As we enter the age of robotics and Artificial Intelligence (AI), let’s get prepared to encounter innovation, disruption and new business models very similar to what happened when the internet caught fire and led to the creation of many different ways of doing things.
The chat bot market which would be part of the overall AI business which was US$113 million in 2015 is projected to grow at a CAGR (compound annual growth rate) of 27.8 percent to US$994.5 million. Cognitive Computing is expected to grow even faster at a CAGR of 49.9 percent with projected global revenues close to US$1 billion in 2025.1
The APAC region is predicted to be the second largest region after North America to consume these products and services.
In order to meet both employee engagement and business requirements, HR will, therefore, need to change and bring about efficiencies based on these new technology innovations. Companies which base their workforce and talent management on Data Analytics will be ahead of their competition. And a function likely to be impacted very soon will be recruitment and staffing – this is the most transaction-heavy, cost-impacting function within a typical HR department.
With the introduction of machine learning-based search tools and chat bots, a recruiter can now go through large databases and identify the best talent in terms of experience and skills a lot faster, easier and cheaper. Today, there is no need to build complex Boolean syntax which, in the past, would have been show-stoppers for recruiters in terms of finding the right resume even if the resume was present in the database searched.
What might other advantages be to a recruitment function?
Standardising output from a sourcing viewpoint
Since it is based on machine learning, every search and shortlist gets the machine to learn better and reconfigure the search to attract resumes which are far closer to the job description. Once the algorithm matches the resumes to the job, it would run perpetually and churn out resumes. This standardises output from a sourcing viewpoint, the bane of the recruitment function at present.
Further, it helps in defining the exact strategy for finding talent and removes any emotional judgement human beings may have in shortlisting candidates for a particular job. This allows the recruiter to provide more value-added work like spending more time in engaging and negotiating with the candidate.
This is also useful for candidates on the lookout for a suitable opportunity. Currently, an individual browsing career sites would typically find it a nightmare – there are so many jobs available and he would not know where he is best suited. But with contextual search tools, a candidate is able to see which jobs he will rank best based on his skills and experience.
The additional introduction of chat bots can also screen and remove mismatches through the use of screening questions or suggestions as to potential courses which can bridge the skill gap. This would help in reducing the time taken to fulfil jobs where there is a dependency on applicants and also helps the individual candidates to follow through jobs where they will have a high probability of getting selected compared to the “spray and pray” approach prevalent in many job searches today.
Reducing hiring time
How many resume databases do you think a super recruiter might be able to scan and then come up with matches that work? With machine learning tools, you can now search millions of resumes in a fraction of the time you took before and with much higher probability of a match.
What does this mean? It means that recruitment could now potentially be measured in days and perhaps even in hours. This means hiring from campus grounds and at recruitment events can also be drastically reduced with machine-based tools.
Stronger buy-in from hiring managers
When you have a more standardised output which leads to a better selection ratio for interviews, this will lead to stronger buy-in from hiring managers. These managers will consequently rely on recruiters even more. From a recruiter’s point of view, this would certainly negate the negative perception a hiring manager typically has towards this function.
When you are able to leverage the internal and ATS (applicant tracking system) databases even better, this leads to reduced cost. You are sourcing more effectively and there is better throughput in the interview pipeline, which thereafter helps reduce operational costs even more.
Cost can also be reduced when there are more internal candidates identified and relevant skills training is provided.
Bots usage is trending
The use of Bots for candidate screening and interview scheduling is showing a positive trend. Algorithms are available for asking certain hygiene questions directly with candidates.
Bot usage, therefore, helps in further qualifying candidates for roles and interviews are scheduled based on the availability of candidate and the interview panel.
Flexibility in the service offering
Many of the products now available are offered as SAAS (solution as a Service) offerings. These are effectively cloud-based services with an option to pay per usage. What this affords is a level of flexibility for you to use such tools within an ATS as well as outside it and allows a degree of customisation as needed. Added to this, is an increasingly strong focus, in the market, on data protection and security which makes these platforms increasingly more reliable.
Overall, there seems to be a global trend to look at AI products and platforms in a more positive light. By all indications, AI appears to be moving rapidly into the realm of everyday business.
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Opinions expressed in this article are those of the author and not of The HR Gazette or its team members.