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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue III, March 2026
A Study on the Influence of AI on Time Optimization of HR
Functions
Rishashri M
1*
,Mr. Vasantha Jayaseelan
2
1
MSW HRM student, Madras School of Social Work
2
Assistant Professor, Madras School of Social Work
*
Corresponding Author
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.150300118
Received: 22 March 2026; 27 March 2026; Published: 23 April 2026
ABSTRACT
This study examines the influence of Artificial Intelligence (AI) on time optimization within Human Resource
(HR) functions, focusing on how AI-driven tools enhance efficiency and organizational outcomes. With HR
departments increasingly expected to act as strategic partners, the integration of AI helps automate repetitive
tasks such as recruitment, payroll, and performance management, thereby reducing time consumption and
improving decision-making. Using a descriptive research design, data was collected from 70\ HR professionals
in IT companies in Chennai through a structured questionnaire. The study highlights that AI significantly
improves time efficiency, enabling HR professionals to focus more on strategic and employee-centric roles. It
also addresses concerns related to adoption, ethical considerations, and human acceptance, emphasizing the need
for a balanced, human-cantered approach. Overall, the research contributes to understanding how AI-powered
time optimization can transform HR functions and support organizational effectiveness.
Keywords: Artificial Intelligence, Time Optimization, Human Resource Management
INTRODUCTION
Artificial Intelligence (AI) is the current growing technology in the digital world. It resembles human intelligence
through machines or computers. It does everything exactly same as human brain which is carried out through a
machine. AI can even bring creativity; problem solving and make decisions. Artificial intelligence can also
respond to human beings by understanding them. This advanced technology is going to be the new technological
revolution. Before the emergence of Artificial Intelligence, there was the invention of Machine Learning which
can make decision and predict based on data given which is dependent on the algorithm. The algorithm can make
conclusion based on the human provided data. Machine learning was discovered before artificial intelligence.
Machine Learning are of various types. Some of the Machine Learning types are logistic regression, linear
regression, support vector machines, clustering, etc. the type of Machine Learning is chosen based on the type
of data they need to predict or make decision. Most popular type of Machine Learning is Neural Network. In
neural network the nodes are interconnected which processed together to analyse complex data. The other
common Machine Learning is Supervised Learning. It is paired label sets that analyse data.
After Machine Learning, there emerged Deep Learning. Deep Learning is nothing but the extended version of
Machine Learning. They have multiple layered neural networks that closely knit together to do the task. They
somewhat resemble the decision-making power of the human brain. This Deep Learning technology has many
layers and so they are advanced than Machine Learning. Because Deep Learning has a lot of hidden layers, they
can make interpretation and take decisions even if the data is not properly given. They can make their own
prediction which slightly look the same way in which the human brain works. Most of the AI used in current
days have Deep Learning algorithm. Deep learning can be used in areas where we need to make accurate pattern,
fast decision and draw relationship even if we have a lot of data.
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The advanced model of Deep Learning is Generative AI. This is also known as Gen AI”. When an individual
uses a prompt in Gen AI, they can receive long text, quality images, good video and audio content and even
creative contents. This is very convenient and recent form of Artificial Intelligence used by many individuals.
These generative models are previously used to analyse data but as times pass by now Gen AI is used to generate
data.
Gen AI works on the basis of 3 phases. First a training is been given to the AI to create a fundamental model.
This serves as foundation for the AI. The common training model given to most AI is large language model.
There are sperate models for images, videos, audios, etc. to train the algorithm, the trainer uses a large number
of unstructured raw data. When training is given continuously to the neural network creates a systematic pattern
in relation to the data given and based on it the algorithm can create a content autonomously. Then the AI is now
capable to generate content by reading the prompt or request given by the individual. The training process in
time consuming and expensive, but it is the fundamental in developing any AI and this is a very essential phase.
Only when the training is done properly the AI can perform accurately.
Then comes the Tuning. In tuning the AI will be capable enough to adapt to specific application. This is the
second phase. Tuning can be done in many ways. Most prominent types of tuning are fine tuning and
reinforcement learning with human feedback. In fine tuning the algorithm give specific labelled data. This helps
to receive correct answer in the wanted format. This helps in creating more reliable data and so make the AI
more efficient. The other type of tuning, which is Reinforcement Learning with Human Feedback (RLHF), here
the individual using it gives feedback on the relevance of the content generated by the Artificial Intelligence and
based on the feedback the AI can improve itself. This is very simple and easy as people have to talk back to the
algorithm by correcting the chatbot.
The Last Phase is Generation and more tuning. This ensures the AI works more accurately and there is no error
or at least minimized error. In this phase the developer asses the output to ensure the algorithm gives more
accurate and relevant content. It is the phase where evaluation takes place. This is very important in fine tuning
the artificial intelligence. Here they also use a unique technique called Retrieval Augmented Generation (RAG)
in which the algorithm can rely upon outside source and not just on the data given while training. This helps in
more refine and accurate output.
Benefits of Artificial Intelligence:
Artificial Intelligence has lot of benefits in day-to-day life. But some of the common benefits includes doing
repetitive task. Artificial Intelligence can automate repetitive digital task such as collection of data, entering and
preprocessing. They also assist in better decision making. They help in taking more accurate and reliable data
driven decisions. We might forget to consider certain aspects but when using AI, the decisions made are more
reliable. And so, the benefit of AI in making decision is very prominent.
Using Artificial Intelligence definitely contribute in minimizing the errors made by humans. Through proper
steps and processes they have the ability to reduce the errors that might occur if the same task is done by humans.
