Artificial Intelligence in Talent Acquisition

This essay explores the topic of Artificial Intelligence in Talent Acquisition. It discusses the definition of talent acquisition and artificial intelligence, as well as the benefits and potential risks of using AI in talent acquisition. The essay also examines how AI supports business functions and provides cognitive insights. The use of AI in talent acquisition can broaden the applicant pool, reach passive job applicants, enhance e-recruiting effectiveness, simplify job analysis, facilitate online employment testing, enable remote video interviewing, and support effective selection decision-making.

NATIONAL ECONOMICS UNIVERSITY
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HUMAN RESOURCES MANAGEMENT ESSAY
TOPIC: ARTIFICIAL INTELLIGENCE IN TALENT ACQUISITION
Name: Ngô Kim Ngân
Class: QTNL CLC 61
Mã SV: 11193682
GVHD: THS. Nguyen Duc Nhan
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HA NOI, 2022
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TABLE OF CONTENTS
I. INTRODUCTION...............................................................................................4
II. ARTIFICIAL INTELLIGENCE IN TALENT ACQUISITION......................5
2.1 Definition:....................................................................................................5
2.1.1 Talent acquisition......................................................................................5
2.1.2 Artificial Intelligence.................................................................................6
2.2 Supporting of AI in business function.......................................................8
2.2.1 Increasing business process automation.......................................................8
2.2.2 Providing cognitive insights..........................................................................9
2.2.3 Support cognitive engagement.......................................................................9
2.3 The benefit of using Artificial Intelligence in Talent Acquisition.........10
2.3.1. Broadening the Applicant Pool..................................................................11
2.3.2 Reaching Passive Job Applicants................................................................12
2.3.3 Enhancing effectiveness of e-recruiting.......................................................12
2.3.4 Simplifying Job Analysis..............................................................................13
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2.3.5 Facilitating online Employment Testing......................................................14
2.3.6 Enabling Remote Video Interviewing..........................................................15
2.3.7 Supporting Effective Selection Decision-Making.........................................16
2.4 Potential risks of AI..................................................................................16
III. CONCLUSION..............................................................................................18
REFERENCES.........................................................................................................19
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I. INTRODUCTION
In the digital age, artificial intelligence (AI) is not only useful for healthcare and
education, but is also being applied to all aspects of social and economic life, such
as human resources and employment. AI in recruitment has been and is the
mainstream of the future, bringing a lot of benefits to both employers and
candidates.
Revolution 4.0 makes terms like artificial intelligence no stranger to most
people, no matter what field you work in. The use of AI in recruiting, managing and
improving the quality of human resources has only been developed and known more
in the last few years, but has gradually proven its advantages and provides many
effective solutions. results for employers as well as job seekers. To understand the
benefits of AI in talent acquisition, I will elaborate further in the sections below.
Specifically, it discusses how e-recruiting and e-selection and AI tools can help
organizations improve recruiting and selection outcomes.
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II. ARTIFICIAL INTELLIGENCE IN TALENT
ACQUISITION
2.1 Definition:
2.1.1 Talent acquisition
The “war for talent” has focused on acquiring and assimilating “the best”.
Human resources play a significant role in reaching organization effectiveness and
performance (Huselid, 1995). Talent has become the key differentiator for human
capital management and for leveraging competitive advantage (Bhatnagar, 004).
With better talent acquisition, employee engagement improves and so does the
productivity. Maximizing team engagement, motivation, and retention through due
diligence in talent acquisition is vital in today's highly competitive environment.
Only a talent resourcing process, that is well‐defined and well‐executed from start to
finish yields consistent, compliant results – and is a competitive advantage in the
war for talent (Ronn, 2007).
With talent acquisition becoming an area of growing concern in the literature,
this paper investigates talent management and its relationship to levels of employee
engagement through the lens of a case study. Employee engagement is an indicator
of high productivity and employee retention as reflected in extant literature.
