Revue Internationale des Sciences de Gestion

ISSN: 2665-7473
Volume 7 : Numéro 3

The Impact of Artificiel Intelligence on Recruitment Processes

L'Impact de l'Intelligence Artificielle sur les Processus de

Recrutement

SALAM Ghizlane
Enseignante chercheure
Faculté des sciences juridiques économiques et sociales Ain Sebaa
Laboratoire de Recherches sur La Nouvelle Economie et
Développement LARNED
Maroc

AOUADE Oumaima
Doctorante
Faculté des sciences juridiques économiques et sociales Ain Sebaa
Laboratoire de Recherches sur La Nouvelle Economie et
Développement LARNED
Maroc

Date submitted : 13/05/2024

Date of acceptance : 02/07/2024
To cite this article :
SALAM G . & et AOUADE O. (2024) «The Impact of Artificial Intelligence on Recruitment Processes», Revue
Internationale des Sciences de Gestion « Volume 7 : Numéro 3 » pp : 1 -16

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Abstract

Over the past few years, the integration of Artificial Intelligence (AI) into recruitment
processes has significantly transformed how organizations find and choose potential
employees. This article explores the substantial influence of AI technologies on recruitment
practices, focusing on how they streamline procedures, enhance decision-making, and boost
overall efficiency. Through a thorough examination of current literature and empirical data,
this study sheds light on the main advantages and challenges of using AI in recruitment.). This
study aims to offer a comprehensive overview of the effects of AI-driven recruitment
strategies on organizational success and workforce changes. By critically analyzing and
synthesizing existing research, this study seeks to provide insights into the potential
opportunities and challenges of adopting AI in recruitment, guiding strategic decisions and
practices in HR management.

Keywords: Artificial Intelligence; Recruitment; Decision-Making, Efficiency;

Challenges.

Résumé

Au cours des dernières années, l'intégration de l'Intelligence Artificielle (IA) dans les
processus de recrutement a considérablement transformé la manière dont les organisations
trouvent et sélectionnent les futurs employés. Cet article explore l'influence significative des
technologies d'IA sur les pratiques de recrutement, en mettant l'accent sur la manière dont
elles rationalisent les procédures, améliorent la prise de décision et augmentent l'efficacité
globale. À travers un examen approfondi de la littérature actuelle et des données empiriques,
cette étude met en lumière les principaux avantages et défis de l'utilisation de l'IA dans le
recrutement. Cette étude vise à offrir un aperçu complet des effets des stratégies de
recrutement basées sur l'IA sur le succès organisationnel et les changements dans la main-
d'œuvre. En analysant de manière critique et en synthétisant les recherches existantes, cette
étude vise à fournir des informations sur les opportunités et les défis potentiels de l'adoption
de l'IA dans le recrutement, guidant ainsi les décisions et les pratiques stratégiques en gestion
des ressources humaines.

Mots clés: Intelligence Artificielle ; Recrutement ; Prise de Décision ; Efficacité ;Défis.

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Introduction
The introduction of Artificial Intelligence (AI) into recruitment has dramatically changed the
way companies find and hire new talent. This article delves into the effects of AI technologies
on recruitment, examining how they can make processes smoother, decisions better, and the
whole system more efficient. (McAfee & Brynjolfsson, 2012) highlight how AI could
transform traditional recruitment by using data to guide decisions and automating parts of the
process. (Staney, 2014) adds that AI can help organizations use data analytics to find and
screen candidates more efficiently. (Kuncel and al 2014) and (Gillies , 2014) show that AI
algorithms can lead to better hiring choices and less bias in the process. However, using AI in
recruitment isn't without challenges, such as the need for appropriate tech skills and managing
data privacy and security concerns, as discussed by (Stone et al. 2015). This study aims to
provide a comprehensive overview of how AI-based recruitment strategies affect
organizational performance and workforce dynamics.