The algorithm keeps on increasing its accuracy and therefore the errors are reduced. The other important benefit
of using Artificial intelligence is, it has zero error. This can be used in diffusing bomb, going to space or can
even go deep into the sea. One best example in manufacturing sector is that, AI can be used in the risky
production line. They can perform task and reduce human errors and accidents. This is one big benefit of the use
of AI.
AI is enthusiastic all the time. They can work 24/7. They usually don’t get tired and so AI can be used efficiently
in areas we need work all the time. They can do multi task with minimized errors. Another key benefit is
personalization; AI tailors services and recommendations based on user’s behaviour. AI reduces the operational
costs and boosts productivity. By automating routine work, AI reduces cost and increases efficiency. AI can be
used to improve safety. These are some of the benefits of Artificial Intelligence.
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Evolution of Artificial Intelligence
AI has evolved due to advancement of technology. In 1940s to 1950s, Alan Turing, John McCarthy generated
an idea of Machine Learning”. In 1956 the term artificial intelligence was coined by Dartmouth Conference by
John McCarthy and his colleagues. This was the beginning for AI research. The early years of AI focused on
reasoning, problem saving, simple task and logic theorist. However, due to lack of computational power and
practical applications, progress was slow, leading to first “AI winterin 1970s, when funding and interest in AI
research declined. The 1980s saw the rise ofexpert systemsthat used rules to replicate human decision making
in specific domains. Although these systems showed promise, they were rigid and expensive, which led to a
second AI winter by the late 1980s. A major shift occurred in the 1990s and early 2000s when researchers began
focusing on machine learning using statistical meths and data driven approaches instead of hard coded logic.
This era saw the emergence of algorithms like decision trees, neural networks and support vector machines. The
real transformation came in the 2010s with the availability of big data, increased computing power and the
advancement of deep learning. Deep learning is a subset of machine learning involving multi-layer neural
network. AI systems began achieving impressive results in image recognition, natural language processing and
speech synthesis and real-world application of virtual assistants. In recent years, AI has moved towards
generative models, capable of creating human like text, images, music and videos. At the same time ethical
concerns around bias, job displacement, privacy and misuse of AI have sparked global debates on AI safety,
governance and regulation. The current focus is on responsible and explainable AI. A future possibility where
machines could exhibit human level intelligence. Overall, AI has evolved from theoretical ideas to becoming
one of the most transformative technologies of the 21
st
century, deeply embedded in our daily lives and
continuing to shape the way we work, communicate and live.
Human Resource Management
Human Resource Management is referred to management of organization’s work force. They are responsible for
creating and overseeing policies for the workforce. The term “Human Resource Managementwas first used in
1900s. human resource is a strategic approach to manage workforce. The main goal of HRM is to effectively use
the human resource and enhance their performance to the maximum that they contribute to the organization. The
functions of human resource management are aligned in such a way that it effectively uses the human resources
in an organisation. It uses strategic approaches to manage workforce.
It is also concerned with policies and governing systems to ensure smooth organisation in the workplace. Since
business stated growing and lot of changes has evolved over period there came a need for effective human
resource management. They need to look at proper organization of human resource in an organisation. Human
resource is an essential department in any organisation since the success of the organisation solely depends on
its workforce. Thus, human resource management is an essential role in a business organisation. Earlier human
resource management was only for recruitment and payroll but the emerging trends made human resource
management focus on strategies for workforce and also to the contribution of success of the organisation. The
human resource management functions are evolving continuously.
Functions of Human Resource Management
Functions of Human Resource Management is classified in Managerial functions and Operational functions. The
Managerial functions involve in the management activities like Planning, Organizing, Directing and Controlling.
Planning is the process of forecasting the future needs and framing steps to prepare to work on those needs.
Organizing is the process of creating tasks that aligns with the goals to be achieved. When these tasks are carried
out the goals can be achieved. Directing is the process of guiding and motivating to take up task that is aligned
with the goals. Employees often lose motivation and therefore it is necessary to guide them whenever necessary,
motivate them to reach the goal. Controlling is overseeing what is being carried out and corrections are made
whenever necessary to achieve the goal. These are the managerial functions of Human Resource Management.
They are called as managerial functions since they are employed to manage the task to achieve the desired goals.
Operational functions of Human resource management comprise of functions that Human Resource carry out.
Recruitment is the process of seeking candidates and choosing the right candidate at the right place and at right
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time. Training and Development is an other operational function of Human Resource Management. It is the
process by which training is given to the employees to equip them with skills necessary to do the task. It helps
in enhancing the existing abilities. Training and development help in doing the task better and also helps the
person to grow professionally. Performance Appraisal is the process to evaluating the performance of the
employees and giving them proper feedback. This process helps in improvement of the employees. Performance
Appraisal is done on proper intervals. It is very essential to track the improvement or performance lag of an
employee. Compensation and benefits is the process of fixing salary for employees and also determine other
benefits for a particular role looking at the market value and the role of a particular employee. Employee
Relations focus on having a healthy relationship between employer and employee. Human resource information
system is using technology to maintain employee records. Every organisation has a unique HRIS software. This
helps in maintaining employee records properly. These are the managerial and operational functions of Human
Resource Management.
Statement of the problem
In todays fast and competitive business world organizations are under immense pressure to maximize efficiency,
reduce operational costs and enhance productivity. Human Resource departments are now expected to act as
strategic partners in driving organizational success. However, many HR functions continue to go down by time
consuming and manual processes such as recruitment, payroll, training, and performance management which
often results in delayed decision-making and inefficiencies. This creates a barrier in achieving organizational
goals and maintaining long-term sustainability.
