AI adoption linked to economic, social, and environmental gains in SMEs

Efforts to promote artificial intelligence (AI) as a tool for sustainable entrepreneurship may fall short unless adoption is supported by strong ecosystem conditions, according to new academic research. The study shows that access to infrastructure, competitive signals, and social influence matter more than internal readiness when firms decide whether to invest in AI.

The analysis, titled Drivers and Sustainable Performance Outcomes of AI Adoption Intention: A Multi-Theoretical Analysis in the Entrepreneurial Ecosystem and published in Sustainability, links AI adoption to strategic renewal and holistic sustainability across entrepreneurial firms.

Ecosystem pressures shape AI adoption decisions

AI adoption among entrepreneurial firms is primarily driven by external and contextual factors rather than internal managerial attitudes. Facilitating conditions, social influence, and competitive pressure emerge as the strongest positive predictors of adoption intention.

Facilitating conditions refer to the availability of technical infrastructure, financial resources, skills, and institutional support required to implement AI solutions. Firms operating in environments with access to digital infrastructure, supportive policies, and knowledge networks are significantly more likely to pursue AI adoption. This finding underscores the importance of ecosystem readiness in determining whether AI diffusion occurs at scale.

Social influence also plays a decisive role. As AI adoption becomes more visible within entrepreneurial networks, firms face increasing normative pressure to follow suit. Adoption decisions are shaped not only by direct competitors but also by industry peers, partners, and ecosystem actors who signal that AI is becoming a standard component of modern business operations.

Competitive pressure further accelerates adoption. Firms are more inclined to invest in AI when they perceive a risk of falling behind technologically advanced rivals. In fast-moving markets, AI adoption is framed less as an opportunity for differentiation and more as a necessity for survival.

On the other hand, several internal factors commonly assumed to drive technology adoption are found to have limited or no direct influence. Top management support, often cited as a critical enabler of digital transformation, does not significantly affect adoption intention in this context. Similarly, effort expectancy, or the perceived ease of using AI systems, does not emerge as a decisive factor.

The study also identifies a negative relationship between performance expectancy and adoption intention. This counterintuitive result reflects a reality check effect, where early exposure to AI implementation challenges tempers overly optimistic expectations. Initial complexity, integration difficulties, and skill gaps reduce confidence in immediate performance gains, discouraging adoption when expectations are not aligned with practical constraints.

AI as a catalyst for strategic renewal

While adoption decisions are shaped by external pressures, the study finds that the consequences of AI adoption are largely strategic and transformational. Firms that successfully adopt AI gain access to new opportunities for strategic renewal, enabling them to rethink business models, operational processes, and value creation mechanisms.

AI adoption enhances firms' ability to analyze data, anticipate market trends, and respond dynamically to environmental changes. These capabilities support more informed decision-making and improve organizational agility, particularly in volatile and uncertain markets where entrepreneurial firms often operate.

By improving sensing, learning, and reconfiguration processes, AI enables firms to identify emerging opportunities, reallocate resources efficiently, and adapt to shifting competitive conditions. This capability-based perspective moves beyond simplistic automation narratives and highlights AI's role in shaping long-term competitiveness.

Importantly, the study shows that AI adoption supports business model innovation rather than incremental optimization alone. Firms leverage AI to redesign customer interactions, enhance product and service personalization, and streamline value chains. These changes contribute to strategic renewal by allowing firms to scale innovation and differentiate themselves in crowded markets.

The findings also demonstrate that AI adoption strengthens competitive advantage in entrepreneurial contexts. Firms that integrate AI into their strategic core outperform peers in terms of adaptability and resilience. This advantage is not derived solely from cost reduction but from enhanced strategic positioning within the ecosystem.

Linking AI adoption to holistic sustainability

Firms that adopt AI report improvements across economic, environmental, and social dimensions, supporting a holistic view of sustainability rather than a narrow financial focus.

Economically, AI adoption contributes to improved efficiency, productivity, and profitability. Enhanced analytics and automation enable firms to optimize resource allocation and reduce operational waste, strengthening financial performance over time.

Environmentally, AI supports more efficient use of energy and materials by enabling predictive maintenance, process optimization, and smarter supply chain management. These improvements reduce resource intensity and environmental impact, aligning entrepreneurial activity with sustainability objectives.

Social performance also benefits from AI adoption. The study finds that AI-enabled firms are better positioned to create skilled employment, support learning and innovation, and enhance workplace decision-making. Rather than displacing entrepreneurial activity, AI reinforces human capabilities when adoption is strategically managed.

Theoretically, the research advances resource-based and dynamic capability perspectives by demonstrating that AI functions as a strategic asset rather than a generic technology. Its value lies in how it is combined with organizational processes, ecosystem support, and strategic intent.