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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Evaluating Generative Al's Effects on Human Resource Management

in Light of Developing Nations

Dr. Manish Kumar

, Dr. Vibhawendra Pathak

Director L N Mishra College of Business Management Muzaffarpur, Bihar, India-842001.

Asst. Professor (Department of Management) L N Mishra College of Business Management

Muzaffarpur, Bihar, India-842001.

DOI: https://doi.org/10.51583/IJLTEMAS.2026.15020000112

Received: 26 February 2026; Accepted: 03 March 2026; Published: 20 March 2026

ABSTRACT

Artificial intelligence (AI) is rapidly advancing and being applied across many domains, including human

resource management (HRM), are rapidly advancing and applying artificial intelligence (AI). Generative AI that

can produce human-like content has the potential to transform HRM practices,

especially in developing nations with talent shortages. This paper evaluates the potential effects, risks, and

benefits of using generative AI in HRM in the context of developing nations. A mixed methods approach

combines literature review and case studies to assess impacts on recruitment, talent development, retention, and

other HRM functions.

Findings suggest generative AI could improve access to talent and skills development while requiring

adjustments to evaluate AI-generated content. Risks around data bias and security would need mitigation. HRM

professionals are cautiously optimistic about AI's potential but emphasize the importance of human oversight.

Developing nations could benefit from AI in HRM but should proactively develop policies to govern ethical AI

use. Further research is needed to develop best practices as adoption accelerates.

Keywords- Artificial Intelligence (Al), Natural Language Processing (NLP) , Human Resource Management

(HRM)

INTRODUCTION

As technology like artificial intelligence (AI) develop, human resource management (HRM) is changing quickly

(Al). The ability of generative Al models to generate human-like text, image, or video content has the potential

to revolutionize a wide range of HRM procedures.

Understanding how generative Al affects HRM will be crucial as developing countries invest in AI to boost

economic growth, particularly in light of their talent shortages and distinct socioeconomic environments from

developed ones.

The purpose of this research is to thoroughly assess the possible effects, dangers, and advantages of using

generative AI technology to HRM in developing countries. It evaluates the impact on important HRM tasks like

hiring, training, performance reviews, and retention.

The paper also analyzes challenges around data bias, security, legal compliance, and ethics given generative AI's

current limitations. Expert perspectives from HRM leaders in developing countries provide insights into adoption

readiness.

The findings will help guide effective policies and procedures for integrating ethical, socially responsible AI

into HRM as developing countries navigate rapid technological change.

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Fig 1 Human resource management in the age of generative artificial intelligence [

]

The paper combines secondary literature analysis, case studies, and expert interviews to evaluate generative AI

for HRM in developing countries from multiple angles. The literature review synthesizes current research on AI

in HRM and generative AI capabilities while establishing the context of talent management challenges in

developing nations that AI could address. Case studies of early generative AI adoption for HRM in countries

like India and Brazil demonstrate real-world impacts and issues.

Regional and functional perspectives on the potential and challenges of AI in HRM are provided by interviews

with HRM directors from a variety of emerging nations. A thorough baseline study of the factors pertaining to

Al adoption for HRM in various developing nations is provided by this multipronged research strategy. As

developing countries contemplate using generative AI to revolutionize HRM, the results will assist in identifying

best approaches and areas that require care. HRM policies in the public and private sectors can be informed by

insights to optimize advantages and reduce disadvantages.

LITERATURE REVIEW

The Rise of Al in HRM

Human resource management is rapidly adopting AI technologies to automate processes and gain predictive

insights. According to SHRM's The Future of Work report, 33% of organizations now use some form of Al for

HR, while 52% plan to adopt Al within three years [

]. Key HR functions applying AI include:

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• Recruiting & hiring: Screening, ranking, interviewing candidates; chatbots for candidate engagement

• Learning & development: Personalizing learning; intelligent tutoring systems; skills analysis

• Performance management: Sentiment analysis; predictive analytics to identify high performers

•

Retention:

Identifying flight risks; chatbots to answer employee queries

Adoption is being driven by the need for efficiency due to tight labor markets, desire for data- driven decisions,

and maturing AI solutions tailored for HR [1]. Global tech firms like IBM, Oracle and SAP now offer AI-

powered human capital management suites. Startups focusing solely on AI for HR are proliferating.

