Even though Human Resources (HR) has been laggard at warming up to AI & ML, the acceptability quotient is rising exponentially. Today’s talent leaders are more curious than ever about the potential applications of AI & ML in their daily tasks. Automation has been the theme over the past few years. As the world becomes more impatient, HR too is emphasizing on reducing the Time-to-Hire. The task of AI & ML is not to replace the human presence but to act as a catalyst in decision-making and to a certain extent expedite execution of activities considered mundane and administrative in nature.

While Artificial Intelligence (AI) relates to building technologies / machines that can exhibit intelligent behavior using cognitive functions, Machine Learning (ML) is an application of AI that helps systems learn/behave without an external stimulus. The most common application of such an approach is in fastening the shortlisting process within Recruitment Management Systems(RMS). Some organizations have developed AI chatbots that answer applicant queries regarding the job application on a real-time basis. This conversation along with information screened from the public domain builds a good enough corpus for the HR to take an informed decision on the candidate applications.

Benefits:
Improved & Uniform decision-making:
Any decision that can be justified through the data-points in consideration can be termed as a quality-decision. One of the major challenges for HR has always been finding quality talent. In this regards, organizations are now busy targeting passive job seekers as well. The RMS can shortlist CVs based on keywords, region, experience, expertise etc. Use of technology ensures enhancement of decision-making but also overcomes biases like – age, gender, ethnicity etc. that a human mind may introduce unconsciously. Through ML, background verification gets a boost. ML can apply predictive models to scan the authenticity of information provided by a prospect.

Focus on qualitative tasks:
With an estimated average of 1000 applications received per job requirement, CV screening is the most time-consuming task for a recruiter. The automation of this activity ensures that the HR spends more time establishing qualitative tasks like interviewing while the monotonous tasks are handled by the technology.

Personalization:
Today’s HR faces the challenge of providing tailored-solutions to its multi-generational workforce. This workforce is diverse in every aspect, be it – learning styles, ambitions or even patience with queries. With the help of AI, HR not only delivers individualized solutions to all its employees but also measures the outcome to make the process more rewarding

Simplification of Processes:
Process automation through AI & ML can be extended to other HR functions like payroll, learning & development, employee onboarding etc. as well. Filing of invoices, approvals from managers whether for leaves or other matters are few examples wherein technology can play a big role. Prediction Models can ascertain the need and success of various training programs. Thus, an organization can allocate capital effectively. Use of ML can also help in detecting Attrition. HR can proactively engage with these employees to retain them.

Barriers:

Lack of in-house Expertise:
This is a typical problem with HR, wherein majority workforce comes from a non-technical background. While AI & ML are often outsourced, HR remains at the mercy of the vendor. An incorrect comprehension of the engagement costs could repel the HR from committing fully to the change. Lack of infrastructure and support from Senior Management will lead to the disinclination toward AI & ML. Thus affecting the organization’s growth in the mid to long run.

Data dependency:
Success or failure of AI & ML usage in HR depends heavily on the quality of data and exhaustiveness of the data points. This could be challenging for smaller firms where in application volumes may not provide volume and variety of applications. One should also bear in mind that the purpose of AI & ML adoption is to alleviate the efficiency of the HR. Improvement in Employee engagement and experience should remain the biggest concern.

Perception:
High stake decisions will require human intervention. In most cases, setting up clear rationales is of utmost importance. HR has to ensure every single stakeholder is on-board with the data-points and the check matrix. The decision-maker should ensure they and their audience know the difference between, say ‘somewhat agree’, ‘agree’ and ‘completely agree’. Eliminating the perception biases of the users is the biggest challenge.

Human Intervention:
No matter how extensive the data points are, every technology needs human intervention. The extent of this intervention will decide the need for the technology in the first place. While, too much human intervention may not be good for the organization, too less of it may turn the users away. In fact, most of the conversations on chatbots end abruptly because of limited responses to the queries or the inability of the user to form a comprehensible query. Experience has shown that AI & ML will command more time from the HR in the first few iterations.

Fear Psychosis:
Let us be very clear, machines are not going to replace humans. At least not in Human Resources which is expected to address sensitive concerns within its workforce in addition to candidate recruitments. Technology will be instrumental in bettering the decision-making capabilities of the HR.

As discussed above, the purpose of AI & ML is to improve employee experience, which in turn will provide better Employee Value Proposition. With vast possibilities of applications, HR has to ensure agility in adopting AI & ML and a roadmap that mandates continuous learning for all its stakeholders.