Evidence map›Paper›PMID 40424309›Full record

ArticlePloS one2025

Navigating the AI landscape in SMEs: Overcoming internal challenges and external obstacles for effective integration.

Roziah Mohd Rasdi, Nordahlia Umar Baki

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Roziah Mohd RasdiDepartment of Professional Development and Continuing Education, Faculty of Educational Studies Universiti Putra Malaysia, Serdang, Selangor, Malaysia.ORCID https://orcid.org/0000-0002-4789-9792
Nordahlia Umar BakiDepartment of Professional Development and Continuing Education, Faculty of Educational Studies Universiti Putra Malaysia, Serdang, Selangor, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) integration in small and medium-sized enterprises (SMEs) is hampered by a variety of internal and external challenges. This article analyses these hurdles and underlines the need of an adaptation framework designed particularly for SMEs. The study collects primary data by conducting detailed interviews with representatives from six different SMEs in distinct sectors. The results indicate substantial internal obstacles, including reluctance to change, fear of job displacement by technology, and restricted resources, all of which impede the incorporation of AI. SMEs encounter a dynamic technological landscape, stringent regulatory requirements, and intense rivalry externally, necessitating agile and prompt strategy adjustments, a challenge often faced by SMEs. Ultimately, this research highlights the need of creating AI implementation plans that are customized to the distinct requirements and situations of SMEs. More adaptable and supportive legislative frameworks are essential to assist these enterprises in efficiently using AI and staying competitive in the digital era. This study contributes to the current discourse on technological progress in SMEs and establishes the foundation for next policies and initiatives designed to enhance their competitive edge using AI technology.

Identifiers

PMID40424309
PMCPMC12111296

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.