A mere year ago, most HR leaders would have never imagined that the question of whether or not they could successfully adopt AI solutions would impact their ability to recruit top talent. Now, according to Gartner research, 76% of HR leaders agree that they will be lagging in organizational success if they do not incorporate AI into their hiring processes in the next 12 to 24 months. Despite all the hype around AI, driven by ChatGPT, plenty of AI applications existed before this renaissance. Technologies like predictive analytics, sentiment analysis, and search and matching have been widespread among enterprises, and even used within HR and recruiting, laying the foundation for the talent acquisition transformation we’re experiencing today.
Much of the increased pressure on HR departments to adopt AI and manage its use across the organization has been re-invigorated by the excitement around generative AI tools like ChatGPT. While generative AI does play a role in transforming talent acquisition, there are several other AI use cases that can be applied to the life cycle of recruiting and talent acquisition. Here’s how you can leverage them to set your organization ahead of the curve:
- AI-Enhanced Resume Analysis
Resume analysis solutions are not new. They enable recruiters to filter applicants based on qualities like skills, education, training, location, and job titles and have been in use for many years. Applicant Tracking Systems (ATS) and Recruiting Management Systems (RMS) serve as the foundation for the hiring process in most organizations, and 90% of employers use their RMS to filter middle-skills and high-skills candidates, according to a Harvard Business School study. However, the same study found that 88% of employers say qualified high-skills candidates are missed because screening tools are too simplistic, causing applicants to be overlooked if they don’t match the exact criteria established by the job description. When it comes to middle-skills candidates, 94% of employers agree that qualified candidates are being vetted out. Even though resume analysis technology has been widely adopted, it’s far from an infallible method of filtering candidates.
AI has the potential to elevate resume analysis to a more robust, equitable level. Using natural language processing, AI solutions can extract topics more efficiently from both resumes and job descriptions to complete a more sophisticated matching. Rather than simplistic keyword searches that ATS or RMS use, AI can understand the nuances of language and phrasing, putting together a more complete view of the candidate. For example, suppose a company is looking for someone with leadership experience. In that case, a simplistic tool might filter out someone whose resume reads “management” rather than “leadership,” whereas a more sophisticated AI tool is more likely to analyze the term’s context. AI solutions can look deeper at the functions of the role and the experience of the candidate even if the wording isn’t an exact match, resulting in a way to prioritize applicants more efficiently and accurately, without inadvertently filtering out qualified candidates.
- AI-Generated Interview Guides
The quality of your hiring and interview processes shapes the candidate experience, which significantly impacts your ability to recruit top talent and influences your overall brand image. HCI reports that 60% of job seekers have a negative candidate experience with the employers they engage. A key contributor to poor candidate experiences is when interviewers appear unprepared or disengaged during conversation. This can be attributed to the fact that interviewing is a learned skill. It’s not a hiring manager’s job to conduct interviews, but they must do so as applicants progress past the initial HR screening. Because a hiring manager must fit interviews in on top of their other responsibilities, it’s common for the questions to be cobbled together at the last minute or even improvised on the spot.
AI can help address this shortcoming by auto-generating a meaningful set of job-specific interview questions. The questions generated by AI may not always be perfect but can save a lot of time even if the questions only serve as a starting point to be further refined by the hiring manager. Establishing a list of questions also ensures consistency across candidate interviews, allowing for easier and more fair candidate comparison. Auto-generated questions are also invaluable to recruiters who must screen applicants for a role they may not be familiar with. For example, recruiters may need to screen forklift operators for a warehouse but have no idea what the job entails. Using generative AI to come up with reasonable questions saves recruiters from spending extensive time researching the role or requiring clarification from the hiring manager.
- Automatic Candidate Notes and Insights
Bottlenecks in the hiring process are another key contributor to a negative candidate experience, not to mention a drain on company resources and efficiencies. One of the biggest hindrances in the hiring process is manually taking notes during an interview, summarizing them in an email or ATS, and then waiting to hear back from all stakeholders involved. Overly lengthy hiring processes have caused 54% of HR directors to lose a qualified candidate to another opportunity, indicating the importance of automating manual tasks to promptly move the hiring process forward.
Interview intelligence solutions capture detailed notes during interviews and automatically interpret and generate organized insights about the conversation. Examples of insights include a concise candidate synopsis, an evaluation of potential strengths and potential concerns, and a summary of what topics were discussed. Automating note-taking and insights improves the hiring process in a few ways:
- Removes bottlenecks that slow down the hiring process.
- Improves engagement with candidates by allowing the hiring manager to focus on them instead of taking notes.
- Helps recruiters to understand why hiring managers reject candidates. There’s often a frustrating disconnect when a hiring manager can’t articulate why a candidate a recruiter screened shouldn’t move forward. Having an intelligent summary of the conversation allows recruiters more insight into the conversation so they can see the areas where the candidate may have fallen short.
- Enables the entire hiring team to use objective data to support their decisions rather than relying mainly on gut instinct.
- Interview Performance and Compliance
When it comes to deciding whether or not a candidate should progress in the hiring process after an interview, 85% to 97% of hiring managers rely on intuition, making them vulnerable to bias. When AI listens to candidate interviews, it can detect if interviewers ask questions that potentially violate Equal Employment Opportunity Commission (EEOC) compliance standards, such as those related to age, race, gender identity, or religion. Compliance risks are summarized with every interview, allowing hiring managers to recognize their mistakes and helping HR departments assess their employees’ interview skills with real-time data. This information can then be used to shape additional training sessions for the whole organization and targeted coaching for hiring managers who need one-on-one guidance.
- Augment ATS for Candidate Rediscovery
Rediscovery is a preferred way to find candidates for newly opened roles because tapping into qualified candidates who have already been interviewed previously is more cost-effective and time-effective than beginning the whole process over again. Of the 43% of organizations that use rediscovery for talent acquisition, 97% say it has benefited them, according to an Agency Central survey.
Although your ATS has the potential to harbor lots of data on previous candidates, the challenge is that recruiters and hiring managers may struggle to provide detailed notes on every candidate, making the data mediocre at best and nonexistent in the worst case. But when you use interview intelligence to extract detailed notes from interviews, the candidate record is updated automatically with rich information that is also searchable, making it valuable for candidate rediscovery efforts later on.
Explore HRtech News for the latest Tech Trends in Human Resources Technology.
Summary
The ways that AI is transforming the talent acquisition process center on gaining efficiencies, improving the candidate experience, enforcing compliance, and achieving better hiring outcomes. While there may be an adjustment period for some professionals to adopt AI tools, most of the functions discussed in this article are so straightforward and intuitive that some users may not even realize they’re using AI. The most crucial consideration when adopting AI tools for your talent acquisition process is that AI must be used to augment human decision-making rather than replace it, the same way that many years ago, ATS platforms complemented HR employees by automating the manual tasks of job board postings, application collection, and candidate management.
ABOUT THE AUTHOR
Richard Mendis
CMO at HireLogic
Richard Mendis is the CMO at HireLogic. He has more than 20 years of experience in the enterprise software industry and currently helps companies hire smarter and faster using interview intelligence platforms
