From Idea to Impact: The Role of Artificial Intelligence in the Transformation of Business Models

Authors

Keywords:

artificial intelligence; startup; digital transformation; business model; innovation management

Abstract

Artificial intelligence (AI) is becoming a significant driver of transformation in the business environment, particularly for startups, which are characterized by high levels of adaptability, innovation orientation and strategic flexibility. Startups that incorporate artificial intelligence into their structures and processes are reshaping traditional approaches to value creation. This study aims to explore how artificial intelligence influences the formation and evolution of business models in startups across different industries. The analysis draws on academic literature, case study evidence and current market observations in order to identify key areas where artificial intelligence may affect fundamental business model components. The comparative part of the study focuses on differences between AI-driven startups and traditional companies that did not emerge as startups and do not rely on artificial intelligence as a core strategic technology. The comparison is carried out within three selected industries, namely banking, automotive manufacturing and retail. The study applies the Business Model Canvas (BCM) as a conceptual tool to evaluate the configuration of business models in both categories of companies. The results of the analysis indicate that startups using artificial intelligence are creating new types of business models in which AI plays a significant role in shaping the value proposition, sales channels, and revenue streams. Traditional companies, on the other hand, are not transforming their business models radically but are integrating artificial intelligence mainly in support areas. Building on these findings, this research contributes to the broader understanding of business model innovation under the influence of artificial intelligence and outlines the analytical foundations for identifying structural distinctions between AI-driven startups and traditional companies.

References

Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2024). AI-powered innovation in digital transformation: Key pillars and industry impact. Sustainability, 16(5), 1790. https://doi.org/10.3390/su16051790

Balcerzak, A. P., & Valaskova, K. (2024). Artificial intelligence: Financial management under pressure of transformative technology. Equilibrium Quarterly Journal of Economics and Economic Policy, 19(4), 1127–1137. https://doi.org/10.24136/eq.3394

Balcerzak, A. P., Nica, E., Rogalska, E., Poliak, M., Kliestik, T., & Sabie, O. (2022). Blockchain technology and smart contracts in decentralized governance systems. Administrative Sciences, 12(3), 96. https://doi.org/10.3390/admsci12030096

Barker, M. (2023). Artificial intelligence-based Internet of manufacturing things systems, digital twin data modeling and visualization tools, and multi-sensory extended reality and geospatial mapping technologies in the immersive industrial metaverse. Economics, Management, and Financial Markets, 18(1), 41-56. https://doi.org/10.22381/EMFM18120233

Brem, A., Giones, F., & Werle, M. (2021). The AI digital revolution in innovation: A conceptual framework of Artificial intelligence technologies for the management of innovation. IEEE Transactions on Engineering Management, 70(2), 770–776. https://doi.org/10.1109/tem.2021.3109983

Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534. https://doi.org/10.1126/science.aap8062

Carayannis, E. G., Dumitrescu, R., Falkowski, T., Papamichail, G., & Zota, N. R. (2025). Enhancing SME resilience through artificial intelligence and strategic foresight: A framework for sustainable competitiveness. Technology in Society, 81, 102835. https://doi.org/10.1016/j.techsoc.2025.102835

Carter, M., & Carter, C. (2020). The creative business model Canvas. Social Enterprise Journal, 16(2), 141–158. https://doi.org/10.1108/sej-03-2019-0018

CB Insights. (2024). Median valuations for artificial intelligence (AI) and non-AI startups worldwide in 2023, by stage (in million U.S. dollars). https://www.statista.com/statistics/1446293/median-valuations-ai-startups-by-stage/

Climent, R. C., Haftor, D. M., & Staniewski, M. W. (2024). AI-enabled business models for competitive advantage. Journal of Innovation & Knowledge, 9(3), 100532. https://doi.org/10.1016/j.jik.2024.100532

Consuegra, L. C., Vasquez, P. a. M., & Perez, A. M. M. (2023). Algoritmos de inteligencia artificial basada en perfiles socio conductuales para la segmentación inteligente de clientes: Estudio de caso. Ingeniería Y Competitividad, 25(3). https://doi.org/10.25100/iyc.v25i3.12658

Dabija, D. C., & Vătămănescu, E. (2023). Artificial intelligence: The future is already here. Oeconomia Copernicana, 14(4), 1053–1056. https://doi.org/10.24136/oc.2023.031

De Vasconcelos Gomes, L. A., Farago, F. E., Facin, A. L. F., Flechas, X. A., & Silva, L. E. N. (2023). From open business model to ecosystem business model: A processes view. Technological Forecasting and Social Change, 194, 122668. https://doi.org/10.1016/j.techfore.2023.122668

