predictive analytics using natural language will address unstructured data to allow candidate matching faster and with fewer mistakes. Imagine a world where companies can qualify candidates by having them interact with a chatbot or AI tool, answering common candidate questions about the role while also asking for feedback and information about the candidate. Compared to approximately 10 résumés per minute, an AI can potentially process thousands of résumés in the same timeframe, effectively removing initial screening time from the recruiting process.

In addition to screening a larger number of candidates, advanced programs today are also taking into account more detailed information. This deeper analysis gives each candidate a chance to be considered on terms other than job titles, company names, and start and end dates. In addition to improving screening speed and intelligence, AI applications are also notable for their objective analysis capability. An AI solution can help remove bias in candidate selection, as well as help organizations avoid the unconscious bias that often taints the development of job descriptions and requirements.

Candidate Care: Engagement, Communication, and Scheduling

The human face of AI today is the chatbot. It is the function that “talks” with the recruiter in defining job requirements and recommending candidates, and it can be a primary point of communication to candidates themselves. With its human communication ability, the chatbot presents a unique opportunity to solve some of the most long-standing issues of candidate engagement.

Wade and Wendy is representative of early-stage, AI-driven chatbots. In this example, a virtual career guide (Wade) or virtual hiring assistant (Wendy) can ask and answer questions with prospective candidates in a dynamic and intelligent fashion. This function can play an active role in bringing more candidates into the process and move them along toward selection without requiring recruiter interaction.

In addition to intelligent conversations with the candidate, AI assistants can also automate the high volume of administrative interaction, providing automatic, real-time, unique reminders and messages that are often neglected by human recruiters. One example is scheduling. Alex, an assistant developed by AI developer, MyAlly, is touted as “a blend of AI, traditional software and human supervision.” The solution coordinates and understands email threads from multiple parties, understands context, and takes action as needed. As a result, companies have a solution for booking interviews or coordinating colleagues, teams, and candidates while eliminating the wasted time and email traffic associated with scheduling.

By maintaining candidate contact through even the simplest interactions, AI services can eliminate the “black hole” of non-communication that often loses candidates or creates a poor reputation for the company. Whether scheduling through MyAlly or guiding candidates through Wade and Wendy, AI applications are already beginning to offer practical solutions for candidate care.

These solutions represent just a few of the ways that AI is changing talent acquisition. Today, most developments are aimed at solving specific issues in the life of the recruiter, the candidate, and the hiring manager. As the technology matures, the real impact of AI will be felt as the roles of recruiters and sourcers are transformed into more strategic functions and freed of repetitious review and communications tasks. The effect will be to raise the standards in competing for talent. The need for humans will not disappear. Instead, look for human skills to be more crucial than ever as the role of the recruiter shifts from an administrative guide to become a more strategic partner to the candidate and the business.

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