Case Study: Transforming the Recruitment Industry with Generative AI

As the workforces are expanding the recruitment industry is going through significant changes. The latest advancements in Generative AI has the potential to transform this industry. We have recently been engaged with one of our clients to modernize the recruitment process using Artificial Intelligence and Cloud technologies. This article is a summary to share the benefits and challenges of the solution with the community. Our client is a consulting firm with steady hiring requirements.

Some of the salient features of the solution are

Expedite High Volume Hiring:

GenAI automated the interview process through video interviews.

• Job descriptions are uploaded to the system
• GenAI will “read” the resume and identify the best set of questions
• Executives will mark the questions they would like to ask and add any pre-configured questions to the set of questions. No answers are provided to the model.
• When candidate applies for the position, GenAI will conduct video interview of the candidate. GenAI will transcribe each answer from candidate and give marks for each answer.
• Finally GenAI will rate the candidate on various skills like Technology, Domain, Business Acumen, People management, Soft skills etc. and then based on pre-defined thresholds will select or reject the candidate for next step

Result: In first 3 months there was 25–30% reduction in time to fill as AI can manage multiple candidates, independent of time zones or working hours without any human intervention, translating to a more efficient hiring and operations.

Improving Candidate Experience:

It is important to introduce a ease of operation when building automated systems. Some of the features of this solution that helped in improving the candidate experience are:

• Seamless integration of the GenAI models with career page on client’s website.
• Query and comment pages for candidates in creating a closed feedback loop
• Crafting creative personalized messages helped in bringing the human touch
• Using a human Avatar makes sure that there is a face to talk and not just text coming up on screen
• Mechanisms introduced in the system to check the integrity and honesty of candidate
• Flexible interview scheduling as it can conducted any time

Result: Rate of candidate applications improved almost 1.5x with increase in conversion ratio.

Breaking The Language Barriers:

One of the biggest challenges in hiring in US market is the language barrier.

Removing language barriers help corporations build more inclusive work force. With the GenAI model’s multilingual capabilities we could break down the language barriers making job descriptions and interview screenings in the language of choice for candidates.

Result: In first 3 months almost 8–10% candidates opted for non-English languages for their interviews. We observe that this rate is going further up.

Intelligent Candidate Scoring:

The advanced mechanism at the backend in scoring the candidates helped in creating more comparative and visually indicative charts for the selectors. GenAI system could also provide the relevant risks associated with each hire.

What are the achievements of this project?

Improved efficiency:

• Obviously we could process now large amount of data and also create a large treasure trove of data for the organization for future utilization.
• Time-to-fill the vacancy is reduced.
• Recruiters could focus on more strategic tasks.
• Company gets better candidates at a faster rate at much lower cost.

Reduction in bias:

• This system removes the unconscious bias in the hiring process.? The potential qualities or subjective judgments, such as gender, age, or background, which can influence a human has lesser to no effect on AI systems.
• It can provide fairer and more objective ratings, helping our client introduce diversity and inclusion.

Reduction in cost:

• This system has faster and more accurate candidate screening.
• It significantly reduced recruitment costs, introducing savings on advertising, time spent on manual review, and potential rehiring costs due to poor candidate selection.

What were the challenges addressed during the project??

Every new system and technology comes with a set of challenges. So is introducing GenAI in the hiring process. Below is the summary of some of the top challenges we faced and our way of resolving them.

Human Factor: Hiring humans through AI itself was an idea hard to accept. AI excels in analyzing the data and making objective decisions however it will lack the emotional intelligence and personal touch that human recruiters have.? To address some of these limitations we took steps like:

1. Give a face to the AI system during video interviews.
2. Allow the candidates to write or comment their views apart from the questions asked
3. Introducing a human review meter to capture how much of the interview needs to be reviewed by a human
4. Make sure to introduce counter questions, trivia, humor and subjective responses to make the interviews more natural.

Ethical concerns: Since beginning there were ethical concerns on the idea of using GenAI for candidate selection. Issues like data privacy, algorithmic transparency, potential bias or discrimination were handled through processes with human review over and above the other concerns.

Limitations to evaluate some job roles: There are some skill sets that AI may not be able to evaluate with current capabilities. It can not gauge creativity, empathy or critical thinking. So roles which primarily need such qualities cannot be evaluated using this solution. However this system can still be utilized as an entry gate for screening candidates and for roles that do not need above qualities.

The videos below will give a high level idea of the product. We cannot put the actual product videos, however the MVP which is the lean version of the actual product, was created in the beginning of the project to assess the effectiveness of the final product.

MVP Demo?—?1: This video shows the usage of pre-defined questions and way the answers given by candidate will be rated by AI system.

MVP Demo?—?2: This video shows the usage of dynamic questions identified by AI using job description provided with answers from candidate rated by AI system.

Future Advancements: As this system runs, it is going to be more efficient in utilizing the existing data to source inward and improve overall selection efficiency with more data generated.

Content for this article is prepared by Nidhi Dangar under the guidance of Sairam Pillai.

Nidhi is machine learning enthusiast with a keen interest in leveraging data-driven solutions to tackle complex problems. She is dedicated to creating impactful and scalable solutions using Machine Learning and Artificial Intelligence that bridge the gap between theory and real-world applications.

Sairam leads the technology group at Arocom. He brings a solid industry experience and expertise in generative ai, deep learning, advanced data modeling, and the design of sophisticated machine learning & data pipelines.

Arocom IT Solutions Pvt. Ltd. helps your company build state-of-the-art, functional and easy to use GenAI workflows to realize the full potential of GenAI at your company. To discuss how we can enable you to achieve similar data transformations, write us at [email protected]. Visit our website for more information.