Organisational Readiness for AI Adoption in South African Enterprises: A TOE Framework Study

Background and Context

Small and medium enterprises (SMEs) are the cornerstone of South Africa’s economy, accounting for over 90% of formal business entities and contributing between 51% and 57% to the country’s GDP (Makwara, 2019). SMEs also provide close to 60% of employment opportunities, making them essential to the country's socio-economic fabric. However, in an increasingly competitive and technologically driven global landscape, traditional business models in South Africa are being disrupted. The rise of new technologies, including Artificial Intelligence (AI), Internet of Things (IoT), and data analytics are transforming industries and changing business practices (Taljaard and Gerber, 2022).

This Fourth Industrial Revolution (4IR) came with an immediate need for digital transformation. As a world phenomenon and at a local level, 4IR is viewed as a positive force in creating jobs, re-inventing industry and delivering better services (World Economic Forum, 2016). Both government initiatives and private sector policies in South Africa have prioritised innovation through technology adoption, with SOEs and financial services receiving close attention (Malope et al., 2021; Qwabaza, 2022).

Well, there should be little doubt by now that this promise exists, and yet, SMEs have lagged behind when it comes to leveraging AI, which is some of both systemic as well as governance hurdles preventing AI seems to be out of the ability for SMEs to configure their efforts over the benefits of low strategic support. However, the future viability of both SMEs and the broader South African economy may depend on the widespread adoption and scaling of technology such as AI, and the reality is that as a component of digital transformation, this now becomes essential. For SMEs, the importance of AI has to do with its ability to automate repetitive tasks, improve decision-making, personalize services, reduce costs and find new business models. This study fills this gap by identifying the major motivators and obstacles of AI adoption in South African SMEs and can help these businesses in the transition path to a digitally inclusive economy.

Problem Statement

South African SMEs trail the rest of the world by a significant margin in the adoption of Artificial Intelligence (AI) solutions, even as digital technologies disrupt global industries. This digital back-foot puts their sustainability and competitiveness at risk. However, as cited by Hassan (2024), despite AI offering unprecedented opportunities for improved efficiency, innovative potential and market growth, the majority of SMEs are failing to take advantage of these benefits. Major factors include limited availability of financial resources, lack of technical skill, low digital maturity and ambiguity related to AI governance and data ethics.

However, Mangundu (2023) mentioned that there are not many formal structured AI governance frameworks at institutions in the South African context, especially at education institutions — and this points to a more systemic problem across various sectors. For SMEs, this lack of clarity in the regulatory and operational framework makes the integration of AI even more challenging. Likewise, Akin-Adetoro & Kabanda (2021) suggested that factors, such as the lack of organizational support structures such as leadership awareness, lack of formal digital strategy and shortage of skilled personnel, impede SME technology adoption in the developing countries.

These barriers are not simply operational – they are strategic holes. But in the absence of proper frameworks around AI readiness, training and ethical supervision, AI adoption is sporadic, fragmented or simply abandoned. In addition, external environmental factors exacerbate the challenges like inconsistent government support, erratic market dynamics, weak industry partnerships etc.

Although 4IR alignment is being advocated for at a national level, SMEs, the backbone of South Africa's economy, will struggle to find the funding and resources needed to facilitate the transition into the new digital economy to the same extent as larger firms or multinationals. The implication translates into stagnant growth, digital insecurity and pulled back on the world stage. This study, therefore, aims to investigate the nuanced factors that either impede or enable AI adoption in the context of South African SMEs and seeks to shed light on this multidimensional challenge by using the Technology-Organization-Environment (TOE) framework.

Research Aim and Objectives

The primary aim of this study is to explore and evaluate the key factors influencing the adoption of Artificial Intelligence (AI) in South African SMEs through the lens of the Technology-Organization-Environment (TOE) framework. The TOE framework provides a structured approach for assessing how internal and external factors influence technological innovation in organizations.

Research Objective:

1. To identify of leadership support, skills, and change readiness as primary enablers of AI readiness.
2. To propose recommendations for improving for AI adoption
3. To recommendations for strategic alignment and transformational leadership to enable AI adoption.

 Research Questions

Main Research Question:

1. ‎What are the key organisational readiness factors that influence AI adoption in South African enterprises?

Sub research questions

What technological, organizational, and environmental factors influence AI adoption in South African SMEs? This question probes the core components of the TOE framework—technology, organization, and environment—to understand the drivers and barriers to AI integration. It will explore issues such as infrastructure readiness, management capability, policy support, and market dynamics (Qwabaza, 2022; Hassan, 2024).

How does perceived compatibility affect the influence of management support and technological competence on AI adoption? This question investigates the moderating role of compatibility. Studies show that even if firms possess technological know-how and executive support, poor alignment between AI solutions and existing workflows can derail adoption efforts (Hassan, 2024).

What are the key enablers of AI governance and digital readiness in SMEs? Governance is crucial for managing AI risks, ensuring compliance, and building stakeholder trust. This question explores how SMEs can institutionalize AI governance mechanisms, drawing from models such as the IT Governance Maturity Model (Mangundu, 2023) and best practices in other sectors.