Many people think that the terms recruitment and talent acquisition are
synonymous, but companies who keep an eye on the big picture know there’s a
crucial difference between the two. Recruitment and talent acquisition are
comparable to short-term and long-term—quick fixes versus long-term planning.
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Both approaches may be used depending on the circumstances, but one tends to be
tactical in nature and the other, strategic.
Internal hiring managers can improve overall recruitment planning with a
basic understanding of the strategic nature of talent acquisition. Independent
recruiters can likewise improve relationships with employers by better
understanding the unique role that acquisition plays in helping a company achieve
its strategic vision.
Recruitment is about filling vacancies. Talent acquisition is an ongoing
strategy to find specialists, leaders, or future executives for your company. Talent
acquisition tends to focus on long-term human resources planning and finding
appropriate candidates for positions that require a very specific skill set.
Ryan Naylor, founder and President of LocalWork.com, a company
connecting local jobs with local job seekers, said that talent acquisition, “is more
focused on the strategic side of tougher positions to fill.”
“It’s important to project three to six months ahead of when you need to fill
leadership and specialty positions. Many tech positions take six months or longer to
fill. If your company is awarded a new client and you need to deliver the work
ASAP, it can be tough to recruit for those positions in a short turnaround.”
‘Companies must ask themselves which positions will be difficult to fill when
a vacancy or need arises. Niche markets, technology skills, highly specific
experience, and leadership roles call for a thoughtful, long-term approach to talent
acquisition.
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2.1.2 Artificial Intelligence
In 1950, Alan Turing proposed that humans would consider a machine
intelligent if the human interacting with that machine could not tell whether he or
she was interacting with a human or a machine (Turing, 1950). Subsequently,
cognitive scientists, computer scientists, and others have attempted to design
intelligent systems. These intelligent, or artificial intelligence (AI), systems were
originally designed to mimic human cognitive decision processes, and to support
and solve a well-defined or structured problem. The earliest systems focused on
solving “common sense” problems using algorithmic logic (McCarthy, 1958). Not
surprisingly, AI often refers to a class of technologies, whose goal is to replicate
human cognitive processing (Flasiński, 2016).
Artificial intelligence (AI) refers to the simulation of human intelligence in
machines that are programmed to think like humans and mimic their actions. The
term may also be applied to any machine that exhibits traits associated with a human
mind such as learning and problem-solving.
The ideal characteristic of artificial intelligence is its ability to rationalize and
take actions that have the best chance of achieving a specific goal. A subset of
artificial intelligence is machine learning, which refers to the concept that computer
programs can automatically learn from and adapt to new data without being assisted
by humans.Deep learning techniques enable this automatic learning through the
absorption of huge amounts of unstructured data such as text, images, or video.
Artificial intelligence uses pre-programmed algorithms to make real-time
decisions, as well as coherent computing approaches. Artificial intelligence will
have an impact on the human resources department.
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Companies will experience an upgraded and evolved condition for their
applicants and workers as a result of the panoptic human component of Human
Resources combined with the intelligence of technology. Not only that, but AI in
HR will also contribute to the promotion of the value of producing better and faster
outcomes
2.2 Supporting of AI in business function
Artificial intelligence, which is one of the most advanced and growing
technologies today, AI plays a significant role in business fuction. Besides HR
department as talent acquisition is an important link in business operation and
development. AI automates and completes the majority of low-value HR tasks so
that more attention may be focused on the strategic scope of work.
With the global deployment of information systems and the growth in
organizational understanding of business processes, organizations continue to
explore ways to improve decision-making, prediction, and performance (Dulebohn
and Johnson, 2013; von Krogh, 2018). Core to these advances in decision-making
capabilities is the use of AI, which the Society of Human Resource Management
(SHRM) has identified as one of its top technology trends and a key driver of HRM
in the future (Wright, 2017).
AI has the potential to revolutionize employee experiences in a variety of
ways, from recruiting to talent management, by processing massive amounts of data
quickly and accurately AI provides capabilities that support three areas of business
functioning (Davenport and Ronanki, 2018).
