ArticlePloS one2025
Navigating the AI landscape in SMEs: Overcoming internal challenges and external obstacles for effective integration.
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.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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.