JOURNAL
OF STRATEGY
& INNOVATION

Keywords:
Artificial intelligence Generative AI High-tech Micro-firms Risk management Technology adoption

Contents lists available at cienceDirect
Journal of Strategy & Innovation
journal homepage: www.elsevier.com/locate/hitech
AI for the underdogs: Navigating risk and growth in high-tech micro-firms through generative artificial intelligence
Faisal Shahzada’, Mohammad Tayeenul Hoque , Iqra Sadaf Khanc, Ahmad Arslan
a HAMK University of Applied Sciences, Finland
b Norwich Business School, University of East Anglia, UK
c Department of Management Studies, Aalto University, Finland
d Department of Marketing, Management & International Business, Oulu Business School, University of Oulu, Finland
ARTICLEINFO	ABSTRACT
Generative Artificial Intelligence (Gen-AI) has gained significant traction in larger firms, yet its adoption among micro-firms remains underexplored particularly in contexts marked by resource scarcity and heightened operational risk. This study addresses this gap by investigating how hightech micro-firms adopt Gen-AI for risk management and growth. Drawing on semi-structured interviews with decision-makers from eight Finnish micro-firms, the research applies the Technology-Organization-Environment (TOE) framework to identify critical enablers and barriers. The findings highlight five key dimensions influencing adoption: technological readiness, leadership engagement, regulatory compliance, data-driven decision-making, and competitive pressures. While Gen-AI fosters operational resilience and strategic agility, its impact is constrained by limited data quality and high implementation costs. By offering a holistic and theoretically grounded perspective, this study advances understanding of Gen-AI adoption in microfirms and contributes to literature on digital transformation under resource constraints. The insights also inform policymakers and practitioners aiming to enhance AI accessibility and governance for micro-enterprises.
1.		Introduction
Generative Artificial Intelligence (Gen-AI) has emerged as a transformative digital technology, reshaping decision-making, innovation, and operational processes across industries (Giuggioli & Pellegrini, 2023; Gupta, Nair, Mishra, Ibrahim, & Bhardwaj, 2024). However, its rapid proliferation also raises critical concerns about long-term implications for equitable access, responsible adoption, and business sustainability (Bouchetara, Zerouti, & Zouambi, 2024). High-tech micro-firms typically employing fewer than 10 people and generating under €2 million in annual turnover represent a vital but underrepresented segment of the entrepreneurial ecosystem. These firms drive technological innovation and regional competitiveness, but face pronounced constraints in capital, data, and managerial capacity compared to larger enterprises (Brynjolfsson & McAfee, 2014).
Risk management in such firms is especially complex, as traditional approaches based on historical analysis and manual processes
*This article is part of a Special issue entitled: ‘AI for Business Strategy’ published in Journal of Strategy & Innovation. are ill-suited for dynamic and uncertain environments (Davenport & Ronanki, 2018). In this context, Gen-AI holds potential as a lowcost, scalable tool for improving risk awareness, accelerating data-driven decisions, and enabling adaptive strategies (Rodriguez- Espindola, Chowdhury, Dey, Albores, & Emrouznejad, 2022). Tools such as machine learning and natural language processing (NLP) can enhance predictive risk analytics, streamline operations, and reduce human error (Kaplan & Haenlein, 2019; Zhu, Li, & Mishra, 2023). Yet, despite growing interest in AI applications, its strategic role in micro-firm transformation remains insufficiently theorized and empirically examined (Kinkel, Baumgartner, & Cherubini, 2022).
Existing literature highlights Gen-AI's contributions to risk resilience and innovation across sectors including public health (Panch et al., 2019), SMEs (Cannas, 2023), and family-owned firms (Upadhyay, Upadhyay, Al-Debei, Baabdullah, & Dwivedi, 2023) but largely neglects micro-firms. These firms are often presumed to lack the digital maturity and absorptive capacity necessary to adopt Gen-AI, leading to a research gap around how they navigate technological integration amid constraints. Furthermore, emerging studies suggest that without adequate governance structures, Gen-AI may amplify performance inequality across firms rather than level the playing field (Capraro et al., 2024).
To address this gap, the present study explores how high-tech micro-firms adopt Gen-AI for risk identification, mitigation, and strategic evolution. Situated within the Nordic context specifically Finland, where digital maturity is high, but resource asymmetries persist the study employs the Technology-Organization-Environment (TOE) framework to analyze internal and external enablers of AI adoption. Through qualitative interviews with decision-makers from eight micro-firms, this research contributes both theoretical and practical insights on digital transformation under resource constraints.
The remainder of the paper is structured as follows: Section 2 presents a literature review of Gen-AI adoption and its enablers in micro-firm contexts. Section 3 outlines the methodology, Section 4 presents the empirical findings, and Section 5 discusses theoretical and practical implications. The final sections cover limitations and future research directions.
2.		Literature review
2.1.		Technology-organization-environment (TOE) framework in gen-AI context
The Technology-Organization-Environment (TOE) framework (Tornatzky & Fleischer, 1990) is primarily used to conceptualize adoption across three dimensions: technology (e.g., compatibility, perceived value), organization (e.g., top management support, resources, capabilities), and environment (e.g., regulation, partners, competition). The strength of this framework lies in its ability to frame adoption as a multidimensional and contextual process, rather than as a purely technical phenomenon. However, it has been criticized for being overly descriptive and lacking in-depth explanation of industry-specific challenges, power asymmetries, and institutional constraints (Baker, 2011; Oliveira & Martins, 2011).
This study examines why high-tech micro-firms struggle to manage the risks associated with integrating generative AI into their operations. Whether an organization adopts a Gen-Al-driven Industry 4.0 strategy is contingent on firm-level decisions. Thus, organizations must assess the technological, organizational, and environmental factors that shape their readiness for digital transformation. In this context, the TOE framework provides a solid theoretical foundation, as it is widely applied to analyze both internal and external factors influencing AI adoption (Chatterjee, Rana, Dwivedi, & Baabdullah, 2021). The TOE model offers a comprehensive view of the elements that influence technology adoption decisions (Hossain & Quaddus, 2011). It captures the interaction between an organization’s internal capabilities and the external forces shaping its strategic direction. Recent studies emphasize TOE's relevance in analyzing adoption of advanced technologies—especially artificial intelligence (AI) and generative AI (Gen-AI)—in entrepreneurial and industrial contexts.
For example, Gupta et al. (2024) investigate the use of generative AI technologies, such as ChatGPT, in marketing and highlight how skill shortages hinder adoption. This underscores the importance of both digital readiness and internal capabilities. Similarly, Shore, Tiwari, Tandon, and Foropon (2024) show that SMEs are using Gen-AI for predictive analytics and automation to respond to turbulent market conditions, while Jiang, Qin, Virtanen, and You (2022) highlight that external market pressures significantly influence AI readiness. Singh, Chatterjee, and Mariani (2024) argue that regulatory environments and compliance hurdles are often underestimated barriers to Gen-AI deployment.
Digital transformation literature has further linked Gen-AI adoption to the advancement of Industry 4.0 ecosystems, especially for smart production and efficiency (Battisti, Agarwal, & Brem, 2022). Despite such potential, concerns about change resistance, disruption, and unclear returns inhibit adoption in practice (Chen, Gao, Mangla, Song, & Wen, 2020; Muller, Buliga, & Voigt, 2018). High-tech micro-firms due to their agility, lean structures, and innovation capacity are increasingly seen as fertile grounds for Gen-AI experimentation (Kumar, Raut, Mangla, Ferraris, & Choubey, 2024; Wan, Tang, & Jiang, 2023). They may reap faster benefits but also face compounded risks from limited managerial support and financial resources (Wan et al., 2023). Despite their adaptability, literature remains sparse on how such firms specifically navigate Gen-AI adoption.