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2.2.1 Increasing business process automation
First, AI can enhance business process automation by providing cognitive
capabilities within the software. For example, organizations are using AI to augment
automated decision-intensive tasks such as loan processing and supply chain
management, and provide cognitive insights about customer purchasing behavior. In
support of HRM, the National Aeronautics and Space Administration (NASA) found
that the use of AI enhanced HR processes allowed them to complete 86% of HR
tasks without human intervention (Davenport and Ronanki, 2018). In a sense, with
AI-enabled process automation the software algorithmically makes and implements
decisions with little human intervention. Given that researchers have found that AI-
based expert systems can mimic the decision recommendations of employee benefits
experts (Sturman et al., 1996), AI-enabled eHRM would not simply recommend a
set of benefits from which employees can choose, but would choose these benefits
for employees, and automatically sign them up.
2.2.2 Providing cognitive insights
Second, AI can provide cognitive insights that facilitate decision-making.
This form of AI utilizes algorithms and machine learning to interpret vast amounts
of data, looking for hidden patterns not previously identified by organizations.
Organizations have used AI in this fashion to predict customer-purchasing behavior,
identify fraud and create customized advertisements. For example, in the last two
presidential election cycles, candidates used AI to target messages to unique groups
of individuals to maximize the impact of each ad and reduce costs (Smith, 2018). In
addition, AI was able to more accurately diagnose cancer patients, and to do so in a
fraction of the time needed by a panel of medical experts (Miller, 2018). AI has also
helped assess the link job characteristics to employee satisfaction (Tian and Pu,
2008).
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2.2.3 Support cognitive engagement
Finally, using intelligent agents and chatbots, AI can support cognitive
engagement. These agents are the closest thing to AI as theorized by Turing,
because the goal of chatbots and intelligent agents is to retrieve and share
knowledge from a knowledge base, and share that with individuals through natural
and human-like social interaction. One of the reasons for deploying chatbots in
organizations is to handle the ever-increasing number of applicant, employee, and
customer requests for information. For example, many e-commerce vendors deploy
intelligent agents to help customers make purchase decisions (Xiao and Benbasat,
2007), and organizations are starting to use chatbots to help customers and
employees. For example, SEBank uses an intelligent chatbot to provide support for
employees that have IT related questions (Davenport and Ronakai, 2018).
2.3 The benefit of using Artificial Intelligence in Talent Acquisition
All industries faces a number workforce challenges. For instance, the
hospitality industry is labor intensive, with estimates suggesting that hoteliers spend
approximately 33% of revenue on labor costs (Deloitte, 2015). One of the primary
labor challenges is the very high turnover rate in the industry. Estimates indicate that
the turnover rate for the hospitality industry in the United States is over 50%, with
some sectors (e.g. hotel and food) experiencing turnover rates of over 70% (Bureau
of Labor Statistics, 2019). These estimates are similar to those found in other
countries, such as the UK (30%) (KPMG, 2019) and the Netherlands (30%)
(Vellenga, 2016). In addition, these turnover rates have been growing for the past
ten years (Bureau of Labor Statistics, 2019). Turnover also has a dramatic impact on
labor costs, with a recent industry survey suggesting 41% of organizations found
that an entry-level hire who subsequently leaves could cost the organization over
$30,000 (McConway, 2019).
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Thus, a great deal of time and money are spent attracting new applicants, and
dealing with the “churn in the system” associated with constantly replacing
employees. Artificial intelligence (AI) can help the industry reduce this turnover
rate, and ensure that they can attract, motivate, and retain talented employees. For
instance, the use of electronic recruitment (e-recruitment) and electronic selection
(e-selection) methods can help companies in the industry uncover talented
applicants, and hire those that fit with the requirements of jobs (P-J fit) and
organizations (P-O fit) (Kristof-Brown, 2000). Increased job and organizational’ fit
should enable organizations to hire talented employees, reduce attrition rates, and
decrease the heavy time and workload associated with the constantly replacing
employees (Johnson and Stone, 2019; Kristof-Brown, 2000).