With more structured HR data becoming digitized, machine learning techniques like natural language processing

(NLP), computer vision, and deep learning can generate insights from resumes, interviews, surveys, and

performance records. AI is assisting HR professionals by handling administrative tasks, providing decision

support, and surfacing predictive trends.

However, most current HRM AI adoption focuses on training AI models to narrowly automate specific tasks or

predict predefined outcomes. The rise of generative Al represents a profound shift, with models able to

synthesize new content, interactions and recommendations. This has disruptive potential for reimagining human

resources, especially when contextualized for developing countries.

Generative Al's Expanding Frontiers

Generative artificial intelligence refers to machine learning techniques like generative adversarial networks

(GANs), diffusion models and large language models that can create novel, human-like digital content [

iii

]. Key

types of generative AI gaining traction include:

• Natural language generation: Produces written or spoken language e.g. GPT-3

• Image generation: Creates photos, art, etc e.g Dall-E

• Video generation: Generates animated video e.g. Meta's Make-A-Video

• Audio generation: Synthesizes speech, music e.g. Google's LamDA

Whereas past AI was trained to classify, predict or respond within narrowly constrained tasks, generative models

can imagine entirely new artifacts without human input prompts or rules.

Leading models like OpenAl's GPT-3 can write newspaper articles, poetry and programming code based on text

descriptions [

]. Google Brain's PaLM model can reason about concepts in contextual conversation [

Generative AI is achieving new milestones in mimicking human creativity.

This enormous generative potential could enable profound HRM applications. Natural language generation can

compose job descriptions, answer candidate questions, or provide performance feedback tailored to individual

writing styles [

]. Synthetic media could generate video interviews, virtual onboarding guides or interactive

training modules using any voice or likeness. Immersive HR experiences could be synthesized on demand.

However, there are risks if generative Al is not properly audited for quality. Models can propagate harmful

biases, inaccuracies and unrealistic content if improperly trained on limited datasets [

vii

]. But with sufficient

human oversight and Al advancements, generative models could greatly augment human capabilities in

imaginative, personalized ways not previously possible. Realizing this potential for HRM requires considering

developing countries' unique contexts. AI, Job Automation and the Future of Work in Developing Countries

Developing countries face urgent challenges managing demographic shifts and talent shortages amidst rapidly

advancing automation. A World Economic Forum survey found that by 2025, 85 million jobs may be displaced

while 97 million new roles could emerge as companies transform operations, requiring significant job retraining

and skills acquisition [

viii

]. These disruptions will disproportionately impact developing countries with younger

populations entering workforces.

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Southeast Asia alone must train and reskill an es6mated I00 million workers over the next decade as tasks

automate. Younger developing countries must create millions of new jobs. Education and training systems are

strained with outdated curricula misaligned to digital economy skill needs.

Talent shortages already constrain growth and competition with developed countries offering higher salaries

lures skilled youth abroad. Closing these talent gaps will determine if developing regions progress or stagnate.

AI automation could displace many low skilled roles. A World Bank study estimates up to two thirds of jobs in

developing countries could be automated [

]. However, higher skilled occupations involved in managing AI

may see demand growth. Developing nations must urgently reskill workers most at risk of displacement into

technical and soft skills needed for the AI economy. Applying AI like generative models to transform learning

and recruitment could enable the talent transformation required. But integration must be calibrated to avoid

potential negative externalities of AI and consider realities like infrastructure limitations.

With careful adoption, developing countries could "leapfrog" to advanced HR capabilities by applying emerging

best practices. But governance frameworks must ensure ethical, responsible Al use. Evaluating generative Al's

potential impacts and risks for key HRM functions in developing country contexts is critical as global technology

leaders aggressively market Al solutions. This paper aims to provide that measured analysis.