The emergence of Artificial Intelligence (AI) presents a transformative opportunity for HR functions to evolve.
AI-powered tools offer automation, real-time data processing, predictive analytics and intelligent decision-
making capabilities. These technologies have the potential to drastically reduce the time spent on repetitive HR
tasks, allowing professionals to redirect their focus toward strategic and people-centric initiatives. Despite the
growing availability of AI solutions, many organizations especially in developing countries are still in the early
stages of adoption or lack a clear understanding of its tangible benefits.
Furthermore, while many studies have examined AI adoption in general business contexts, there is limited
empirical research that specifically focuses on how AI-driven time optimization impacts various HR functions
and contributes to organizational effectiveness. This lack of specific data and understanding poses a challenge
for HR leaders who are uncertain about investing in or relying on AI technologies to enhance their processes.
Another important concern is the human element whether employees and HR professionals feel displaced,
empowered, or burdened by AI adoption. While technology may offer speed, its success also depends on the
cultural and behavioural acceptance of AI within the organization. Hence, a deeper investigation is needed to
explore not only the technical outcomes of AI but also the human response it invokes within HR settings.
Therefore, this study aims to fill this critical gap by assessing the real-time impact of AI-powered time
optimization on HR operations and analysing how such optimization translates into improved organizational
performance. By doing so, the research intends to offer actionable insights to HR professionals, organizational
leaders, and policymakers for making informed decisions about AI integration in human resource management.
Need of the study
The importance of this study if focused on demand of the innovation which is the main factor behind the
technology growth. Everyone knows time is very precious and we all work towards using the time efficiently.
When we use AI in certain functions of Human Resource Management, reduces time and this is really a game
changer. This can reduce a lot of time and make the work efficient in very less time and this strengthen HR
capabilities. Many organisations understand the benefits of using Artificial intelligence in their regular
functioning and few adopt these practices, few delay to adopt or implement. The study focuses on the ways in
which Artificial intelligence can support an organisations HR function to transform and create an organization
efficiency.
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HR professional has multiple roles to look. The handle roles from recruitment to compliance and lack of
technological support and thus this consumes a lot of time particularly for an HR professional as they mostly
rely on manual tasks. Usage of AI can contribute a lot in time optimization and can provide faster activities and
enable the professional to focus more on other roles such as employee engagement and retention strategy
building. This strategy of using Artificial intelligence in certain aspects of HR functions that are routine and
doesn't require much attention can enable them to save a lot of time and ultimately contribute to organizational
effectiveness.
Additionally, this research holds importance for organizational decision-makers who are hesitant about investing
in AI due to lack of clarity or fear of job displacement. By showcasing evidence-based outcomes this study can
encourage responsible AI adoption that enhances both technological and human potential.
This study contributes to the relatively underexplored field of AI in HR-specific time management. Most
literature either discusses AI in broad business contexts or focuses on automation in isolation. This research
narrows the lens to examine AI through the specific function of time optimization in HR, which contributes to
more studies in this area of study.
Ultimately, this study is significant not only for business growth but also for employee wellbeing and satisfaction.
When HR functions become more efficient and strategic, employees benefit from faster services, better
communication, and more focused developmental support. Therefore, understanding the role of AI in optimizing
time within HR is not just a technological advancement its a human centred move toward building better,
smarter, and more sustainable workplaces.
REVIEW OF LITERATURE
Bain & Company (2025) A study by Bain & Company highlights how Generative AI is transforming Human
Resource functions from task-oriented roles to strategic roles. Using their Generative AI Workforce Impact
Explorer tool, HR functions can save 15–20% of labour time on average, with HR operations saving up to 35%,
talent acquisition up to 20%, and HR business partners up to 15%. This indicates that AI has a greater impact on
routine and operational tasks compared to strategic roles, reflecting principles of Scientific Management Theory,
where efficiency and task optimization are emphasized. However, the study extends beyond traditional efficiency
models by showing that time savings enable HR professionals to transition into roles such as culture designers
and strategic advisors, aligning with modern Strategic Human Resource Management (SHRM) perspectives.
Compared to other studies, Bain focuses more on quantifiable efficiency gains, but gives limited attention to
ethical and human-centered concerns.
Erik (2025) explains that Artificial Intelligence in HR transforms key organizational functions such as
recruitment, employee engagement, performance management, and workforce planning. AI tools like machine
learning, chatbots, and analytics help streamline administrative tasks including resume screening, onboarding,
and job description creation, allowing HR professionals to focus on strategic initiatives such as talent
development and inclusivity. Unlike Bain, Erik emphasizes ethical concerns, including algorithmic bias, data
privacy, and governance, highlighting the importance of transparency and policy frameworks. This aligns with
Socio-Technical Systems Theory, which stresses the balance between technology and human factors. The study
critically contributes by arguing that AI adoption is not just technological but requires organizational trust and
governance, an aspect less emphasized in efficiency-driven studies.
Gartner (2025) reports a significant increase in Generative AI adoption in HR, from 19% in 2023 to 61% in
2025, indicating a shift from experimentation to strategic implementation. Organizations are creating new roles
such as AI product leaders, HR technologists, and establishing AI centres of excellence. The study predicts that
37% of employees will be impacted by AI within 2–5 years, while overall job numbers remain stable with
significant job creation expected by 2036. This reflects Human Capital Theory, where employee adaptability and
skills become critical assets. However, Gartner presents a techno-optimistic view, which contrasts with other
studies that highlight concerns about job displacement and ethical risks, indicating a gap between future
projections and present organizational challenges.
