I.
Introduction
In 2023, the world
witnessed the rapid rise of ChatGPT and the generative artificial intelligence
technology it represents, accelerating the process of interaction between
humans and AI and becoming a new milestone in the history of artificial
intelligence development. Discussions by The MakerBay Foundation (2024) show
that 86% of educators believe technology should be the core of teaching.
AI-driven enterprises with data as their core assets are adopting a
people-centered strategy to systematically deploy and expand AI technology in
core business processes. They leverage data-driven decision-making capabilities
to enhance employee and customer experiences to stay ahead of the competition
and continually innovate. Professionals should reframe their professional
duties (Lifshitz-Assaf 746), which is a key element that constitutes their
identity. This professional role identity is the self-perception of
professionals based on their personal identities and duties (Reay et al.). It
is particularly important in the context of technological change as it helps
maintain the integrity of the profession amid external changes. According to
Chreim, Williams, and Hinings, the formation of professional role identity is a
multi-level process, and professional organizations frequently advocate and
give frameworks at the scene level (1515). These frameworks
greatly influence the shaping of professionals’ personal identities in their
daily work (Doe). Relevant professionals can create their professional
identities at the collective level via interactions with peers and other
stakeholders, helping them to better adjust to field-level changes. The
Deloitte AI Institute and Deloitte Integrated Research Center survey reveals
how global enterprises are progressing toward this vision, from overall AI
strategy and leadership to technology and data approaches, and how they are
assisting employees in all aspects of AI operations. The results highlight the
behavioral initiatives most associated with strong outcomes, as well as support
from partners within the ecosystem, providing the technical foundation and
external perspective needed for large-scale implementation and continued
innovation. In fact, AI has advanced significantly in the past 18 months,
moving from a role once viewed as a nuisance to employees to a trusted
lieutenant that operates autonomously and generates insights via cloud
computing technology. and trends.
In addition, AI not only
improves management efficiency, but also improves management quality by
changing managers’ working methods and organizational operating models. The
impact of AI on management activities covers multiple levels, starting with
decision-making. By analyzing large amounts of data, AI can quickly make
accurate predictions and decisions, provide in-depth insights beyond human
capabilities, and assist managers in making smarter choices. For example, AI
can provide accurate financial projections based on past data and build
complicated models to simulate the financial consequences of various actions. When
it comes to automating basic, repetitive administrative tasks, businesses can
reduce human error and improve efficiency by automating billing, invoicing, and
other financial tasks. In particular, generative AI (like AIGC)
can complete some creative work that traditional AI cannot handle, such as
automatically generating reports and data analysis, allowing managers to obtain
information more quickly and reducing the burden of manual operations. When it
comes to employee management, AI improves recruitment, evaluation and
management processes. For instance, AI is used to parse resumes, predict job
performance, and provide real-time feedback on employee performance. According
to research articles by MIT Sloan Management Review and BCG in May 2023, Companies
are using AI to not just enhance but also redefine performance (Ransbotham
et al.). Effective KPI governance enables leaders to turn data into competitive
advantages and analyze the relationships and underlying components between KPIs
through AI algorithms to better balance competing or complementary
relationships. Advances in AI also help managers make better decisions and lead
organizations. For example, Copilot, a new feature
of Microsoft 365, uses AI technology to help users perform various tasks more
efficiently, from composing emails to preparing PPT to organizing meetings, etc (Liang
et al.). It understands user intentions and provides suggestions and
operations. Similarly, Copilot aims to create new ways of working.
Microsoft Chairman and CEO Satya Nadella said at the press conference that by
changing the way you interact with computers. it will fundamentally change the
way you work and stimulate a new round of productivity growth. However,
these advantages of AI technology also bring ethical and moral challenges,
especially in areas where automated decision-making may affect employee careers
and the public interest. As AI decision-making systems
become more and more widely used in areas such as recruitment, credit approval,
and law enforcement, how to ensure that the decisions of these systems are fair
and transparent has become an urgent problem to be solved (Ransbotham
et al.). In this regard, researchers in different fields are
actively exploring possible regulatory frameworks and ethical guidelines to
ensure the healthy development and widespread acceptance of AI technology.