Deligianni, I., Voudouris, I., Spanos, Y., & Lioukas, S. (2019). Non-linear effects of technological competence on product innovation in new technology-based firms: Resource orchestration and the role of the entrepreneur’s political competence and prior start-up experience. Technovation, 88, 102076. https://doi.org/10.1016/j.technovation.2019.05.002

Dokumacı, M. (2024). Legal frameworks for AI regulations. Human Computer Interaction., 8(1), 133. https://doi.org/10.62802/ytst2927

Fakieh, B., Al-Ghamdi, A. S. A., & Ragab, M. (2022). The effect of utilizing business model canvas on the satisfaction of operating electronic business. Complexity, 2022(1). https://doi.org/10.1155/2022/1649160

Farahani, M. S., & Esfahani, A. (2022). Opportunities and challenges of applying artificial intelligence in the financial sectors and startups during the coronavirus outbreak. International Journal of Innovation in Management Economics and Social Sciences, 2(4), 33–55. https://doi.org/10.52547/ijimes.2.4.33

Fu, D., Jenkinson, T., & Rauch, C. (2022). How do financial contracts evolve for new ventures? Journal of Corporate Finance, 81, 102222. https://doi.org/10.1016/j.jcorpfin.2022.102222

Gambardella, A., & McGahan, A. M. (2009). Business-model innovation: General purpose technologies and their implications for industry structure. Long Range Planning, 43(2–3), 262–271. https://doi.org/10.1016/j.lrp.2009.07.009

Gentsch, P. (2019). AI in marketing, sales and service: How marketers without a data science degree can use AI, big data and bots. Springer. https://doi.org/10.1007/978-3-319-89957-2

Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057

Gültekin, D. G., Pinarbasi, F., Yazici, M., & Adiguzel, Z. (2024). Commercialisation of artificial intelligence: A research on entrepreneurial companies with challenges and opportunities. Business Process Management Journal, 31(2), 605–630. https://doi.org/10.1108/bpmj-10-2023-0836

Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2016). Capturing value from big data – a taxonomy of data-driven business models used by start-up firms. International Journal of Operations & Production Management, 36(10), 1382–1406. https://doi.org/10.1108/ijopm-02-2014-0098

Hossain, M. A., Akter, S., Yanamandram, V., & Wamba, S. F. (2023). Data-driven market effectiveness: The role of a sustained customer analytics capability in business operations. Technological Forecasting and Social Change, 194, 122745. https://doi.org/10.1016/j.techfore.2023.122745

Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, A. (2022). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216, 119456. https://doi.org/10.1016/j.eswa.2022.119456

Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence? Discover Artificial Intelligence, 2(1). https://doi.org/10.1007/s44163-022-00022-8

Jin, Y., Ji, S., Liu, L., & Wang, W. (2021). Business model innovation canvas: A visual business model innovation model. European Journal of Innovation Management, 25(5), 1469–1493. https://doi.org/10.1108/ejim-02-2021-0079

Kaggwa, S., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., & Akinoso, A. (2024). AI in decision making: Transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423–444. https://doi.org/10.51244/ijrsi.2023.1012032

Kaplan, A., & Haenlein, M. (2018). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004

Kellogg, K. C., Valentine, M. A., & Christin, A. (2019). Algorithms at work: the new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174

Kerzel, U. (2020). Enterprise AI canvas integrating artificial intelligence into business. Applied Artificial Intelligence, 35(1), 1–12. https://doi.org/10.1080/08839514.2020.1826146

Khan, I. U., Taherdoost, H., Madanchian, M., Ouaissa, M., Hajjami, S. E., & Rahman, H. (2024). Future tech startups and innovation in the age of AI (1st ed.). CRC Press. https://doi.org/10.1201/9781032715957

Kliestik, T., Kral, P., Bugaj, M., & Durana, P. (2024). Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse. Equilibrium Quarterly Journal of Economics and Economic Policy, 19(2), 429–461. https://doi.org/10.24136/eq.3108

Kuteesa, N. K. N., Akpuokwe, N. C. U., & Udeh, N. C. A. (2024). Navigating the digital transformation journey: Strategies for startup growth and innovation in the digital era. International Journal of Scholarly Research in Multidisciplinary Studies, 4(2), 038–053. https://doi.org/10.56781/ijsrms.2024.4.2.0031

Lee, B., Kim, B., & Ivan, U. V. (2023). Enhancing the competitiveness of AI technology-based startups in the digital era. Administrative Sciences, 14(1), 6. https://doi.org/10.3390/admsci14010006

Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51(1), 44–56. https://doi.org/10.1016/j.intmar.2020.04.002