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HR Convo (2025) discusses how AI integration in HR functions enhances efficiency by automating repetitive
tasks such as resume screening, interview scheduling, and onboarding. It also highlights AIs role in tracking
employee sentiment, identifying behavioural patterns, and improving retention strategies. The study emphasizes
improved decision-making and organizational efficiency through data-driven insights. This aligns with the
Resource-Based View (RBV), where AI acts as a strategic resource that enhances organizational capabilities.
However, compared to Erik (2025), the study provides limited discussion on ethical challenges and governance,
focusing primarily on operational benefits.
IBM (2025) highlights the role of AI, particularly through its Watson platform, in transforming HR by automating
routine processes such as onboarding, payroll, and leave management. AI chatbots reduce time spent on routine
tasks by up to 75%, enabling HR professionals to focus on strategic and human-centered roles. The study reports
measurable outcomes, including a 10% improvement in hiring quality and a 33% reduction in attrition,
demonstrating strong organizational impact. IBM emphasizes transparency, fairness, and human oversight,
aligning with Ethical AI frameworks and Contingency Theory, where technology must align with organizational
needs. Compared to Bain, IBM provides both quantitative results and ethical considerations, offering a more
balanced perspective.
SHRM (2025) notes that AI usage in HR has increased from 26% to 43%, particularly in recruitment activities
such as job advertisement creation, resume screening, and candidate sourcing. AI is also used in employee
engagement, predictive analytics, and workforce planning. The study emphasizes that while AI improves
efficiency and decision-making, human control is essential to ensure fairness, transparency, and legal
compliance. This reflects a Human-AI collaboration approach and aligns with Strategic HRM theories, where
technology supports but does not replace human judgment. Compared to Gartner’s optimistic outlook, SHRM
provides a more balanced and practical perspective, highlighting both opportunities and risks.
Adecco Group (2024) reports that employees using AI save approximately one hour per day, contributing to
improved productivity, work-life balance, and job satisfaction. The study suggests that time saved from routine
tasks can be redirected toward creative and strategic activities. This aligns with Herzbergs Two-Factor Theory,
were reduced workload and increased efficiency act as motivators for job satisfaction. However, the study does
not critically examine potential negative outcomes such as work intensification or role ambiguity, which limits
its analytical depth compared to other studies.
Ali Fenwick et al. (2024) provide a theoretical perspective on the evolution of HRM in the AI era, describing
three phases of AI integration: technocratic, integrated, and fully embedded. This progression reflects Lewins
Change Management Theory, where organizations gradually adapt to technological transformation. The study
emphasizes the importance of a human-centered approach, ensuring that AI enhances rather than replaces human
capabilities. Compared to other studies, this work offers a strong theoretical foundation, bridging the gap
between technological advancement and humanistic management, and highlighting the need to balance
efficiency with ethical and social considerations.
METHODOLOGY
Title of the Study
A study on the influence of AI powered time optimization on HR functions.
General and Specific Objectives
General objective:
To examine how AI powered time optimization influences HR functions and organizational outcomes.
Specific objectives:
To study the demographic profile of the respondents.
To evaluate the relationship between use of AI in HR functions and time efficiency.
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Field of Study
The study was conducted among HR professionals working in medium and large IT companies located in
Chennai. Chennai was chosen because of its dense concentration of firms with HR technologies and the easy
accessibility for the researcher.
Pilot Visit
A pilot visit was conducted with two organizations and an interaction was held with their HR teams to understand
current AI use, feasibility of data collection and permission procedures.
Research Design
Descriptive research design was adopted. Descriptive research design is a systematic methodology used to
describe the characteristics of a population, event or phenomenon.
Selection of Sample
Population/Universe: HR professionals employed in registered IT organization in Chennai.
Sampling frame: HR professionals from IT companies in Chennai.
Sampling technique: Purposive Sampling technique was used. Purposive sampling, also known as judgmental
or selective sampling, is a non-probability sampling technique in which researchers select participants based on
their knowledge, relevance or expertise concerning the research topic. Purposive sampling because HR
professionals who use AI can be more suitable to be the respondents of the study.
Sample size: 100 respondents.
Inclusion criteria: HR professionals in IT company.
Tools of Data Collection
A self-structured questionnaire based on the objectives. A reliability test was carried for the tool and the reliability
score is 0.91 and therefore, the tool was validated for data collection.
Sources of Data
Primary data: Collected directly from HR professionals working in IT sector.
Secondary data: Published literature, articles, research papers, website articles, journals, etc on AI in HR
functions.
Pre-testing
The questionnaire was pre-tested with 10 HR professionals similar to the target sample. Items will be checked
for clarity, length and relevance
Data Collection
Data was collected using printed questionnaire. This will be circulated among the HR professionals working in
IT sector to collect data for the research.
Definition of Terms
Artificial Intelligence is a branch of computer science that focuses on creating systems or machines capable of
performing tasks that normally require human intelligence.
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Human Resource Management is the process of planning, organizing, directing, and controlling the functions of
procurement, development, compensation, integration, maintenance, and separation of human resources to
achieve organizational goals effectively.
Analysis
Data was analysed using SPSS and Microsoft Excel.
Limitations and Scope of the Study
The study was limited to HR professionals in Chennai, which may restrict generalization. Results were based on
self-reported perceptions and so it might vary with different population.
