In today’s fast-evolving recruitment landscape, companies have more interview options than ever. From traditional in-house processes to leveraging AI and outsourcing to Interview-as-a-Service platforms, choosing the right approach can make or break your hiring process. In this post, we’ll compare these three methods to help you understand which might be the best fit for your organization’s needs.
IaaS platforms are highly scalable. They can handle varying volumes of candidates, which is ideal for companies with fluctuating hiring needs. Whether you need to interview 10 candidates or 100, an IaaS provider can typically adjust quickly and scale up to meet demand without additional strain on your internal resources.
However, scalability depends on the provider’s infrastructure and availability. During peak hiring seasons or large recruitment campaigns, some providers might face delays or struggle to keep up with a sudden surge of candidates, especially if specialized recruiters are required.
In-house interviews have a limited capacity based on the size and availability of your HR team and interviewers. If you have a large recruitment team, you can handle a decent volume of interviews, making this approach well-suited for companies with steady or predictable hiring needs.
However, capacity can become overwhelmed during large recruitment drives or when hiring for multiple positions simultaneously. Scaling up means adding more resources—either through temporary interviewers or by hiring additional HR staff. This is a slower process than IaaS or AI solutions since you’re limited by the human resources available.
AI-powered systems have virtually unlimited capacity. They can handle a massive volume of candidates at any time, 24/7. This is ideal for high-volume recruitment, especially in situations like screening hundreds or even thousands of candidates at once, without delay or human resource limitations. AI can be used for tasks like initial screenings, phone interviews, and even analyzing responses.
While AI can process large volumes, the quality of the interaction is limited compared to human interviewers. AI excels at repetitive tasks and structured interviews, but it can struggle with more complex or nuanced conversations that require empathy, problem-solving, or deeper insight into a candidate’s personality.
IaaS providers typically offer quick turnaround times. Once you share the job requirements and your needs with the provider, they can quickly mobilize a team of experienced interviewers to begin the process. They handle everything from candidate screenings to interviews, allowing you to move swiftly through the hiring pipeline.
The time to hire for in-house interviews can vary significantly depending on your team’s capacity and resources. If you have a well-organized internal HR team with sufficient capacity, the turnaround time can be fairly quick. However, if your HR team is small, or the process is unstructured, it might take longer to schedule and conduct interviews, review candidates, and make decisions.
AI-driven interviews typically offer the quickest turnaround. AI can conduct initial screenings and even first-round interviews automatically—this can save days or even weeks in the early stages of hiring. Candidates can be evaluated, and decisions can be made in real-time with automated scoring.
While AI speeds up the process, it’s not ideal for roles where cultural fit and empathy are crucial, which may require human interaction.
IaaS offers a good balance between efficiency and personalization. Candidates typically feel they’re engaging with professional interviewers who are skilled in making the process smooth. Since IaaS platforms often assign dedicated interviewers who are experienced, candidates might feel more confident in the quality of the process.
However, while the interviewers are human, they might not have the same cultural insight as your internal team. As a result, candidates may feel somewhat disconnected from the organization. Hence, it is important to set clear expectations with the candidate before embarking them on their assessment process.
In-house interviews offer the best candidate experience when it comes to building a connection with the company. Since these interviews are conducted by your internal team, candidates feel as though they’re engaging directly with the organization, which gives them a better sense of its culture and values. However, the in-house process can sometimes feel longer due to scheduling conflicts, multiple rounds of interviews, and waiting for feedback.
AI-driven interviews are efficient but often lack the personalization and emotional engagement of human interactions. While AI can handle initial screenings and provide immediate feedback, it evaluates all candidates the same way, ensuring fairness and transparency. Available 24/7, AI interviews offer candidates the flexibility to complete them at their convenience, without concerns about time zone differences.
However, the absence of a human interviewer can leave candidates feeling disconnected. Without the opportunity for natural conversation, candidates may perceive the process as impersonal, particularly if they have specific questions or concerns. AI also struggles to establish trust, which is essential in creating a positive candidate experience.