Based on the above
discussion, this study wants to discuss the disruptive influence
of AI on the labor market, specifically its rise in job hierarchies, ethical
implications for recruitment, and implications for job sustainability. This
study uses qualitative analysis methods and combines with specific cases to
gain an in-depth understanding of how enterprises are developing towards the
vision of using AI to improve workflow and organizational structure. The
study’s goal is to demonstrate how artificial intelligence is becoming more
integrated across sectors and how it has the potential to change employment
roles and organizational structures. Through this research, it is expected to
provide a new theoretical perspective for the academic community, provide
practical reference for industry practitioners. It also provides
decision-making support for policy makers, especially when formulating ethical
standards and policies related to the application of AI technology. The
article will first analyze the relationship between AI and career levels, then
consider the ethics of AI in recruitment, and finally analyze the impact of AI
on job sustainability to comprehensively explore how this technology can
reshape people’s work and social structures.
II.
Literature
Review
With
the rapid development and application of these technologies, enterprises are
also developing innovative working methods and business models while utilizing
AI technology to optimize existing processes. Its purpose is to maintain a
competitive edge in the fierce market competition. Artificial intelligence
and machine learning have moved from theoretical research to real-world
applications, covering a wide range of areas from data analysis to customer
service and even people’s lives (Liang et al.). The application of artificial
intelligence enables enterprises to optimize inventory management and supply
chains through large databases, while automation technology reduces
manufacturing costs by improving production efficiency. At the same time, the large
model of AI has become the main innovation drivers, and enterprises need to
re-examine and adjust existing innovation mechanisms to adapt to the rapid
development of AI technology and big data. These technological advances have
not only changed the way businesses operate but have also had a profound impact
on employees’ daily work and career development.
To
begin with, as an effective instrument, ai has been consistently coordinates
into the world of selecting and ability administration. Its enchantment lies in
its capacity to filter through gigantic work applications and single out the
sparkling stars through prescient analytics and machine learning. Based on
application sort, the worldwide generative AI in enrollment and human assets
showcase is mainly driving within the enrollment and ability procurement
fragment, bookkeeping for 28% of the showcase share. Generative AI is broadly
utilized to mechanize and streamline distinctive angles of the enlistment
handle, from candidate sourcing to candidate coordinating. Whereas it gives
comfort, characteristic separation rules lead to predisposition in generative AI.
This thought was replied sideways in Jacques Bughin’s article Why AI Will Not
Cause the Passing of Employments. Bughin recommended that companies that
utilize ai to drive advancement tend to see business increments, not diminishes
(Bughin 42-46). The effect of ai is distant from a single decrease in work but
is complex and has numerous conceivable outcomes. Moreover, Bughin gives a
critical counterpoint to the role of ai within the work advertise, and it
highlights important factors that policymakers and commerce pioneers have to be
consider when creating ai techniques that advance work development instead of
work misfortune (Bughin 42-46). But at the same time, a senior supervisor
rejects a candidate for the off-base reasons since of inclinations based on
sexual orientation, age, ideology, or race. Without cautious screening, AI may
confuse these designs as markers of ineptitude, compounding the avoidance of
qualified candidates from minority foundations. Amazon’s AI-driven selecting
approach has moreover experienced a difficulty in favor of male candidates for
specialized positions. In reaction to this address, inquire about by Serge P.
