The implementation rate for AI systems in South Africa is about half the implementation rate in the US, which is owing to differences in skills, data infrastructure, organisational alignment and the integration of AI into core business strategy, says commercial software design company Specno.

While global benchmarks show South Africa's AI readiness is about 35% to 40%, which is behind the US's AI readiness, enterprise data reveals an even wider gap in execution or about half the level of the US, says Specno head of growth Joshua Harvey.

“South Africa is not short on ambition when it comes to AI. What we are short on is execution,” he says.

South Africa’s AI challenge stems from several execution barriers, including a skills shortage and lack of workforce readiness. South Africa faces a critical shortage of AI-related skills, which threatens to limit the country’s competitiveness and the ability of organisations to realise value from AI technologies.

Without coordinated investment in training, certification and workplace upskilling, this gap will widen, warns Harvey.

The second barrier is organisational and data readiness. Successful AI implementation requires robust organisational infrastructure and data readiness.

Research on AI adoption frameworks shows that readiness factors, including data quality, executive leadership support, IT capacity and available resources, are core determinants of whether AI initiatives succeed or fail, he notes.

Additionally, where organisations lack integrated data systems or strong governance structures, AI pilots often stall and fail to scale, he adds.

A third barrier South Africa faces is low adoption despite the acknowledged value of AI because, even where business leaders recognise the benefits of AI, adoption lags.

Many executives understand AI value in theory, but are constrained in practice by limited IT maturity, risk aversion and organisational culture factors that inhibit more transformational adoption.

In contrast, US firms and institutions have aggressively pushed AI into operational workflows, talent development and business strategy. Even amid challenges, including debates about deployment scale and workforce impact, US companies maintain strong investments in practical AI applications, cross-functional teams and data architectures that support industrialisation, says Harvey.

Specifically, in the US, the concerted focus on problem-first deployment, where AI is aligned to cost drivers and operational impediments, has influenced both innovation and productivity, he adds.

While adoption is still uneven across sectors, the integration of AI into core functions such as supply chains, customer service and decision support is more advanced than in South Africa, he says.

To narrow the gap between South Africa and the US AI implementation rates, South Africa must improve AI literacy at executive and board level, but this must be matched with practical implementation skills, from data engineers and machine learning operators to product managers who can translate business needs into technology outcomes, recommends Harvey.

Further, South Africa’s strongest opportunities lie in sectors such as financial services, healthcare, energy and logistics, which are areas where inefficiency is measurable and improvements deliver real value.

However, AI must address tangible, locally relevant problems that matter to the economy and citizens, he says.

“Filling the skills gap and strengthening data ecosystems requires coordinated action across all sectors. National initiatives that support training, certification and research collaborations can accelerate readiness and ensure South Africa’s workforce is prepared for the demands of AI-driven economic participation,” says Harvey.

South Africa stands at a pivotal point. The next 12 months will determine whether the country remains a follower, or whether it narrows the execution gap and builds AI systems that are robust, ethical and commercially viable in its context.