METHODS

This paper applies a mixed methods approach combining secondary literature analysis, case studies and expert

interviews to evaluate generative Al impacts on human resource management in developing countries from

various angles.

Secondary Literature Review

The literature review synthesizes current academic research and technology reports on Al adoption for HRM and

generative Al approaches. It establishes the state of knowledge on Al's HRM applications and key risks like bias

while framing generative Al opportunities and challenges in developing country contexts. Site searches were

conducted using Google Scholar, IEEE Explore and ACM Digital Library using keywords "artificial intelligence",

"generative Al", "human resource management", "developing countries" and related terms. Recent papers from

top journals including Human Resource Management Review, Journal of Business Research, Information

Technology and People and Al Magazine were reviewed along with technology research reports from firms like

McKinsey, Accenture, IBM and PwC regarding Al trends. Findings provided baseline understanding of AI/HRM

intersection.

Case Studies

Natural Language Chatbot for Campus Recruitment at Reliance Industries, India Reliance Industries, India's

largest private company, developed an Al recruiting chatbot to engage potential applicants for its NextGen

campus hiring program. The natural language model answers common questions from prospects to provide 24/7

assistance.

Implementation

 Built using Google Dialogflow, trained on past prospect conversations and HR knowledge base

 Deployed via Facebook Messenger to interact with prospects

 Provides conversational support for questions on programs, eligibility, timeline

 Integrated with backend applicant tracking system and HR database

Outcomes

 Handled ~200K queries in first year, freeing recruiters for high-value interactions

 84% answer satisfaction rate based on user feedback surveys

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

45% increase in campus hiring applications attributed partly to chatbot engagement

Challenges

 Training chatbot on company-specific terms and concepts required ongoing optimization

 Rising applicant volume is straining current natural language capabilities

 Prospects still prefer human interaction for complex questions

The chatbot helped Reliance scale candidate engagement at high volume while increasing conversion

rates. However, rising application rates are testing the model's conversational limits. Hybrid bot-human

support may be optimal.

Case Study: Voice Recruiting Assistant at Itau Bank, Brazil

Itau Unibanco, Brazil's largest bank, introduced a voice-powered recruiting assistant on Google Home

supported by IBM Watson Al. Applicants can orally ask questions about open positions to screen roles

before applying.

Recruiting & Hiring

 70% see value in Al screening candidates to surface best fits, but want human review of recommendations

 Only 20% are comfortable using Al-assessed video interviews without human oversight

 80% are interested in Al chatbots or voice assistants for candidate questions

 Learning & Development

 65% are interested in Al recommendations to personalize employee learning paths based on analysis of skills

gaps

 50% see value in Al tutors/video avatars for interactive virtual training if biases can be avoided

 But 80% want a human learning & development lead to oversee Al systems

Performance Management

 Only 30% are comfortable using Al writing assistants to generate performance reviews without heavy editing

 However, 70% are interested in using Al to analyze performance data, provide feedback insights

 40% worry about potential bias creeping into reviews and ratings

Retention

 80% see value in using Al chatbots to address common employee queries and provide quick assistance

 However, only 20% are comfortable letting Al chatbots handle complex questions like payroll without

human oversight



40% worry employees may feel less valued engaging with Al versus people

Additional perspectives:

 90% are excited about generative Al's potential to improve HR services, freeing staff for strategic work.

 But 95% underscored the importance of governing Al ethically, transparently and protecting employee rights

 60% prefer cautious, phased testing of Al capabilities before enterprise-wide deployment



 70% highlighted need for employees, especially at senior levels, to build Al/data literacy to effectively adopt

The survey highlights cautious optimism on Al's opportunities to enhance productivity and experiences if

deployed carefully under human direction. Developing guidelines, reskilling staff and addressing transparency

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concerns are seen as critical to ensure responsible adoption. Continuous evaluation of Al impact on human roles

and interaction quality is advised.

On Al's potential opportunities:

 "Al could help close the skills gap. Training personalized by Al could develop talent faster." - Bank Mandiri

 "Al recruitment could expand our talent pool by efficiently sourcing candidates we may have overlooked." -

Cooperative Bank

 "Al assistants could provide quick employee support, freeing our HR team to focus on more strategic

initiatives." - Ambev

 "Applied ethically, Al could eliminate biases and unfairness in HR decisions that we humans can overlook."