A significant growth in data collection and management systems has occurred due to new
technologies (Searle, 2006). The world is being transformed by big data, making it essential
for organizations to confront this radical change (Mayer-Schönberger & Cukier, 2013). The
potential transformation that recruitment might undergo due to big data analytics and AI is
particularly fascinating. Big data is expected to strongly impact every organization and its
operations today and in the future (Scholz, 2017). While big data might currently be seen as a
technological phenomenon, it will have a profound impact on a social level and on personnel
within organizations. Recruitment professionals will have the opportunity to focus on
individuals while observing and noting the changes big data brings about (Scholz, 2017).

High volumes, velocity, and variety are distinct characteristics of the big data phenomenon,
defined as information assets requiring specific technological and analytical methods for their
transformation into value (De Mauro, Greco & Grimaldi, 2016). Although the future of big
data analytics remains uncertain, the roles and professional skills in this field are likely to be
altered (De Mauro, and al 2017). Today, big data is utilized by organizations in recruitment,
as they argue that the subjective nature of individuals hinders their business, while big data is
considered to be less biased (Scholz, 2017). Digital data analysis methods contribute to
making decision-making more objective, which is challenging with traditional judgments
involving some degree of subjectivity, which can be useful in recruitment (Bondarouk &
Brewster, 2016). Although big data is conceptualized as objective by eliminating people's

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subjective instincts, the subjectivity of big data must also be considered. Big data contains
errors, blind spots, and subjectivity through algorithms constructed by people (Scholz, 2017).

New technological solutions provide a quick way to search and analyze huge amounts of data
using algorithms, making the criterion no longer just a keyword but a complete concept,
which can support the recruitment process (McLean,and al, 2013). Training these algorithms
requires massive amounts of data (Jordan & Mitchell, 2015). Today, it is increasingly
important for organizations and professional recruiters to understand and learn from big data
(Christozov & Toleva-Stoimenova, 2015).

Undertaking a scientific literature review focused on "The Impact of AI on the Recruitment

Process in Enterprises," this study adopts a rigorous methodological approach to synthesize
existing knowledge on the subject. The main objective is to deeply analyze the relationship
between AI and the recruitment process, identifying key trends, dominant themes, and
research gaps. This involves defining the research objective, examining existing theoretical
frameworks and conceptual models, deploying a comprehensive research strategy to identify
relevant sources, and categorizing and synthesizing information regarding the relationships
between AI and the recruitment process. Thematic analysis will group existing studies based
on common themes, methodologies used, and their respective conclusions, helping to identify
recurring patterns, gaps, and challenges in the current literature on the impact of AI on the
recruitment process.

The research methodology of this article is based on an exhaustive review of scientific

literature to analyze the impact of Artificial Intelligence (AI) on the recruitment process. This
rigorous methodological approach includes several key steps: defining the research objective,
examining existing theoretical frameworks and conceptual models, implementing a
comprehensive research strategy to identify relevant sources, and categorizing and
synthesizing information regarding the relationships between AI and the recruitment process.
Thematic analysis groups existing studies based on common themes, methodologies used, and
respective conclusions, which helps to identify recurring patterns, gaps, and challenges in the
current literature on the impact of AI on recruitment.

How does the integration of Artificial Intelligence (AI) and big data impact the
recruitment process, and what are the key benefits and challenges associated with its
adoption in organizational hiring practices?

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The article begins with an Introduction, followed by a comprehensive Literature Review. The first
part of the literature review provides an Overview of AI in Recruitment, and then offers a
Historical Perspective on AI Adoption in HR.

The next section, How AI Helps Streamline Recruitment, details the efficiencies AI brings to the
recruitment process.

The subsequent section, Enhancing Decision-Making with AI, is divided into two parts: The Role
of Data-Driven Insights and Transforming Recruitment Strategies with AI.

Following this, the article explores Boosting Efficiency with AI, which includes a discussion on
how Automation Makes HR's Life Easier and addresses HR's New Challenges and Opportunities.

The article then navigates the challenges and considerations of AI adoption. This section covers
Building the Right Tech Foundation, The Importance of Adequate Infrastructure, and Investing in
People and Processes.

