Article Contents
Introduction
AI interviews are becoming popular in the corporate world. This new technology is being adopted slowly but surely. There are many benefits, but there are also common mistakes that companies can avoid to make the most of AI interviews.
In this article, we will discuss features you can implement and mistakes you can avoid to succeed with AI interviews.
Top 5 Mistakes to Avoid in AI Interviews
1. Making AI Interview Generic
First, a common mistake is making AI interviews too generic.
AI works well when it gets proper input. Think of a face-to-face interview where a subject matter expert asks questions based on their knowledge. Similarly, for an AI interview, you need to provide specific inputs related to the job role or description.
If the AI interview is too generic, it will ask similar questions to all candidates in the same order. This makes the process less effective. To avoid this, customize the AI interview for each job role.
Why it’s a problem:
- Generic questions fail to evaluate role-specific skills or competencies.
- Candidates may feel the process is impersonal, leading to a poor candidate experience.
How to fix it:
- Customize the AI interview process for each job role. For example, a software developer should be assessed on coding skills, while a sales professional should be evaluated on communication and negotiation abilities.
- Use the job description to create tailored questions that align with the role’s responsibilities.
- Incorporate industry-specific scenarios or case studies to make the interview more relevant.
If you’re hiring a data scientist, include questions about machine learning algorithms, data visualization, and problem-solving with real-world datasets. Avoid generic questions like, “Tell me about yourself,” which don’t provide meaningful insights.
2. Ignoring feedback from interview recommendation
Another key mistake is not taking feedback after the interview process. After candidates complete their interviews, gather feedback about their experience.
Ask if they faced any challenges or if everything worked well. Analyzing this feedback helps you understand if the AI interview process is effective and what improvements may be needed. Without feedback, you won’t know if your AI interviews are working properly.
In this context, Companies are increasingly adopting a hybrid approach, combining AI interviews with human touchpoints. This ensures a balance between efficiency and personal connection.
The hybrid approach helps to refine the system and it would help AI interview process improvement. A hybrid approach in the initial phase and moving towards automation can be a good approach.
How to fix it:
- After each interview, send candidates a short survey to gather feedback on their experience. Ask questions like:
- Was the interview process clear and easy to follow?
- Did you face any technical difficulties?
- How would you rate the relevance of the questions asked?
- Use this feedback to refine the AI system and improve the interview process.
Example: If multiple candidates report that the AI system struggles to understand their accents, you can work with the platform or technology to improve speech recognition capabilities.
- Conduct interviews virtually at your convenience.
- Assess multiple skills with detailed feedback and rating.
- Eliminate bias and errors in assessing candidate skills.
- Record responses and evaluate them later as per your time.
3. Relying 100% on the AI Interview process
A big mistake with AI interviews is relying too much on them. Technology is growing, and AI is getting better, but you can’t trust it 100%. Sometimes, human help is needed.
When using AI for interviews, always check if it’s doing a good job. Make sure you’re getting useful feedback about the candidate. To be sure, you can do an extra interview round after AI picks some candidates.
Compare the feedback from human interviews with the AI’s feedback to see how accurate it is. AI interview tools should be trained continuously to have a proper feedback loop in place.
AI interviews are great for handling lots of candidates quickly. For example, if you have over 1,000 applicants and need to choose the top 100, you can use online tests and AI interviews to narrow them down. Then, do a few manual interviews to double-check.
For the AI system to work well, keep giving it feedback and refining its criteria. If you provide good input and clear skill evaluations, your trust in AI interviews will grow a lot.
How to fix it:
- Use AI interviews as a first-round screening tool to narrow down large applicant pools.
- Follow up with human-led interviews to evaluate soft skills, cultural fit, and other intangible qualities.
- Compare AI-generated insights with human evaluations to ensure accuracy and fairness.
While AI can reduce human bias, it can also perpetuate bias if not properly trained. Companies must ensure their AI systems are trained on diverse datasets and regularly audited for fairness.
Example: Amazon scrapped an AI recruitment tool in 2018 because it showed bias against female candidates.
4. Not defining detailed evaluation criteria
One of the most important things about an AI interview is setting clear evaluation criteria. The AI interview system lets you decide how many questions to ask, what topics to cover, and the difficulty level of each topic.
You can also choose to interview specific groups of candidates, like those with more or less experience, or those who show leadership skills.
You need to be very clear about your evaluation criteria and the overall difficulty of the interview process. You should provide detailed input to the AI system, including uploading certain documents. A clear job description is crucial. You must know exactly what kind of candidate you are looking for.
If you provide the right input and have a clear job description, the AI system is likely to work well. However, if your evaluation criteria are not well-defined and your job description is too vague, the AI might not assess candidates accurately. This could lead to choosing the wrong person for the job.
Many people make mistakes by not defining proper evaluation criteria. Avoiding this mistake will help you succeed in using an AI interview system.
How to fix it:
- Clearly define the skills, traits, and qualifications required for the role.
- Provide the AI system with a detailed job description and evaluation rubric.
- Specify the weightage for each skill or competency to ensure the AI prioritizes the most important factors.
Example: For a marketing role, specify that creativity (40%), data analysis (30%), and communication skills (30%) are the key evaluation criteria. This ensures the AI system focuses on the right attributes.
- Measure skills, knowledge, and abilities.
- Performance breakdown and scoring.
- Key action points for interview checklist.
5. Not defining the AI Interview process according to roles and responsibilities
When using AI for interviews, it’s important to tailor the process to specific roles and responsibilities. This means you need to clearly define what the candidate’s job will be and what is expected of them.
If you don’t do this, the AI might not work as you hope. It could end up choosing someone who isn’t a good fit for the job.
To avoid this, make sure to document all roles and responsibilities well. Provide clear information about the AI interview system.
This way, the AI can conduct interviews based on the specific needs of the role. Doing this will improve the accuracy of your hiring process and help you select the right candidates.
How to fix it:
- Tailor the AI interview process to the specific role and responsibilities.
- For technical roles, include coding challenges or technical assessments.
- For leadership roles, focus on situational judgment and decision-making scenarios.
Example: For a customer service role, the AI interview could include simulated customer interactions to assess problem-solving and communication skills.
Check out our deep dives
- The Hybrid AI Interview Process
- AI Video Interviews and Hiring in 2025
- AI Interviews: The Future of Recruitment
- The Rise of Empathy-Driven AI Interviews
Conclusion
AI interviews are here to stay, and we’re just scratching the surface of what they can do. For organizations ready to embrace this technology, the benefits are huge.
By integrating AI into your recruitment process, you can save thousands of hours spent on manual interviews, optimize resources, and improve the overall experience for candidates.
When done right, AI interviews can also boost your company’s image, making you look like a forward-thinking, tech-savvy organization that candidates want to work for.
By addressing common mistakes and fine-tuning the process, you’ll not only attract better talent but also make smarter, more accurate hiring decisions. The future of recruitment is evolving, and AI interviews are a big part of it.
By adopting this technology now, you’ll stay ahead of the curve and build a stronger, more efficient hiring process that benefits both your organization and the talent you bring on board. So, don’t wait—start leveraging AI interviews today and watch your recruitment game transform!
