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All Journal Blockchain, Artificial Intelligence, and Future Research Techno Agriculturae Studium of Research
Rajkumar, S.R.
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT

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Will Artificial Intelligence Reshape the Global Workforce by 2030? A Cross-Sectoral Analysis of Job Displacement and Transformation Chhibber, Sugandha; Rajkumar, S.R.; Dassanayake, Sandun
Blockchain, Artificial Intelligence, and Future Research Vol. 1 No. 1 (2025): May 2025
Publisher : WISE Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70211/bafr.v1i1.178

Abstract

The rapid advancement of artificial intelligence (AI) is transforming the global labor market, presenting both opportunities and challenges. This study investigates the extent of AI-driven job displacement and task transformation across industries, highlighting sector-specific vulnerabilities and workforce perceptions. Using secondary data from Statista’s global surveys (2022–2023), involving 22,816 employees, 1,684 business leaders, and 803 corporations, the study employs descriptive statistical analysis to identify patterns of job disruption and skill adaptation. The findings reveal that AI primarily reshapes job functions rather than eliminating entire occupations, with 57% of respondents reporting task augmentation and 36% expressing concern about job loss. Routine-based sectors, such as manufacturing and customer service, face higher displacement risks, while knowledge-based professions, including healthcare, education, and creative industries, experience AI as a complementary tool. Additionally, disparities in AI adoption are evident between large corporations and small-to-medium enterprises (SMEs), often due to resource limitations and varying digital readiness. The study concludes that successful AI integration hinges on proactive strategies, including continuous workforce reskilling, adaptive education systems, and ethical AI deployment. Policymakers, industry leaders, and educational institutions must collaborate to ensure an inclusive transition, prioritizing digital literacy and skills development. Future research should explore regional variations, firm-level case studies, and the long-term socio-economic impacts of AI adoption. Ultimately, this study underscores the importance of balancing technological advancement with workforce resilience to foster sustainable economic growth in an AI-driven era.
ENERGY EFFICIENCY, DEMAND-SIDE MANAGEMENT STORAGE TECHNOLOGIES A CRITICAL ANALYSIS OF INTEGRATION PATHWAYS IN AGRICULTURAL SYSTEMS Rajkumar, S.R.; Raheem, Abdel; Cankirli, Nebahat Ozaydin; Ibekeme, Merit; Audia, Chaira
Techno Agriculturae Studium of Research Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v3i1.3614

Abstract

This paper presents a review of residential demand-side management (DSM) focusing on modeling approaches, optimization techniques, and future perspectives. Deterministic, stochastic, and data-driven models are analyzed to capture residential load behavior. Various optimization methods, including classical and artificial intelligence-based techniques, are discussed for improving energy efficiency and reducing peak demand. The role of smart grid technologies and IoT in enabling DSM is also examined. Key challenges and future research directions are highlighted.
Co-Authors Audia, Chaira Cankirli, Nebahat Ozaydin Chhibber, Sugandha Dassanayake, Sandun Ibekeme, Merit Raheem, Abdel