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Exploring artificial intelligence adoption among Italian firms: the AI readiness level

G. Costa et al.

International Journal of Business Information Systems2026https://doi.org/10.1504/ijbis.2026.152513article
AJG 1ABDC C
Weight
0.50

What the paper says

This study presents the first comprehensive analysis of artificial intelligence (AI) adoption among Italian firms, with a particular focus on the emerging impact of generative AI.Drawing upon the diffusion of innovations theory and the technology-organisation-environment (TOE) framework, we surveyed 237 managers from an Italian business school to examine their perspectives on both predictive/analytical AI and generative AI implementation.Our findings revealed varying levels of adoption and identified a significant gap in organisational AI readiness assessment tools.In response, we developed the AI readiness level (AIRL) framework, grounded in the TOE model, to evaluate AI maturity and guide strategic planning.This framework enables organisations to assess their AI capabilities, align initiatives with business objectives, and address key barriers such as skills shortages and data quality concerns.The study's limitations include the Italian context and specific executive sample.Future research should expand the sample size, diversify industries, and use randomised sampling.

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https://doi.org/https://doi.org/10.1504/ijbis.2026.152513

Or copy a formatted citation

@article{g.2026,
  title        = {{Exploring artificial intelligence adoption among Italian firms: the AI readiness level}},
  author       = {G. Costa et al.},
  journal      = {International Journal of Business Information Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijbis.2026.152513},
}

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Exploring artificial intelligence adoption among Italian firms: the AI readiness level

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.