Macha-Huamán, R., Zavala-Zavala, O. M., Soto, F. C. N., Suárez, J. S. Z., Castañeda, D. R. Y., Lucar, R. G. C., Jibaja, L. C., Mejía, P. J. C., Montoya, C. M. S., Casco, R. J. E., & Romero-Carazas, R. (2023). Business model canvas in the entrepreneurs’ business model: A system approach. ICST Transactions on Scalable Information Systems, 10(5), 1–9. https://doi.org/10.4108/eetsis.3594

Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045

Mao, H., Zhang, T., & Tang, Q. (2021). Research framework for determining how artificial intelligence enables information technology service management for business model resilience. Sustainability, 13(20), 11496. https://doi.org/10.3390/su132011496

Martinović, M., Barać, R., & Maljak, H. (2024). Exploring Croatian consumer adoption of subscription-based e-commerce for business innovation. Administrative Sciences, 14(7), 149. https://doi.org/10.3390/admsci14070149

Metzker, Z. (2024). Selected demographic determinants of CSR, financial & environmental management and business ethics in SMEs. Deleted Journal, 2(1), 79–88. https://doi.org/10.62222/fend1256

Mohammadi, N., & Shafiee, M. (2022). Predicting the success of seed-stage startups to enter the acceleration program and receive seed money. International Journal of Entrepreneurial Venturing, 14(2), 168. https://doi.org/10.1504/ijev.2022.122654

Murray, A., & Scuotto, V. (2016). The business model canvas. Symphonya Emerging Issues in Management, 3, 94–109. https://doi.org/10.4468/2015.3.13murray.scuotto

Musch, S., Borrelli, M. C., & Kerrigan, C. (2024). Bridging compliance and innovation: A comparative analysis of the EU AI Act and GDPR for enhanced organisational strategy. Journal of Data Protection & Privacy, 7(1), 14. https://doi.org/10.69554/fwhu3837

Nagy, M., Figura, M., Valaskova, K., & Lăzăroiu, G. (2025). Predictive maintenance algorithms, artificial intelligence digital twin technologies, and internet of robotic things in big data-driven industry 4.0 manufacturing systems. Mathematics, 13(6), 981. https://doi.org/10.3390/math13060981

Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023). Machine intelligence and autonomous robotic technologies in the corporate context of SMES: Deep learning and virtual simulation algorithms, cyber-physical production networks, and industry 4.0-based manufacturing systems. Applied Sciences, 13(3), 1681. https://doi.org/10.3390/app13031681

Ozay, D., Jahanbakht, M., Shoomal, A., & Wang, S. (2024). Artificial intelligence (AI)-based customer relationship management (CRM): A comprehensive bibliometric and systematic literature review with outlook on future research. Enterprise Information Systems, 18(7). https://doi.org/10.1080/17517575.2024.2351869

Piotrowski, D., & Orzeszko, W. (2023). Artificial intelligence and customers’ intention to use robo-advisory in banking services. Equilibrium Quarterly Journal of Economics and Economic Policy, 18(4), 967–1007. https://doi.org/10.24136/eq.2023.031

Purwanto, A. N. I., Fauzan, M., Widya, T., & Azzaky, N. S. (2024). Ethical Implications and challenges of AI implementation in business operations. Techcomp Innovations, 1(2), 68–82. https://doi.org/10.70063/techcompinnovations.v1i2.52

Rahim, M. J., Afroz, A., & Akinola, O. (2025). Predictive analytics in healthcare: big data, better decisions. International Journal of Scientific Research and Modern Technology, 4(1), 1-21. https://doi.org/10.5281/zenodo.14630840

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072

Riedl, R. (2022). Is trust in artificial intelligence systems related to user personality? Review of empirical evidence and future research directions. Electronic Markets, 32(4), 2021–2051. https://doi.org/10.1007/s12525-022-00594-4

Rios-Campos, C., Zambrano, E. O. G., Vargas, D. J. C., Merino, L. a. A., Vallejos, P. a. A., Alcantara, I. M. B., Rubio, D. E. D., Rodriguez, D. S., Tomanguilla, J. H., & Calderón, E. V. (2024). Startups and artificial intelligence. South Florida Journal of Development, 5(2), 950–969. https://doi.org/10.46932/sfjdv5n2-042

Salwin, M., Jacyna-Gołda, I., Kraslawski, A., & Waszkiewicz, A. E. (2022). The use of business model canvas in the design and classification of product-service systems design methods. Sustainability, 14(7), 4283. https://doi.org/10.3390/su14074283

Sarma, M., Senaratne, C., & Matheus, T. (2023). Challenges and opportunities of ethical AI and digital technology use in emerging economies. In M. Findlay, L. M. Ong & W. Zhang (Eds.), Elgar Companion to Regulating AI and Big Data in Emerging Economies (pp. 42–58). Edward Elgar Publishing. https://doi.org/10.4337/9781785362408.00009

