“What is our strategy to incorporate AI and Generative AI into our business?” – said everyone’s management team this year. The reality is that organisations across the world are all asking remarkably similar questions. Unfortunately, the answers remain elusive and overwhelming for most.
Over the past year, conversations about AI have evolved from theoretical use-cases about how companies can benefit from the technology to how organisations should incorporate real-life traditional ML and Generative AI models into their businesses. At the core of this conversation lies a fundamental focus on delivering competitive advantages and business value for organisations. Freely available AI models like ChatGPT, combined with advances in other foundational data technologies, allow us the unique and once in a lifetime opportunity to create a more profitable, efficient, and prosperous future for our businesses and organisations.
However, despite these developments, there is a perception that Africa is lagging behind the rest of the world with respect to AI adoption in business. An index compiled by Oxford Insights, a UK-based consultancy, placed Mauritius as the most advanced sub-Saharan country as far as AI adoption is concerned, but only 57th globally. South Africa followed, and is the only country in the region with commercially available 5G infrastructure and other essential technologies required to build an AI-enabled future.
While the study may be accurate as far as the criteria are concerned, these statistics do not paint the entire picture. The African continent is not only ready to join the AI revolution, but also contributing in terms of labour and research. There is a lot to be said about AI solutions that are primarily geared towards solving problems in the US and Europe, while neglecting an entire continent with use-cases that could help leapfrog multiple economic infrastructure and societal issues. This makes the adoption and development of AI tools and technology an impractical dream for some mainstream organisations on the continent.
Making AI simple
As with any other technology geared at international markets, a great deal of customisation is required for AI solutions to be able to solve local challenges. Solving specific problems may even require additional ML specialist resources, because Africa’s unique needs are not catered for in most solutions.
For example, most solutions geared towards agriculture do not allow for the wide variety of climate differences seen across Africa, and many logistics solutions do not make provision for things like dirt roads which are a common on the continent. Taking language into consideration, Africa requires home grown solutions to gain the full benefits offered by AI.
AI platforms are largely rooted in English and make no allowances for contextual biases. While there are AI models that have been created to facilitate the use of different languages, there are many nuances that can be missed. With anywhere between 1000 and 2000 languages, Africa is home to approximately one-third of the world’s languages. Many of these languages are spoken by small minorities, but there are at least 75 languages in Africa which have more than one million speakers. An AI solution that only allows for one or two languages in a country that has dozens spoken daily will obviously not be as effective as one that has been designed to incorporate all the differences and subtleties of interactions between the different languages.
It is therefore not surprising that local organisations have been tentative in their approach to AI. Many are not aware that there is a local solution, designed not only to solve their specific challenges, but developed with Africa’s specific macroeconomic conditions in mind.
iOCO’s Machine Learning as a Service (MaaS) solution has been designed to make AI simple for African organisations. Using models built to take specific local dynamics into account, while creating specific solutions for each customer’s needs, MaaS combines global standards and local requirements to provide business-oriented outcomes. Supported by iOCO’s extensive skills and expertise, MaaS is also extremely cost effective compared to solutions billed in Dollars.
Use cases
A deep understanding of Africa’s specific use cases was one of the incentives for the creation of MaaS. From agriculture, to mining, to healthcare, to energy – and in every other industry – AI has the potential to transform businesses and economies across Africa.
AI is already being used in a variety of ways across the continent. In South Africa, there are drones helping in the fight against rhino poaching; in Mauritius and Ghana, there are computers crunching health data for better outcomes for patients; and in Rwanda, AI efficiently schedules the delivery of medicine to patients in remote areas via drones.
There are a great many use cases where Africa’s specific operating environments can benefit from AI. In the finance sector, AI could help automate more customer transactions in the commercial banking space, or AI models can be used to make predictions and recommendations on the market or investments. In the energy sector, AI can be invaluable in managing renewable resources. It can also be used to improve efficiencies and reduce costs in a traditional generation network through demand response management, predictive maintenance, and energy trading.
With thousands of mines across Africa – and hundreds in South Africa alone – mining is one of the industries that stands the most to gain from AI implementations. From predicting supply chain disruptions, to energy optimisation, to reducing environmental impact and risk, AI has already started fundamentally transforming how mines operate. Similarly, farmers across the continent have started to use AI to obtain useful insights like choosing the right time to sow seeds, determine crop choices, detect disease in plants, and even to target weeds and then decide which herbicide to apply within a region.
There are a multitude of other use cases. Sadly, more AI implementations in Africa use technology from international vendors than local ones. As the continent starts expanding its AI horizons, that must change. By 2030, AI is projected to contribute a staggering $15.7 trillion to global GDP, with $6.6 trillion coming from increased productivity and $9.1 trillion from consumption effects. It is time for companies with a footprint in Africa to make it a priority to use local solutions to ensure the continent gets the lion’s share of these benefits.
Read the original Building an AI-enabled Africa article on ITWeb.
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