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The African Generative AI market is on a rapid growth trajectory, with revenue projected to reach $ 1.54 billion in 2025. Forecasts indicate an annual growth rate of 41.52 % expected between 2024 and 2030, resulting in a market volume of $ 8.75 billion by 2030.

This is according to Statista, which states that AI adoption is still at an early stage within Africa. However, these developments are being driven by rising demand for AI-powered chatbots in customer service and sales, due in turn to the increasing use of smartphones and internet penetration across the region.

Statista further affirms that there is a growing focus on the use of AI for predictive maintenance in industries such as agriculture and manufacturing.

Monitoring and feedback loops are important, so businesses must look at establishing dashboards to track costs, performance and outcomes, enabling continuous improvement.

Shakeel Jhazbhay, General Manager: Digital Business Solutions, Datacentrix

PwC’ s report, entitled the 28th Annual Global CEO Survey: Sub-Saharan perspective, shows that, while AI adoption rates in the Sub-Saharan African region are slightly lower, 75 % than the global figure of 83 %, the impact data reveals encouraging signs of effective implementation, with 72 % planning to adopt or expand their AI initiatives in the next 12 months, compared to 80 % globally.

“ To implement Generative AI successfully, businesses should focus on several critical factors,” says Shakeel Jhazbhay, General Manager: Digital Business Solutions, Datacentrix.

While Generative AI does hold transformational potential, companies must quantify where its actual value lies. This requires a combination of traditional business metrics and tailored AI-specific measures. This requires a mix of key performance indicators, KPIs such as:

Standard business metrics

Revenue growth, cost savings, customer satisfaction and operational efficiency are important gauges here. For example, measuring time saved in content creation or personalised customer interactions can help to quantify ROI.

Strategic alignment is important, as bringing Generative AI initiatives in line with business goals ensures relevance and value creation.

Next is data management. The success of Generative AI projects is very much dependent on the availability of high-quality data. In addition, data security, privacy and governance must all be prioritised.

KPIs for accuracy

Metrics such as accuracy in meeting business needs can be used to evaluate Generative AI’ s utility, as well as quality of information, which affects the customer experience.

Customer and employee experience

Talent and training are additional important factors. Building a skilled workforce capable of using and managing Generative AI tools is critical, while upskilling existing employees and hiring specialised talent are additional key components.

Identification of high impact use cases that align with organisational priorities plays a significant role in ensuring focused deployment.

Improvements in customer experience, faster response times, for instance or employee efficiency can also serve as measurable outcomes.

By leveraging these metrics, organisations can move beyond the hype to make data-driven decisions about their AI investments. Businesses should also remain mindful of hidden costs that could impact on their AIrelated ROI.

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