Start-ups and artificial intelligence: From application to value, and the five problems that shape effective implementation

Start-ups and artificial intelligence: From application to value, and the five problems that shape effective implementation

Článek - ostatní

This paper examines the growing role of artificial intelligence (AI) in start-ups and digital entrepreneurial ventures, emphasizing the transition from simple technological application to the generation of sustainable business value. The authors argue that the effective adoption of AI in start-ups depends on several interrelated organizational and ecosystem factors that shape innovation outcomes. The discussion identifies five key challenges influencing AI implementation. First, the ability of start-ups to absorb external technologies and data determines whether open innovation and access to cloud-based AI tools translate into real competitive advantage. Second, the transition from experimental prototypes to scalable solutions remains a major operational bottleneck, often requiring structured proof-of-concept processes and support from innovation ecosystems. Third, human factors - such as organizational culture, employee attitudes, and empowerment - play a decisive role in determining whether AI is perceived as a supportive tool or a threat. Fourth, ethical considerations, trust, and regulatory compliance are increasingly becoming integral elements of AI-based value propositions, particularly in consumer-oriented markets. Finally, ecosystem inequalities, including geographical concentration of AI start-ups and unequal access to capital and talent, shape the diffusion and commercialization of AI innovations. The editorial concludes that successful AI-driven entrepreneurship requires treating AI not merely as a technological solution but as an organizational system combining technological capabilities, human competencies, and institutional support structures.

This paper examines the growing role of artificial intelligence (AI) in start-ups and digital entrepreneurial ventures, emphasizing the transition from simple technological application to the generation of sustainable business value. The authors argue that the effective adoption of AI in start-ups depends on several interrelated organizational and ecosystem factors that shape innovation outcomes. The discussion identifies five key challenges influencing AI implementation. First, the ability of start-ups to absorb external technologies and data determines whether open innovation and access to cloud-based AI tools translate into real competitive advantage. Second, the transition from experimental prototypes to scalable solutions remains a major operational bottleneck, often requiring structured proof-of-concept processes and support from innovation ecosystems. Third, human factors - such as organizational culture, employee attitudes, and empowerment - play a decisive role in determining whether AI is perceived as a supportive tool or a threat. Fourth, ethical considerations, trust, and regulatory compliance are increasingly becoming integral elements of AI-based value propositions, particularly in consumer-oriented markets. Finally, ecosystem inequalities, including geographical concentration of AI start-ups and unequal access to capital and talent, shape the diffusion and commercialization of AI innovations. The editorial concludes that successful AI-driven entrepreneurship requires treating AI not merely as a technological solution but as an organizational system combining technological capabilities, human competencies, and institutional support structures.

Artificial Intelligence; Start-ups; Digital Entrepreneurship; AI Adoption and Implementation; Innovation Ecosystems

Artificial Intelligence; Start-ups; Digital Entrepreneurship; AI Adoption and Implementation; Innovation Ecosystems

BALCERZAK, A.; ŠKAPA, S.; ZINECKER, M.

2026

01.12.2025

Institute of Economic Research (Poland)

Oeconomia Copernicana

16

4

Polská republika

1369

1376

8

https://oeconomia.pl/index.php/oc/article/view/4057

http://hdl.handle.net/11012/256413