Building on this foundation, recent literature from 2020 to 2024 has identified three major growth avenues for Gen-AI in microfirms:
(1)		automated content/prototype generation to accelerate innovation and cut costs;
(2)		augmented reality to enhance decision-making;
(3)		Al-based customization to scale operations (Cheng et al., 2023; Kim et al., 2025; Gonzàlez-Pérez, Garcia-Pehalvo, & Arguelles- Cruz, 2025).
However, these growth channels carry substantial risks. Emerging studies show Gen-AI adoption increases outcome variance in micro-firms where some firms reap exponential benefits, others suffer losses, especially when governance capabilities are weak. Governance mechanisms such as model traceability, documentation, and human oversight, not just technical model quality are stronger predictors of realized returns (Capraro et al., 2024; Kim et al., 2025). Without these, Gen-AI risks amplifying inequality between highly capitalized startups and smaller firms with fewer resources (Capraro et al., 2024). This underscores the need for research focused not only on Gen-AI's technical potential, but also on the socio-organizational structures that moderate its impact especially in under-resourced micro-firm contexts.
This implies the need for a thorough assessment of the obstacles that may arise during Gen-AI implementation, specifically focusing on internal and external determinants within the Technology-Organization-Environment (TOE) framework. In such context, the TOE framework provides a solid foundation for evaluating AI adoption in high-tech micro-firms, by analyzing technology, organization, and environment (Tornatzky & Fleischer, 1990). The enhanced TOE framework can prove effectiveness in analyzing the complex socio-environmental and technological challenges faced by high-tech micro-firms. By structuring the analysis around organizational, environmental, and technological dimensions, this approach offers a detailed understanding of both the barriers and enablers relevant to Gen-AI adoption in these firms.
In line with this, this study seeks to explore and examine the impact of internal and external risks on the adoption and implementation of Gen-AI in high-tech micro-firms. Table 1 illustrates essential elements by integrating previous research on AI adoption, emphasizing the challenges encountered by enterprises in many industries, while Fig. 1 depicts the integrated framework of Gen-AI evolution. By examining past studies reported in the Appendix, we highlight the multifaceted risks and strategic considerations critical for successful Gen-AI adoption including resource limitations, technical proficiency, and organizational preparedness, as well as external factors such as regulatory compliance, market competition, and advancing technological standards.
2.2.		Enablers of AI adoption in high-tech micro-firms
High-tech micro-firms typically consist of less than employees generating less than €2 million in turnover and are characterized as the most knowledge-intensive and innovation driven part of the entrepreneurial ecosystem (Eurostat, 2023). While they predominantly operate in technical and engineering niches such as digital design, software, computational sciences, and biotech, these firms utilize fewer resources, execute short strategy implementation cycles, and maintain flat organizational structures (e.g., Coad, Daun- feldt, Holzl, Johansson, & Nightingale, 2014). Their growth and survival are highly dependent on factors such as absorptive capacity, interdependencies, and rapid technical decision-making (Autio & Rannikko, 2016). Failure to meet any of these conditions can derail the adoption process and compromise the value trajectory of Gen-AI within these firms (Von Krogh, 2018).
Understanding how these firms evaluate, absorb, and operationalize Gen-AI despite its enormous benefits and equally novel risks makes this context both theoretically rich and practically urgent. This study positions high-tech micro-firms as an important yet underexplored unit of analysis for understanding how generative AI can be leveraged to enhance competitiveness and resilience under resource constraints.
Table 1
A Review of Key Enablers of Gen-AI.
Type of Risks	Type of Technology	Industry Type Context	References	
Operational Risks, Ethical Risks, Legal Compliance Risks, Cybersecurity Risks	Explainable AI Tool - Facilitates transparency in decision-making and risk evaluation, addressing operational, ethical, legal, and cybersecurity risks.	SMEs	Han et al. (2023)	
Supply Chain Disruption, Operational Vulnerability	Industry 4.0 Technologies (AI, Blockchain, Cloud Computing, Big Data) - Enhances resilience and decision-making in manufacturing.	SMEs in manufacturing	Rodriguez-Espindola et al. (2022)	
Supply Chain Disruption, Demand	Deep Learning and AI - Enhances supply chain agility through	SMEs in supply chain	Wong, Tan, Ooi, Lin, and	
Uncertainty	predictive analytics and scenario modeling.	management	Dwivedi (2024)	
Market Turbulence, Entrepreneurial	Generative AI - Supports adaptive decision-making and resilience	SMEs in various	Shore, Tiwari, Tandon,	
Challenges	building amid market crises.	sectors	and Foropon (2024)	
Market Adaptability, Operational Flexibility	Agile AI and E-Commerce Integration - Enliances adaptability and operational efficiency through iterative updates and data- driven insights.	SMEs in e-commerce	Barata, Ferreira, Carayannis, and Ferreira (2024)	
Financial Constraints, Data	Business Intelligence Systems (BIS) - Enhances decision-making	SMEs in various	Stjepic, Pejic Bach, and	
Management Challenges	by analyzing data and managing resources effectively.	sectors	Vuksic (2021)	
Credit Risk, Financial Distress	Hybrid Ensemble Machine Learning (RS-MultiBoosting) - Improves accuracy in forecasting SMEs' credit risk in supply chain finance.	SMEs in finance	Zhu, Zhou, Xie, Wang, and Nguyen (2019)	
Financial, Operational, Strategic	Enterprise Risk Management (ERM) Framework — Integrates	SMEs in various	de Araujo Lima, Crema,	
Risks
Workforce Planning, Employee Retention	financial, operational, and strategic risk management processes for holistic risk mitigation.
Al-Powered Workforce Management (WFM) - Automates HR processes, improves workforce planning, and enhances employee engagement and retention.	sectors MSMEs	& Verbano (2020)
Kumar et al. (2024);
Sattu et al., 2024	
Operational Inefficiency, Competitive Disadvantage	Al-Integrated Business Analytics (AI-BA) - Provides analytical insights but may lead to inefficiency and competitive loss due to opacity and poor governance.	SMEs in various sectors	Rana, Chatterjee, Dwivedi, and Akter (2022)	

Fig. 1. An integrated framework of Gen-AI adoption. Source(s): Authors' own work.

Advancements in digital and automation technologies have significantly transformed high-tech micro-firms (Matarazzo, Penco, Profumo, & Quaglia, 2021). Entrepreneurship and management research scholars are increasingly examining how effectively these firms are leveraging Gen-AI to drive innovation across products, processes, and business models (Radicic & Petkovic, 2023; Sitaridis & Kitsios, 2024; Upadhyay et al., 2023). This growing interest underscores the need to better understand the practical implementation of AI in fostering innovation and mitigating risks within the unique operational constraints of these organizations.
The adoption of generative AI when facilitated by enabling conditions permits entrepreneurs to transform conventional business models and operational strategies, creating new opportunities for value generation (Marr, 2018). Gen-AI serves as a transformative digital tool that, when supported through appropriate organizational and technological enablers, offers entrepreneurs innovative methods to improve collaboration, coordination, innovation, and competitiveness in the marketplace.
High-tech micro-firms, which produce products or services with significant technological value, typically depend on resources and competencies that are rare, valuable, difficult to imitate, and non-substitutable (Chen, 2024; Musetti, Nagano, & Filho, 2024). This context highlights the significance of analyzing entrepreneurial behavior in these firms, especially regarding how they identify and leverage the specific dimensions and enablers of Gen-AI to improve operational performance and sustain competitiveness.