An Overview of the Theoretical Framework is provided next, summarizing the theoretical

underpinnings of AI's impact on recruitment.

Finally, the article concludes with a Conclusion and is followed by a comprehensive

Bibliography.

1. Literature Review
1.1. Overview of AI in Recruitment
Artificial Intelligence (AI) is a broad term that covers a variety of technologies designed to
give machines the ability to think and learn like humans. This includes tasks like solving
problems and making decisions. In the world of recruitment, AI is a game-changer, offering
tools like natural language processing (NLP) that let computers understand and respond to
human language, whether it's written or spoken. Machine learning algorithms are another AI
tool that can sift through huge amounts of data to spot patterns and insights that help in
choosing the right candidates. Predictive analytics uses past data to predict future trends and
outcomes, helping organizations to automate tasks, gain valuable insights from candidate
information, and make smart decisions to improve their hiring strategies.

Understanding intelligence has been a perplexing quest for humanity throughout history, as we
strive to unravel the complexities of cognition. Artificial intelligence (AI) emerges from this
quest, seeking to comprehend and emulate intelligent behavior. Simply put, AI refers to

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computers or computer programs capable of performing tasks that typically require human
intelligence. However, defining AI precisely proves challenging, given its broad applicability to
various intellectual tasks across numerous subfields (Stuart & Norvig, 2016).

Various perspectives exist on what constitutes AI. Stuart and (Norvig, 2016) present four
distinct approaches to AI, along with eight diverse definitions provided by different scholars
using different methodologies. At the top lie approaches linked to human thought processes,
while at the bottom are those associated with observable behavior. On the left side are
definitions rooted in human-centered perspectives, measuring success based on human
performance. Conversely, on the right side are definitions reflecting rationalist viewpoints,
gauging success against an ideal standard of rationality. The human-centered approach relies
on observations and hypotheses regarding human behavior, whereas the rationalist approach
combines mathematical principles with engineering concepts (Stuart & Norvig, 2016).

When we talk about using AI, it's like looking at a piece of art—everyone sees it through their
own lens. (Stuart & Norvig, 2016) suggest that before diving in, it's important to ask yourself
what you're aiming for: are you more interested in understanding how we think, or how we
behave? Do you want AI to mimic human behavior, or do you prefer it to aspire to an ideal
standard?

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Table N°1 : Definitions of AI, organized into four categories (Stuart & Norvig,
2016)
Approach Definition
"The exciting new effort to make computers think … machines with minds, in the full and
Thinking literal sense." (Haugeland, 1985)
Humanly "[The automation of] activities that we associate with human thinking, activities such as
decision-making, problem solving, learning …" (Bellman, 1978)
"The study of mental faculties through the use of computational models." (Charmiak &
Thinking McDermott, 1985)
Rationally "The study of the computations that make it possible to perceive, reason, and act."
(Winstron, 1992)
"The art of creating machines that perform functions that require intelligence when
Acting performed by people." (Kurzweil, 1990)
Humanly "The study of how to make computers do things at which, at the moment, people are better."
(Rich & Knight, 1991)
"Computational Intelligence is the study of the design of intelligent agents." (Poole et al.,
Acting
1998)
Rationally
"AI… is concerned with intelligent behavior in artifacts." (Nilsson, 1998)

Source: (Stuart & Norvig, 2016)

Human behavior, you see, is a bit of a puzzle. We like to think we're rational creatures, but let's face
it—nobody's perfect. Our brains can only handle so much information at once, which means we
can't always make the smartest choices (Simon, 1968) (Omohundro, 2008).