Inferential Statistics
Corelation was identified using SPSS to understand the association between variables and to test the hypothesis.
It was also compared with the literatures and interpreted.
TABLE 1: AGE OF THE RESPONDENTS
S.NO
AGE
FREQUENCY
PERCENTAGE
1
18-24
25
35.7
2
25-34
45
64.3
TOTAL
70
100
Chart 1: Age of the Respondents
The above table and charts show that majority of the respondents (64.3%) belong to the age group 25-34 age
and a small size (35.7%) of the respondents belong to the age group 18-24 age.
TABLE 2: Gender of the Respondents
S.NO
GENDER
FREQUENCY
1
Male
36
2
Female
34
TOTAL
70
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Chart 2: Gender of the Respondents
The above table and charts show that more than half of the respondents (51.4%) are Male and almost half of the
respondents (48.6%) are Female.
Table 3: Qualification of the Respondents
S.NO
QUALIFICATION
FREQUENCY
PERCENTAGE
1
UG
9
12.9
2
PG
61
87.1
TOTAL
70
100
Chart 3: Qualification of the Respondents
The above table and charts show that a sizeable majority of the respondents (87.1%) has postgraduate
qualification and a meagre (12.9%) of the respondents has undergraduate qualification.
Table 4: Current Role of the Respondents
S.NO
CURRENT ROLE
FREQUENCY
PERCENTAGE
1
HR trainee
35
50
2
HR BP
32
45.7
3
HR manager
3
4.3
TOTAL
7
100
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Chart 4: Current Role of the Respondents
The above table and charts show that half of the respondents (50%) are HR Trainees, almost half of the
respondents (45.7%) are HRBP and a meagre (4.3%) of the respondents are HR Manager.
Table 5: Years of Experience of the Respondents
S.NO
YEARS OF EXPERIENCE
FREQUENCY
PERCENTAGE
1
Less than 2 years
22
31.4
2
2-5 years
47
67.1
3
6-10 years
1
1.4
TOTAL
70
100
Chart 5: Years of Experience of the Respondents
The above table and charts show that majority of the respondents (67.1%) have 2-5 years of experience, a small
size (31.4%) have less than 2 years of experience and a meagre (1.4%) of the respondents have 6-10 years of
experience.
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Table 6: Work Mode
S.NO
WORK MODE
FREQUENCY
PERCENTAGE
1
On site
64
91.4
2
Remote
4
5.7
3
Hybrid
2
2.9
TOTAL
70
100
Chart 6: Work Mode
The above table and charts show that almost all of the respondents (91.4%) work on-site, a meagre (5.7%) of the
respondents work in remote mode and a meagre (2.9%) of the respondents work in hybrid mode.
Table 7: AI has Simplified Mass Campus Hiring and Lateral Hiring in It Companies
S.NO
AI HAS SIMPLIFIED MASS CAMPUS
HIRING AND LATERAL HIRING IN IT
COMPANIES
FREQUENCY
PERCENTAGE
1
Strongly disagree
4
5.7
2
Disagree
1
1.4
3
Neutral
7
10.0
4
Agree
38
54.3
5
Strongly disagree
20
28.6
TOTAL
70
100
The above table show that more than half (54.3%) of the respondents agree that AI has simplified mass campus
hiring and lateral hiring in IT companies, a small size (28.6%) of the respondents strongly agree, a meagre (10%)
of the respondents are neutral, a meagre (5.7%) of the respondents strongly disagree and a meagre (1.4%) of the
respondents disagree that AI has simplified mass campus hiring and lateral hiring in IT companies.
Table 8: AI Tools Have Reduced Hiring Limitations in it Sector
S.NO
AI TOOLS HAVE REDUCED HIRING
LIMITATIONS IN IT SECTOR
FREQUENCY
PERCENTAGE
1
Strongly disagree
1
1.4
2
Disagree
3
4.3
3
Neutral
7
10.0
4
Agree
41
58.6
5
Strongly disagree
18
25.7
TOTAL
70
100
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue III, March 2026
The above table show that more than half (58.6%) of the respondents agree that AI tools have reduced hiring
limitations in IT sector, a small size (25.7%) of the respondents strongly agree, a meagre (10%) of the
respondents are neutral, a meagre (49.3%) of the respondents disagree and a meagre (1.4%) of the respondents
strongly disagree that AI tools have reduced hiring limitations in IT sector.
Table 9: AI -Powered Learning Management Systems have Made Skill Upgradation Faster
S.NO
AI-POWERED LEARNING MANAGEMENT SYSTEMS
HAVE MADE SKILL UPGRADATION FASTER
FREQUENCY
PERCENTAGE
1
Strongly disagree
3
4.3
2
Disagree
2
2.9
3
Neutral
11
15.7
4
Agree
30
42.9
5
Strongly disagree
24
34.3
TOTAL
70
100
The above table show that almost half (42.9%) of the respondents agree that AI-powered learning management
systems have made skill upgradation faster, a small size (34.3%) of the respondents strongly agree, a meagre
(15.7%) of the respondents is neutral, a meagre (4.3%) of the respondents strongly disagree and a meagre (2.9%)
of the respondents disagree that AI-powered learning management systems have made skill upgradation faster.
