ADOPTION OF AI AND MACHINE LEARNING FOR STRATEGIC DECISION-MAKING IN MSMES: EVIDENCE FROM RAJASTHAN

Micro, Small, and Medium Enterprises (MSMEs) are vital to Rajasthan’s economy, contributing significantly to employment and industrial output. This study examines the adoption of Artificial Intelligence (AI) and Machine Learning (ML) in strategic decision-making among 357 MSMEs in Jaipur District, Rajasthan, using a quantitative survey. Results indicate that only 27.5% of MSMEs adopt AI/ML, with higher adoption in services (34.7%) and manufacturing (30.9%) compared to tourism/handicrafts (20.4%). Small and medium enterprises (mean adoption score = 3.12) outperform micro-enterprises (2.45, p < 0.01), and sectoral differences are significant (F = 5.89, p < 0.003). AI/ML adoption enhances decision accuracy (mean = 3.85 for adopters) and cost efficiency (mean = 3.65), but barriers such as high costs (68% rating 4/5) and skill gaps (55%) limit uptake, particularly among microenterprises (75% non-adopters). The study highlights the need for cost-effective AI solutions, skill development, and infrastructure support to bridge the adoption gap. Findings offer empirical evidence from a developing economy, emphasizing Rajasthan’s unique challenges and opportunities. Recommendations include subsidized AI tools and sector-specific training to empower MSMEs, fostering competitiveness and growth.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-22
DOI
https://doi.org/10.5281/zenodo.18020867
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

ADOPTION OF AI AND MACHINE LEARNING FOR STRATEGIC DECISION-MAKING IN MSMES: EVIDENCE FROM RAJASTHAN

DR KAMAL KANT
Zenodo (CERN European Organization for Nuclear Research)
Digital Transformation in Industry
article

ADOPTION OF AI AND MACHINE LEARNING FOR STRATEGIC DECISION-MAKING IN MSMES: EVIDENCE FROM RAJASTHAN

DR KAMAL KANT
article en

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are vital to Rajasthan’s economy, contributing significantly to employment and industrial output. This study examines the adoption of Artificial Intelligence (AI) and Machine Learning (ML) in strategic decision-making among 357 MSMEs in Jaipur District, Rajasthan, using a quantitative survey. Results indicate that only 27.5% of MSMEs adopt AI/ML, with higher adoption in services (34.7%) and manufacturing (30.9%) compared to tourism/handicrafts (20.4%). Small and medium enterprises (mean adoption score = 3.12) outperform micro-enterprises (2.45, p < 0.01), and sectoral differences are significant (F = 5.89, p < 0.003). AI/ML adoption enhances decision accuracy (mean = 3.85 for adopters) and cost efficiency (mean = 3.65), but barriers such as high costs (68% rating 4/5) and skill gaps (55%) limit uptake, particularly among microenterprises (75% non-adopters). The study highlights the need for cost-effective AI solutions, skill development, and infrastructure support to bridge the adoption gap. Findings offer empirical evidence from a developing economy, emphasizing Rajasthan’s unique challenges and opportunities. Recommendations include subsidized AI tools and sector-specific training to empower MSMEs, fostering competitiveness and growth.

Zenodo (CERN European Organization for Nuclear Research)
Central University of Rajasthan (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 33%
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.