Published: 14/01/2025

You may remember me writing in mid-2023, "On 30 November 2022, the world changed forever with the public launch of OpenAI's ChatGPT". I stand by that statement. From Day 1, I was a super user, trying to determine how I could use Generative AI to create value in my business by making me more productive and improving the quality of my work. From newsletter editing to ideation to delicate emails ("we really need you to pay that invoice - it's been three years..."), I spent countless hours experimenting. But GPT didn't, and sometimes still doesn't, meet our expectations.

Me: "Hey GPT, write this in a young, informal voice.
"ChatGPT: "Yo Homie...."

I ignored GPT's suggestion this time to avoid getting in trouble.

Additionally, sometimes GPT behaves like a five-year-old, making things up when they don’t have a good answer. One of the early feedback options used to be "Lazy/Non-Helpful Response". We call these "hallucinations", which is a playful term but also a difficult problem to fix. Generative AI isn't like traditional code - you can't simply fix a bug. (This literally happened to me with Claude, which seems to have gotten tired and stopped producing.

Interestingly, when Sam Altman and his team launched GPT-3, they were so focused on GPT-4, which was in testing, they didn't think much of it. They had no idea how good GPT-3 would be. They were as surprised as everybody else.

Up until that moment, AI had primarily been a tool for automation, organisation, and problem-solving. But ChatGPT represented a leap forward, capable of generating human-like responses, coding applications, and creating content ranging from business plans to creative writing. This breakthrough has since been accompanied by models like GPT-4 and Claude 3.5 Sonnet, pushing GenAI options even further.

ChatGPT's rapid adoption—reaching 100 million users in its first two months—has solidified AI's transformative potential. However, the generative AI market is rapidly evolving. Recent market data from Menlo Ventures highlights a decline in ChatGPT's market share from 50% in 2023 to 34% in 2024, while Anthropic's Claude has risen from 2.1% in January 2024 to 24% by the end of the year, reflecting its rapid adoption and growing competitiveness. These trends reflect the dynamic competitive landscape and the rapid adoption of emerging AI models like Claude. Claude 3.0 was considered the best model for coding, and that was before their upgrade to 3.5.

AI's Transformative Potential for Africa

‍The rapid advancements in artificial intelligence have profound implications for the workforce, education, and economies. In the past 18 months, Africa has witnessed a surge in AI research centres, policy frameworks, and tailored innovations addressing local challenges.

Google AI Research Centre in Ghana:
Google has enhanced its AI research efforts in Africa by expanding its AI research centre in Accra, Ghana. This centre focuses on leveraging AI to address key challenges in healthcare, agriculture, and education across the continent.

African Union's AI Policy Framework
The African Union recently introduced a policy framework to guide member states in responsibly adopting AI. This framework aims to drive innovation while addressing ethical concerns and mitigating risks.

AI-Driven Agricultural Innovation in Kenya
Kenya is leading in AI-powered agriculture with platforms like Twiga Foods. These solutions use AI to optimise supply chains, reduce food waste, and empower smallholder farmers.

South Africa's National AI Strategy
South Africa has made significant strides with the launch of its National Artificial Intelligence Institute (NAII) and the development of a national AI strategy. The focus is on skills development and using AI to drive economic transformation.

AI-Enabled Healthcare in Nigeria
Nigeria is emerging as a hub for AI healthcare solutions. Startups like Helium Health are using AI to digitise medical records, improve patient management, and enhance diagnostic accuracy.

Yet, whilst the promise of AI looms large, the challenges—from infrastructure to skills gaps—are equally significant.

The key question for AI experts, COOs, CFOs, and workforce management specialists in Africa is: Will GenAI take all the jobs? This is an important question, and since Africa is a continent and not a country, the impact on jobs and workforces will likely vary by country or region.

To take a step back, most developed (rich) countries, and almost every country in Eastern and Northern Europe and Asia, are facing a demographic crisis. They simply don't have enough young people to fill the jobs, and they won't have enough workers to pay for the retirement of older workers in the near future. So the ideal outcome for these countries is higher productivity means higher wagers and more tax revenue for a smaller workforce.

The challenge in most African countries is not shrinking availability of workers, but rather a skills shortage, lack of resources, and poor energy and internet connectivity in most areas. So the challenges are decidedly different. Africa’s best outcome is better skilled workers (due to availability of free online training and education programmes, plus GenAI’s important ability to be the best tutor in the world.

Despite these hurdles, Africa has the opportunity to leapfrog traditional development pathways.

Opportunities & Considerations for African Businesses

1. AI Will Replace the Unskilled, Not the Skilled
‍The narrative that AI will replace people doesn't hold water. Rather, although it may sound like a cliché these days: AI won't replace people. People who know how to use AI will replace those who don't.

