ISLAMIA UNIVERSITY OF BAHAWALPUR

Department of Project and Operations Management

REPORT SUBMITTED BY:
1. ROOMAN SHAHID
ROLL NO: F21BDPOM1M01037
2. MUZNA KHALIQ
ROLL NO: F21BDPOM1M01006
PROGRAM:
Supply chain Management
7th semester
Course Instructor: Dr Muhammad Nazim

The impact of AI and machine learning on Recruitment, Performance management,

and Employee development and what challenges companies were facing before AI?
Introduction:
1.Background

The integration of Artificial Intelligence (AI) and machine learning into organizational
operations marks a transformative shift in the way companies approach recruitment, performance
management, and employee development. The concept of "artificial intelligence," first
introduced in the 1950s, aimed to create systems capable of simulating human intelligence
(McCarthy et al., 2006; Pillai & Sivathanu, 2020). Over the years, advancements in AI have
redefined traditional practices, enabling organizations to harness data-driven insights and
automate complex tasks with unprecedented precision and efficiency. Gherghes (2018)
highlights that the development of advanced AI systems has the potential to generate new
professions and skill sets while addressing operational inefficiencies through enhanced
productivity. Machine learning, a critical subset of AI, focuses on deriving meaningful insights
from data using statistical techniques, which has proven instrumental in optimizing
organizational processes. Deep learning, an advanced form of machine learning, employs
hierarchical frameworks to transform raw data into intricate representations, enabling more
effective decision-making and predictive analytics (Goodfellow et al., 2016). The recent progress
in machine learning is largely attributed to innovations in deep learning methodologies, which
have enabled organizations to tackle challenges related to recruitment, performance evaluation,
and workforce development more effectively (LeCun et al., 2015). These technological
advancements have laid the foundation for a more agile, data-informed, and efficient
organizational structure.

2.Objective of the Research

The objective of this research is to explore the transformative impact of Artificial Intelligence
(AI) and Machine Learning (ML) on the organization, specifically in recruitment, performance
management, and employee development. The study aims to identify how AI and ML address
the limitations and inefficiencies of traditional practices, such as time-consuming manual
processes, unconscious bias, limited reach in recruitment, and subjective performance
evaluations. It seeks to examine how these technologies enhance organizational productivity by
providing data-driven insights, automating repetitive tasks, and enabling personalized learning
and development programs. Furthermore, the research investigates the challenges faced by
organizations before the integration of AI and ML, including fragmented data systems, biased
decision-making, and limited employee engagement, to assess the potential of these technologies
in creating more efficient, equitable, and scalable HR processes. Ultimately, the research aspires
to provide insights into how organizations can leverage AI and ML to overcome operational
inefficiencies, ensure fairness, and drive strategic workforce development.

3. Problem statement and factors influencing problem

The human resources landscape is changing dramatically as a result of the rise of Artificial
Intelligence (AI). These powerful technologies have the potential to transform the way firms
recruit, manage performance, and develop their employees. Before AI and machine learning
organizations are having issues in recruitment like manual screening of resumes and applications
was time-consuming. unconscious bias could lead to overlooking qualified candidates based on
factors like gender or name. Limited reach is also major issue in recruitment because traditional
job advertising may not attract a diverse range of applicants. Managing employee performance
and organizational performance were so hard and exhausting for HR to evaluate performance,
the results are not precise before AI and machine learning. Performance evaluations were
subjective and influenced by personal relationships. There was a Lack of real-time data to track
employee performance and identify areas for improvement. Before these technologies
organization and HR mangers were also facing issue in employee development like they were
providing Generic training programs instead of Personalized Skills Development Program.
Managers had a limited ability to track the impact of training programs on employee
performance. There was also a engaging issues between employee and upper level management.