The cost of using an IaaS provider is typically variable and depends on factors like the provider’s fees, the number of candidates you need to interview, and the complexity of the interviews. The key benefits are that it saves your internal resources and time, allowing you to focus on core tasks. Plus, there’s no need to train or onboard interviewers in-house.
IaaS tends to be cheaper than in-house options if the total cost of evaluation is considered (direct time investment by hiring managers & TA teams plus indirect opportunity cost of longer hiring turn-around-time).
In-house interviews typically involve internal resources (e.g., HR staff, hiring managers, and interviewers) who are already part of the company. The main costs here are time and labour, as the employees conducting the interviews are likely working at their regular salaries, but you still need to factor in the opportunity cost of their time spent interviewing rather than focusing on their usual responsibilities.
Since there are no external fees for interview services, you’re just allocating your existing staff’s time. But if your team needs training or lacks interview experience, that can add hidden costs. Additionally, if you’re conducting a high volume of interviews, you may need to hire temporary staff or train current employees, which can drive up costs. In-house interviews are cost-effective if you have enough internal resources.
Lastly, since internal hiring capacity tends to be limited, relying just on in-house interviewers adds delay to the hiring process. A delayed hire means that the incoming candidate is deployed by that much delay. Valuable billable hours or productive hours are also a hidden cost that gets incurred in this case.
AI-driven interviews are typically the most cost-effective in the short term since they eliminate the need for human involvement during the interview process. Most AI tools are priced via subscriptions or per-interview costs, and once the system is in place, there are few additional operational costs.
You may incur upfront costs to purchase software or integrate the system into your existing systems. Subscription or per-interview costs can add up, especially for high-volume hiring. Tailoring the AI for your specific needs could involve additional costs.
When comparing IaaS (Interview as a Service), In-House, and AI in terms of types of evaluations during recruitment, they each bring distinct approaches and processes. Below, we’ll break down how each model typically handles different evaluation types, like technical questions, coding tests, scenario-based questions, and cultural fit assessments:
IaaS platforms provide a highly consistent interview process. With structured formats and professional interviewers, the evaluations are fair and standardized. The downside? While they eliminate internal biases, IaaS interviewers may not be fully in sync with your company’s culture, which could affect how they assess a candidate’s “fit.”
In-house interviews can be consistent if interviewers are trained well and follow a clear structure. However, they’re more prone to inconsistency due to varying interview styles, personal biases, subjective opinions, and lack of well-designed interview tools. The real strength? In-house interviewers understand your company’s culture, making them great at evaluating cultural fit. But without standardized training, the evaluation can be a bit hit-or-miss and prone to inherent biases.
AI-driven interviews are fairly consistent especially given the capability of existing LLMs to evaluate all kinds of answers against an ideal solution. Since AI evaluates every candidate based on predefined criteria, you’re guaranteed a fair and bias-free process. The catch? AI can miss the subtleties of human interactions like emotional intelligence or cultural fit. If you’re looking for data-driven decisions and fast scalability, AI is a game-changer.
IaaS reduces bias by using trained, external interviewers who follow a structured framework to assess candidates. Since these interviewers aren’t part of your internal team, they’re less likely to be influenced by personal biases or internal company dynamics. However, even with standardized assessments, there’s always a small chance of bias creeping in, especially in subjective areas like cultural fit.
In-house interviews are more prone to bias because they involve human interviewers who may have unconscious preferences or preconceived notions. Bias can come from personal opinions, experiences, or even factors like a candidate’s appearance or communication style. However, bias can be minimized with proper training and structured interview processes, but it’s still a risk, especially when interviewers don’t follow a uniform set of criteria.
AI-driven interviews eliminate human bias entirely. Since AI evaluates candidates purely based on data and predefined algorithms, it ensures a consistent, objective evaluation. However, the risk of bias can still exist in the algorithm itself if the data used to train the AI isn’t diverse or comprehensive enough. That said, AI is one of the best models for minimizing bias in hiring.
IaaS platforms typically assess a wide range of skills using structured formats. These providers often focus on core technical skills, problem-solving, and basic soft skills. While the skill coverage is broad, it may not dive deeply into very specific or advanced skills unless tailored for a particular role. It’s standardized, which means all candidates are evaluated using the same criteria, but it may not capture every nuanced skill your team requires.