da Motta Veiga, Maria Figueroa-Armijos, and Brent B. Clark dives into how moral
discernments affect an organization’s engaging quality and innovativeness when
utilizing fake insights in enlistment (Thida). Agreeing to Yeqing Kong and
Huiling Ding, AI instruments have moral issues with protection infringement,
algorithmic predisposition, and far-reaching social media observing in
assessing candidates (Kong et al.). These questions reflect that indeed in the
event that AI may be more unbiased in a few regards, its moral and interpretive
challenges are critical. In addition, the darkness of the AI decision-making
prepare might turn into a legitimate problem on the off chance that work
tribunals address their capacity to explain the thinking behind ai-driven
enlisting judgments. Their research unfolded across two studies, including job
seekers and individuals with recent recruiting experience, to
better understand their attitudes to the usage of artificial intelligence in
the recruiting process. It results show that when the application of AI in
recruitment is considered to be ethical, this positive ethical perception
significantly improves people’s attractiveness and innovative perception of the
organization, especially when the practice of AI is seen as When it is
innovative or invasive in some way.
As
the saga of generative AI in recruiting continues to unfold, it is critical
that organizational leaders deeply understand the inner workings of this
technology. While AI may have advantages in impartiality compared
to its human counterparts, a dilemma arises when these machine-generated
decisions need to be explained to human candidates. Ferrario, Loi, and Vigan
proposed an “incremental trust model” to understand how humans build
trust in AI systems (Ferrario et al.). They
believe that if an AI system performs well in terms of impartiality, this may
help build trust. However, this trust can be challenged when it comes to
explaining its decisions to candidates. Because AI’s decision-making process
may not be as transparent or easy to understand as human. Regarding this idea, Erdinç
Aydın and Metin Turan gave the same explanation when discussing the utilization
of AI to streamline the recruiting and screening process. Concurring to Brad A.
M. Johnson, Jerrell D. Coggburn, and Jared J. Llorens, counterfeit insights may
computerize various tedious and time-consuming forms, boosting proficiency and
diminishing human mistake (Johnson et
al.). In other words, ai innovation can offer assistance directors make more
objective and successful choices by giving data-driven bits of knowledge. This
incorporates utilizing ai apparatuses to analyze worker execution information,
anticipate worker potential, and optimize ability allotment and training plans.
Myo Thida made utilize of ChatGPT to analyze and summarize information from
online work postings and social media to superior get it the labor advertise
request for Myanmar laborers in Japan and Thailand (Thida). It was too
illustrating the potential of expansive dialect models for analyzing energetic
labor showcase patterns in genuine time.
In
general, these studies provide a relatively comprehensive examination of the application
of artificial intelligence in company operations and human resource management. Bughin
stated that the role of AI in improving corporate innovation and recruitment
processes (Bughin 42-46). Ferrario, Loi, and Viganò and
Erdinç Aydın and Metin Turan provide a creative application of AI in building
and maintaining organizational trust and improving recruitment efficiency. These
studies analyze the effectiveness of AI in understanding data and automating
mechanical tasks (Ferrario et al.). Yeqing Kong and
Huiling Ding, along with Brad A. M. Johnson, Jerrell D. Coggburn, and Jared J.
Llorens, present some new perspectives, particularly on the application of AI
in public sector human resource management and how to deal with potential
biases introduced by AI and ethical issues. However, none of these studies
systematically answers the question of the increasing integration of artificial
intelligence across industries and its potential to redefine job roles and
organizational structures.
III.
