With the increasing
demand for technical talent, it’s no surprise that the recruiting arms race
continues to push companies to create new ways of attracting the very best
people… One of the newest weapons in the arsenal in machine learning.
What is Machine Learning?
Machine learning is giving computers the ability to learn without
explicit programming. This is primarily accomplished through various pattern
recognition processes. For example, if you tell a computer to find the best
candidates for a job by identifying patterns in data that produce the best
results. In this way, a computer will find correlations and patterns that a
human would overlook — leading to increasingly higher quality candidates.Beyond
that, the possibilities are infinite! Imagine that we’re able to predict the
crash of a certain market, and know that there will be a surplus of available
candidates in that specific field, you can then focus your efforts as a
recruiter to take advantage of that situation. The ability to predict upticks
and downticks in a given market is a revolutionary new possibility thanks to
machine learning. It would give recruiters a serious advantage over their
competitors and would allow them to reach out and make connections much earlier
in the recruiting process.
How is machine learning changing
recruiting?
The biggest issue for recruiters right now is that we all have
these massive networks but we haven’t really had an effective way to leverage
those connections without committing a significant amount of time and
resources. If you have 5,000 LinkedIn connections, what does that do except
allow you to brag about it?This is where machine learning comes into play.
Recruiters can start to recognize pure data points of candidates’ contact
information, their profile, their work history, etc. and be able to match those
with opportunities. Machine learning does not automatically select the best
candidate, instead it narrows the field of search and allows us to focus on
analyzing the intangibles. From this, a stronger hire is made, leading to a
greater R.O.I. as well as a high L.T.V. from each candidate.Going deeper, on
top of quickly sorting through all of this data, machine learning will be able
to take a broader view of trends in specific industries and even specific job
titles.For example, machine learning could determine that a certain developer
has been at their job for a year and a half and there is a, let’s say, 98%
chance that they will leave their job in the next three months. That is a huge
insight that can be determined very quickly with machine learning. There is an
enormous R.O.I. for every singular effort that the recruiter makes. Time equals
money, and machine learning will save recruiters unimaginable amounts of time.
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Today, machine learning is emerging as a strategy to help employers more efficiently conduct talent sourcing and recruitment.
The biggest
issue for recruiters right now is that we all have these massive networks but
we haven’t really had an effective way to leverage those connections without
committing a significant amount of time and resources. If you have 5,000
LinkedIn connections, what does that do except allow you to brag about it?This
is where machine learning comes into play. Recruiters can start to recognize
pure data points of candidates’ contact information, their profile, their work
history, etc. and be able to match those with opportunities. Machine learning
does not automatically select the best candidate, instead it narrows the field
of search and allows us to focus on analyzing the intangibles. From this, a
stronger hire is made, leading to a greater R.O.I. as well as a high L.T.V.
from each candidate.Going deeper, on top of quickly sorting through all of this
data, machine learning will be able to take a broader view of trends in
specific industries and even specific job titles.For example, machine learning
could determine that a certain developer has been at their job for a year and a
half and there is a, let’s say, 98% chance that they will leave their job in
the next three months. That is a huge insight that can be determined very
quickly with machine learning. There is an enormous R.O.I. for every singular
effort that the recruiter makes. Time equals money, and machine learning will
save recruiters unimaginable amounts of time.
To gauge the
role of machine learning in recruitment and hiring, we researched this sector
in depth to help answer questions business leaders are asking today, including:
·
What types of AI applications are currently in use for recruitment and
hiring?
·
How has the market responded to these AI applications?
·
Are there any common trends among these innovation efforts – and how
could these trends possibly affect the future of recruitment and hiring?
In this article
we break down applications of artificial intelligence in the domain of
recruitment and hiring to provide business leaders with an understanding of
current and emerging trends that may impact their sector. We’ll begin with a
synopsis of the sectors we covered:
Recruitment and Hiring AI Applications Overview
Based on our
assessment of the applications in this sector, the majority of talent
acquisition use-cases appear to fall into three major categories:
1. Talent Recruitment: Companies
are training machine learning algorithms to help employers automate repetitive
aspects of the recruitment process such as resume and application review
2. Talent Sourcing: Companies
are using machine learning to help identify top candidates from large candidate
pools.
3. Candidate Screening and Engagement: Companies
are developing AI assistants to pre-screen candidates and to respond to
inquiries regarding positions using natural language processing.
we’ll explore the AI applications of each application by
section and provide representative examples.
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