Before AI and Machine leaning many tasks were performed manually, leading to slow
processing times, increased error rates and a heavier workload for employees. Information was
often fragmented and stored in separate systems, making it difficult to access and analyze data
effectively. Employees spent a considerable amount of time on repetitive administrative tasks,
hindering productivity and limiting their ability to focus on more strategic work. For example
HR departments relied on manual resume screening and scheduling, leading to delays in the
hiring process. Finance departments spent hours on manual data entry and reconciliation,
increasing the risk of errors. Marketing teams struggled to personalize campaigns due to limited
access and analysis of customer data. Subconscious biases could influence hiring, performance
evaluations, promotions, and other decision-making processes. Factors like race, gender, or
personal relationships could unfairly impact outcomes. Decision-making was often based on
limited information, increasing the likelihood of bias and the rationale behind decisions was not
always transparent, making it difficult to identify and address potential biases. For example,
Recruitment biasness occurs in resumes with traditionally "male" names might be
subconsciously prioritized. Promotion bias occurs when employees who share similar
backgrounds or characteristics with managers might have a higher chance of promotion.
Reaching employees and customers often relied on traditional methods like emails, flyers, or
phone calls. This limited the ability to segment audiences and deliver targeted communication.
The inability to analyze customer data effectively made it difficult to personalize marketing
campaigns and promotions, hindering reach and engagement. Like Training programs might
not have been easily accessible to employees in remote locations.

4.Importance of study

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Human
Resource Management (HRM) is transforming recruitment, performance management, and
employee development. These technologies address inefficiencies in traditional HR practices,
such as manual processes, biases, and fragmented systems, by enabling automation, data-
driven decision-making, and personalized solutions. AI streamlines recruitment through tools
like resume screening and chatbots, reduces bias, and enhances diversity. In performance
management, AI improves accuracy, tracks real-time metrics, and offers personalized
feedback. For employee development, it tailors training programs to individual needs,
boosting engagement and productivity. Ultimately, AI and ML empower organizations to
build more efficient, equitable, and adaptive HR processes.

REVIEW OF RELATED LETERATURE

1. Literature review:

AI and machine learning technologies have revolutionized recruitment processes by enhancing

efficiency, accuracy, and decision-making in talent acquisition. These technologies have
significantly impacted human resource management practices, particularly in the areas of
selection and recruitment. By leveraging AI, HR professionals can streamline recruitment
processes, analyze vast amounts of candidate data, and identify top talent more effectively.
Studies have shown that AI's influence on recruitment and selection processes has wide-ranging
implications for training, performance management, and compensation. Moreover, the adoption
of generative artificial intelligence in recruiting tasks has become more prevalent among HR
professionals, indicating a growing reliance on AI technologies for talent acquisition and
development.

AI-powered machine learning technologies have also proven crucial in changing how companies
handle performance management. These technologies may evaluate performance information,
spot patterns, and offer insightful analysis that raises worker engagement and productivity. AI-
driven performance management systems are becoming more and more popular among HR
managers as a means of supporting data-driven decision-making, facilitating continuous
feedback, and gaining a deeper knowledge of individual and team performance indicators.
Research directions on AI adoption in HR place a strong emphasis on the necessity for HR
managers to become proficient in employing AI technology to improve performance
management procedures. Artificial Intelligence (AI) technologies are essential for identifying
skill shortages in businesses and for offering individualized learning experiences in the field of
employee development and training. AI-powered systems have the ability to evaluate employee
performance information, provide specialized training courses, and provide chances for ongoing
education. HR managers may increase employee skill development and career progression
chances by leveraging AI to build more focused and efficient training efforts. The
comprehensive analysis of the body of literature highlights how automation and artificial
intelligence are revolutionizing human resource management, especially when it comes to
programs for employee training and development. Businesses may anticipate more developments
in employee learning and development plans as AI continues to improve and become more
integrated into HR procedures.