Throughout history, major advancements in innovation have in every instance created more, better-paying jobs...eventually. For example, when automobiles replaced horses, it catalysed industries employing millions globally and 25% of the American workforce. However, that transition took more than half a century. The same happened for the printing press, the cotton gin (the Luddites!), electricity and the light bulb, PCs, the Internet, and more. Unfortunately, we don't have half a century. We have at most three years to transition our workforces in most industries.

Importantly, in history, the first jobs to go are those that are too dangerous, too unpleasant and boring to be attractive to most people. Think of the workers cleaning up after horses, spinning cotton, or working a kilometre below ground in a mine.

The challenge lies in preparing Africa's workforce for these roles. As AI disrupts fields from agriculture to medicine, proactive upskilling and reskilling initiatives can ensure the continent's human capital is future-ready. Precious few countries and corporations are addressing this productively.

‍2. SMEs: The Agile Contenders
‍The agility of Small and Medium-sized Enterprises (SMEs) gives them a huge advantage. However, if they are beaten to the punch by larger corporate executives, it will mean big trouble for some, depending on the industry. Here are some examples:

AI-Driven Marketing Operations
A marketing manager at a growing SME can use a platform like Jasper for content creation (or use ChatGPT with clever prompting), Canva's AI design tools for visuals, and HubSpot's AI for automating email campaigns. This streamlined approach eliminates the need for external marketing agencies, enabling faster campaign turnarounds and cost savings.

AI-Powered Compliance in Financial Services
In Kenya, SMEs in financial services are adopting tools like PesaCheck's AI-enabled systems to ensure regulatory compliance. These tools analyse transactions in real-time, flagging potential issues, and assisting in regulatory reporting, thus reducing human error and time spent on manual processes.

South African Fintech and Micro-Lending
Fintech startups in South Africa, such as Yoco, are utilising AI to streamline payment processing and micro-lending. By integrating AI for real-time credit scoring and fraud detection, they're enabling faster loan approvals and catering to previously underserved small business owners.

AI in Customer Personalisation for E-Commerce
A Nigerian SME in e-commerce uses AI platforms like Recommend to deliver personalised product suggestions based on customer behaviour. This not only enhances user experience but also boosts conversion rates and average order values.

AI in Predictive Maintenance for Manufacturing SMEs
A Ghanaian manufacturing SME has implemented AI-powered predictive maintenance systems, such as Senseye. These systems monitor machinery in real-time and predict failures before they occur, significantly reducing downtime and maintenance costs.

AI in Retail Analytics
A South African retailer used AI to analyse purchasing patterns in cash-dominated markets, leading to tailored promotions that significantly boosted sales. Similarly, AI-driven chatbots customised with internal knowledge bases have improved customer service efficiency in sectors like banking and education.While most professionals are now using GenAI for their own work, integrating AI into business teamwork and workflows remains challenging.

‍3. The True Battleground: Internal Data, not GenAILast year, we spoke at about 40 industry conferences, and in these keynotes, we always ask for a show of hands on who is using ChatGPT or similar. Invariably, all or nearly all of the hands go up. Now, when we ask how many are using the paid versions, that's normally around 20-25%.

While most of the public chatter is around AI tools like ChatGPT, the real value lies in leveraging internal data, and that requires a $20 monthly investment. By now, African businesses should be utilising generative AI and machine learning to unlock insights from operational data, enabling smarter decision-making and process optimisation. But most are not.

4. Navigating AI Adoption Challenges
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‍Despite its promise, AI adoption faces significant barriers in Africa:
Language: It's improving fast, but frontier AI model companies and their partners are rapidly expanding the availability of African languages. Amazon Web Services (AWS) now supports six South African languages and is working with partners to enhance those languages and add the other five.
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Infrastructure: Limited access to high-speed internet and computing resources hampers progress. However, with the collapse of solar pricing and Starlink wireless internet available in many African countries, AI will be available to more and more people in Africa.

Skills Gap: While AI adoption is growing, the shortage of skilled professionals remains a bottleneck. There is no avoiding this - African countries and corporations need to step up skills development in GenAI, Data Science, and Machine Learning.

Ethics & Governance: Ensuring ethical AI deployment and addressing biases in AI models are critical challenges.

Conclusion

The accelerating evolution of AI presents African leaders with an unprecedented opportunity to reshape industries, create jobs, and address systemic challenges. By focusing on upskilling the workforce, leveraging internal data, and fostering local innovations, Africa can carve a unique path in the AI-driven future.

To succeed, African leaders must embrace AI not as a threat but as a tool for empowerment. The road ahead requires strategic investments, robust policy frameworks, and a commitment to inclusivity. With these elements in place, Africa stands poised to lead in the global AI economy.

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