Why some small businesses win with AI while others fall behind

Small and medium-sized enterprises are increasingly turning to generative artificial intelligence to boost competitiveness, streamline operations, and unlock new growth opportunities. However, new research suggests that the value created by these technologies is far from uniform, with outcomes varying widely depending on how firms deploy AI and how prepared they are to integrate it.

A study titled "How Generative Artificial Intelligence Creates Value: A Function and Readiness Perspective in Small and Medium-Sized Enterprises," published in Administrative Sciences, offers a detailed framework explaining why some firms achieve strong gains from GenAI while others see limited impact. The research shifts the focus from simple adoption to a more nuanced understanding of how specific AI functions align with strategic goals and how readiness conditions shape outcomes.

GenAI functions split between efficiency gains and growth opportunities

The research identifies generative AI as a multifunctional strategic capability rather than a single-purpose tool. Through a Delphi study involving experts from industry, academia, and policy, the authors classify six core functional domains that shape how firms extract value from GenAI.

Two of these functions are closely tied to efficiency-oriented strategies. Operational automation enables firms to streamline repetitive administrative tasks, improve customer service processes, and integrate AI into existing workflows. This reduces costs and allows human resources to shift toward higher-value activities. Alongside this, data intelligence and predictive analytics enhance decision-making by enabling faster analysis of large datasets, forecasting demand, and supporting scenario planning.

These functions reinforce what the study describes as exploitation-oriented strategies, where firms prioritize efficiency, cost control, and process optimization. SMEs, often constrained by limited resources, stand to benefit significantly from such improvements.

On the other side, four GenAI functions are more strongly associated with growth-oriented strategies. Market and competitive intelligence allows firms to monitor trends and competitors in real time, while linguistic and cultural market expansion enables businesses to localize content and enter new geographic markets more effectively. Market testing and validation supports rapid experimentation with new products or services, and idea generation and prototyping accelerates innovation cycles.

These functions align with exploration-oriented strategies, where firms focus on innovation, market expansion, and opportunity discovery. The study highlights how GenAI enables faster iteration, reduced time-to-market, and the creation of entirely new business models.

The research finds that these functions are not mutually exclusive. While certain applications lean toward efficiency or growth, all functions show some degree of overlap. This suggests that GenAI's value is inherently flexible, but only when deployed in alignment with a firm's strategic priorities.

Organizational readiness emerges as the decisive factor

While much of the current discourse around AI adoption focuses on technology and infrastructure, the study presents a different conclusion. It finds that organizational readiness, rather than technological capability, is the most critical determinant of whether GenAI delivers meaningful value.

Among all identified barriers, cultural resistance to change ranks as the most significant obstacle. Many SMEs struggle with entrenched routines, skepticism toward AI outputs, and reluctance among leadership to integrate new systems into core operations. Closely related is the absence of a clear GenAI strategy, which leads to fragmented and opportunistic adoption rather than structured implementation.

The shortage of skilled personnel further complicates adoption. Without employees capable of understanding, managing, and scaling AI systems, firms are unable to translate technological potential into tangible outcomes. Limited awareness of GenAI's benefits also contributes to underutilization.

On the other hand, technological barriers such as infrastructure limitations or system compatibility are considered secondary. While still relevant, they are not seen as the primary bottleneck. Environmental factors, including regulatory uncertainty and market resistance, are ranked even lower, suggesting that external conditions play a relatively minor role compared to internal organizational dynamics.

On the enabling side, a culture that supports change and experimentation emerges as the strongest driver of success. Firms that foster openness, encourage learning, and align leadership around AI initiatives are better positioned to integrate GenAI effectively. Internal capabilities, including digital skills and resource availability, also play a significant role.

The study also highlights the importance of iterative experimentation and the development of clear AI usage guidelines. These factors help firms move beyond isolated pilot projects and embed AI into broader strategic processes.

A contingency framework explains diverging outcomes

The contingency-based framework explains why similar GenAI deployments produce different results across firms. According to the study, the impact of GenAI depends on the alignment between three key elements: strategic orientation, functional deployment, and readiness conditions.

Strategic orientation defines whether a firm prioritizes efficiency or growth. Functional deployment determines which GenAI applications are used. Readiness conditions shape how effectively these applications are integrated and scaled.

When these elements are aligned, firms can achieve significant performance gains. For example, an efficiency-oriented SME with strong organizational readiness can integrate automation and analytics into core processes, leading to sustained cost reductions and improved decision-making. Similarly, a growth-oriented firm with high readiness can use GenAI to drive innovation, expand into new markets, and develop new business models.

However, when alignment is lacking, outcomes are limited. Firms with low readiness may adopt GenAI tools in a fragmented manner, resulting in isolated improvements rather than systemic change. Efficiency-focused firms may see only incremental gains from automation, while growth-oriented firms may struggle to scale experimental initiatives.

The framework also highlights that readiness acts as a moderating factor rather than a direct source of value. GenAI functions themselves do not automatically generate strategic benefits. Instead, their effectiveness depends on the broader organizational and environmental context.

This insight challenges the prevailing assumption that adopting advanced AI technologies is sufficient to drive performance improvements. Instead, the study emphasizes that value creation requires a combination of strategic clarity, functional alignment, and internal capability development.

Implications for SMEs navigating the AI transition

Efficiency-oriented firms should prioritize automation and data-driven decision support, ensuring that these tools are integrated into core workflows. Growth-oriented firms, meanwhile, should focus on applications that enhance market intelligence, enable experimentation, and support innovation.

Equally important is the need to invest in organizational readiness. This includes building digital skills, fostering a culture of openness and experimentation, and developing a coherent AI strategy. Without these elements, even the most advanced technologies are unlikely to deliver meaningful results.

The study also reinforces the importance of moving beyond isolated use cases. Firms that embed GenAI into their broader operational and strategic systems are more likely to achieve sustained value, while those that rely on ad hoc applications risk limited impact.