AI-powered initial screening automation is a potent option in recruitment and selection. AI

systems can quickly and efficiently search resumes for relevant keywords and abilities, freeing
up HR people to concentrate on more strategic duties like applicant assessment and interview
preparation. This increases productivity and lessens the possibility of unconscious bias
influencing human screening. This targeted approach saves time and money by streamlining the
application process for candidates through automated scheduling and accelerated screening.
However, it's critical to remember that AI is a tool that advances human understanding.
Organizations may improve hiring decisions, attract top talent, and expedite their recruiting
process by utilizing AI for preliminary screening.

The recruiting process may now be more individually tailored thanks to AI which are completely
changing the way HR engages with candidates. Envision a scenario wherein candidates get
customized correspondence contingent upon their credentials and advancement during the
recruitment process. AI is able to find relevant expertise and talents by analyzing resumes and
job applications. Personalized emails or messages explaining the next stages or updating them on
their eligibility can be sent based on this data. For example, if a candidate shows signs of a
communication gap, they may receive a kind reminder or an update on the application process.
Employing a tailored approach improves corporate branding, fosters a positive application
experience, and attracts top talent who are made to feel valued throughout the whole hiring
process.
The way HR departments review resumes and profiles is being completely transformed by AI
and Machine Learning (ML), which is bringing a powerful combination of speed, efficiency, and
impartiality to the hiring process. Imagine AI algorithms acting as tireless resume screeners,
meticulously scanning through large applicant pools. These algorithms can analyze keywords,
skills, and experience mentioned in resumes and profiles, matching them against the specific
requirements of the open position. This significantly reduces the time HR professionals spend on
manual screening, allowing them to focus on more strategic tasks. Furthermore, Research
conducted by Nayab et al. (2011) AI helps mitigate bias by using objective criteria to assess
qualifications. Resumes are evaluated based on pre-defined skills and experience, reducing the
influence of unconscious human biases that might favor certain candidates based on factors like
gender or name. This leads to a more diverse talent pool with the best fit for the role. However,
it's important to remember that AI is a tool that complements human expertise. While AI excels
at identifying relevant skills, human judgment remains crucial for evaluating cultural fit, written
communication style, and overall candidate suitability for the role. the integration of Machine
Learning (ML) technology has revolutionized various processes, particularly in conducting
preliminary interviews. ML algorithms are adept at sifting through large volumes of data,
enabling HR professionals to streamline candidate selection processes efficiently. In the context
of preliminary interviews, ML algorithms can analyze resumes, cover letters, and online profiles
to identify key qualifications and match them with job requirements. These algorithms can also
predict candidate suitability based on historical data of successful hires, minimizing biases and
improving the overall quality of candidate shortlisting.

There are few challenges that AI can face are:

1. Avoiding algorithmic biases

2. Ensuring fairness and avoiding biases.
 3. Recognizing diverse qualifications.

In Performance Management, AI plays a crucial role in enhancing Key Performance Indicators

(KPIs), Automating evaluations, Identifying performance trends and offering personalized
development plans.
In Performance Management, AI plays a crucial role in enhancing Key Performance Indicators
(KPIs). By analyzing vast amounts of data from various sources such as employee performance
metrics, project outcomes, and customer feedback, AI algorithms can identify the most relevant
KPIs for each role and department. This ensures that KPIs are aligned with organizational goals
and provide meaningful insights into individual and team performance.

Automating evaluations is another area where AI brings significant benefits. AI-powered

systems can collect and analyze performance data in real-time, allowing for continuous feedback
and assessment. These systems can identify patterns and anomalies in performance metrics,
flagging areas that require improvement or recognition. By automating evaluations,
organizations can streamline the performance review process, reduce biases, and provide more
timely feedback to employees.

Identifying performance trends is another valuable application of AI in Performance

Management. AI algorithms can analyze historical performance data to identify trends and
patterns across individuals, teams, and departments. By spotting trends such as productivity
fluctuations, skill gaps, or engagement levels, organizations can proactively address performance
issues and capitalize on opportunities for improvement.

