Artificial Intelligence of Leadership Resource Management and

Talent System

N. Vinodh and A. K. Subramani

Saveetha School of Management, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai,

Tamil Nadu, India

Keywords: Artificial Intelligence, Cloud Platform, Human Resource Development, Talent System.

Abstract: The first thing the paper does is use the Java EE or Python programming language to develop and implement

a human resources management system with a solid human-computer interface. The goal of human resource

management is to remove obstacles that stand in the way of business expansion. By doing this, the company's

human resource development may operate in an environment that is conducive to growth. The coupling issue

between multiple levels can be effectively resolved by it. The system's benefits include an easy-to-use

interface, secure functioning, and effective human-computer interaction. It fulfils numerous purposes. This

paper then suggests a method for estimating the cost of human resources. The research employs the chaos

principle to gather the enterprise's data for human resource cost estimation, restructure it, and bring back its

evolving features. The "extreme learning machine" was employed to assess the performance of the

organization and the expenses related to its human resources. The study's findings demonstrate that this

approach can enhance the effectiveness of enterprise performance evaluation and more accurately reflect the

cost-changing characteristics of enterprise personnel.

1 INTRODUCTION

Human resource management has grown in

importance and necessity as a crucial component of

enterprise management as a result of the economy's

rapid development and the rising level of management

(

Uriarte,S.,et al., 2025). However, a lot of conventional

HRM techniques still rely heavily on experience and

subjective assessment rather than statistical or data

analytic support, which can easily result in poor

decisions and resource waste. The goal of this article

is to provide a decision support system for human

resource management that can lessen related

issues.Advanced technologies, organizational

strategy, and sustainable human resource [HR]

practices are coming together to change the field of

human resource management (

Köchling, A., 2024). By

highlighting the importance of managing crucial data,

ethical issues, and responsible AI governance all of

which would develop a means to enhance and attain

HR excellence the study provides the foundation for

successful HR analytics. It also examines how cutting-

edge analytical techniques and artificial intelligence

(AI) technology might raise the efficacy and

sustainability of HR management procedures.

Investigating these technologies' revolutionary

potential is its goal. By streamlining decision-making

procedures, boosting employee productivity, and

creating long-lasting HR practices, the research seeks

to advance HR excellence. In order to foster

excellence in the field of HR management as a whole

and aid in the development of sustainable HR

practices, this article aims to offer important insights

and theoretical underpinnings. It offers a theoretical,

integrated paradigm to aid in the comprehension of the

aforementioned viewpoint.

1.1 Integration of Human-AI Teams

with Human Systems

These days, given that artificial intelligence (AI) is

included into the majority of vital systems, human

systems integration (HSI) needs to be expanded.

Block Diagram Shown in Figure 1.

Vinodh, N. and Subramani, A. K.

Artiﬁcial Intelligence of Leadership Resource Management and Talent System.

DOI: 10.5220/0013900800004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

511-515

ISBN: 978-989-758-777-1

511

Figure 1: Block diagram.

As a result, the new viewpoint of human-AI

teaming (HAT) must guide the development of

systems engineering and AI. In order to tackle this

task, this essay takes into account human aspects like

situation awareness, risk-taking, and decision-making

(

Halid, H.,2024)

. It brings up questions about the

division of labor, the flexibility of design and

operations, and the incremental design of

organizations, technology, and human capital. More

precisely, it raises the significant problem of AI

systems' need for qualification and the impossibility of

certification, turning them from partners into tools.

Consequently, research on human resource

management and predictive models is evaluated, and

a Neural Network Algorithm-based study on these

topics is presented. In order to reduce interference

factors in the management and prediction models, the

influencing aspects are first found using the gradient

descent theory, and the indicators are then divided

accordingly. Then, a neural network algorithm

management and prediction model scheme is

developed using the gradient descent theory, and the

results of the management and prediction models are

carefully scrutinized (

Jonczyk, N.,. et al., 2025)

. The

MATLAB simulation results show that the neural

network algorithm performs better than the typical

neural network algorithms in specific assessment

conditions with respect to time of influencing

variables, accuracy of prediction models, and

management (

Ribeiro, M.F,. et al., 2024)

. The rules of

the enterprise's employment demand provide a more

informative basis for the enterprise to present the

correct strategy, which has greater practical value. The

human resource demand prediction model is

established based on the RBF neural network, and a

significant amount of disordered data are trained,

learned, and tested.

1.2 Computer-Based Network

Management System for University

Human Resources

The business process of the university's personnel

information management system is examined in this

essay. The university personnel management system

was then created using the university cloud platform,

big data, business services, and diversified business

services (

Kanade A,., 2024)

. This system creates a new

kind of university human resource management

system based on Microsoft Biztalk server. A service

mode based on Microsoft Biztalk Server is suggested

as a solution to the issues with the way university

people are currently working. A genetic algorithm-

based optimization technique is suggested as a

solution to this issue. A novel intelligent sorting

algorithm is suggested based on this foundation.

A novel intelligent sorting algorithm is suggested

based on this foundation. This approach reduces the

operating time by 7.261 seconds when compared to

the current algorithm. The algorithm is put into

practice on the Biztalk server, and its applicability is

confirmed (

Schlichte., 2024)

. Through experimental

testing, the suggested optimization technique can

reduce the inference time when compared to the

current algorithms. The Biztalk server is optimized in

this study using an optimization technique based on

genetic algorithms. The algorithm runs faster,

according to experimental results. This has some

reference value to the BizTalk server design. In a

situation with large data, this technique can

significantly lessen the manual allocation burden

(

Wang, et al., 2025)

. The thesis holds some reference

value in advancing the process of informatizing school

personnel work. Task distribution, system maturity,

and human expectations for flexible Human Liberty

Collaboration (

Muss, C.,, 2025)

. The propose to

investigate, in turn, the consideration of Human

cognition, System maturity, and Task allocation in

order to achieve adaptable Human Autonomy

Teaming. We suggest current, real study projects done

by our research team for each of these three courses.