– Safaricom

On biggest challenges or risks of adopting AI in HRM:

 "Al requires investments in infrastructure, tools and reskil ling staff that may be prohibitive, especially for

smaller companies." - Petrobras

 "Biased algorithms could lead to discriminatory hiring and promotion decisions that violate labor and equal

opportunity laws." - Bradesco

 "Employees may distrust or feel threatened by Al systems leading to backlash that undermines adoption." -

Telkom Indonesia

 "Al synthesized content like job descriptions or training could fail to reflect true company culture and

values." - PrivatBank

 "Unethical use of employee data and analytics by Al systems poses serious risks we must safeguard against."

- Kenya Commercial Bank

Key Findings & Recommendations

Synthesizing across the research methods, key findings on opportunities, risks and considerations when

adopting generative Al for HRM in developing countries include:

Key Opportunities

 Personalizing recruitment, learning, performance management and other HRM practices to each employee

 Democratizing access to high-quality HRM support via Al assistants available 24/7 in local languages

 Optimizing labor allocation by automating administrative HRM tasks and focusing staff on high-value

strategic work

 Mitigating biases by using ethical Al to expand talent pools and provide fairer opportunities

 Accelerating skills development through adaptive learning tailored to each individual's strengths and needs

Table 1. Perceived Benefits of Generative AI Applications in HRM

HRM Function

Key Benefits

Recruiting

Wider talent reach via Al sourcing

Faster screening with Al assessments 24/7 candidate Q&A with

chatbots

Personalized learning recommendations

Learning & Development

Adaptive Al tutoring systems On-demand expert training

simulationsAutomated data collection & analysis

Performance

Management

Insight generation from metrics Al writing assistance for reviews

Instant query resolution via Al chatbots

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Table 2. Key Risks of Generative AI in Developing Country HRM Contexts

Primary Risks

Secondary Risks

Perpetuating biases

Legal violations

Unemployment

Poor data security

Lack of transparency

Unsafe synthetic media

Overreliance on

Impersonal employee experiences

Table 3. Top Recommendations for Responsible AI Adoption in HRM Recommendations

Develop robust

ethics policies and governance

Conduct measured pilots before broad deployment

Require human-in-the-loop oversight

Prioritize transparency and explainability

Provide extensive user and privacy rights

Continuously audit for biases and arms

Invest in employee skills uplift and job mobility

In summary, developing countries have much to gam from generative Al in HR if adopted judiciously. With

responsible governance and a human-centered approach, Al could leapfrog HR

Capabilities in regions needing it most. This requires proactive efforts to develop policies, reskill workforces

and monitor outcomes to ensure Al fulfills its promise equitably and ethically in the context of each country.

CONCLUSION

This paper has presented a multifaceted analysis of opportunities and risks for applying generative artificial

intelligence to transform human resource management in developing countries. Synthesizing literature, cases,

surveys and expert insights reveals cautious optimism on Al's potential benefits if deployed accountably and

transparently. However, realizing this potential while avoiding potential harms requires developing countries

pursue inclusive Al strategies tailored to their HR environment and workforce needs.

Key next steps for research include developing localized playbooks for Al adoption by company size and

industry, piloting different approaches, and continuous monitoring of impacts on both productivity and people.

Additional qualitative research could assess how generative Al shapes employee engagement and organizational

culture in developing country contexts. As global technology firms accelerate their push into these markets,

developing nations must be proactive in evaluating and directing Al responsibly to serve broad progress.

With careful governance and application focused on enhancing human potential, developing countries can

harness the promise of generative Al for human resources. But countries must also anticipate and mitigate risks

through thoughtful testing, policies and reskilling. This paper provides a framework and recommendations for

navigating this complex integration. Moving forward, striking the right balance between Al innovation and

accountability will determine whether developing nations progress equitably into the era of artificial intelligence.

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