Offering personalized development plans is an area where AI excels in tailoring learning and
development opportunities to individual employee needs. AI-powered systems can analyze
employee performance data, career aspirations, and skill gaps to recommend personalized
training programs, coaching sessions, or mentorship opportunities. By offering personalized
development plans, organizations can empower employees to enhance their skills, achieve their
career goals, and contribute more effectively to the organization's success.
There are few challenges that AI and machine learning can face are:

1. Defining clear and fair metrics

2. Avoiding algorithmic biases
3. Ensuring transparency
4. Integrating with existing systems.

In Employee development, AI plays role in real time feedback and performance Management
Tools. AI plays a pivotal role in providing real-time feedback to employees. Organizations may
gather and examine data on worker performance, interactions, and results in real time using AI-
powered solutions. These methods enable managers to provide their staff with fast feedback by
quickly identifying areas of strength, weakness, and progress. Artificial Intelligence (AI)
facilitates fast behavior and action modification by employees, resulting in improved
performance and ongoing development.
AI also greatly improves performance management tools, increasing their effectiveness and
efficiency. Goal-setting, tracking progress, and assessment are just a few of the performance
management tasks that AI-powered solutions can automate. These tools leverage machine
learning algorithms to analyze performance data, identify trends, and provide insights to
managers and employees. Additionally, AI can personalize performance management processes
based on individual preferences, priorities, and learning styles. By streamlining performance
management tasks and providing actionable insights, AI-powered tools enable organizations to
foster a culture of accountability, transparency, and continuous improvement.

There are few challenges that AI and machine learning can face are:

1. Ensuring accurate and relevant content

2. Adapting to various learning styles
3. Addressing privacy concerns
4. Integrating with existing systems.

Primark

 Issues:
 o Manual hiring processes and fragmented systems lead to inefficiencies in
recruitment.
o Limited use of data in performance evaluation results in subjective assessments.
 Impact:
Primark's traditional approach struggles to meet modern employee development
expectations, such as personalized training.

2. Theoretical framework

Primark's adoption of the Resource-Based View (RBV) framework in integrating Artificial

Intelligence (AI) and Machine Learning (ML) highlights its strategic approach to leveraging
technology for organizational success. The company has established valuable resources by
utilizing AI and ML as tools for efficiency. Automation of time-intensive tasks such as resume
screening, data collection, and performance tracking has significantly reduced manual labor,
leading to cost savings and better allocation of HR resources. Additionally, Primark enhances
employee development programs through AI-powered personalized learning pathways, enabling
employees to acquire relevant skills that directly improve organizational productivity and agility.

The organization has also created rare resources through its customized AI solutions. Unlike
generic applications of AI, Primark tailors its tools to address inefficiencies specific to its
operational structure, particularly in recruitment and performance management. Moreover,
access to real-time, actionable insights derived from employee and operational data provides a
competitive edge, enabling the company to adapt quickly to market changes and workforce
needs.

Primark's expertise in AI implementation serves as an inimitable resource. Successfully

integrating AI into recruitment and employee management requires a deep understanding of
organizational workflows and technology, combined with years of historical hiring and
performance data. This integration process creates a resource that competitors find challenging to
replicate. Furthermore, the company’s extensive organizational knowledge, encompassing
employee performance metrics, customer engagement, and operational requirements, allows it to
configure AI systems uniquely suited to its needs.
Finally, Primark has developed non-substitutable resources by fostering a culture of data-
driven HR practices. The combination of AI-driven tools and human oversight ensures fairness,
reduces biases, and enhances decision-making quality in recruitment and performance
evaluation, surpassing the capabilities of traditional HR practices. Additionally, AI’s capacity to
design individualized training programs keeps employees engaged and aligned with
organizational goals, creating a competitive advantage that cannot be substituted by conventional
approaches.