In-house interviews are highly customizable, allowing you to focus on the specific skills that matter most to your company and team. Interviewers can probe deeper into both technical and soft skills, often tailoring the questions to the company’s needs. However, the scope can be limited by the interviewer’s expertise or focus areas, meaning you may not cover all relevant skills, especially if interviewers have varying levels of knowledge in certain areas.
Additionally, if you are trying to bring new skills into the organization, existing talent pool might not be sufficient to conduct a fair evaluation of the candidates.
AI-powered interviews can evaluate a vast number of skills, especially if the system is designed to assess both technical and soft skills. The advantage of AI is that it can analyze a candidate’s responses across a variety of scenarios (technical questions, problem-solving, behavioral traits, etc.) and give a comprehensive overview of a candidate’s abilities. However, while AI can handle multiple skill types, it might not fully capture every nuance, like interpersonal dynamics or complex problem-solving that requires a human touch.
IaaS platforms provide structured feedback reports that are typically based on standardized evaluation criteria. These reports often include scores or ratings for various skills, along with comments on strengths and areas for improvement. The feedback is professional and consistent, but it might lack the personal touch that comes with knowing the candidate in-depth, such as insights on how they might fit with your specific team dynamics.
In-house interviews tend to offer highly personalized feedback because the interviewers have direct knowledge of the candidate and can provide detailed insights based on their interactions. Feedback typically focuses on both technical skills and cultural fit, giving a holistic view of how the candidate aligns with the company. However, the feedback may be subjective due to individual biases or the interviewer’s interpretation of certain qualities.
AI-powered feedback reports are generated based on data and algorithms, offering an objective, metrics-driven evaluation. These reports typically provide detailed assessments of a candidate’s technical proficiency, problem-solving skills, and even behavioral traits if the system is designed to evaluate them. While the feedback is highly objective and bias-free, it may lack nuance and deeper insights into how the candidate might perform in real-world situations or fit into the company culture.
IaaS platforms typically have professional proctoring in place, especially for technical assessments. They use software to monitor candidates during coding tests or live interviews, ensuring that candidates aren’t cheating or using outside help. Proctoring can include features like screen recording, keystroke tracking, and browser lockdowns to prevent cheating. However, for live interviews, the level of proctoring can vary depending on the platform, but it’s usually more standardized than in-house setups.
In-house interviews generally don’t have formal proctoring unless they are conducted remotely using proctoring software. If done in-person, interviewers rely on their own judgment to ensure that candidates aren’t cheating or using unauthorized resources. For remote in-house interviews, proctoring can vary—some companies use video monitoring or third-party tools to oversee the candidate during coding tests or technical assessments, but it’s not always consistent across all interviews.
AI-powered platforms typically provide automated proctoring for assessments, especially during coding or technical evaluations. These systems can track the candidate’s screen, monitor their environment via webcam, and analyze patterns in their responses to ensure they are not using outside help. AI can enforce strict proctoring guidelines without human intervention, making the process highly consistent and secure. However, for behavioral or non-technical assessments, proctoring might not be as relevant or intense.
In conclusion, the decision between Interview-as-a-Service (IaaS), in-house interviews, and AI-driven solutions depends largely on your organization’s specific needs, priorities, and resources. If scalability and speed are key factors for your hiring process, IaaS or AI might offer the best solutions. IaaS platforms provide flexibility with a balance of human touch and efficiency, while AI-driven systems excel in handling large volumes of candidates with objective, data-driven assessments.
However, if your priority is a deeper understanding of cultural fit and nuanced evaluations, in-house interviews remain the most effective choice. They allow you to evaluate candidates in a way that aligns with your company culture and values, though they may be resource-intensive and less scalable.
Each method brings its own set of strengths and weaknesses when it comes to cost, capacity, candidate experience, and the quality of evaluations. By carefully considering your hiring goals—whether that’s reducing time-to-hire, ensuring a personalized candidate experience, or improving consistency and scalability—you can make an informed decision that will streamline your recruitment efforts and help you secure the best talent for your team. Ultimately, a blended approach that leverages the strengths of each method may prove to be the most effective for achieving long-term hiring success.
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