2.3.1. Broadening the Applicant Pool
Given the large turnover rates, the organizations regularly need to identify
and attract a large number of applicants to fill critical job vacancies. By listing
openings on recruitment websites, applicants can easily access them any time of the
day or night. Organizations have found that the use of e-recruitment has led to a
much larger applicant pool than traditional recruitment processes (Chapman and
Gödöllei, 2017). For example, Kia Motors received 43,000 applications within 30
days for 2500 open positions when they opened a new plant in Georgia (Adams,
2008). In addition, increasing the size of the applicant pool will also increase the
likelihood that organizations will uncover individuals who fit the desired hospitality-
oriented and organizational specific values and goals. Recruiting individuals that fit
with organizational goals should increase the likelihood that they will accept jobs,
and remain with the organization over time (Cable and Judge, 1996).
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AI can also help enlarge the applicant pool and attract applicants who fit with
the organization. Specifically, organizations can utilize AI cognitive insight
capabilities to identify the characteristics of high performing employees and those
who have remained with the organization for a long time. For example, some
research has shown that employee needs for good relationships with co-workers and
supervisors, job enrichment, developmental opportunities, and satisfaction with pay
influence retention rates (Hom and Griffeth, 1995). Organizations can then identify
the characteristics of high performing employees, including those who have
remained with the organization for a long time, and develop targeted recruitment
messages that can motivate similar applicants to apply for jobs.
2.3.2 Reaching Passive Job Applicants
Another major advantage is that organizations can identify and attract
applicants who may be currently employed, and not actively seeking another job
(i.e., passive applicants). For example, organizations can scan professional social
media sites (e.g., LinkedIn) to uncover individuals who meet job requirements, and
contact the person about applying for a job. One benefit of identifying passive
applicants is that they may already have the needed job experience, and should be
less likely to be job hoppers. Research has found that using professional social
media sites such as LinkedIn were very effective in identifying and attracting
qualified job applicants (Nikolaou, 2014).
Using data stored in the HRIS, organizations can also scan the employee
records of current employees to identify those who meet the requirements of the
positions. A major advantage of this approach is that current employees may gain
promotion opportunities, and the process may lead other employees to believe that
the company offers advancement opportunities, which should increase job
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satisfaction rates. Although this strategy appears feasible, research has yet to
examine its effectiveness.
2.3.3 Enhancing effectiveness of e-recruiting
AI can also enhance the effectiveness of e-recruiting. For example,
companies can use AI to develop games that help prospective applicants understand
what it is like to work for the organization, and can help them assess the extent to
which their personality or values matches those in the organization. For example,
Shell created a game that allows players to explore for oil, and Siemens created a
game that allows individuals to practice managing a plant (Smurthwaite, 2019).
Results showed that these games increased the extent to which high fit employees
applied for jobs with the companies.
Taken together, the brief review above suggested that e-recruiting and AI can
help organizations (a) broaden their applicant pool, (b) reach passive job applicants,
(c) increase the timeliness and efficiency of the recruiting process, (d) enhance
applicants’ attraction to organizations and (e) improve the degree to which
applicants can assess fit with the organization
2.3.4 Simplifying Job Analysis
As noted above, e-selection can help hospitality organizations hire and retain
talented employees. Two factors thought to influence individuals’ retention in
organization are the degree to which they fit with the job requirements, and fit with
the goals and values of the organization (Cascio, 2019). Quite simply, when
individuals’ knowledge, skills, abilities, and other requirements (KSOAs)
correspond to the requirements of the job (P-J fit), their performance should be
higher and they should have more positive job attitudes toward the organization than
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when their KSAOs do not match job requisites (Chatman, 1989; Kristof-Brown,
2000)
One of the first steps in the selection process is conducting a job analysis,
which identifies task requirements and uncovers the KSAOs (job specifications)
needed to perform the job (Gatewood et al., 2016). Although organizations use a
variety of techniques to conduct job analyses (e.g., questionnaires, interviews), the
process can be very time consuming, and the use of online job analysis will greatly
simplify and streamline this process Thus, an electronic job analysis approach uses
geographically dispersed SME panels to discuss and make final decisions about job
requirements and job specifications. The identification of tasks and KSAOs is
important for every aspect of HRM, but is especially important in the recruitment
and selection process. Organizations use data from a job analysis to develop
recruiting websites, and is the basis for initial screening criteria and selection
decisions (Stone et al., 2013). Although there has been relatively little research on
the effectiveness of this process, a study by Reiter-Palmon and colleagues (2006)
found that electronic job analysis methods were less time consuming, more flexible,
and easier to use than traditional methods. Thus, organizations can identify the job-
related tasks and specifications quickly using an online job analysis technique that
reduces the time needed for the overall selection process.
