Efficiency of Artificial Intelligence among Engineers in Manufacturing Firms in Nigeria

This study examined “Efficiency of Artificial Intelligence among Engineers in Manufacturing Firms in Nigeria”. Methodology: Relevant data were drawn from selected journals, textbooks. Qualitative content analysis was used to analyze the data. Study conclusion and policy recommendations: The study concluded that the efficiency of artificial intelligence (AI) among engineers in manufacturing firms in Nigeria reflects both promising potential and significant challenges. Empirical evidence suggests that AI technologies, when effectively adopted, enhance productivity, accuracy, and task performance by automating routine engineering processes, enabling real-time monitoring, and improving data-driven decision-making. In firms with sufficient infrastructure and skilled personnel, AI integration has already resulted in measurable gains, such as reduced downtime, faster maintenance cycles, and improved product quality. Finally, the study recommends that to enhance the efficiency of artificial intelligence among engineers in manufacturing firms in Nigeria, it is recommended that firms invest in continuous training and capacity-building programs to equip engineers with the necessary AI-related technical skills. Government and private sector collaboration should be strengthened to improve digital infrastructure, such as stable power supply and high-speed internet, which are critical for the smooth operation of artificial intelligence systems.

Authors

Institutions

Publication Details

Journal
International Journal of Sustainability in Research
Published
2025-10-02
DOI
https://doi.org/10.59890/ijsr.v3i5.160
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficiency of Artificial Intelligence among Engineers in Manufacturing Firms in Nigeria

Godspower Onyekachukwu Ekwueme
International Journal of Sustainability in Research
Digital Transformation in Industry
article

Efficiency of Artificial Intelligence among Engineers in Manufacturing Firms in Nigeria

Godspower Onyekachukwu Ekwueme
article en

Abstract

This study examined “Efficiency of Artificial Intelligence among Engineers in Manufacturing Firms in Nigeria”. Methodology: Relevant data were drawn from selected journals, textbooks. Qualitative content analysis was used to analyze the data. Study conclusion and policy recommendations: The study concluded that the efficiency of artificial intelligence (AI) among engineers in manufacturing firms in Nigeria reflects both promising potential and significant challenges. Empirical evidence suggests that AI technologies, when effectively adopted, enhance productivity, accuracy, and task performance by automating routine engineering processes, enabling real-time monitoring, and improving data-driven decision-making. In firms with sufficient infrastructure and skilled personnel, AI integration has already resulted in measurable gains, such as reduced downtime, faster maintenance cycles, and improved product quality. Finally, the study recommends that to enhance the efficiency of artificial intelligence among engineers in manufacturing firms in Nigeria, it is recommended that firms invest in continuous training and capacity-building programs to equip engineers with the necessary AI-related technical skills. Government and private sector collaboration should be strengthened to improve digital infrastructure, such as stable power supply and high-speed internet, which are critical for the smooth operation of artificial intelligence systems.

International Journal of Sustainability in ResearchVol. 3(5)
Nnamdi Azikiwe University (NG)
Openalex Percentile: Top 59%
Digital Transformation in Industry
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.