Table 10: AI-Enabled Performance Management System Helps Manage Remote/Hybrid it Employees
Efficiently
S.NO
AI-ENABLED PERFORMANCE MANAGEMENT
SYSTEM HELPS MANAGE REMOTE/HYBRID IT
EMPLOYEES EFFICIENTLY
FREQUENCY
PERCENTAGE
1
Strongly disagree
2
2.9
2
Disagree
3
4.3
3
Neutral
3
4.3
4
Agree
36
51.4
5
Strongly disagree
26
37.1
TOTAL
70
100
The above table show that more than half (51.4%) of the respondents agree that AI-enabled performance
management system helps manage remote/hybrid IT employees efficiently, a small size (37.1%) of the
respondents strongly agree, a meagre (4.3%) of the respondents is neutral, a meagre (4.3%) of the respondents
disagree and a meagre (2.9%) of the respondents strongly disagree that AI-enabled performance management
system helps manage remote/hybrid IT employees efficiently.
Table 11: Resume Screening Through AI Saves Significant Time Compared to Manual Screening
S.NO
RESUME SCREENING THROUGH AI SAVES
SIGNIFICANT TIME COMPARED TO MANUAL
SCREENING
FREQUENCY
PERCENTAGE
1
Strongly disagree
3
4.3
2
Disagree
1
1.4
3
Neutral
10
14.3
4
Agree
30
42.9
5
Strongly disagree
26
37.1
TOTAL
70
100
The above table show that almost half (42.9%) of the respondents agree that resume screening through AI saves
significant time compared to manual screening, a small size (37.1%) of the respondents strongly agree, a meagre
(14.3%) of the respondents is neutral, a meagre (1.4%) of the respondents disagree and a meagre (4.3%) of the
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue III, March 2026
respondents strongly disagree that resume screening through AI saves significant time compared to manual
screening.
Table 12: Chatbots and AI -Based Employee Helpdesks Save HR Time in Responding to Employees
S.NO
CHATBOTS AND AI-BASED EMPLOYEE
HELPDESKS SAVE HR TIME IN RESPONDING TO
EMPLOYEES
FREQUENCY
PERCENTAGE
1
Strongly disagree
2
2.9
2
Disagree
2
2.9
3
Neutral
9
12.9
4
Agree
35
50.0
5
Strongly disagree
22
31.4
TOTAL
70
100
The above table show that half (50%) of the respondents agree that chatbots and AI-based employee helpdesks
save HR time in responding to employees, a small size (31.4%) of the respondents strongly agree, a meagre
(12.9%) of the respondents is neutral, a meagre (2.9%) of the respondents disagree and a meagre (2.9%) of the
respondents strongly disagree that chatbots and AI-based employee helpdesks save HR time in responding to
employees.
Table 13: AI Reduces Time in Managing Employee Attendance and Scheduling
S.NO
AI REDUCES TIME IN MANAGING EMPLOYEE
ATTENDANCE AND SCHEDULING
FREQUENCY
PERCENTAGE
1
Strongly disagree
3
4.3
2
Disagree
1
1.4
3
Neutral
10
14.3
4
Agree
26
37.1
5
Strongly disagree
30
42.9
TOTAL
70
100
The above table show that almost half (42.9%) of the respondents strongly agree that AI reduces time in
managing employee attendance and scheduling, a small size (37.1%) of the respondents agree, meagre (14.3%)
of the respondents is neutral, a meagre (1.4%) of the respondents disagree and a meagre (4.3%) of the
respondents strongly disagree that AI reduces time in managing employee attendance and scheduling.
Table 14: AI Saves Time in Training Employees
S.NO
AI SAVES TIME IN TRAINING EMPLOYEES
FREQUENCY
PERCENTAGE
1
Strongly disagree
2
2.9
2
Disagree
4
5.7
3
Neutral
7
10.0
4
Agree
34
48.6
5
Strongly disagree
23
32.9
TOTAL
70
100
The above table show that almost half (48.6%) of the respondents agree that AI saves time in training employees,
a small size (32.9%) of the respondents strongly agree, a meagre (10%) of the respondents is neutral, a meagre
(5.7%) of the respondents disagree and a meagre (2.9%) of the respondents strongly disagree that AI saves time
in training employees.
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue III, March 2026
Table 15: AI-Based Performance Management Systems Save Significant Time Compared to Traditional
Appraisal Methods
S.NO
AI-BASED PERFORMANCE MANAGEMENT
SYSTEMS SAVE SIGNIFICANT TIME COMPARED TO
TRADITIONAL APPRAISAL METHODS
FREQUENCY
PERCENTAGE
1
Strongly disagree
2
2.9
2
Disagree
2
2.9
3
Neutral
8
11.4
4
Agree
35
50.0
5
Strongly disagree
23
32.9
TOTAL
70
100
The above table show that half (50%) of the respondents agree that AI-based performance management systems
save significant time compared to traditional appraisal methods, a small size (32.9%) of the respondents strongly
agree, a meagre (11.4%) of the respondents is neutral, a meagre (2.9%) of the respondents disagree and a meagre
(2.9%) of the respondents strongly disagree that AI-based performance management systems save significant
time compared to traditional appraisal methods.
Table 16: AI-Based Compensation Systems Reduce Time Taken for Salary and Incentive Decisions
S.NO
AI-BASED COMPENSATION SYSTEMS REDUCE
TIME TAKEN FOR SALARY AND INCENTIVE
DECISIONS
FREQUENCY
PERCENTAGE
1
Strongly disagree
1
1.4
2
Disagree
3
4.3
3
Neutral
10
14.3
4
Agree
30
42.9
5
Strongly disagree
26
37.1
TOTAL
70
100
The above table show that almost half (42.9%) of the respondents agree that AI-based compensation systems
reduce time taken for salary and incentive decisions, a small size (37.1%) of the respondents strongly agree, a
meagre (14.3%) of the respondents is neutral, a meagre (4.3%) of the respondents disagree and a meagre (1.4%)
of the respondents strongly disagree that AI-based compensation systems reduce time taken for salary and
incentive decisions.
