Artificial intelligence in business: challenges and prospects for development
DOI:
https://doi.org/10.3846/bm.2026.2538Abstract
Artificial intelligence (AI) has become a transformative force in contemporary business management, yet its practical integration remains uneven due to barriers such as technological readiness, competence shortages, and regulatory uncertainty. This study aims to identify the key challenges and development prospects of AI implementation to enhance business efficiency and ensure sustainable development. Employing a foresight research methodology – including expert evaluation, scenario analysis, and semantic modeling – the study utilizes data from a 2025 session at Vilnius TECH University involving interdisciplinary experts. The findings highlight four critical success factors: technologies and innovation; human capital; financing and partnerships; and risk, security, and ethics. While AI’s strategic importance is growing, organizations must address the need for continuous workforce upskilling, stronger ethical governance, and more adaptive regulatory frameworks. Based on these results, a structured roadmap for AI implementation was developed, covering stages from preparation and pilot implementation to scaling and innovation-driven growth. Ultimately, this study contributes an integrated framework for understanding AI in management, providing strategic guidance for organizations to balance technological innovation with responsibility and risk management.
Keywords:
artificial intelligence, business management, innovation, human capital, risk management, digital transformation, ethics, foresight analysisHow to Cite
Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation and work (NBER Working Paper No. 24196). National Bureau of Economic Research. https://doi.org/10.3386/w24196
Begenau, J., Farboodi, M., & Veldkamp, L. (2018). Big data in finance and the growth of large firms (NBER Working Paper No. 24550). National Bureau of Economic Research. https://doi.org/10.3386/w24550
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (NBER Working Paper No. 31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161
Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Applications and value of deep learning (Discussion Paper). McKinsey Global Institute.
Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L., & Zemmel, R. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey & Company.
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747-019-00696-0
DeepL SE. (2026). DeepL Write [AI writing assistant]. https://www.deepl.com/write
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., Conboy, K., Doyle, R., Dubey, R., Dutot, V., Felix, R., Goyal, D. P., Gustafsson, A., Hinsch, C., Jebabli, I., … Wamba, S. F. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
European Union Agency for Cybersecurity. (2023). ENISA threat landscape 2023. https://www.enisa.europa.eu
European Commission. (2022). Competence framework for strategic foresight. Publications Office of the European Union.
European Investment Bank. (2026). Artificial intelligence adoption, productivity and employment: Evidence from European firms. https://www.eib.org/economics
European Union. (2024). Artificial Intelligence Act: Regulation laying down harmonised rules on artificial intelligence. https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
Felten, E. W., Raj, M., & Seamans, R. (2023). How will language modelers like ChatGPT affect occupations and industries? SSRN. https://doi.org/10.2139/ssrn.4375268
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2022). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26, 1771–1796. https://doi.org/10.1007/s11948-020-00213-5
Grammarly, Inc. (2026). Grammarly [AI writing assistant]. https://www.grammarly.com/
International Labour Organization. (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality. https://www.ilo.org/publications/generative-ai-and-jobs-global-analysis-potential-effects-job-quantity-and
Ivanov, D., Dolgui, A., & Sokolov, B. (2023). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/00207543.2018.1488086
Jöhnk, J., Weißert, M., & Wyrtki, K. (2022). Ready or not, AI comes – An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63, 5–20. https://doi.org/10.1007/s12599-020-00676-7
Khan, S. A. R., Yu, Z., & Umar, M. (2022). How environmental awareness and corporate social responsibility practices benefit the enterprise? An empirical study in the context of artificial intelligence. Management of Environmental Quality: An International Journal, 32(5), 863–885. https://doi.org/10.1108/MEQ-08-2020-0178
Klucznik, T., Teodoridis, F., & Zhou, N. (2023). Artificial intelligence and innovation: Evidence from patent data. Research Policy, 52(7), Article 104798.
Korinek, A., & Stiglitz, J. E. (2017). Artificial intelligence and its implications for income distribution and unemployment (NBER Working Paper No. 24174). National Bureau of Economic Research. https://doi.org/10.3386/w24174
Li, L., & Wang, X. (2022). Artificial intelligence and economic growth: Evidence from global data. Technological Forecasting and Social Change, 177, Article 121528.
Li, L., Su, F., Zhang, W., & Mao, J. Y. (2022). Digital transformation by SME entrepreneurs: A capability perspective. Information Systems Journal, 32(3), 528–557.
McKinsey Global Institute. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey & Company. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
McKinsey & Company. (2024). The state of AI: How organizations are rewiring to capture value. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
National Institute of Standards and Technology. (2023). AI Risk management framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1
Parkes, D. C., & Wellman, M. P. (2023). Economic reasoning and artificial intelligence. Science, 349(6245), 267–272. https://doi.org/10.1126/science.aaa8403
PwC. (2023). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? (PwC Global Report). https://www.pwc.com.au/government/pwc-ai-analysis-sizing-the-prize-report.pdf
Raford, N. (2022). Foresight, futures thinking, and scenario planning: Foundations and applications. World Futures Review, 14(1), 3–15.
Raisch, S., & Krakowski, S. (2022). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Rowe, G., & Wright, G. (2011). The Delphi technique: Past, present, and future prospects. Technological Forecasting and Social Change 78, 1487–1490. https://doi.org/10.1016/j.techfore.2011.09.002
Stanford Institute for Human-Centered Artificial Intelligence. (2025). AI Index Report 2025. Stanford University. https://aiindex.stanford.edu/report/
The Organisation for Economic Co-operation and Development. (2023a). OECD framework for the classification of AI systems: A tool for effective AI policies. OECD Publishing. https://doi.org/10.1787/cb6d9eca-en
The Organisation for Economic Co-operation and Development. (2023b). Financing SMEs and entrepreneurs 2023: An OECD scoreboard. OECD Publishing.
The Organisation for Economic Co-operation and Development. (2023c). OECD AI principles overview. OECD Publishing.
The Organisation for Economic Co-operation and Development. (2023d). Skills for the digital age: Insights from OECD research. OECD Publishing.
The Organisation for Economic Co-operation and Development. (2024). Artificial intelligence, productivity, and the future of work. OECD Publishing.
Vecchiato, R. (2022). Scenario planning and strategic foresight: How organizations use the future to make better decisions. Technological Forecasting and Social Change, 174, Article 121250.
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2022). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11, Article 233. https://doi.org/10.1038/s41467-019-14108-y
World Bank. (2023). Public-private partnerships reference guide (4th ed.). World Bank Group.
World Economic Forum. (2023). The future of jobs report 2023. https://www.weforum.org/reports/the-future-of-jobs-report-2023
Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes. Journal of Cleaner Production, 142, 626–641. https://doi.org/10.1016/j.jclepro.2016.07.123
Downloads
Published
Conference Event
Section
Copyright
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