To help high-tech micro enterprises succeed in complex operational environments and achieve long-term development, it is essential to identify the specific enablers involved in adopting generative AI. In this section, the study introduces Table 1 as an overview of the enabling factors essential for Gen-AI implementation in micro-sized firms. The identified enablers derived from a comprehensive review of the literature underscore the technological, organizational, and environmental conditions that support Al- driven risk identification and mitigation. Table 1 provides a synthesis of key enablers and associated risks as identified in recent empirical studies.
2.3.		AI enablers for micro-sized firms
Within the dynamic landscape of science and technology, the rapid evolution of Gen-AI holds transformative potential for microfirms, reshaping their use of digital technologies, enhancing risk mitigation systems, and strengthening operational capabilities (Giuggioli & Pellegrini, 2023). Despite the enormous potential Gen-AI offers, its integration into the cross-functional business units of small and medium-sized enterprises (SMEs) faces significant challenges. This underscores the critical need for micro-firms to effectively address these hurdles by leveraging Gen-AI to identify and mitigate operational risks, ensuring sustainable business operations. To navigate the complexities of risk identification, evaluation, and mitigation, high-tech micro-firms must focus on identifying the key enablers of Gen-AI technologies that support its implementation. However, the literature on key enablers for Gen-AI adoption remains sparse, particularly within the context of high-tech micro-firms (Schwaeke, Peters, Kanbach, Kraus, & Jones, 2024). Failure to pinpoint and integrate these enablers within cross-functional business units can lead to severe consequences for micro-firms, including operational inefficiencies and competitive disadvantages (Baabdullah, Alalwan, Slade, Raman, & Khatatneh, 2021). Unlike their larger counterparts, which possess extensive resources and capabilities (Cook, Hagiu, & Wright, 2024), high-tech micro-firms often face greater barriers to technology adoption due to their limited operational capacity and resource constraints, resulting in lower levels of innovation (Baumann & Kritikos, 2016; Nafizah, Roper, & Mole, 2024).
This study examines key factors that facilitate the adoption of Gen-AI that are suited to the resource constraints and agility needs of microbusinesses, followed by the Technology-Organization-Environment (TOE) framework. The TOE framework provides a systematic approach to evaluate the technological, organizational, and environmental elements that enable successful integration of Gen-AI. Table 02 synthesizes key enablers of Gen-AI, highlighting their function in mitigating various risks across different industrial contexts, as evidenced by existing literature. This table offers an overview of how these enablers facilitate risk identification and mitigation, demonstrating the practical relevance of the TOE framework in guiding Gen-AI adoption for micro-firms. Micro businesses can better leverage Gen-AI's disruptive potential to improve their risk management tactics and operational resilience by concentrating on these enablers.
2.4.		Technological enablers
High High-tech micro-firms necessitate specialized digital tools to effectively reconfigure multi-stage production operations, thereby ensuring a smooth and flexible production process (Dachs et al., 2019). This underscores that an organization's technological capabilities are central to recognizing and utilizing emerging digital alternatives to achieve strategic objectives. The digital technological roadmap is fundamental to the adoption of Gen-AI, facilitating improvements in operational performance for micro-firms (Dwivedi et al., 2021). In his seminal work, Giuggioli & Pellegrini (2023)asserts that a well-defined digital roadmap serves not only as a guide but also as a driver of AI integration, particularly in resource-constrained micro-firms. Such roadmaps offer a strategic framework to identify and utilize ideal technological enablers, ensuring alignment between AI initiatives and corporate objectives. For Gen-AI to be successfully integrated, there must be robust technological capabilities and clear alignment with long-term innovation strategies (Baabdullah et al., 2021).
Technological enablers such as explainable AI, machine learning systems, and predictive analytics equip micro-firms with sophisticated tools for real-time decision-making, especially in the context of risk assessment and mitigation (Makri, Hitt, & Lane, 2010). Additionally, digital infrastructure, including interoperable systems and high-speed networks, facilitates seamless AI integration and boosts organizational resilience (Luo et al., 2025). However, despite these benefits, scholars have raised concerns about overreliance on technological enablers without adequate investment in complementary human, structural, and governance capabilities (Arrieta et al., 2020; Bughin et al., 2019). In many micro-firms, the absence of technical expertise and weak systems integration capacities can reduce the efficacy of even the most advanced AI tools. Furthermore, without a critical evaluation of context-specific needs and long-term scalability, digital roadmaps risk becoming symbolic rather than functional (Pereira et al., 2024). These concerns suggest that technological readiness, while necessary, is not sufficient on its own. It must be embedded within a broader digital transformation strategy that accounts for organizational agility, ethical deployment, and capacity-building efforts. Thus, while Gen-AI offers compelling opportunities for micro-firms, its successful deployment demands a more holistic approach than is often assumed in technology-led adoption frameworks.
2.5.		Organizational enablers
The integration of modern technology into operational processes is a significant factor in organizational transformation, especially with the emergence of Industry 4.0 in micro-sized enterprises (Zoppelletto, Orlandi, Zardini, & Rossignoli, 2020). Industry 4.0 technologies, such as Gen-AI, enable firms to improve operational efficiency through the utilization of advanced data analytics. Gen-AI processes sensor data to detect and predict machinery malfunctions, overloads, or other operational challenges, facilitating proactive decision-making and reducing downtime (Carayannis, Dumitrescu, Falkowski, & Zota, 2024). Access to technology alone is insufficient for micro-sized businesses to effectively adopt and integrate Gen-AI. The accumulation of critical resources, such as skilled human capital, alongside the development of organizational capabilities, is essential for the effective utilization of these technologies. This includes developing an organizational culture that supports cross-functional collaboration, continuous learning, and innovation. Digital leadership is essential in this transformation, as it promotes a culture that embraces technological advancements. In the meantime, effective knowledge management systems facilitate the dissemination and application of insights from Gen-AI across business units (e.g., Schwaeke et al., 2024). Micro-firms must adopt a continuous capabilities reconfiguration-based strategy to successfully deal with the rapidly changing technological landscape. This approach allows firms to consistently reconfigure their resources and capabilities, facilitating the identification of new opportunities, effective seizing of those opportunities, and transformation of operational processes in response to changing challenges. Micro-firms can achieve strategic adaptation in response to Industry 4.0 advancements by leveraging dynamic capabilities.
Even though, the Resource-Based View (RBV) serves as a fundamental framework that highlights the significance of acquiring and utilizing valuable, rare, inimitable, and non-substitutable (VRIN) resources for attaining competitive advantage (Agyapong, Essuman, & Yeboah, 2021). In rapidly changing environments characterized by accelerated technological advancements like Gen-AI, the Resource-Based View's static emphasis on resource possession illustrates inadequate. The concept of dynamic capabilities enhances the Resource-Based View (RBV) by emphasizing that organizations must proactively reconfigure their resource base to respond to evolving challenges and opportunities. Dynamic capabilities denote a micro firm's capacity to identify emerging opportunities, effectively allocate resources to capitalize on them, and adapt its operations to remain aligned with a changing competitive environment. This indicates that micro-firms, especially in high-tech sectors, need to implement a strategy based on dynamic capabilities to maintain competitiveness. Micro-firms, in contrast to larger firms, frequently function with constrained resources, rendering their capacity for adaptation and innovation essential in unstable technological contexts. A strategy based on dynamic capabilities allows firms to effectively reconfigure their operational processes, facilitating a swift response to rapid technological changes and disruptions (Heider, Gerken, van Dinther, & Hulsbeck, 2021). In this context, leveraging Gen-AI is essential, as it prepares high-tech micro-firms with tools to enhance decision-making, streamline operations, and foster continuous innovation, thereby ensuring resilience and agility in a dynamic industry landscape (Verma & Singh, 2022).