Table 17: Correlation Between AI -Enabled Recruitment Simplification and Time Efficiency in Resume
Screening
Correlations
AI has simplified mass
campus hiring and lateral
hiring in IT companies
Resume screening through AI
saves significant time compared
to manual screening
AI has simplified mass
campus 1 and lateral 1
in IT companies
Pearson
Correlation
1
.734
**
Sig. (2-tailed)
.000
N
70
70
Resume screening
through AI saves
significant time
compared to manual
screening
Pearson
Correlation
.734
**
1
Sig. (2-tailed)
.000
N
70
70
** Correlation is significant at the 0.01 level (2-tailed).
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue III, March 2026
Upadhyay and Khandelwal (2018) stated that AI-based recruitment tools significantly reduce time-to-hire and
improve operational efficiency by automating resume screening and candidate shortlisting. The strong positive
correlation (r = 0.734) in the present study supports these earlier findings, demonstrating that AI not only
simplifies recruitment processes but also contributes significantly to time efficiency in resume screening.
Table 18: Correlation Between AI -Based Training Time Efficiency and Skill Upgradation Through AI -
Powered Learning Management Systems
Correlations
AI saves time in
training employees
AI-powered Learning Management
Systems have made skill
upgradation faster
AI saves time in
training employees
Pearson
Correlation
1
.553
**
Sig. (2-tailed)
.000
N
70
70
AI-powered Learning
Management Systems
have made skill
upgradation faster
Pearson
Correlation
.553
**
1
Sig. (2-tailed)
.000
N
70
70
**. Correlation is significant at the 0.01 level (2-tailed).
Bersin (2020) highlighted that AI-powered learning platforms personalize training content, thereby reducing
learning time and improving competency development. The moderate positive correlation (r = 0.553) in this
study aligns with these scholarly contributions, confirming that AI integration in training functions contributes
not only to time efficiency but also to faster skill development.
Table 19: Correlation Between AI -Based Attendance Management Efficiency and Time Savings in AI-
Driven Performance Appraisal Systems
Correlations
AI reduces time in managing
employee attendance and
scheduling
AI-based performance
management systems save
significant time compared to
traditional appraisal methods
AI reduces time in
managing employee
attendance and
scheduling
Pearson
Correlation
1
.594
**
Sig. (2-tailed)
.000
N
70
70
AI-based performance
management systems
save significant time
compared to traditional
appraisal methods
Pearson
Correlation
.594
**
1
Sig. (2-tailed)
.000
N
70
70
**. Correlation is significant at the 0.01 level (2-tailed).
Kapoor and Sherif (2019) observed that AI-based HR systems significantly reduce administrative workload in
attendance tracking and appraisal documentation. The moderate positive correlation (r = 0.594) found in this
study supports these scholarly contributions, indicating that AI integration across different HR functions such as
attendance management and performance appraisal collectively contributes to time efficiency.
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue III, March 2026
TABLE 20: Correlation Between AI -Driven Time Optimization and Employee Productivity with Employee
Satisfaction and Retention
Correlations
AI-driven time optimization
improves employee productivity
in IT firms
Time efficiency in HR
processes improves
employee satisfaction and
retention
AI-driven time
optimization improves
employee productivity
in IT firms
Pearson
Correlation
1
.584
**
Sig. (2-tailed)
.000
N
70
70
Time efficiency in HR
processes improves
employee satisfaction
and retention
Pearson
Correlation
.584
**
1
Sig. (2-tailed)
.000
N
70
70
**. Correlation is significant at the 0.01 level (2-tailed).
Deloitte (2022) reported that digital HR transformation positively influences employee engagement and
retention through streamlined HR services. The moderate positive correlation (r = 0.584) found in this study
aligns with these scholarly insights, suggesting that AI-driven time efficiency not only improves productivity
but also indirectly enhances employee satisfaction and retention.
MAJOR FINDINGS
To study the demographic profile of the respondents
Majority of the respondents (64.3%) belong to the age group 25-34 age.
More than half of the respondents (51.4%) are Male.
Majority of the respondents (87.1%) has postgraduate qualification.
Half of the respondents (50%) are HR Trainees.
Majority of the respondents (67.1%) have 2-5 years of experience.
Almost all of the respondents (91.4%) of the respondents work on-site.
To evaluate the relationship between use of AI and time efficiency
More than half (54.3%) of the respondents agree that AI has simplified mass campus hiring and lateral
hiring in IT companies.
More than half (58.6%) of the respondents agree that that AI tools have reduced hiring limitations in IT
sector.
Almost half (42.9%) of the respondents agree that AI-powered learning management systems have
made skill upgradation faster.
More than half (51.4%) of the respondents agree that AI-enabled performance management system
helps manage remote/hybrid IT employees efficiently.
Almost half (42.9%) of the respondents agree that resume screening through AI saves significant time
compared to manual screening.
Half (50%) of the respondents agree that chatbots and AI-based employee helpdesks save HR’s time in
responding to employees.
Almost half (42.9%) of the respondents strongly agree that AI reduces time in managing employee
attendance and scheduling.
Almost half (48.6%) of the respondents agree that AI saves time in training employees.
Half (50%) of the respondents agree that AI-based performance management systems save significant
time compared to traditional appraisal methods.
