Why AI Readiness in African SMEs Starts With Leadership, Not Just Infrastructure

AI adoption among SMEs is not only a technology problem. It is also a leadership and organisational readiness problem. Firms with similar resources may make different choices because their leaders differ in vision, communication ability and capacity to create a climate where innovation feels possible.

Artificial intelligence (AI) is often discussed as a question of tools, capital and infrastructure. For small and medium enterprises (SMEs) in Nigeria, a new study suggests another factor may be just as important: digital leadership.

SMEs are not a small part of Nigeria's economy. The country has more than 41 million SMEs, contributing about 48 percent of national GDP and 84 percent of employment. If these firms struggle to participate in the AI economy, the consequences could go beyond individual businesses. They could affect jobs, productivity, competitiveness and the wider promise of inclusive digital transformation.

The study published in Systems finds that the intention of Nigerian SMEs to adopt AI is strongly shaped by the digital leadership capabilities of owner-managers. The research, conducted by Ayodeji Idowu and Yemisi Tomilola Babalola of Babcock University, surveyed SME owners and managers across six South-West Nigerian states and analysed 306 valid responses. It asserts that firms are more likely to consider AI adoption when their leaders can think strategically, communicate change, and create a workplace climate where new ideas feel possible.

The missing piece in the AI adoption debate

For years, weak AI adoption among African SMEs has often been explained through familiar barriers: limited finance, poor infrastructure, scarce digital skills and institutional gaps. Those barriers remain real. However, the study adds a more human layer to the debate.

Even when businesses operate in difficult environments, some leaders are better able to recognise AI's value, explain its relevance, and prepare their firms for change. Others may hesitate, not because the technology is unavailable, but because they lack the confidence or organisational readiness to take the first step.

The research focused on registered SMEs in Lagos, Ogun, Oyo, Osun, Ekiti and Ondo states. These states form one of Nigeria's most commercially active regions and include Lagos, the country's main business hub. The researchers drew their sample from a population of 23,290 registered SMEs listed in the SMEDAN database.

The study examined four types of digital leadership capability: strategic capabilities, delivery-related capabilities, interpersonal capabilities and personal attributes. It also tested whether organisational innovation climate, meaning the extent to which a workplace supports new ideas and experimentation, helps explain the link between leadership and AI adoption intention. Firm size was also assessed to see whether leadership works differently in micro, small and medium-sized firms.

Strategic capabilities had the largest positive effect on AI adoption intention, with a coefficient of 0.298. In practical terms, this means SME leaders who can set direction, manage risk, understand digital change and connect AI to business goals are more likely to consider adoption.

Put simply, AI adoption does not begin with software. It begins with a business question: what problem can this technology solve? For many SMEs, that question is answered by the owner-manager. If the leader cannot see a clear use case, AI remains distant, abstract or risky. If the leader can connect AI to customer service, inventory control, market insight, decision-making or process improvement, adoption becomes more realistic.

Trust may decide whether AI gets accepted

The second strongest driver was interpersonal leadership, with a coefficient of 0.245. This includes communication, coaching, collaboration, relationship-building and psychological safety. These may sound like soft skills, but in a small business they can determine whether change is accepted or resisted.

AI can easily create anxiety in the workplace. Employees may worry about job loss, new skills demands, monitoring or unfamiliar systems. In a smaller firm, where teams often work closely and informally, these concerns can quickly shape whether a new technology is welcomed or avoided. A leader who explains why AI is being considered, listens to staff, and builds trust is more likely to move the firm from curiosity to readiness.

Personal attributes also mattered, though less strongly. Adaptability, ethical awareness, empathy, initiative and willingness to learn had a positive effect on AI adoption intention, with a coefficient of 0.129. The finding suggests that leaders who are open to learning and comfortable with uncertainty are more likely to explore AI. But personal openness alone is not enough. It needs to be paired with strategy and the ability to bring others along.

Execution skills may come later

Surprisingly, delivery-related capabilities, including technological proficiency, analytical thinking, team performance and results orientation, did not significantly predict AI adoption intention at the pre-adoption stage. However, this does not mean execution skills are irrelevant. Rather, the study suggests they may become more important later, when a firm moves from intention to implementation. Before that stage, what matters most is whether the leader understands AI's value and can build organisational confidence around it. In other words, technical execution may matter after the decision has been made.

This has clear implications for SME support programmes. Many digital transformation initiatives rush quickly into tools, platforms and technical training. This study suggests that for firms still deciding whether AI is useful, the first priority may be strategic awareness. Business owners need to understand what AI can and cannot do, where it fits, what risks it carries, and how adoption can be staged without overwhelming limited resources.

Innovation climate turns interest into readiness

The study also found that organisational innovation climate partly mediates the effect of strategic and interpersonal leadership on AI adoption intention. Put simply, leadership works better when the workplace itself supports experimentation.

A leader may personally believe in AI, but that belief needs to become part of the organisation's daily behaviour. Employees need to feel that new ideas are welcome, that reasonable mistakes will not be punished, and that innovation is connected to business improvement rather than disruption for its own sake.

Firm size added another layer. The link between interpersonal capabilities and AI adoption intention was stronger in medium-sized firms than in micro-enterprises. This is understandable. In a micro-enterprise, the owner may work directly with a handful of people, leaving fewer layers of coordination. In a medium-sized firm, communication, coaching and collaboration become more important because more employees need to understand and support the change.

This finding argues against one-size-fits-all SME policy. Micro firms may need simple, practical AI awareness and business-case guidance. Small firms may need help formalising basic innovation practices. Medium-sized firms may need stronger support in change management, employee communication and internal alignment.

What policymakers and SME leaders should take from this

For governments, the key policy message is that AI-readiness programmes for SMEs should not be limited to broadband, grants or digital tools. They should also invest in leadership development. Short-term support could help owner-managers identify realistic AI use cases and understand risks. Longer-term support should build innovation culture, peer learning networks and sector-specific pathways for adoption.

Development agencies and international organisations can draw a similar lesson. If AI is to support inclusive growth in the Global South, assistance must go beyond access to technology. It must also help firms build the human and organisational capacity to use technology well. That includes coaching leaders, preparing workers and designing programmes that match the realities of micro, small and medium enterprises.

For SME owners, the study offers a practical reminder: start with the business problem, not the tool. AI adoption should not be treated as a race to buy the latest system. It should begin with a careful look at where the firm is struggling, where data or automation could help, and whether employees understand the purpose of the change.

Why the findings need cautious reading

The study measures intention to adopt AI, not actual adoption. A firm may intend to use AI but later face barriers such as cost, weak infrastructure, limited skills or regulatory uncertainty. The research is also cross-sectional, so it cannot prove cause and effect. Its regional focus on South-West Nigeria means the findings may not fully reflect conditions in less digitally mature parts of the country. The data are self-reported, and the exact fieldwork period is not specified.

Even with these limitations, the research brings the AI adoption debate closer to the everyday reality of small businesses. For many SMEs, the first barrier is not an algorithm. It is whether the leader can make sense of AI, build trust, and create a workplace ready to try something new.

The development stakes behind SME AI readiness

If SMEs in developing economies are excluded from AI adoption, productivity gains may become concentrated among larger firms and richer economies. However, if smaller firms are supported with the right mix of infrastructure, finance, skills and leadership capacity, AI could become a tool for resilience and growth.

For Nigerian SMEs, and for many similar firms across the Global South, adopting AI may begin not in a data center or software platform, but in the decisions of business owners who must lead their people through uncertainty with clarity, trust and purpose.