This in-depth analysis explores artificial intelligence (AI) from various perspectives,
emphasizing its definition, implications in recruitment, historical evolution, and underlying
theoretical perspectives. It begins by defining AI as a technology aimed at endowing
machines with human cognitive abilities, highlighting its significance in recruitment through
tools like natural language processing and machine learning algorithms. It then examines the
nature of AI as the capacity of computers to perform human intellectual tasks, while noting
the complexity of its definition due to its multiple applications. The analysis also presents
various approaches to AI according to (Stuart & Norvig ,2016), categorizing them into four
distinct categories with definitions provided by various researchers. Finally, it concludes by
addressing the complexity of human behavior and the limitations of our rationality,

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emphasizing that our decisions are often influenced by emotional and cognitive factors.
Overall, this analysis provides a comprehensive perspective on AI, examining its conceptual,
theoretical, and practical aspects, as well as its implications for understanding intelligence and
human behavior.

1.2. Historical Perspective on AI Adoption in HR

The story of AI in HR began in the late 20th century, with its first uses focused on simplifying
routine tasks like handling payroll and benefits (Marler & Boudreau, 2017). As AI technology got
better and more advanced, its role in HR grew to include more complex functions.

AI's progress has led to its integration into many parts of human resource management, such
as recruitment, talent management, and keeping employees engaged (Davenport, 2018). By
analyzing large amounts of data, spotting trends, and making predictions based on data, AI
systems have transformed how organizations find, screen, and choose candidates, making the
process more efficient and effective. AI-driven talent management platforms also provide
insights into employee performance, skill development, and career paths, helping
organizations make better decisions about workforce planning and development.

The use of AI in HR shows a shift in how organizations manage their workforce, highlighting
the importance of using technology to improve HR processes and boost overall performance.
As AI continues to develop, its potential to change the HR field and drive innovation in talent
management is huge.

2. How AI Helps Streamline Recruitment

(McAfee & Brynjolfsson, 2012) present a compelling argument regarding the transformative
potential of AI in revolutionizing the hiring process. They assert that AI has the capacity to
automate mundane tasks such as sifting through resumes and identifying suitable candidates.
By leveraging AI technologies, companies can significantly expedite their hiring procedures,
streamline onboarding processes, and enhance overall operational efficiency (McAfee &
Brynjolfsson, 2012).

Furthermore, (Staney, 2014) expands upon this notion by highlighting AI's prowess in the
realm of data analytics. With AI-driven analytics tools, recruiters can delve into vast pools of
candidate data, discern pertinent patterns and insights, and gain a more nuanced
understanding of which individuals align best with job requirements (Staney, 2014). This

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analytical capability empowers organizations to make more informed decisions at every stage
of the recruitment process, thereby fostering better outcomes and cultivating stronger teams.
The insights gleaned from the works of (McAfee & Brynjolfsson, 2012) and (Staney, 2014)
underscore how AI has the potential to revolutionize hiring practices, rendering them more
efficient, insightful, and beneficial for all stakeholders involved. As AI continues to advance,
it is poised to play an increasingly prominent role in talent acquisition and management,
reshaping the landscape of recruitment for the better.

3. Enhancing Decision-Making with AI

3.1. The Role of Data-Driven Insights
In the world of recruitment, making the right hiring decisions is crucial for the success of any
organization. AI has emerged as a powerful tool in this process, offering data-driven insights that go
beyond traditional methods (Bock et al., 2018). By analyzing candidate data and performance
metrics, AI algorithms provide recruiters with deep insights that enable them to make informed
choices. This isn't just about finding candidates with the right skills; it's about identifying
individuals who will thrive in the organization's culture and excel in their roles.

The integration of AI in recruitment allows organizations to move beyond guesswork and

intuition. With data-driven insights, recruiters can pinpoint candidates who are not only
technically proficient but also culturally aligned and likely to be successful in the long term.
This approach not only improves the quality of hires but also reduces turnover by ensuring
that new employees are well-matched to their positions from the outset.

3.2. Transforming Recruitment Strategies with AI

AI has revolutionized the way organizations approach recruitment, particularly through the
use of predictive analytics (Davenport, 2018). This technology enables recruiters to analyze
vast amounts of data, uncovering patterns and trends that were previously invisible. By
leveraging these insights, recruiters can anticipate the success of potential candidates and
tailor their strategies to target individuals who are most likely to be a good fit.