2 TALENT ACQUISITION

The improving HRM through ai-driven talent

acquisition approach employing deep ResNet

Artificial intelligence (AI) is gaining popularity as a

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

512

technology in human resource management (HRM) to

help businesses expedite and enhance their talent

acquisition procedures. Analysis Output of AI-Driven

Talent Acquisition in HRM S Shown in Figure 2.

It is challenging to swiftly and efficiently find and

hire excellent people when using the traditional HRM

approach. The suggest that the existing systems be

integrated with Deep ResNet, a state-of-the-art deep

learning architecture with remarkable feature

extraction capabilities, as a solution to this issue. The

application of artificial intelligence to human resource

management is gaining popularity, however research

into how Deep ResNet might be utilized to the talent

acquisition process is trailing behind. This study uses

Deep Residual Networks (ResNet) to offer a fresh way

to talent acquisition in HRM.

Figure 2: Analysis output of AI-driven talent acquisition in

HRM.

The study uses a rigorous methodology that

combines real-world case studies, machine learning

approaches, and data analysis. This methodology's

goal is to determine the viability of the suggested AI-

powered talent acquisition plan. To improve its search

and grading capabilities, Deep ResNet will be trained

on many datasets. This directly leads to a considerable

improvement in the accuracy and efficiency of the

process of acquiring new talent.The aim of this

research is to present empirical data that validates

Deep ResNet's effectiveness in enhancing HRM

procedures.

3 HRM EFFECTIVENESS IN THE

CONTEMPORARY

WORKPLACE

This study, "Strategic Leadership: A Driver for

Enhancing Human Resource Performance in the

Contemporary Workplace," explores the complex

relationship that exists between HR performance and

strategic leadership. Through a thorough assessment

of pertinent academic literature and the use of

comparative analysis, this research illuminates the

significant influence of strategic leadership on worker

engagement, HR innovation, and the overall health of

companies. The study's main findings show that

strategic leadership generally has a positive impact on

HR performance. It is imperative to acknowledge that

the effectiveness of strategic leadership in this context

varies between organizational environments and

cultures. The study also highlights the challenges

faced by strategic leaders, specifically when it comes

to balancing organizational objectives with the diverse

needs of workers in a global business environment. A

comparative analysis of leadership styles highlights

the importance of adaptation and context-specific

techniques. By providing fresh insights on the

expanding role that strategic leadership plays in

enhancing the performance of human resources in

modern work settings, the study contributes to the

ongoing scholarly discussion on the topic. The article

offers helpful advice on how to improve leadership

abilities and highlights the necessity of constant

adaptation and academic research in this field that is

always changing. Academics and experts in the

domains of organizational leadership and human

resource management will find great value in this

research.

4 ARTIFICIAL INTELLIGENCE

BASED HUMAN-COMPUTER

INTERACTION

It is challenging to accomplish effective task

allocation and resource scheduling with traditional

scheduling algorithms when dealing with complicated

workloads and dynamically changing resource

settings. This study describes the research methods

used in the development of an artificial intelligence-

based data scheduling algorithm for human-computer

interaction. By analyzing historical and real-time data,

the algorithm learns and optimizes resource allocation

tactics, increasing resource utilization and scheduling

accuracy. This is accomplished through the use of

intelligent optimization technology and machine

learning. The algorithm can dynamically adapt to task

characteristics and resource status to determine the

best scheduling approach. It also possesses the

features of adaptive adjustment and decision-making

optimization. Figure 3 Shows the Artificial

Intelligence HRM Management System.

Artiﬁcial Intelligence of Leadership Resource Management and Talent System

513

Figure 3: Artificial intelligence HRM management system.

5 RESULT AND DISCUSSION

This paper obtains research results by comparing and

experimentally evaluating the traditional scheduling

algorithm and the artificial intelligence-based human-

computer interaction data scheduling algorithm. AI-

Based HCI Scheduling Algorithm Shown in Figure 4.

Figure 4: AI-based HCI scheduling algorithm.

The algorithm's mean square error is maintained

within the range of. The algorithm presented in this

research has clear advantages in terms of resource

usage. It can forecast job resource requirements

precisely and prevent idleness and waste of resources.

Figure 5 Shows the Analysis Output of Predictive

Analytics in HR Decision-Making.

This approach can better match jobs and resources,

increase task execution efficiency, and enhance

reaction time in terms of scheduling accuracy. By

defining task requirements and resource allocation

models, the algorithm in this research enhances

system stability and performance while reducing

prediction mistakes in terms of mean square error.

Figure 5: Analysis output of predictive analytics in HR

decision-making.

6 CONCLUSIONS

This is accomplished by utilizing data-driven

methodologies and predictive analytics, which enable

well-informed decision-making processes. The report

highlights how these technological tools can improve

operational efficacy and help HR professionals move

from handling administrative tasks to taking on

strategic roles in talent management, workforce

planning, and organizational expansion. This study

examines how technological innovations affect both

employers and employees, with a particular emphasis

on the benefits and drawbacks of integrating

technology into human resource management. This

research attempts to build a link between theoretical

ideas and their practical application through an

extensive review of current scholarly literature and

empirical investigations. Researchers, organizational

leaders, and human resource professionals can all

benefit greatly from this resource. It supports them in

successfully navigating and handling the complex

difficulties of contemporary HRM in a high-tech

setting.

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