Primark's strategic implementation of the Resource-Based View (RBV) framework, powered by

AI, delivers significant benefits across key HR functions. One notable advantage is the
improvement of recruitment processes, where AI eliminates inefficiencies associated with
manual hiring. By automating tasks such as resume screening, Primark accelerates the hiring
process and minimizes unconscious biases, fostering a more diverse and skilled workforce—a
valuable asset under the RBV framework. In performance management, Primark leverages AI
to provide real-time feedback and develop tailored development plans, ensuring continuous
employee improvement. The use of sophisticated tools to monitor and enhance performance not
only boosts workforce productivity but also drives organizational success. Similarly, the
company's focus on strengthened employee development through personalized, AI-powered
training ensures that employees acquire the necessary skills for their roles. This approach
enhances engagement and reduces turnover, while the insights derived from training data help
guide strategic workforce planning.

Moreover, Primark achieves cost efficiency and scalability by automating repetitive tasks with
AI, significantly reducing operational expenses. This allows HR teams to allocate more resources
to strategic functions. The scalability of AI solutions enables the company to grow without a
corresponding increase in HR costs, making the integration of AI a pivotal component of
Primark’s HR strategy.

3. conceptual framework
 The conceptual framework illustrates the relationship between AI/ML technologies and their
impact on HR functions, focusing on operational efficiency, fairness, and workforce
development.

 Independent Variables: AI and ML technologies (e.g., automated screening,

performance tracking, personalized training systems).
 Mediating Variables: Data-driven decision-making, transparency in HR processes,
employee engagement metrics.
 Dependent Variables: Enhanced recruitment outcomes, unbiased performance
evaluations, and effective employee development.

Framework

Recruitment
and Selection

Artificial Performance Organizational

intelligence Management Performance

Employee
development
4.Hypothesis

1. Recruitment

 H1: AI can significantly improve the efficiency of the recruitment process.

 H2: AI can create a more personalized and engaging candidate experience during
the recruitment process.
 H3: AI can help reduce unconscious bias in candidate selection.

 H4: Using AI for initial candidate screening can help reduce unconscious bias in
the recruitment process by focusing on objective criteria.

2. Performance Management

 H5: AI can analyze data to provide more objective performance evaluations for
employees.
 H6: AI can identify skill gaps and recommend relevant training programs for
employees.
 H7: AI-powered performance management systems can improve communication
and feedback between employees and managers.
 H8: AI should completely replace traditional performance review methods.
 H9: AI can personalize learning paths for employees based on their individual
needs and goals.

3. Employee Development

 H5: AI-powered chatbots can provide employees with immediate access to learning
resources and support.
 H6: AI tools can help identify employees with high potential and provide them
with targeted development opportunities.
 H12: AI tools can help identify employees with high potential and provide them
with targeted development opportunities.
 H13: AI can replace the need for human interaction and mentorship in employee
development
 H14: AI can significantly improve the efficiency of the recruitment process by
automating tasks like resume screening, leading to faster time-to-hire.

4. Organizational Efficiency
  H15: Effective implementation of AI and Machine Learning in HR practices can
lead to a more engaged and productive workforce.
 H16: AI-powered talent management can help organizations attract and retain top
talent, leading to a competitive advantage.
 H17: ethical considerations regarding data privacy, algorithmic bias, and
transparency in AI decision-making need to be addressed for HR to maximize its
positive impact.
 H18: Overall, AI and Machine Learning hold significant potential to improve HR
practices and contribute to enhanced organizational performance when
implemented strategically and ethically.

DATA ANALYSIS

We’ve used Likert scale to conduct the questionnaire survey and our focus of interest was the business
and education sector of Bahawalpur.

Section no 1: Recruitment and Selection

section no2: Performance Management
Section no 3: Employee development
Section4: Organizational performance
Future Recommendations
To further optimize the integration of Artificial Intelligence (AI) and Machine Learning (ML) in
human resource management, organizations should focus on refining their approach to ethical AI
implementation. This includes conducting regular audits to detect and mitigate algorithmic bias,
ensuring transparency in AI-driven decision-making, and addressing data privacy concerns to
foster trust among employees. Additionally, companies can enhance the scalability of AI systems
by integrating them seamlessly with existing HR platforms and developing adaptive AI tools that
accommodate diverse learning styles and evolving workforce needs.