For successful implementation of Gen-AI, micro-firms need to ensure organizational digital readiness by aligning resources and capabilities with operational objectives (Pingali, Singha, Arunachalam, & Pedada, 2023). This preparedness encompasses digital leadership practices, technology-enabled knowledge management systems, a culture of digital innovation, and training programs aimed at enhancing employees’ digital skills (Omrani et al., 2022). Supportive, digitally oriented leadership is crucial for improving the perceived utility of Gen-AI and increasing the data-driven decision-making as a standard practice in micro-firms (Schwaeke et al., 2024). Leadership behavior must embrace a culture that promotes knowledge enhancement in digital technologies can substantially enhance employees' proficiency in utilizing digital tools (Schwaeke et al., 2024). This facilitates a smoother transition to Gen-AI adoption, as employees gain confidence and capability in utilizing AI technologies to improve their performance, von Garrel and Jahn (2023) highlight that employees' readiness to adopt AI is closely associated with their assessment of the technology's value and ease of use.
The human component is an essential enabler of implementing Gen-AI, as the expertise and knowledge of employees significantly impact the success of technological transformations. Choudrie et al. (2025) emphasize the significant role of expertise in the diffusion of innovation. This exchange enhances employees' digital skills, allowing for effective utilization of new technologies. Employees who recognize that AI improves their performance are more inclined to adopt the technology, fostering a culture of ongoing innovation and adaptability. Both resource-Based View and dynamic Capability Theoretical perspectives collectively offer a comprehensive framework for analyzing how micro-sized firms cope with resource limitations and leverage Gen-AI to facilitate operational transformation and innovation.
2.6.		Environmental enablers
When deciding whether to integrate and successfully deploy Gen-AI into their operational processes, micro-sized businesses should not only look at technology and organizational enablers, but also at how environmental factors play a crucial role. A recent study indicates that regulatory frameworks, a favorable entrepreneurial ecosystem, and competitive pressures are fundamental external factors influencing technology adoption (Sharma, Singh, Islam, & Dhir, 2022). External pressures drive micro-firms to implement advanced technologies, including Gen-AI, to improve resilience and sustain competitiveness in unstable markets. Research indicates that competitive pressure significantly influences AI adoption, with firms experiencing intense market rivalry more inclined to adopt technological advancements to secure a competitive advantage (Pillai & Sivathanu, 2020).
The effective integration of Gen-AI in micro-firms is significantly influenced by supportive strategic policies and government regulations. These policies address the technical and operational challenges of adoption while ensuring the workforce is equipped with necessary skills through targeted training programs. This is especially important for high-tech micro-firms, which, despite their substantial contributions to national economies and innovation, frequently face challenges in digital transformation due to the lack of supportive regulatory frameworks (Schwaeke et al., 2024). Micro enterprises may overcome these obstacles and make good use of Gen- AI, if the government adopts laws and programs that encourage digitalization (Galera-Zarco, Opazo-Basaez, Maric, & Garcia-Feijoo, 2020).
For instance. The European Union's AI Act represents the inaugural comprehensive legal framework for artificial intelligence, facilitating support for small businesses, including micro-sized enterprises, through the establishment of clear requirements for AI utilization and the alleviation of compliance burdens for SMEs (Regulation (EU) 2024/1689, 2024). Building on different variables such as technological readiness, organizational preparedness, and external factors discussed in the preceding sections, Fig. 1 illustrates a structured framework for Gen-AI adoption. This framework promotes reliable AI systems that reconcile innovation with risk management, offering high-tech micro-firms a systematic model for secure and efficient AI integration. It supports innovation while addressing challenges such as data privacy, algorithmic bias, and ethical deployment. As shown in Fig. 1, the integrated framework illustrates how the TOE dimensions interact in the Gen-AI adoption process among micro-firms.
To fully utilize Gen-AI technology, high-tech micro-firms must have external elements including supportive ecosystems, friendly policies, and competitive pressures. The safe and efficient integration of Gen-AI into company processes is ensured by regulatory frameworks such as the EU AI Act, which give important direction and decrease compliance obligations. Innovation and expansion can also flourish in settings where the entrepreneurial ecosystem is robust, providing resources like capital, mentors, and cutting-edge technology (Lue et al., 2025). A favorable entrepreneurial ecosystem enables micro enterprises to adopt innovative technologies such as Gen-AI to stay resilient and stay ahead of the competition. With the help of these external environmental forces, micro-firms can endure the challenges of digital transformation and force themselves to be continued success despite the uncertainty of today's markets. Based on the studies, we develop an integrated framework that depicts the determinants, enabling factors of Gen AI in microfirms as well as gen-AI's implications on risk mitigations and strategic evaluation.
Fig. 2 illustrates the "Gen-AI adoption, risk mitigations, and strategic evolution framework,” which provides a structural view of how organizations navigate AI implementation under evolving regulatory and competitive pressure.
2.7.		Ethical and governance challenges in gen-AI adoption
The adoption of generative AI (Gen-AI) by micro-firms raises several governance and ethical challenges, particularly given their limited technical infrastructure and regulatory resources. These firms often lack formal AI oversight structures, making them more susceptible to risks around algorithmic opacity, data bias, and compliance gaps (Dwivedi et al., 2021). A central issue is the lack of explainability in Gen-AI outputs, which complicates how micro-firms interpret or audit Al-generated decisions. Without domain expertise, these firms may overly rely on such systems, raising the likelihood of biased or misleading outcomes (Schlagwein & Willcocks, 2023). In addition, weak data governance practices—such as poor data quality and lack of version control—can increase exposure to model hallucinations and ethical breaches.
Regulatory developments like the EU AI Act introduce further complexity, as they impose documentation, transparency, and auditability standards that are often difficult for small firms to implement effectively. This may lead to either compliance fatigue or avoidance of adoption altogether (Bracci, 2023). To navigate these tensions, scholars have proposed proportionate governance models suited to smaller firms, including human-in-the-loop systems, lightweight audit trails, and value-sensitive design frameworks that embed stakeholder values into AI development processes (Stahl, 2025). In summary, Gen-AI adoption in micro-firms must be coupled with intentional ethical foresight. Establishing scalable and adaptive governance mechanisms is essential to fostering innovation while maintaining stakeholder trust and regulatory alignment.
3.		Method
3.1.		Research design
This study adopts a qualitative, exploratory research design to examine the perspectives of CEOs and founders of entrepreneurial micro-sized high-tech firms in Finland. Qualitative inquiry is particularly suitable for investigating complex and context-dependent organizational phenomena, as it enables researchers to explore the meanings and interpretations that individuals attribute to their lived experiences (Lincoln, 1985; Denzin & Lincoln, 2011; Creswell & Poth, 2016). The study is grounded in an interpretivist paradigm, which assumes that reality is socially constructed and best understood through interaction and interpretation. This paradigm is especially appropriate for examining the adoption of generative artificial intelligence (Gen-AI) within small and resource-constrained firms, where subjective decision-making, contextual embeddedness, and experiential learning strongly influence strategic behavior (Dana & Dumez, 2015).
Semi-structured interviews were selected as the primary data collection method, as they combine a consistent thematic structure with the flexibility to probe deeper into emerging topics (Kallio, Pietila, Johnson, & Kangasniemi, 2016; Patton, 2014). This approach allows for a nuanced understanding of managerial attitudes and experiences while ensuring comparability across participants. The design is consistent with established qualitative research traditions in entrepreneurship and technology management, which emphasize depth, context, and meaning making over quantification (Mousa, et al., 2024; Shahzad, et al., 2024).