Selvakumar, P., Shanthi, M., Pitchiah, R., Sharma, M., Dahake, P. S., & T. C., M. (2025). The Role of Artificial Intelligence in Business Model Innovation: Overview of AI Technologies. In M. Khokhar (Ed.), AI-Driven Business Model Innovation (pp. 219-246). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9571-4.ch009

Sestino, A., & De Mauro, A. (2021). Leveraging artificial intelligence in business: implications, applications and methods. Technology Analysis and Strategic Management, 34(1), 16–29. https://doi.org/10.1080/09537325.2021.1883583

Sjödin, D., Parida, V., Palmié, M., & Wincent, J. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134, 574–587. https://doi.org/10.1016/j.jbusres.2021.05.009

Smuha, N. A. (2021). From a ‘race to AI’ to a ‘race to AI regulation’: regulatory competition for artificial intelligence. Law Innovation and Technology, 13(1), 57–84. https://doi.org/10.1080/17579961.2021.1898300

Song, X., & Bonanni, C. (2024). AI-driven business model: How AI-powered try-on technology is refining the luxury shopping experience and customer satisfaction. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3067–3087. https://doi.org/10.3390/jtaer19040148

Soussi, A., Zero, E., Sacile, R., Trinchero, D., & Fossa, M. (2024). Smart sensors and smart data for precision agriculture: A review. Sensors, 24(8), 2647. https://doi.org/10.3390/s24082647

Stork, S., Morgenstern, R., Pölling, B., & Feil, J. (2023). Holistic business model conceptualisation—capturing sustainability contributions illustrated by nature-based solutions. Sustainability, 15(19), 14091. https://doi.org/10.3390/su151914091

Svobodova, Z., Vecerova, V., Redlichova, R., & Somerlikova, K. (2024). Evaluation of the performance of SMEs in the context of regional development. Deleted Journal, 2(1), 47–60. https://doi.org/10.62222/riox8892

Tang, X., Du, S., & Deng, W. (2025). Business innovation in digital startups: A case study of an AI startup. International Review of Economics & Finance, 98, 103898. https://doi.org/10.1016/j.iref.2025.103898

Tani, M., Muto, V., Basile, G., & Nevi, G. (2025). A bibliometric analysis to study the evolution of artificial intelligence in business ethics. Business Ethics the Environment & Responsibility. https://doi.org/10.1111/beer.12797

Ting, D. S., Peng, L., Varadarajan, A. V., Keane, P. A., Burlina, P. M., Chiang, M. F., Schmetterer, L., Pasquale, L. R., Bressler, N. M., Webster, D. R., Abramoff, M., & Wong, T. Y. (2019). Deep learning in ophthalmology: The technical and clinical considerations. Progress in Retinal and Eye Research, 72, 100759. https://doi.org/10.1016/j.preteyeres.2019.04.003

Vasenska, I. P. (2024). Economic implications of deep machine learning for tourism time series forecasting. Ekonomicko-manažerske Spektrum, 18(1), 90–101. https://doi.org/10.26552/ems.2024.1.90-101

Wang, Q., Zhang, F., & Li, R. (2024). Artificial intelligence and sustainable development during urbanization: Perspectives on AI R&D innovation, AI infrastructure, and AI market advantage. Sustainable Development, 33(1), 1136–1156. https://doi.org/10.1002/sd.3150

Winecoff, A. A., & Watkins, E. A. (2022). Artificial concepts of artificial intelligence: Institutional compliance and resistance in AI startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES '22) (pp. 788–799). ACM. https://doi.org/10.1145/3514094.3534138

Wu, W., & Liu, S. (2023). Dilemma of the artificial intelligence regulatory landscape. Communications of the ACM, 66(9), 28–31. https://doi.org/10.1145/3584665

Xie, X., Han, Y., Anderson, A., & Ribeiro-Navarrete, S. (2022). Digital platforms and SMEs’ business model innovation: Exploring the mediating mechanisms of capability reconfiguration. International Journal of Information Management, 65, 102513. https://doi.org/10.1016/j.ijinfomgt.2022.102513

Zafar, A. (2024). Balancing the scale: Navigating ethical and practical challenges of artificial intelligence (AI) integration in legal practices. Discover Artificial Intelligence, 4(1), 27. https://doi.org/10.1007/s44163-024-00121-8

Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224. https://doi.org/10.1016/j.jii.2021.100224

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2025-06-25

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FIGURA, M., JURACKA, D., & IMPPOLA, J. (2025). From Idea to Impact: The Role of Artificial Intelligence in the Transformation of Business Models. Management Dynamics in the Knowledge Economy, 13(2), 120–147. Retrieved from https://www.managementdynamics.ro/index.php/journal/article/view/686

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