How Can Structured Interviews Improve AI-Ready Talent Acquisition?

Bad hiring decisions have always been expensive for organizations, but they are especially costly today given the tight labor market and the importance of having the right talent in place in the age of AI. The strategic deployment of AI technologies is central to maintaining and enhancing competitive advantage in today’s digital economy. The cost of poor hiring is amplified by the necessity of having individuals who can navigate the complexities of AI and align its capabilities with the broader business strategy. Employees in the age of AI are expected to focus on higher value-added tasks, such as innovating, exchanging ideas, and finding highly creative solutions to problems. It’s critical to bring new talent into the organization that has the uniquely human skills that AI lacks. This underscores the importance of meticulous talent acquisition.

Precisely Identify Ideal Candidate Traits Based on Key Success Factors (KSFs)

Talent acquisition across numerous organizations is beset with challenges. Hiring decisions are typically made with limited information and under significant time constraints. Most candidate interviewing is unstructured because interviewers typically do not follow a predetermined set of questions or a consistent procedure for evaluating candidates. Instead, the conversation may flow freely, with questions arising organically based on the interviewer’s intuition, something specific on the candidate’s resume, or the direction of the conversation. While the unstructured approach can make the interview feel more relaxed and conversational, Larry Bossidy, a former CEO of AlliedSignal, once called the unstructured interview “the most flawed process in American business.” Unstructured interviewing is problematic because there is significant unconscious bias built into the process, particularly confirmation bias and stability bias.

The lack of standardization can also lead to evaluations that are more influenced by the interviewer’s personal feelings and less by the candidate’s actual competencies and skills. As a result, unstructured interviews tend to be unreliable in ensuring a candidate has the critical qualities for a position. Therefore, it’s essential to have a clear idea of the knowledge, skills, and abilities, as well as the key success factors (KSFs) required to succeed in the role for which you are hiring. The job description is often the best starting place for developing the list of KSFs, but you can supplement it with focus groups, statistical analysis, and other sources to complete the list. Most knowledge worker roles will increasingly require individuals to have the following capabilities: strong social interaction skills, creativity, critical thinking ability, and curiosity.

Select the Optimal Interview Technique for Each KSF

As a result, every job candidate in the age of AI should be evaluated against those four KSFs in addition to the KSFs identified for the specific role. Once you have precisely identified the KSFs, you can create a scoring rubric to evaluate candidates effectively. On the left side of the rubric are the KSFs you’ve identified, and along the top is a 1–5 rating scale—from weak to strong. Once you have the evaluation rubric, you can decide what interviewing technique to use. There are three common techniques. The first are experience-based interviews. These interviews ask candidates to share something from their past that is related to the KSF being evaluated. For example, to evaluate creativity using the experience-based technique, you might say to a candidate: “Tell me when you found a creative solution to a problem in your previous role.”

The second are scenario-based interviews. A candidate is given a specific business problem that tests a KSF and is asked to respond to that problem in real-time. Scenario questions are often “think on your feet” type questions. These questions present a scenario that requires the interviewee to logically deduce the solution through a process of elimination and inferential reasoning. The third are real-time skill test interviews. These interviews test more technical KSFs (coding or data analysis skills, for example). A candidate is asked to answer a predefined set of questions. An interviewing technique used should be mapped to each KSF to ensure a thorough evaluation of the candidate’s abilities.

Create the Interviewing Guide

Every job candidate in the AI era should be assessed against four Key Success Factors (KSFs) as well as the KSFs relevant to the specific role. After pinpointing the KSFs, you can develop a scoring rubric to evaluate candidates. On the rubric’s left are the identified KSFs, and across the top is a 1–5 rating scale, from weak to strong.

Once the rubric is ready, choose an interviewing technique. There are three common methods. First are experience-based interviews, where candidates share past experiences related to the KSF. For instance, to evaluate creativity, ask, “Describe a time you found a creative solution to a problem at work.”

Second, scenario-based interviews present a specific business problem testing a KSF, requiring candidates to respond on the spot. These questions often need logical deduction and inferential reasoning. Lastly, real-time skill test interviews assess technical KSFs, such as coding or data analysis. Candidates answer predefined questions to demonstrate their skills.

Each interviewing technique should align with specific KSFs to thoroughly assess the candidate’s competencies, ensuring a comprehensive evaluation process.

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