Depending on how AI interviews are used, companies themselves will also be evaluated by job seekers.
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AI Job Interviews Are Unstoppable
The term “AI interviews” has been appearing with increasing frequency since 2026. Major trading houses have announced full-scale adoption for 2027 graduates, while large corporations such as Kirin Holdings, Lawson, and MUFG Bank have already begun real-world implementation.
For students, this trend brings growing anxiety—“Will I be rejected by AI?”
For HR professionals, it offers clear advantages—reduced workload and improved fairness.
In other words, the very premise of hiring is beginning to shift.
However, what truly matters is how AI is used. If it is merely deployed as an efficient filtering tool, the outcome will not differ much from the era of application forms. To unlock its real potential, AI must be positioned as a tool for collecting large volumes of evaluation data—and used as a platform for meaningful encounters between companies and applicants.
Expanding the points of contact between both sides—that is the essence of AI interviews.
Chapter 1: AI Interviews Are Already Expanding Among Major Corporations
As of 2026, AI interviews have moved beyond the trial phase. Kirin Holdings uses AI for everything from resume screening to first-round interviews, achieving a 95% satisfaction rate among over 1,000 applicants. Lawson, on the other hand, does not use AI for pass/fail decisions; instead, all candidates proceed to human interviews, resulting in a 98% positive evaluation rate and a 10% increase in offer acceptance.
Major trading houses—Mitsubishi Corporation, Sumitomo Corporation, and Marubeni—have also committed to adopting AI interviews for 2027 graduates. Today, roughly half of companies are incorporating AI into their recruitment processes.
This shift is driven by two factors: the growing number of applicants and the limits of HR capacity. AI enables 24/7 operations, eliminates scheduling burdens, and reduces human biases that are difficult to avoid in traditional interviews.
AI Interview Implementation Among Major Companies (2026–2027 Graduates)
| Company | Outcome |
|---|---|
| Kirin Holdings | 95% satisfaction among 1,000+ applicants |
| Lawson | 98% positive feedback, 10% higher acceptance rate |
| Mitsubishi Corporation | Improved fairness in handling large applicant volumes |
| Sumitomo Corporation | Significant reduction in selection workload |
| Marubeni | Enhanced discovery of diverse talent |
| MUFG Bank | Improved convenience and reduced burden for candidates |
These examples show that major corporations are shifting not only toward efficiency, but toward discovering talent that cannot be identified through resumes alone. Lawson’s approach—using AI purely as a source of information—is particularly instructive.
Chapter 2: What AI Interviews Can—and Cannot—Evaluate
Currently, AI interviews focus on three primary dimensions:
- Logical structure (consistency of conclusion → reasoning → example)
- Clarity of language (speed and fluency)
- Non-verbal signals (facial expressions, eye contact, posture)
On the other hand, AI struggles with more subjective elements such as depth of motivation, emotional nuance, atmosphere, and originality. As a result, candidates who deliver well-structured, template-based responses (such as PREP or STAR formats) tend to perform better.
Typical pitfalls include:
- Long explanations without a clear conclusion
- Overly abstract statements
- Logical inconsistencies
- Lack of eye contact with the camera
However, AI also has the potential to interpret conversations more accurately than humans. While current implementations often rely on standardized responses, it is technically possible to design systems that prioritize natural, role-specific communication. This remains an area for future development.
Chapter 3: Outdated Prompt Design Limits AI’s Potential
AI’s natural language processing capabilities have already surpassed human levels in many respects. Yet if companies continue to rely on outdated prompt designs—focused solely on structured responses—they fail to leverage AI’s full capabilities.
For example, rather than simply identifying a university name, AI can explore deeper context:
- Why the candidate chose that institution
- Whether they faced setbacks, such as failing their first-choice school
- The role of financial or family circumstances
- How their choices connect to long-term goals
Similarly, AI can uncover the motivations behind certifications, career decisions, extracurricular activities, and preferred working conditions—insights that cannot be captured through written applications alone.
This is the true strength of AI interviews.
The more data collected, the easier it becomes to identify candidates who present well but lack substance—while also revealing the potential of individuals with complex, non-linear backgrounds who may have been overlooked in the past.
Without rethinking prompt design, AI risks becoming nothing more than a faster version of a human interviewer. With proper design, however, it becomes a powerful partner capable of gathering vast amounts of meaningful data around the clock.
Chapter 4: Lessons from Medical AI
The medical field provides a clear example of how to use AI effectively. In Japan, adoption remains limited, but in the United States, over 1,000 FDA-approved AI medical devices are already in use, particularly in diagnostic imaging.
The model is simple:
- AI collects and processes large amounts of data
- Humans make the final decisions
This division of roles minimizes risk while improving accuracy.
The same principle applies to hiring. AI should not function as a gatekeeper for candidate selection, but as a tool for collecting comprehensive information. By applying AI in this way, companies can achieve a depth of evaluation that was impossible in the era of traditional application screening.
Chapter 5: From “Selection” to “Encounter”
The key is to stop treating AI as a gatekeeper.
Instead, it should be used as a data-gathering tool that expands understanding.
The benefits are clear:
- Superficial candidates become easier to identify
- Hidden potential becomes visible
- The overall depth of evaluation increases
An even more effective approach is to incorporate company data and real employee perspectives into AI systems. By doing so, candidates can gain immediate insight into the organization—its culture, challenges, and working environment.
This leads to deeper understanding and significantly reduces dropout rates during the hiring process.
Lawson’s model—advancing all candidates to the next stage without AI-based rejection—is a strong example of this “encounter-based” approach.
AI interviews are not a selection mechanism, but a platform for mutual understanding. This shift in perspective is what unlocks their true value.
Chapter 6: The Rise of Verbalization Skills
As AI interviews become widespread, the skills required of candidates will also change.
The most critical skill is verbalization—the ability to clearly articulate one’s thoughts, experiences, and intentions.
Because AI can conduct continuous, in-depth questioning, superficial answers are quickly exposed. At the same time, individuals who may have struggled in traditional interviews—those with less polished communication but deeper substance—gain new opportunities to be evaluated fairly.
Across all aspects of a candidate’s profile—education, career, achievements, and preferences—the key question becomes:
“Why did you make this choice?”
Those who can answer this consistently and authentically will thrive in the age of AI interviews.
Conclusion: Substance Will Define the Future of Hiring
AI interviews are unstoppable. And precisely because they are unstoppable, how they are used will determine everything.
If companies rely on them for superficial screening, diversity will shrink and only “safe” candidates will remain. But if AI is used as a data-collection tool—combined with thoughtful design and transparency—it can enable a more informed, fair, and meaningful hiring process.
In time, candidates will also use AI interviews to evaluate companies, just as companies evaluate candidates.
This is no longer a one-sided process.
Companies must learn to assess true substance.
Candidates must learn to articulate it.
When both sides embrace authenticity, AI interviews will transform hiring into something far richer than before.
Read in Japanese ↓（For Japanese learners!）↓
AI面接は「選考」ではなく「出会い」と「相互理解」に使え（2026.4.21）
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