AI can also support electronic job analysis in several ways. First, AI can help
job analysts develop job descriptions and specifications. For example, AI could
parse any previous descriptions for positions, match them with job descriptions for
external positions or from systems such as O*Net . Thus, AI could be used in the
first step of job analysis and help analysts understand the nature of the job and skill
requirements prior to collecting data from SMEs. These data could also be used to
develop job analysis questionnaires. Second, organizations could use AI to update
job descriptions so that they match the job that is currently being done by
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employees. Over time, job descriptions and specifications can become outdated, and
AI could be used to scan existing job descriptions and identify parts that need to be
updated.
2.3.5 Facilitating online Employment Testing
AI can also facilitate selection in several ways. For example, AI-enabled
business process automation can be used to develop computer adaptive tests (CAT)
which tailor test items to each test taker (Tippins et al., 2006). CAT is designed so
that when test takers answer a question it is tailored to the person’s skill level, and
when questions are answered correctly a more difficult test items is presented.
However, when a question is answered incorrectly, an easier question is presented.
Thus, AI can help organizations develop targeted tests based upon test takers’ skill
level and an analysis of the answering patterns. This approach can maintain the
validity of the tests, and reduce test administration time and costs (Overton et al.,
1997).
2.3.6 Enabling Remote Video Interviewing
There are several ways that AI can improve online interviews. First, setting
up interviews can be time-consuming and cumbersome. Chatbots (using cognitive
engagement) can automatically handle the scheduling and coordination of interviews
with candidates, including any updates regarding future meetings and locations
(Kharkovyna, 2018). Research has found that 66% of candidates were comfortable
using chatbots in this way (Min, 2018). In addition, AI can support the analysis of
digital interviews. Organizations are using AI-enabled business process automation
to automatically analyze muscular contractions (e.g. frowning while talking about
prior employment), voice tone (e.g., enthusiastic about this position or company),
and proficiency in English from the video interview. (Kharkovyna, 2018). Finally,
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AI has the potential as a coaching tool for applicants. It is likely that mentors and
coaches will work with individuals to improve their interviewing skills, and can use
these same tools to provide feedback to their mentees. In addition, as AI-enabled
social technologies evolve, it is possible that AI may provide more direct coaching
and support to employees in the future.
2.3.7 Supporting Effective Selection Decision-Making
The last step in the selection process is to combine all the information
collected in the process to make a final selection decision. For example,
organizations can merge information gathered from job applications, employment
tests, personality inventories, and interviews, and AI can develop algorithms that
weight and combine the information to make overall hiring recommendations. The
use of AI should be very beneficial in this process because it does not include biases
or affect reactions to applicants, which often mislead decision-making. Thus, the use
of AI algorithms may make more effective selection decisions than traditional
decision-making, but research is needed to examine this prediction. Industry
research supports this argument, finding that AI enabled hiring decisions result in a
20% increase in employee performance and a 35% decrease in turnover rates (Ideal,
2019). Overall, e-selection and AI can help hospitality organizations hire employees
with a better P-O fit and increase retention rates. The paper identified several
benefits of using e-selection and AI-enabled hiring decisions including: simplifying
job analysis, facilitating employment testing, enabling remote video interviewing,
and supporting overall selection decisions.