Theoretical Framework

The study is grounded in the TOE (technology–organization–environment) framework which was originally presented by Tornatzky and Fleischer (1990). This framework is one of the most widely used for the assessment of technology adoption across different organizational contexts. Changes flavour, as it divides the influencing factors into three domains which are technological (e.g., complexity, compatibility), organizational (e.g., management support, human capital), and environmental (e.g., government policy, market competition), respectively.

As such, the TOE framework takes a wide view of the internal capabilities as well as external pressures (e.g., social, market) imposed on South African SMEs adopting AI. The research design is well-suited for capturing the complexity and dynamism of AI adoption in developing economies' (Hassan, 2024; Shonubi, 2024).

In analysing this more deeply, this study also includes considerations from the IT Governance Maturity Model as applied by Mangundu (2023) that highlights the significant role of readiness, risk and ethical compliance in technology governance. In addition, elements of the theory of Diffusion of Innovation (DOI) are also used to better understand the process of innovation spreading within and between organizations (Rogers, 2003; Qwabaza, 2022)

The combined theoretical lenses of TTF and TOP help to consider both structural and behavioral aspects of AI adoption. The TOE framework, for example, describes what is important, the DOI framework describes how adoption diffuses, and the IT governance maturity model describes why governance and oversight are needed in such a complex technology landscape.

Such a multi-theoretical way of combining existing theoretical lenses improves the analytical rigor of this study and allows deriving recommendations that are more sensible and applicable for SMEs and policy-makers.

Significance of the Study

The study is an important addition to the literature, both in terms of theory and practice, and comes at a timely point in time. Second, it contributes to international AI adoption literature in developing countries, which is still in the early stages of empirical investigation. Although research studies in mature economies provide important evidence, they seldom consider contextual impediments and opportunities prevalent in the African environment, such as infrastructural deficits, informal approaches to business, and ambiguous policy (Akin-Adetoro & Kabanda, 2021).

Second, this study is novel in that it is the first empirical study on the South African SME context, addressing the lack of empirical research on innovative strategies of Haas et al. (2020) of how these enterprises cope with the complexities of AI. The study adopts the TOE framework and combines governance and innovation theories to develop a different contextualised understanding that is close to the actual experience of SME owners and decision makers.

Third, the results are not meant to serve as broad guidance for AI readiness and adoption. The results provide a basis for targeted interventions by policymakers and support institutions such as digital training programs, subsidized AI solutions, and regulatory support according to SME type. Leadership development, external partnerships, and data infrastructure improvements can, for instance, help improve digital maturity and competitiveness (Taljaard & Gerber, 2022; Hassan, 2024).

Lastly, the research also feeds into South Africa's wider 4IR agenda, informing national efforts around equitable digital transformation. AI rapid adoption by SMEs ensures greater innovation and growth by SMEs which leads the way for employment and exports for enhanced economic resilience of the country.

Lastly, a wellbeing-oriented AI governance is recommended to the SMEs, who do not just incorporate AI but apply responsibly. By concentrating on governance and preparedness, it guarantees that the advantages of AI are sustainable and synchronized with long-term development aspirations.

Assessment Summary and Guided Approach Explanation

Summary of Assessment Requirements

The assessment required the student to examine the adoption of Artificial Intelligence (AI) within South African Small and Medium Enterprises (SMEs), using the Technology–Organization–Environment (TOE) framework as the guiding theoretical model. Students were expected to:

• Provide a contextual background explaining the role of SMEs in the South African economy and the disruptive influence of 4IR technologies.

• Identify and articulate a clear problem statement outlining the challenges SMEs face in adopting AI.

• Formulate the research aim, objectives, and key research questions, ensuring alignment with the TOE framework.

• Apply the TOE framework and additional theoretical lenses, including Diffusion of Innovation (DOI) and the IT Governance Maturity Model.

• Explain the significance of the study, demonstrating theoretical, contextual, and policy-related contributions.

• Present the content in an academically coherent manner using evidence-based citations and logical flow.

The assessment required a structured argument that connected South Africa’s economic context, AI readiness challenges, and organisational capability gaps to broader strategic, technological, and environmental considerations.

How the Academic Mentor Guided the Student

The academic mentor supported the student through a systematic and phased approach to ensure clarity, academic depth, and alignment with the assessment requirements.

Clarifying the Task and Requirements

The mentor began by breaking down the assignment instructions into key components:

• Background and context

• Problem statement

• Research aim and objectives

• Research questions

• Theoretical framework

• Significance of the study

This helped the student understand exactly what the assessment needed and prevented overlap or unnecessary content.

Developing the Background Section

The mentor guided the student to:

• Use credible statistics about SMEs in South Africa

• Establish the relevance of AI in the 4IR era

• Highlight gaps in SME digital readiness

• Connect global trends to national challenges

This ensured the background offered a strong foundation leading naturally into the problem statement.

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