3.2.		Data sampling and collection
The study employed a combination of purposive and snowball sampling to identify and recruit participants, ensuring access to information-rich cases and diversity of perspectives (Bryman, 2016; Patton, 2014). Purposive sampling targeted CEOs and founders of high-tech micro-firms that met specific criteria: (i) operating with fewer than 15 employees, (ii) demonstrating innovation intensity and engagement in digital transformation, and (iii) active participation in Finland's high-technology sector. This approach ensured that participants had direct, relevant experience with the strategic and operational aspects of AI adoption in micro-firm contexts. To supplement purposive recruitment, snowball sampling was used to access additional participants through professional and entrepreneurial networks. This method was particularly effective given the small size and relational interconnectedness of Finland's hightech ecosystem (e.g., Denzin & Lincoln, 2011).
A total of eight participants were interviewed between March 2024 and September 2024. Each interview was conducted online via Microsoft Teams or Zoom to allow geographical flexibility and maintain conversational depth. The interviews ranged from 45 to 90 min, depending on participants' availability and the richness of their narratives. An interview guide with open-ended questions was developed and iteratively refined throughout the data collection process to ensure relevance and responsiveness to emerging insights. All interviews were recorded with participants' consent, transcribed verbatim, and anonymized to preserve confidentiality in

Al Adoption Framework for Micro-Firms

Business Outcomes (Operational efficiency, customer engagement, risk management)
Fig. 2. Gen-Ai adoption, risk mitigations and strategic evolution framework. Source(s): Authors' own work.
accordance with established ethical guidelines (Cohen, Manion, & Morrison, 2002; Shenton, 2004).
To enhance methodological trustworthiness, both internal and external validation strategies were employed. Internally, transcripts were independently reviewed and coded by two researchers before jointly developing the final thematic structure, ensuring analytical dependability and reducing interpretive bias (Lincoln, 1985). Externally, emergent findings were compared with prior literature on AI adoption and organizational readiness in small and medium-sized enterprises (Gupta et al., 2024; Hemmler et al., 2022), thus reinforcing theoretical coherence. Data saturation was reached after seven interviews, as no new substantive themes emerged during subsequent discussions consistent with the methodological criteria of Guest, Bunce, and Johnson (2006) and Fusch and Ness (2015). This saturation point confirmed that the collected data captured sufficient depth and diversity of experiences. A descriptive overview of the participating firms is presented in Table 2.
3.3.		Data analysis
The analysis of interview data followed the four-phase analytical framework proposed by Miles and Huberman (1994), encompassing data condensation, data display, drawing conclusions, and verification. This iterative process facilitated systematic organization and interpretation of qualitative material while maintaining openness to emergent meanings. To enhance analytical transparency and theoretical rigor, the study also incorporated the Gioia methodology (Gioia, Corley, & Hamilton, 2013), which provides a structured approach to inductive concept development, progressing from informant-centric codes to researcher-derived themes and higher-order theoretical dimensions. The integration of these frameworks enabled the analysis to combine procedural structure with interpretive depth, reflecting contemporary standards for qualitative research in management and entrepreneurship (Mousa et al., 2024; Shahzad et al., 2024).
All transcripts were read multiple times to achieve familiarity and immersion in the data, ensuring a comprehensive understanding of participants' narratives (Braun & Clarke, 2006). During the first stage, open coding was performed manually by two researchers to identify and label meaningful phrases and expressions that captured participants' perceptions and experiences. These initial codes, or first-order concepts, were grounded in the informants' own language to preserve the authenticity of their perspectives (Patton, 2014). Examples of such first-order concepts included “the founder-initiated AI experimentation,” “we are learning by doing,” and “data is our biggest challenge."
In the next stage, axial coding was conducted collaboratively to group related first-order codes into second-order themes, representing higher-level analytical interpretations. This process followed an inductive and iterative approach consistent with the thematic analysis guidelines of Braun and Clarke (2006) and the systematic data reduction techniques outlined by Miles and Huberman (1994). These second-order themes captured recurring theoretical patterns such as “leadership-driven innovation,” "organizational learning through improvisation," and “resource readiness constraints." Finally, the second-order themes were synthesized into aggregate theoretical dimensions, including “organizational preparedness,” “leadership as catalyst," and “institutional enablers and barriers." The hierarchical coding structure progressing from first-order concepts to second-order themes and aggregate dimensions reflects the integration of thematic and inductive analytical logic, ensuring that the findings remained closely aligned with participants' lived experiences while allowing for conceptual abstraction (Braun & Clarke, 2006; Miles & Huberman, 1994).
Throughout the analysis, the research team adopted a reflexive and collaborative approach to ensure consistency and minimize subjective bias. Coding and theme development were conducted independently by two researchers and subsequently compared in peer debriefing sessions to align interpretations. Rather than relying on statistical measures of inter-coder reliability, interpretive coherence was achieved through iterative dialogue, reflective memoing, and consensus-building, following best practices in interpretive qualitative research (Cohen et al., 2002; Denzin & Lincoln, 2011). This approach aligns with established methodological strategies for ensuring analytic dependability and credibility (Lincoln, 1985; Shenton, 2004).
Verification and validation were embedded throughout the analysis in accordance with Miles and Huberman's (1994) framework and Lincoln (1985) trustworthiness criteria. Credibility was strengthened through triangulation across cases and member reflections on emerging themes, ensuring that interpretations accurately reflected participants' perspectives (Creswell & Miller, 2000). Transferability was enhanced through detailed contextual description of the participating firms, while dependability and confirmability were supported by maintaining an audit trail of coding decisions and analytic memos (Shenton, 2004). Finally, external triangulation was achieved through comparison of emergent findings with extant research on AI adoption and SME innovation. The combined application of Miles and Huberman’s procedural rigor, Braun and Clarke's thematic logic, and Gioia's inductive structure yielded an empirically grounded, theoretically coherent, and methodologically robust interpretation of the data.
The interrelationships among the five core dimensions technological readiness, organizational preparedness, external environmental factors, risk mitigation strategies, and strategic advantages are summarized in Fig. 3, which presents the study's data structure following the Gioia methodology. The figure illustrates the analytical progression from first-order participant expressions to second- order themes and finally aggregates theoretical dimensions. This visual representation reinforces the transparency and systematic nature of the analytical process, demonstrating how the study's conceptual framework was inductively derived from empirical evidence.
4.		Findings
The findings of this study reveal five interconnected dimensions that shape the adoption and implementation of generative AI among Finnish high-tech micro-firms. Together, these dimensions illustrate the multi-layered and context-dependent nature of AI adoption in micro-enterprises. Each theme is supported by direct quotations from participants, which provide authentic insights into
Table 2
Overview of the case firms' characteristics.
Case Company (CC)	Industry	Founded Team Size Stage of AI Implementation	Focus of AI Use	
CC 1
CC 2	IT (Cybersecurity)	2015	8	Fully integrated in products	Threat detection and real-time response
Robotics	2020	7	Core part of operations	Predictive maintenance and process
optimization	
CC 3
CC 4
CC 5
CC 6
CC 7
CC 8	Healthcare Analytics 2017	9	Central to diagnostics tools	Medical data analysis and diagnostics
EdTech	2018	8	Key to adaptive learning platforms Personalized learning and engagement tracking
Agritech	2020	7	Used for predictive farming	Crop health and resource optimization
Robotics	2017	9	Core in automation systems	Autonomous robots for warehouses
FinTech	2012	8	Central to financial analytics	Real-time financial insights and fraud detection
CleanTech	2019	6	Core in energy optimization	Energy usage monitoring and optimization	
Source(s): Authors' own work.