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2.4 Potential risks of AI
The use of AI-enhanced eHRM does not come without risks. First, although
AI will normally outperform individuals in decision-making, not all algorithms are
fully accurate, and some can induce unexpected biases. For example, Amazon had to
scrap an AI-enabled selection tool because it was biased against women in the hiring
process (Dastin, 2018). Firms will need to ensure that AI-enabled selection
processes do not limit the diversity of the applicant pool and comply with relevant
labor laws in each country in which they are applied.
Second, applicants and employees must trust and respond positively to AI if
it is to be a successful HR tool. Research suggests that applicants may not respond
positively to AI-enabled digital interviews because they find them less personal and
creepier than even two-way video interviews (Langer, König, & Krause, 2017). In
addition, research has shown that individuals are likely to resist any system that
captures data that they see as irrelevant to the decisions being made and when they
lack a voice in regards to its use (Lukaszewski, Stone, & Johnson, 2016)).
Organizations need to make sure that they can explain and justify to HR
stakeholders both the data utilized to support AI-based decisions and the decisions
themselves (Tambe, Cappelli, & Yakubovich, 2019).
Moreover, experts agree that job automation is the most immediate risk of AI
applications. According to a 2019 study by the Brookings Institution, automation
threatens about 25 percent of American jobs. The study found that automation
would impact low-wage earners, especially those in food-service, office
management, and administration. Jobs with repetitive tasks are the most vulnerable,
but as machine learning algorithms become more sophisticated, jobs requiring
degrees could be more at risk as well.
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So, are humans going to be replaced by robots in the workplace? Not exactly.
With training and education programs, employees can learn to work alongside the
AI instead of being replaced by it. Many processes require judgement calls — and
AI is still an imperfect technology — so this “attended automation” would be the
ideal model, ensuring the machines produce the desired results.
Some AI technologies, like Automated Speech Recognition (ASR), can
benefit greatly from human input. Take Rev’s industry-leading ASR engine, Rev.ai,
a fully automated solution that has been trained on millions of hours of human-
generated transcripts. This training data comes from our network of more than
60,000 freelancers, who create 99 percent accurate transcripts for customers across
multiple industries. It’s the quality, volume, and varied sourcing of our data that
makes our speech-to-text output the fastest and most accurate in the game.
So, our freelancers provide ground-truth transcripts to our speech recognition
development team, helping them enhance our ASR engine. In turn, Rev.ai allows
our freelancers to produce transcripts faster and more accurately. This combination
of speech recognition AI and trained human professionals ensures that the AI’s
speed and accuracy continually improves, so that customers (both human and
automated) get the highest quality product possible.
III. CONCLUSION
Firms is labor-intensive and faces a number of important workforce
challenges. As a result, AI have the potential to help firms attract, motivate, and
retain talented employees and reduce turnover rates. Through a literature review,
the paper considered the benefit of artificial intelligence for talent acquisition.
Especially, how the use of AI may enhance the fit between employees and
organizations, and increase retention rates. In the case of AI, it has the potential to
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transform how firms recruit and select firms. But without a strong understanding of
both the benefits and the risks of these tools, organizations risk being left behind or
underutilizing these tools. Despite the positive reviews, there are still many doubts
and mixed opinions about the application of AI in recruitment.The main criticisms
are that recruitment and personnel are the building of cooperative relationships, trust
between people and people, and machines cannot support them all, no matter how
superior they are. In addition, the fear of missing data leading to inaccurate analysis
can also lead to wrong assessment, wrong recruitment. However, AI in recruitment
is still an inevitable trend. Organizations who are best able to leverage these tools
will be at the forefront of hiring the most talented employees who will be motivated
to remain with the organization over time.
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