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue III, March 2026
Almost half (42.9%) of the respondents agree that AI-based compensation systems reduce time taken
for salary and incentive decisions.
Suggestion:
The findings of this study indicate that the integration of Artificial Intelligence (AI) into HR functions
significantly enhances time efficiency across various operational areas. HR activities such as recruitment, resume
screening, interview scheduling, attendance management, payroll processing and responding to routine
employee queries are traditionally time-consuming and manual in nature. The adoption of AI-powered systems,
including Applicant Tracking Systems, automated attendance software and AI-enabled chatbots can reduce
administrative workload and processing time. By automating repetitive and data-intensive tasks, AI enables HR
professionals to allocate more time toward strategic functions such as employee engagement, workforce
planning, training and organizational development. Thus, AI contributes not only to operational efficiency but
also to the strategic contribution of the HR function.
The successful implementation of AI in HR requires organizational readiness and capacity building. The study
suggests that management should invest in structured training programs to enhance digital competencies among
HR professionals. Resistance to technological change, fear of job displacement and lack of technical knowledge
may hinder effective adoption. Therefore, pilot testing of AI tools within specific HR functions, is recommended.
This would allow organizations to measure time savings, assess cost–benefit outcomes and address practical
challenges before large-scale implementation.
At the same time, ethical and governance considerations must be prioritized while adopting AI in HR practices.
Organizations must ensure data privacy, confidentiality and transparency in AI-based decision-making
processes. Mechanisms should be established to monitor and minimize algorithmic bias, particularly in
recruitment and performance evaluation. Importantly, AI should function as a decision-support system rather
than a replacement for human judgment. The human aspects of HR, including empathy, interpersonal
communication and emotional intelligence, remain critical for effective people management. Therefore, a
balanced integration of AI efficiency with human expertise is essential to achieve sustainable improvements in
time efficiency and overall organizational effectiveness.
Summary
AI-powered time optimization influences Human Resource (HR) functions in organizations. Artificial
Intelligence helps HR departments save time by automating routine tasks such as resume screening, scheduling
interviews, monitoring attendance and analysing employee performance. By reducing manual work, AI allows
HR professionals to focus more on important activities like employee development, engagement and strategic
decision-making. The study also highlights that AI improves productivity in HR processes. Overall, the study
shows that AI-powered time optimization positively supports HR functions and helps organizations manage their
workforce more effectively.
CONCLUSION
This study concludes that the integration of Artificial Intelligence (AI) in HR functions has a significant positive
influence on time efficiency. The findings indicate that AI-powered tools help reduce the time spent on repetitive
and administrative tasks such as resume screening, interview scheduling, attendance management, payroll
processing and responding to routine employee queries. By automating these processes, HR professionals are
able to minimize manual errors, improve accuracy and complete tasks more quickly. As a result, HR departments
can shift their focus from operational activities to more strategic and value-added functions such as employee
engagement, talent development and organizational planning.
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BIBLIOGRAPHY
1. Agenda, A. I. (2022). Artificial intelligence in human resources management: A review and research
agenda. Pacific Asia Journal of the Association for Information Systems, 14(6).
https://aisel.aisnet.org/pajais/vol14/iss6/1/
2. AIHR. (2025). AI and automation in HR: Impact, adoption and future outlook.
https://www.aihr.com/blog/ai-and-automation-in-hr/
3. Anderson, K. (2024). How artificial intelligence is transforming HR. IHRIM.
https://www.ihrim.org/2020/02/how-artificial-intelligence-is-transforming-hr/
4. Aon. (2024, May 9). How artificial intelligence is transforming human resources and the workforce.
https://www.aon.com/en/insights/articles/how-artificial-intelligence-is-transforming-human-resources-
and-the-workforce
5. Ayondo, O., Karaarslan, E., & Narin, N. G. (2024). Artificial intelligence, VR, AR and metaverse
technologies for human resources management. arXiv. https://arxiv.org/abs/2406.15383
6. Belagalla, N. (2025). The role of artificial intelligence in transforming human resource management:
Opportunities and challenges. Journal of Information Systems Engineering & Management.
7. Chowdhury, S. R. (2024). Artificial intelligence enabled human resource management: A review and
future research avenues. Archives of Business Research.
https://journals.scholarpublishing.org/index.php/ABR/article/view/17050
8. Colvin, C. (2024, January 23). How AI can save HR time on the job, according to one practitioner. HR
Dive. https://www.hrdive.com/news/ai-save-time-hr-tasks/704940/
9. Convo, H. (2025, May 19). Automating HR processes: How AI is saving time and reducing costs. HR
Convo.ai. https://hrconvo.ai/ai-in-hr-process-automation/
10. Fenwick, A. (2024). Revisiting the role of HR in the age of AI: Bringing humans and machines closer
together in the workplace. Frontiers in Research. https://pmc.ncbi.nlm.nih.gov/articles/PMC10822991/
11. Gartner. (2025). AI in HR: Position your organization for success. https://www.gartner.com/en/human-
resources/topics/artificial-intelligence-in-hr
12. GeeksforGeeks. (n.d.). AI in manufacturing: Revolutionizing the industry.
https://www.geeksforgeeks.org/artificial-intelligence/ai-in-manufacturing-revolutionizing-the-industry/
13. GeeksforGeeks. (n.d.). Evolution of AI. https://www.geeksforgeeks.org/artificial-intelligence/evolution-
of-ai/
14. Google Cloud. (n.d.). What is artificial intelligence? https://cloud.google.com/learn/what-is-artificial-
intelligence