Consider a typical scenario related to the hiring process in an organisation, whether private or public. The human resources (HR) department announces a new opening on job boards, only to be inundated with a tsunami of applications. Candidates tend to apply for jobs blindly, without consideration of the requirements and qualifications.
Thus, even for very senior-level positions, you will end up getting a large number of CVs! Thankfully, almost all job boards and application management systems have some amount of automation to weed out such candidates. They can reduce the number of CVs one has to scan through, based on an objective criterion – does the candidate have the same skills that you have asked for in the job description?
But they cannot figure out the best-fit candidates, because here, the criteria are all subjective. And this is where artificial intelligence (AI) and machine learning (ML) can start to help in the recruiting process.
Finding the better fit
Decisions on the best fit are all subjective and can vary depending on the person making the decision and the way the candidate has presented herself/himself. Also, the best fit for the organisation may not be the most qualified candidate or the most affordable one.
The best fit is one who is likely to contribute the most. For this, you need to analyze the performance of existing and past employees. Thus, you have to consider not just the new candidates, but also past data on the candidates who did well in the organisation in a similar position as well as those who did not.
Once you mark the CVs of existing and past employees in this manner, AI systems can learn from those data points. Then, when presented with a bunch of candidate CVs, the systems can identify those most likely to succeed in the organisation.
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Avoiding CV fraud
Depending on the industry you are working in, CV fraud can be pretty rampant. These start with claims of skills that the candidate does not possess. Fraud also includes claims of working on projects and organisations that the candidates have not worked in, claiming incorrect educational credentials, higher salaries and more.
The standard mechanism to filter fraudulent claims is to do a reference check. In the case of freshers, this involves checking an educational institution and marks. These reference checks are time-consuming, cost money and often do not reveal the true picture.
On the other hand, there is a lot of reference information sitting on the CVs of candidates as well as employees. For example, a candidate has claimed to work on a particular project. An existing employee has also claimed to work on the same project. ML systems can link the two and highlight them, making your reference check more effective.
Combined with personality analysis systems, they could also highlight CVs with a higher chance of fraud, much like AI applications can highlight loan applications or credit card applicants that are more likely to default.
Fit for other positions
Candidates apply for a specific position. While candidate management systems can highlight potential fits for other positions, they do it by matching keywords in the CV against keywords in the job profile.
An AI- or ML-based system can proactively scan the incoming CV against other available positions to see whether there is a better fit elsewhere and suggest the candidate to recruiters handling those positions.
Moreover, in any organisation, the work culture across departments could be different. Using AI-based systems could enable us to look for cultural fit across teams.
Automated and self-paced screening
Initial screening tests and interviews are fairly standardised at all organisations and enough resources are available online that tell candidates what to expect at each company. Screening interviews also take up and waste considerable HR manhours, what with candidates seeking rescheduling at the last minute and at times not even turning up.
An AI-based interactive system will be able to tune the screening interview to the candidates’ personalities. It will also be able to let the candidate schedule their test and screening sessions according to their convenience and pace. Such a system will release internal resources from fruitless waits and much angst against and within the HR department.
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Handholding the candidate
Selecting a candidate and making an acceptable offer is only half the battle won. Candidates have a propensity to shop for better offers and not all offers culminate in the candidate joining, particularly at junior levels.
During the interview process and before joining, the candidate would have many questions about the organisation. Typically, they turn to friends or to internet searches for information about the prospective company.
Employer review sites that are popular for this purpose tend to have a lot of negative reviews from disgruntled employees. And the organisation seldom gets to present its view to the candidate. A simple AI-enabled web chatbot would be an excellent starting point for the prospective employee to know more about the employer.
Once the candidate joins, HR has a task at hand, making the candidate familiar with internal systems, processes and people. In large organisations, particularly, new employees are presented with a mountain of multimedia information on the intranet. AI systems would make onboarding and familiarisation much easier for both the new employee and the HR department.
Talking of handholding the candidate, would you also not like to be handheld while shortlisting candidates in your recruitment management system? Why should you have to enter complex search parameters into the system?
Indeed, the candidates need not have all the fun! The same technology that can handhold the candidate can handhold the recruiter, too. Similarly, a chatbot can also take the recruiter through an intuitive shortlisting process.
So, apart from improving the recruitment process, AI/ ML-based systems offer five key benefits to the organisation. These are:
- Get candidates who are a better fit for the organisation
- Reduce subjectivity in recruitment
- Release internal resources from mundane tasks and endless waits
- Save time through automation
- Provide a better candidate experience during the recruitment and onboarding process
Things to look out for
It is only fair that we end this article by highlighting areas of concern when using such systems and what AI-based systems can learn from HR managers on the flip side. Any AI system is only as good as the data that it has been trained on. Biases in the training data have led to enough problems that it is worth highlighting here. Training data for the AI has to be truly representative of the organisation and its departments.
AI and ML require relatively large samples to be effective and credible information therein. Therefore, the larger the organisation or the department that is using the system, the better the result. This also means that such systems have to be used with abundant caution for small organisations or departments.
Conclusion
Used correctly, AI and ML tools have the ability to make the life of the recruiter easier and help source better employees, faster. Such tools are becoming a part of everyday life in diverse environments and systems.
Early adopters are already using AI and ML-based systems in recruitment and candidate assessment. It is only a matter of time before they become widely used in this area.