their organizational realities and sense-making processes.
4.1.		Technological readiness
Technological readiness emerged as a critical determinant of successful generative AI adoption. Participants consistently emphasized that access to appropriate digital infrastructure and tailored AI tools formed the foundation for meaningful implementation. One participant explained that “our IT infrastructure is robust enough to handle AI integration, but the tools themselves are expensive and sometimes hard to justify without clear returns" (Case Company 2). Another participant underscored the role of explainability, noting that “our team needs to trust the Al-generated insights, and without explainable features, it’s difficult to fully adopt these technologies" (Case Company 7).
Affordability and technical expertise were also identified as major constraints. A manager highlighted cost limitations by explaining that “as a small enterprise, we struggle to find AI solutions within our budget. We often have to settle for less advanced tools that don't entirely meet our needs" (Case Company 1). Similarly, another respondent described dependency on external consultants: “our inhouse team lacks specialized AI knowledge, so we frequently rely on external expertise, which adds to the cost and delays implementation" (Case Company 6). Compatibility with existing systems was another recurring concern, as one participant reflected that "we focus on tools that integrate seamlessly with our current infrastructure; otherwise, even the best tools become disruptive" (Case Company 5).
These accounts indicate that while participants recognize AI's transformative potential, its effective adoption depends on technological maturity, cost accessibility, and systems compatibility factors that are especially critical in resource-constrained micro-firm contexts.
4.2.		Organizational preparedness
The findings highlight that organizational culture, leadership engagement, and workforce readiness play decisive roles in the adoption of generative AI. Leadership commitment and employee development were identified as central enablers of successful implementation. One CEO described proactive leadership initiatives, noting that “we actively support AI initiatives by organizing training sessions. This reassures employees and helps them understand the technology better" (Case Company 8).
However, financial constraints frequently impeded long-term strategic planning. One participant observed that “AI projects require substantial investment, and we often need to see immediate returns to justify these expenses. This short-term focus limits our ability to plan strategically” (Case Company 3). Employee resistance was another challenge, though several participants noted that attitudes evolved positively over time. As one manager recalled, "initially, employees were hesitant, fearing job loss, but regular exposure to AI tools has made them see it as an enhancement rather than a threat” (Case Company 4).
The lack of technical expertise within small firms was also a recurring theme. One respondent admitted that “our team is stretched thin, and finding skilled personnel who understand AI is a challenge. Even hiring interns with basic knowledge has helped us move forward" (Case Company 6). Yet, some firms actively encouraged experimentation as a means of learning and innovation. As one manager shared, “our company culture promotes trying new tools without fear of failure, which accelerates adoption and advances innovation" (Case Company 2).

AI tools
Cloud-based platforms
Accessibility to predictive analytics	====> ====>
=>
*	
Leadership commitment Employee readiness Training programs		
Market competition Regulatory pressures Collaboration opportunities		
Data transparency policies AI audits for bias
Vendor partnerships for technical support		
Operational improvements
Addressing inefficiencies
Enhancing strategic decision-making		

First-Order Categories

Cloud-based solutions	_	Technological	
Cost-effectiveness
Predictive analytics		
Leadership support	-—/	Organizational	
Employee training Upskilling initiatives	Preparedness	
Market competition Regulatory compliance	_	_	/	External	
Vendor support
Government workshops	Environmental
		• 	" X	Factors	
Data transparency AI audits		
Modular AI solutions Scenario planning		•	Strategies	
Operational efficiency	=	
Process improvements		==	Advantages	

Second-Order Categories

Aggregated Themes

Fig. 3. Data Structure. Source(s): Authors' own work.

Collectively, these insights show that internal preparedness is not merely a function of resources but also of leadership vision, learning culture, and adaptive capacity.
4.3.		External environmental factors
External environmental conditions including regulatory frameworks, policy clarity, and competitive pressures were also significant in shaping AI adoption. Participants expressed mixed sentiments toward emerging European Union AI regulations. While acknowledging their necessity, several viewed compliance as a burden. One founder explained that "compliance with regulations is costly and time-consuming. Clearer guidelines would help us adopt AI more confidently " (Case Company 7).
Competitive dynamics were another major external driver. A participant noted that "our competitors are adopting AI rapidly, and we cannot afford to lag behind. The market demands quick adaptation to stay relevant” (Case Company 4). Others, however, emphasized that evolving and inconsistent regulations created uncertainty: “the lack of standardized frameworks delays our decision-making. We need consistency to move forward" (Case Company 3).
Despite these challenges, participants widely agreed that AI adoption is becoming indispensable. As one executive asserted, "in today's market, AI is not optional. It's a necessity for maintaining competitiveness and meeting customer expectations”(Case Company 8). This recognition underscores how external institutional and market forces jointly shape firms' strategic decisions toward AI adoption.
4.4.		Risk mitigation strategies
Risk management emerged as another crucial area in which generative AI is reshaping operations. Participants described how Al- based tools are increasingly integrated into processes for identifying and mitigating potential risks, particularly in logistics and operations. One manager explained that AI enables "predicting equipment failures weeks in advance, enabling fewer disruptions and substantial cost savings” (Case Company 1). Similarly, another participant shared that AI “anticipates bottlenecks in supply chain operations, ensuring proactive issue resolution and maintaining workflow continuity" (Case Company 8).
Financial risk management was also enhanced by AI adoption. One respondent noted that "AI tools enhance financial monitoring by identifying irregularities or trends that human oversight might miss, saving the company from penalties and oversights" (Case Company 7). Another emphasized the value of continuous risk tracking: “AI provides a proactive stance by continuously monitoring risks, enabling actions before problems escalate, which was previously unattainable” (Case Company 2).
Nonetheless, limitations were also acknowledged. Several participants cautioned that the accuracy of Al-driven insights depends heavily on data quality. As one manager warned, "AI's accuracy heavily relies on high-quality input data, and poor data could lead to misleading conclusions with severe consequences" (Case Company 6). Despite these constraints, the majority viewed AI as a transformative tool that had significantly strengthened their firms' ability to manage operational and financial uncertainty.
4.5.		Strategic advantages
Generative AI was found to deliver strategic advantages that extend beyond operational efficiency. Participants described tangible benefits including productivity improvements, customer engagement, and cost reduction. One manager observed that AI has “transformed customer interactions by enabling personalized responses, thereby deepening trust and improving client retention rates" (Case Company 3). Similarly, another participant highlighted its productivity-enhancing effects, noting that AI “allowed the company to achieve more without expanding its workforce. Routine tasks are handled by AI, freeing employees for more strategic initiatives" (Case Company 7).
Al also facilitated innovation and market expansion. A respondent explained that “AI accelerates product prototyping and idea refinement, positioning the company ahead of competitors" (Case Company 4). Another described how automation contributed to cost efficiency, stating that "AI has helped reduce operational expenses, allowing the firm to reinvest in growth areas" (Case Company 5).
Importantly, AI was perceived as a tool that compensates for micro-firms' resource limitations. One participant explained that "AI bridges the gap by providing capabilities that would otherwise require substantial investments in human or financial capital" (Case Company 2). This observation was echoed by another executive who concluded that “AI ensures our company remains competitive in a rapidly evolving market" (Case Company 8).
Together, these findings suggest that while micro-firms face notable resource and capability constraints, generative AI has become a critical enabler of strategic resilience, innovation, and competitiveness. The resulting data structure is presented in Fig. 3, which illustrates the progression from first-order concepts to second-order themes and aggregate dimensions using the Gioia methodology.