Predictive analytics gives recruiters a strategic advantage, allowing them to identify high-
potential candidates early in the recruitment process. This proactive approach not only
accelerates the hiring process but also ensures that recruitment efforts are focused on
individuals who have the highest probability of success. In a competitive talent market, this
can be the difference between attracting average candidates and securing top-tier talent.

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Moreover, AI-driven insights provide recruiters with a wealth of information about candidate
preferences, behaviors, and career paths. Armed with this knowledge, recruiters can
personalize their recruitment strategies to resonate with each candidate, creating a more
engaging and effective hiring process. This personalized approach not only enhances the
candidate experience but also increases the likelihood of successful placements and long-term
retention.

4. Boosting Efficiency with AI

4.1. Automation Makes HR's Life Easier
AI has totally changed how HR departments work, and one of the biggest wins is how it takes
care of those boring, repetitive tasks like going through resumes and setting up interviews
(Gartner, 2019). Stuff that used to eat up so much time is now done quickly and accurately by
AI.

AI is like a superhero when it comes to dealing with loads of candidate info. It spots the
important stuff—skills, experience, qualifications—and only picks out the best matches for
job openings. This speeds up the hiring process and makes sure only the right people get
through to the next round. Plus, AI can handle all the scheduling, send out reminders, and
even do some early checks on candidates, taking a ton of work off HR's plate.

But AI isn't just good at what it does today; it keeps getting better. As it learns from more data
and more candidates, it gets really good at matching people to jobs. This means better
predictions about who'll do well, leading to better hires overall.

4.2. HR's New Challenges and Opportunities

AI isn't just about making HR's job easier; it's also about making it more strategic. With AI
handling the day-to-day stuff, HR pros can focus on the bigger picture of finding and
nurturing talent (Cascio & Aguinis, 2008).

To make the most of this, HR folks need to get comfortable with data. They've got to
understand what the numbers are saying, spot trends, and use that info to make smart hiring
decisions. It's about turning all that data from AI into actionable insights that improve hiring
strategies and results.

AI also means HR needs to think more strategically about talent. It's about using the insights
from AI to really connect with potential hires, keep them interested, and make sure the best

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ones stick around. This is where HR can really make a difference, using AI to align hiring
with the company's goals and to build a top-notch team.

In a nutshell, AI is pushing HR to evolve. By getting on board with data and thinking

strategically, HR can use AI to make hiring better and help their organizations succeed.

For the Moroccan case, according to the study by (Hattab, & El Houari, 2023), e-recruitment
4.0 faces numerous cultural and human challenges. The main obstacles identified are the lack
of digital culture and insufficient training in digital tools and new technologies among young
graduates. This situation hinders the effective integration of the e-recruitment 4.0 process.

5. Navigating the Challenges and Considerations

5.1. Building the Right Tech Foundation
In their work, (Stone et al., 2015) shed light on a critical aspect that often gets overlooked
when it comes to integrating AI into recruitment processes: the technological readiness of
organizations. They argue that for AI to truly revolutionize hiring, companies must have the
necessary infrastructure and resources in place. This means more than just having the latest
gadgets; it's about having a solid technological backbone that can support the implementation
and utilization of AI technologies. Without this foundation, the potential benefits of AI in
recruitment could remain just out of reach for many organizations.

5.2. The Importance of Adequate Infrastructure

(Stone and al 2013) concerns are echoed by the broader understanding that a robust
technological infrastructure is non-negotiable for the successful adoption of AI in recruitment.
This infrastructure includes state-of-the-art IT systems that can handle the demands of AI
algorithms, vast data storage and processing capabilities to manage the information deluge,
and sophisticated software platforms that can harness the power of AI. Investing in these
foundational elements is not just a matter of keeping up with the Joneses; it's about ensuring
that the organization can compete in a talent landscape that is increasingly driven by data and
intelligent automation (Stone and al 2013).

