INNOVATIVE AI TOOLS FOR DIGITAL BUSINESS TRANSFORMATION WITH A FOCUS ON DATA MANAGEMENT AND AUTOMATION OF KEY PROCESSE

INNOVATIVE AI TOOLS FOR DIGITAL BUSINESS TRANSFORMATION WITH A FOCUS ON DATA MANAGEMENT AND AUTOMATION OF KEY PROCESSE | Zenodo Skip to main Communities My dashboard Log in Sign up Zenodo is currently experiencing slowness and intermittent outages due to heavy automated traffic from bots and AI crawlers. We are aware of the problem, and our team is focused on stabilizing the service. Thank you for your patience. Journal of Theoretical and Applied Information Technology Published April 30, 2026 | Version v1 Journal article Open INNOVATIVE AI TOOLS FOR DIGITAL BUSINESS TRANSFORMATION WITH A FOCUS ON DATA MANAGEMENT AND AUTOMATION OF KEY PROCESSE Authors/Creators KATERYNA HALAN, NATALIIA HAVADZYN , TETIANA KNIAZIEVA , ELENA LYTVYNENKO, NATALIA DANIK Description This study aims to determine whether companies’ management of data and AI-driven automation contributes to operational flexibility and competitiveness, focusing on highly specialized businesses. The authors employ a global perspective using expert judgment, cross-country comparisons, compliance with international standards (ISO/IEC 27001:2022, ISO/IEC 38505-1:2021, ISO/IEC 42001:2023), and combined quantitative tools within the "Digital Transformation Integration Index" (DTII) model. Empirical research focuses on the UAE, Ukraine, and Singapore, each with distinct regulatory regimes, technical infrastructures, and organizational maturity levels. Digital capability is assessed through three factors: data governance capacity (DGC), AI automation performance (AAP), and organizational readiness for AI adoption (ORA). Results establish a hierarchy of digital maturity. Singapore achieves the highest DTII score (.94) due to robust data governance, advanced automation, and strong organizational capabilities. The UAE scores second (.815), benefiting from rapid public-sector modernization and fast AI implementation, presenting significant scaling opportunities. Ukraine’s DTII score (.584) reflects improving digital skills and automation but highlights the need for enhanced infrastructure and regulation, described through a “transitive growth” model. Sectoral AAP analysis shows Singapore leading (88–95), the UAE with high but uneven performance (79–90), and Ukraine at average levels (63–78). Differences are also observed in cultural orientation toward innovation, managerial digital literacy, and institutional readiness for AI adoption, as measured by ORA. The study provides a comprehensive analytical framework to evaluate a company’s or country’s preparedness for digital transformation and to guide strategic priorities. Its findings can support corporate digital strategy development, enhance data management systems, streamline AI-driven workflows, and inform policies to stimulate digital economy growth, particularly in developing countries. Files 22Vol104No8.pdf Files (895.1 kB) Name Size Download all 22Vol104No8.pdf md5:3447041fdb6692e97ce73de8f8d34717 895.1 kB Preview Download 43 Views 28 Downloads Show more details All versions This version Views Total views 43 43 Downloads Total downloads 28 28 Data volume Total data volume 26.0 MB 26.0 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Digital Transformation, Artificial. Intelligence, Information Governance, Process. Automation, Planning Readiness, AI Integration, Business. Analytics, Digital Strategy, Innovation, Systems, Sustainable. Development. Details DOI DOI Badge DOI 10.5281/zenodo.19977537 Markdown [](https://doi.org/10.5281/zenodo.19977537) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19977537.svg :target: https://doi.org/10.5281/zenodo.19977537 HTML <a href="https://doi.org/10.5281/zenodo.19977537"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19977537.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19977537.svg Target URL https://doi.org/10.5281/zenodo.19977537 Resource type Journal article Publisher Little Lion Scientific Published in Journal of Theoretical and Applied Information Technology, 104(8), ISSN: 1992-8645, 2026. Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Copyright Little Lion Scientific Citation Export Technical metadata Created May 2, 2026 Modified May 2, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies

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