5.		Discussion
This study provides an in-depth understanding of how high-tech micro-firms adopt Generative Artificial Intelligence (Gen-AI) for strategic and operational purposes under resource constraints. Guided by the Technology-Organization-Environment (TOE) framework (Tornatzky & Fleischer, 1990), the findings identify five core dimensions technological readiness, organizational preparedness, environmental pressures, risk mitigation strategies, and strategic benefits that influence AI adoption. These findings extend previous research (e.g., Chatterjee et al., 2021; Oliveira & Martins, 2011) by positioning risk management as a central, not peripheral, factor in AI integration particularly for micro-sized enterprises.
Technological readiness was found to be foundational. Firms with existing digital capabilities, such as cloud platforms, data analytics systems, and Al-compatible infrastructure, were better prepared to leverage Gen-AI applications (Baabdullah et al., 2021; Dwivedi et al., 2021). Explainable AI (XAI) also emerged as a critical enabler, helping to foster trust among employees and nontechnical users (Gupta et al., 2024; Mancuso, Petruzzelli, Panniello, & Frattini, 2025). Unlike larger firms that can afford bespoke AI solutions and internal data science teams, micro-firms adopted open-access or lightweight Gen-AI tools to reduce costs (Kim et al., 2025). However, concerns over affordability, technical complexity, and data quality echoed limitations identified in earlier studies (Gandomi & Haider, 2015).
Organizational preparedness particularly digital leadership and employee engagement significantly influenced adoption success. Leadership vision played a pivotal role in setting digital transformation priorities, while ongoing training programs and inclusive communication strategies reduced internal resistance (Golgeci, Ritala, Arslan, McKenna, & Ali, 2025; Schwaeke et al., 2024). Unlike larger firms, which often institutionalize AI through formal change programs, micro-firms relied on iterative, bottom-up learning and flexible task assignments to build Gen-AI capabilities (Alvesson & Karreman, 2011; von Garrel & Jahn, 2023). These grassroots strategies enabled firms to maintain business continuity while exploring emerging technologies in a controlled, low-risk manner.
Environmental factors notably regulatory pressures and competitive dynamics were also key external influences. The EU AI Act and similar compliance frameworks were seen as both supportive and burdensome. While they offered ethical guidance and credibility to AI deployments, they also required micro-firms to navigate legal complexity with limited internal resources (Albahri, Khaleel, & Habeeb, 2023; Capraro et al., 2024; Singh et al., 2024). Simultaneously, intense market competition was a catalyst for adoption, with many firms perceiving Gen-AI as essential for keeping pace with rapidly digitizing industries (Sharma et al., 2022). These findings reflect institutional theory arguments that highlight how external forces, including norms and competitive benchmarks, drive small firms toward innovation despite limited internal readiness (Baabdullah et al., 2021).
Risk mitigation emerged as one of the most transformative functions of Gen-AI. Participants reported using AI for predictive maintenance, fraud detection, and supply chain resilience confirming findings from Shore et al. (2024), Zhu et al. (2023), and Rodriguez-Espindola et al. (2022). The shift from reactive to proactive decision-making was particularly emphasized, allowing firms to anticipate disruptions before they escalated. However, the quality and availability of data remained a barrier, often limiting the accuracy and reliability of Gen-AI insights (Gandomi & Haider, 2015; Kumar et al., 2024). This issue aligns with broader debates on trustworthy AI, where concerns around transparency, bias, and reliability are amplified in micro-firm settings lacking data governance infrastructure (Albahri et al., 2023).
Strategic advantages were also widely reported. Gen-AI supported operational efficiency by automating routine tasks, enhancing customer personalization, and reducing time-to-market for new offerings (Chatterjee et al., 2021; Davenport & Ronanki, 2018). Several firms leveraged AI for prototype generation, marketing content creation, and decision support—areas where Gen-AI has shown the greatest utility in recent research (Gonzalez-Pérez et al., 2025). However, participants also emphasized the importance of human oversight, echoing concerns that over-reliance on AI could lead to errors or misaligned decisions (Baiyere, Salmela, Nieminen, & Kankainen, 2025; Capraro et al., 2024). As such, Gen-AI was viewed not as a replacement but as an augmentation tool enhancing human intelligence while preserving strategic judgment.
From a comparative perspective, micro-firms approach AI differently than large enterprises. While the latter typically deploy AI at scale using dedicated teams, micro-firms rely on leaner, more adaptive approaches, emphasizing cost-efficiency and rapid experimentation (Goldfarb, 2024; Kumar et al., 2024). This study reinforces that Gen-AI may act as a leveller, reducing historical technologyaccess gaps between small and large firms. However, it also highlights that governance structures such as audit trails, approval gates, and data validation protocols are essential to ensure equitable and safe outcomes (Capraro et al., 2024; Kim et al., 2025). To further
Table 3
Mapping Study Findings to the TOE Framework.
TOE Dimension	Key Findings from Study	Representative Themes / Examples	
Technology (Τ)	Technological readiness significantly influenced Gen-AI adoption feasibility in micro-firms.	•		Robust IT infrastructure (e.g., Case Company 2)
•		Importance of tool affordability and compatibility (e. g., CC1, CC5)
•		Need for explainable AI to build trust (e.g., CC7)	
Organization (0)	Internal leadership, digital culture, and workforce readiness were decisive enablers.	•		Leadership commitment to Al-driven innovation (e. g., CC8)
•		Ongoing training and upskilling (e.g., CC6)
•		Culturally supported experimentation and adaptability (e.g., CC2)	
Environment (Ε)	External factors like regulation and competition acted as both enablers and constraints.	•		Regulatory pressure from EU AI Act (e.g., CC7)
•		Compliance ambiguity delaying adoption (e.g., CC3)
•		Competitive pressure accelerating uptake (e.g., CC4)	
Risk Governance (Extension to TOE)	Risk mitigation emerged as a central driver and outcome of Gen-AI use.	•		Predictive risk analytics in operations/logistics (e.g., CC1, CC8)
•		Real-time financial risl< monitoring (e.g., CC7)
•		Limitations due to data quality concerns (e.g., CC6)	
Strategic Impact (Extension to TOE)	Gen-AI enabled strategic agility, innovation, and resource leverage.	•		Al-facilitated rapid product development (e.g., CC4)
•		Customer engagement through personalization (e.g., CC3)
•		Cost savings and efficiency gains (e.g., CC5)	
Source(s): Authors' own work.
reinforce the theoretical contribution, Table 3 explicitly links the empirical findings with the Technology-Organization-Environment (TOE) framework. In addition to the traditional dimensions (technology, organization, and environment), the table integrates two emergent constructs risk governance and strategic impact that reflect the specific realities of Gen-AI adoption in high-tech micro-firms.
In conclusion, this discussion affirms that Gen-AI is not merely a technological upgrade but a strategic enabler that supports resilience, innovation, and growth in high-tech micro-firms. The study contributes to theory by repositioning risk mitigation as a primary driver of AI adoption within the TOE framework and by situating micro-firms at the center of ongoing debates about responsible AI, strategic agility, and digital transformation. It also reinforces the need for context-sensitive adoption strategies, where technological, organizational, and institutional conditions are closely aligned for successful Gen-AI integration.
6.		Theoretical and practical implications
This study contributes to theory in two major ways: (1) by reconceptualizing generative AI as a capability-equalizing mechanism for high-tech micro-firms and (2) by extending the Technology-Organization-Environment (TOE) framework to incorporate risk governance as a central, not peripheral, dimension in emerging technology adoption.