5.3. Investing in People and Processes

Akrivia HCM. (2023) Furthermore, the effective integration of AI into recruitment is not
solely reliant on technology. It also necessitates a substantial investment in people—the HR
professionals and recruiters who will spearhead these changes. Training and development

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programs are crucial to ensure that these individuals possess the skills and knowledge
required to collaborate effectively with AI systems. Moreover, organizations must establish
supportive structures and processes that facilitate the seamless adoption of AI technologies,
ensuring that these innovations are not merely added on but are ingrained into the
fundamental framework of recruitment practices.

6. Overview of the theoretical framework

In Table 2, there is a summary of the effects of the presented new technologies and their
impact on the various phases of the recruitment process. The table includes the phases of a
recruitment process, along with the new technological solutions studied. The recruitment
process begins with establishing recruitment objectives (such as filling a certain position,
determining the type of candidate sought, etc.) and progresses with strategy development
(defining the strategy for filling the position, where/whom to recruit, etc.). After addressing
strategy-oriented questions, recruitment activities (recruitment methods, etc.) are carried out.
Efforts have been made to take into account the characteristics of each phase of the
recruitment process and compare them with the opportunities offered by these new
technologies.

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Table N°2: Overview of how new technology-based tools can be utilized in the
recruitment process.

Recruitment Strategy Recruitment Benefits of

Technologies
Objectives Development Activities Technology
- Online
- Enhancing the - Easy access to
- Screening recruitment has
visibility of the potential
applicants (Viitala, reduced routine
organization applicants (Searle,
2007). work (Dhamija,
(Searle, 2006). 2006).
Online 2012).
recruitment - Online
- Online - Identifying - CV- and
recruitment reduces
recruitment job passive job application banks
cost & time and
boards (Galanaki, seekers (Searle, (Panayotopoulou et
improves candidate
2002). 2006). al., 2005).
pool & quality.
- Reaching
- Creating a
candidates with - Managing huge
- Employer competitive
targeted job masses of
branding (Scholz, advantage in
advertising applications (Bâra et
2017). recruitment (Bâra
(Aguirre et al., al., 2015).
et al., 2015).
2015).
Big Data - Targeted - Supports in the - Automation of
analytics advertising to gain search for - Screening routine tasks in
visibility (Liu & candidates applicants recruitment
Mattila, 2017). (Scholz, 2017). (Nilsson, 2005, 73).
- Predicting job
- Identifying the - Eliminating
performance of new
most suitable subjectivity
hires (Zang & Ye,
applicant (Scholz, 2017).
2015).
- Automating the
- Speeding up the
process of candidate
- Advertisements to recruitment process
- Job screening
reach the ever- (Faliagka et al.,
Artificial matchmaking (Kaczmarek et al.,
growing audience 2012).
intelligence (Montuschi et al., 2005).
(Montuschi et al.,
2014). - Candidate ranking
2014). - Co-operation with
(Faliagka et al.,
AI (Scholz, 2017).
2012).

Sources : Authors

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Conclusion
In this conclusion, it is evident that the integration of AI into recruitment strategies offers
significant advantages, but also presents complex challenges. The study emphasizes the
necessity for organizations to adopt a strategic approach that considers ethical and privacy
considerations.

Looking ahead, it is clear that AI will continue to evolve and reshape the recruitment
landscape. To remain competitive, organizations must stay informed, flexible, and proactive
in their use of AI.

However, it is important to recognize that AI also has limitations and unresolved issues,
particularly regarding algorithm bias and its impact on diversity and inclusion.

Despite these challenges, the research highlights the key contributions of integrating AI into
recruitment, including improving process efficiency and optimizing talent acquisition. By
adopting a responsible and strategic approach, organizations can unlock the full potential of
AI to drive success in recruitment and beyond.

Thus, this study underscores the importance for organizations to stay at the forefront of
innovation while maintaining a balance between the benefits of AI and the ethical and social
concerns that accompany it. Ultimately, it is through adopting a balanced and thoughtful
approach that organizations can maximize the benefits of AI in their recruitment strategies
while mitigating its potential risks.

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