Organizations should invest in training HR professionals to effectively leverage AI technologies

and ensure a balance between automation and human oversight. Future research should explore
emerging AI applications, such as predictive analytics for workforce planning and virtual reality-
driven employee development programs. By adopting a strategic and ethical approach to AI,
organizations can achieve sustainable growth, improve employee satisfaction, and maintain a
competitive edge.

CONCLUSION

In Recruitment and selection, AI plays a vital role to improve efficiency by automates tasks like
resume screening, filtering applications based on keywords and skills, scheduling interviews, and
even conducting initial pre-screening interviews through chatbots. This frees up HR
professionals' time so they can concentrate on strategic recruitment tasks like relationship-
building and candidate assessment. Moreover, AI helps to reduced biasness by analyze resumes
based on pre-defined criteria, minimizing the impact of unconscious bias based on factors like
gender, name, or university attended. Machine Learning algorithms can analyze past successful
hires and identify patterns in skills, experience, and personality traits. This allows for better
matching of candidates with open positions based on these success profiles. Artificial
intelligence (AI)-driven chatbots may arrange interviews at the candidate's convenience, offer
real-time application process updates, and respond to routine queries from candidates around-
the-clock. IBM (international business machine corporation) also uses AI for candidate
matching, skills assessment, and chatbot-powered candidate engagement.
In evaluating performance management, AI and Machine Learning (ML) offering organizations a
more data-driven and objective approach to employee evaluation by Automating Data Collection
and Analysis because traditional performance management relied on subjective evaluations and
limited data points. AI can automate the collection and analysis of data from various sources.
Focusing on objective performance reduces the risk of bias and ensures a fairer evaluation
process because AI, based on pre-defined criteria and algorithms, can analyze data objectively,
identifying strengths, weaknesses, and areas for improvement. Performance evaluations were
conducted either yearly or biannually in the past. AI is capable of giving staff members
continuous feedback based on analysis of real-time data. This enables year-round course
correction and improvement, resulting in improved performance outcomes. AI is able to provide
individualized development programs based on data analysis, which are customized to meet the
needs of each employee. This guarantees that learning opportunities are pertinent and deal with
particular deficiencies in skills or knowledge.

In employee development, AI and Machine Learning (ML) are transforming employee

development by offering organizations personalized learning experiences to find specific skill
shortages, AI may examine employee data like as performance reports, self-evaluations, and
skills tests. AI can suggest individualized learning routes with pertinent courses, microlearning
modules, or online resources based on this analysis. AI-powered systems have the ability to
modify the learning route in response to an employee's performance and advancement. Employee
attention is directed onto areas that require development, and learning efficiency is maximized.
AI can recommend relevant learning content based on individual needs and interests. This saves
employees time searching for resources and ensures they're accessing the most relevant
materials.

In conclusion, the integration of Artificial Intelligence (AI) and Machine Learning (ML) in
Human Resource Management (HRM) represents a transformative shift in how organizations
approach recruitment, performance management, and employee development. These
technologies have proven instrumental in addressing inefficiencies and challenges of traditional
HR practices, such as manual processes, unconscious biases, and lack of personalization. AI and
ML enable automation, data-driven decision-making, and real-time insights, allowing HR
professionals to focus on strategic initiatives. The adoption of AI and ML not only optimizes HR
processes but also empowers organizations to build a more diverse, engaged, and capable
workforce. However, ethical considerations, such as avoiding algorithmic bias, ensuring
transparency, and addressing data privacy concerns, remain critical. By leveraging these
technologies responsibly, organizations can achieve sustainable growth, foster innovation, and
maintain a competitive advantage in today’s dynamic business environment.

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