First, this study challenges the dominant assumption that micro-firms' technological adoption is inherently limited due to resource scarcity. Traditional models based on the Resource-Based View (RBV) have long emphasized the constraints small firms face in acquiring VRIN resources (Barney, 1991; Agyapong et al., 2021). However, our findings reveal that generative AI due to its relative affordability, accessibility, and low entry threshold acts as a capability-leveling technology. It allows high-tech micro-firms to perform complex tasks like content generation, predictive analytics, and prototyping, functions traditionally reserved for larger, resource-rich organizations (Cannas, 2023; Goldfarb, 2024; Kinkel et al., 2022).
In this sense, Gen-AI functions not only as a disruptive innovation but as a gap-filling mechanism—enabling small firms to leapfrog typical growth stages and engage in higher-order value creation activities (Schwaeke et al., 2024; Verma & Singh, 2022). These findings nuance existing theory by showing that access not just ownership of technological infrastructure can shape competitive dynamics in digital ecosystems.
Second, the study enhances the Technology-Organization-Environment (TOE) framework (Tornatzky & Fleischer, 1990) by introducing risk governance as a core determinant of adoption effectiveness in volatile AI environments. While TOE emphasizes technological readiness, organizational capacity, and environmental pressure, it often under-theorizes how firms manage uncertainty and ethical complexity associated with frontier technologies like Gen-AI (Hossain & Quaddus, 2011; Oliveira & Martins, 2011).
Our findings show that adoption outcomes in micro-firms are less driven by technical sophistication and more by governance maturity including the use of traceability mechanisms, documentation, and human approval loops (Capraro et al., 2024; Kim et al., 2025). This supports the growing recognition in digital transformation theory that risk is not a moderating factor but a primary construct influencing adoption trajectories (Baiyere et al., 2025; Dwivedi et al., 2021).
By incorporating these findings, we propose a risk-aware extension of the TOE framework that centers risk mitigation and strategic alignment as integral to Gen-AI adoption, especially for firms operating under high uncertainty and low resource slack. This reformulation aligns with Dynamic Capabilities Theory, which emphasizes that firms must reconfigure resources in response to turbulent environments (Teece, 2014; Heider et al., 2021).
Lastly, this study also contributes to micro-firm innovation literature by emphasizing that digital maturity in small firms is multidimensional, involving both tangible resources and intangible governance routines. Our work prompts future research to empirically test the effectiveness of this risk-governed TOE model across various firm typologies and industry contexts to evaluate its explanatory power in predicting innovation outcomes, resilience, and competitive differentiation.
The study presents important implications for both practitioners and policymakers seeking to enhance the adoption of generative AI within high-tech micro-firms. From a managerial standpoint, the findings emphasize that Gen-AI adoption should be viewed as a strategic initiative rather than merely a technical enhancement. Successful implementation requires alignment with firm-level goals, especially for micro-firms operating under severe resource constraints. This includes developing basic but effective governance mechanisms such as model documentation, traceability, and human oversight that can manage risks without demanding intensive infrastructure (Capraro et al., 2024; Kim et al., 2025). Such practices offer scalable solutions for small firms to build trust and accountability around AI use.
Equally, digital leadership and employee readiness are central to effective adoption. Managers must foster a culture of digital learning and adaptability by investing in skills development and encouraging experimentation. This prepares the workforce to integrate Gen-AI into operational processes with confidence, enhancing both efficiency and innovation (Schwaeke et al., 2024).
At the policy level, findings indicate that micro-firms require regulatory and institutional support to overcome adoption barriers. Despite Gen-AI's accessibility, structural issues such as limited AI literacy, compliance burdens, and weak ecosystem connectivity persist. Policies like the EU AI Act offer a foundational framework, but tailored interventions such as simplified compliance pathways, AI upskilling programs, and access to shared innovation infrastructure are necessary to support equitable adoption.
In conclusion, advancing Gen-AI adoption in high-tech micro-firms demands a coordinated effort. Firms must cultivate internal readiness and risk governance, while policymakers must foster enabling environments that ensure inclusion, efficiency, and ethical implementation. Without such dual support, Gen-AI may risk reinforcing, rather than resolving, structural inequalities across the entrepreneurial ecosystem.
7.		Conclusion, limitations and future research avenues
This study examined how high-tech micro-firms adopt Generative Artificial Intelligence (Gen-AI) for risk management and strategic growth within resource-constrained environments. Using the Technology-Organization-Environment (TOE) framework, the study identified key internal and external enablers namely, technological readiness, leadership engagement, regulatory compliance, data- driven decision-making, and competitive pressures that shape Gen-AI adoption. Unlike traditional risk mitigation strategies that are reliant on retrospective data, Gen-AI offers real-time predictive capabilities that allow micro-firms to navigate uncertainty more proactively and cost-effectively.
Empirical evidence from eight Finnish micro-firms suggests that Gen-AI adoption is driven as much by risk mitigation as by innovation or productivity goals. Leadership commitment and gradual workforce adaptation emerged as decisive factors in overcoming technical and cultural barriers. While Gen-AI improves operational agility, firms continue to face challenges related to affordability, data quality, and system integration constraints that are more acute in micro-firms compared to larger enterprises.
Theoretically, this study extends the TOE framework by positioning risk management not as a peripheral factor but as a core dimension of AI adoption, particularly in contexts marked by high uncertainty and low resource availability. It also contributes to the literature by demonstrating that Gen-AI functions as both a technological and strategic equalizer for micro-firms, enabling access to advanced capabilities traditionally limited to well-capitalized organizations. These findings respond to calls for more nuanced models of AI adoption that integrate organizational and environmental complexity (Baiyere et al., 2025; Gupta et al., 2024).
However, several limitations must be acknowledged. First, the study is context-specific—focused on Finnish high-tech micro-firms and may not fully capture sectoral or regional variations in Gen-AI adoption. Second, the qualitative nature of the research limits generalizability, though it offers rich insights into underexplored organizational processes. Future research could address these limitations by employing comparative or longitudinal designs across different firm sizes and geographic contexts.
Moreover, integrating alternative theoretical lenses such as the Resource-Based View (RBV) or Dynamic Capabilities Theory may provide further insights into how micro-firms reconfigure scarce resources to create sustained competitive advantage through Gen-AI. Finally, future studies might explore how regulatory developments, such as the EU AI Act, influence AI adoption trajectories and governance practices in micro-firms over time. While this study focuses on Finnish micro-firms, Gen-AI adoption is likely to differ across sectors and regions. In low-resource or heavily regulated environments, such as healthcare or finance, adoption may be constrained by stricter compliance demands or data sensitivity. Conversely, tech startups in emerging economies may demonstrate more agile experimentation due to looser oversight or necessity-driven innovation (Girinskiené, 2024; Nafizah et al., 2024). Cross-country comparisons would provide further insight into these contextual divergences.
CRediT authorship contribution statement
Faisal Shahzad: Writing - original draft. Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Mohammad Tayeenul Hoque: Writing - review & editing, Writing - original draft, Visualization, Validation, Formal analysis, Conceptualization. Iqra Sadaf Khan: Writing - review & editing, Writing - original draft, Visualization, Validation, Formal analysis, Conceptualization. Ahmad Arslan: Writing - review & editing, Writing - original draft, Visualization, Supervision, Methodology, Investigation, Formal analysis, Conceptualization.
Declaration of generative AI and Al-assisted technologies in the writing process
During the preparation of this work the author(s) used Grammarly to improve language. After using this tool, the author(s) reviewed and edited the content as needed and takes full responsibility for the content of the publication.
Declaration of competing interest
None.
Data availability
The data that has been used is confidential.
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