Altius Link Introduces AI Interviews for New Graduate Recruitment
Altius Link Inc. has introduced AI interviews for its first-round new graduate recruitment. This initiative aims to standardize evaluation criteria and significantly reduce the workload of recruiters, allowing them to focus on high-quality communication with applicants.
📋 Article Processing Timeline
- 📰 Published: March 30, 2026 at 20:02
- 🔍 Collected: March 30, 2026 at 22:56 (2h 53m after Published)
- 🤖 AI Analyzed: April 24, 2026 at 06:10 (583h 14m after Collected)
Altius Link Inc. (Headquarters: Shibuya-ku, Tokyo; President and Representative Director: Masatoshi Natani; hereinafter "Altius Link") has introduced AI interviews for the first round of new graduate recruitment as part of its corporate DX*1 initiatives. Through its BPO business, the company has continuously strived to improve operational efficiency while identifying areas where "humans should demonstrate value." This initiative applies that policy to the recruitment process. By standardizing evaluations through AI and visualizing the qualities of applicants, the company aims to enhance the time and quality spent facing each applicant, leading to further improvement in the candidate experience.
Background of Introduction - Standardization of Evaluation Criteria and Strengthening of Selection System as Issues
In recent years, as competition for recruitment intensifies, the importance of strengthening touchpoints and mutual understanding with applicants has grown, leading to a widespread movement to review evaluation concepts and recruitment processes. In our company, variations in the content and perspectives of evaluation reports written by interviewers sometimes resulted in differences in the granularity of applicant information shared with final interviewers. Furthermore, 80 managers outside the recruitment department were in charge of first-round interviews, making the reduction of administrative workload associated with selection an issue for recruiters as well.
Given this situation, we recognized the need to optimize the recruitment process and repeatedly considered whether the knowledge of utilizing AI and technology cultivated in the various services provided by our company could be applied to the recruitment field. On the other hand, the spread of AI utilization in the recruitment field is limited; according to a survey by Nihon Keizai Shimbun, AI utilization in "interviews" is only 4.5%.
Based on these issues and social conditions, we decided to introduce AI interviews as part of our recruitment DX. This aims to standardize evaluation criteria and build a selection system that can focus more on dialogue with applicants, while improving the quality of interviews and the candidate experience.
Effects of Introduction - Improving Interview Quality Through Fair and Unified AI Evaluation Axes and Interview Reports
Before fully introducing AI interviews, we implemented a phased introduction and effectiveness verification in our recruitment activities for 2027 graduates. In addition to confirming the consistency between AI scores across 10 evaluation items and the evaluations by recruiters, we verified the impact on the candidate experience and the efficiency of the recruitment process, leading to the decision to adopt it for main recruitment.
* Improving Interview Quality: Deepening Final Interviews Through Unified and Visualized Evaluation Perspectives
By standardizing the evaluation axes using AI, we suppressed evaluation variations while organizing and visualizing the characteristics and strengths of each applicant, smoothing information sharing for final selections.
Interviewers in charge of final interviews provided feedback such as:
"Because we can grasp the content of the first interview in advance, we can delve deeply into the applicant's thoughts and orientations within the limited time."
"Referring to the AI score clarified the points that needed to be checked."
In the future, we will explore the possibility of utilizing the qualitative information obtained during the selection process for post-hiring training and placement considerations.
* Optimizing the Recruitment Process: Reducing Operational Workload and Reallocating Time to Applicant Communication
We expect an approximately 95% reduction in work hours for recruitment-related tasks that previously required about 1,200 hours, such as interview management and scheduling. This will lead to the creation of a system where recruiters can focus more on communicating with and following up with applicants.
Specifically, we will expand recruitment content and enhance interactions with applicants.
Background of Introduction - Standardization of Evaluation Criteria and Strengthening of Selection System as Issues
In recent years, as competition for recruitment intensifies, the importance of strengthening touchpoints and mutual understanding with applicants has grown, leading to a widespread movement to review evaluation concepts and recruitment processes. In our company, variations in the content and perspectives of evaluation reports written by interviewers sometimes resulted in differences in the granularity of applicant information shared with final interviewers. Furthermore, 80 managers outside the recruitment department were in charge of first-round interviews, making the reduction of administrative workload associated with selection an issue for recruiters as well.
Given this situation, we recognized the need to optimize the recruitment process and repeatedly considered whether the knowledge of utilizing AI and technology cultivated in the various services provided by our company could be applied to the recruitment field. On the other hand, the spread of AI utilization in the recruitment field is limited; according to a survey by Nihon Keizai Shimbun, AI utilization in "interviews" is only 4.5%.
Based on these issues and social conditions, we decided to introduce AI interviews as part of our recruitment DX. This aims to standardize evaluation criteria and build a selection system that can focus more on dialogue with applicants, while improving the quality of interviews and the candidate experience.
Effects of Introduction - Improving Interview Quality Through Fair and Unified AI Evaluation Axes and Interview Reports
Before fully introducing AI interviews, we implemented a phased introduction and effectiveness verification in our recruitment activities for 2027 graduates. In addition to confirming the consistency between AI scores across 10 evaluation items and the evaluations by recruiters, we verified the impact on the candidate experience and the efficiency of the recruitment process, leading to the decision to adopt it for main recruitment.
* Improving Interview Quality: Deepening Final Interviews Through Unified and Visualized Evaluation Perspectives
By standardizing the evaluation axes using AI, we suppressed evaluation variations while organizing and visualizing the characteristics and strengths of each applicant, smoothing information sharing for final selections.
Interviewers in charge of final interviews provided feedback such as:
"Because we can grasp the content of the first interview in advance, we can delve deeply into the applicant's thoughts and orientations within the limited time."
"Referring to the AI score clarified the points that needed to be checked."
In the future, we will explore the possibility of utilizing the qualitative information obtained during the selection process for post-hiring training and placement considerations.
* Optimizing the Recruitment Process: Reducing Operational Workload and Reallocating Time to Applicant Communication
We expect an approximately 95% reduction in work hours for recruitment-related tasks that previously required about 1,200 hours, such as interview management and scheduling. This will lead to the creation of a system where recruiters can focus more on communicating with and following up with applicants.
Specifically, we will expand recruitment content and enhance interactions with applicants.