Case
study
While
generative AI is now utilized mostly in consumer-facing products, it has the
potential to enhance enterprise operations with situational awareness and
human-like decision-making skills, revolutionizing our business models. For
example, Google’s Customer support Center Artificial Intelligence (CCAI) is
intended to facilitate natural language customer support interactions. Hirevue
is an online recruitment and interview platform that provides companies with
services such as virtual interviews, intelligent analysis, talent management
and employee development. The platform uses high-definition video technology
and artificial intelligence algorithms to improve the traditional interview
process and add more interactivity and convenience. In an interview with the
Washington Post, HireVue stated that the technology will examine the smallest
characteristics of the job interview process, such as facial expressions and
eye contact, as well as the applicant’s degree of excitement (Salley). The
system makes judgments based on these details and finally classifies the job
seeker’s probability of success in the interview into three levels: high,
medium and low. Loren Larsen, HireVue’s chief technology officer, stated that
80 to 90 percent of assessments are based on algorithmic examination of a
candidate’s linguistic and verbal skills (Salley). The program can recognize
350 linguistic characteristics and change the assessment criteria for various
roles (Langer). For example, when recruiting doctors, candidates with a more professional
vocabulary may be favored, but for sales roles, speaking speed, facial
expressions, and so on are more relevant (Salley). This technology is presently
utilized for preliminary interviews, and humans will finally determine if the
interviewer is accepted. According to reports, more than 700 organizations
worldwide have conducted approximately 12 million interviews using this
technology, with some of them being huge. For example, hotel giant Hilton’s
revenue management and customer center positions are now mostly recruited
through HireVue’s AI system (Salley). In addition, this system
helps them get rid of past stereotypes and recruit more diverse employees. Although
HireVue aims to improve the fairness of the recruitment process through AI
technology, its methods have caused widespread concern and controversy,
especially regarding privacy violations, data bias and its impact on job
seekers’ psychology. According to Anna Cox, algorithms will favor individuals
who are strong at video interviews, perhaps excluding others who perform well
in actual employment (Langer).
Similarly,
IBM has developed a system that uses AI to assist in internal job promotion
decisions by evaluating employees’ performance data and career development
needs. First, IBM Human Resources filed for a patent for its predictive
attrition program, which was developed in partnership with Watson to predict an
employee’s risk of leaving and provide for managers to engage in employee
actions (Guenole and Feinzig). This effort increases transparency
regarding employee career paths. It focuses on an individual’s strengths by
helping them grasp data patterns and related abilities. On the contrary,
managers will be able to train staff on future prospects identified using
traditional approaches that extrapolate more accurately from the data. Second, IBM
supervisors receive warnings that are tailored to each employee’s specific
needs. For example, if someone has been on the team for a long time and has
specific skills, the manager will be alerted to those facts (Guenole and
Feinzig). Similarly, managers may be notified when employees are more likely to
leave their positions. When salespeople are at risk of missing sales targets,
early action might be suggested to bring work back on track. Ferrario,
Loi, and Viganò propose a trust model in their discussion of how humans
gradually develop trust in artificial intelligence systems (Ferrario
et al.). The establishment of trust is a dynamic process. As
users interact with the AI system, their level of trust may increase or
decrease. This dynamic nature requires a high degree of transparency and
consistency in the design and operation of AI systems. Although this kind of AI
application shows potential in improving decision-making transparency and
efficiency. It will also face problems such as data accuracy, bias filtering,
and employee acceptance.
In
terms of investigating the effect of AI, generative AI has sparked interest
from traditional venture finance, mergers and acquisitions, and developing
ecosystem collaborations. As a leading global consulting and auditing services
company, Deloitte actively explores the application of AI technology in
improving corporate sustainability (Napier). First,
there is the potential for companies to improve energy efficiency and manage
resources through AI technology. The company achieves a greener operating model
by using AI to analyze and optimize energy consumption patterns. Secondly,
Deloitte is applying AI technology to supply chain management, logistics
optimization, and automating routine and repetitive tasks during its
development process, thereby freeing up human resources to focus on
higher-value activities (Napier). The
significance is to elevate artificial intelligence from an “enabler” to a
“collaborator”. According to Brad A. M. Johnson, Jerrell D. Coggburn, and Jared
J. Llorens, AI can handle complex problems and large amounts of data, which is
especially important when managing large-scale data and complex systems (Johnson
et al.). The company also thinks that generative AI has the capacity to
redefine business models, processes, and value dynamics, therefore altering how
people work, learn, and interact.
IV.
Conclusion
Overall,
while the field of generative AI is growing rapidly, it is still in its infancy
and presents many risks. The key issues include privacy and security, bias
management, transparency and traceability of results, intellectual property
protection, and ensuring equal access for vulnerable groups. Therefore,
participants should comprehensively consider factors such as commercialization,
regulation, ethics, and co-creation to expand the participation and
contribution of stakeholders.