From Digital Inclusion to Digital Sovereignty: Building Capacity, Infrastructure, and Governance for Sustainable Digital Transformation

Sama Mbang argues that Africa faces a widening technology gap because the pace of Industry 4.0 advancement is extremely fast, and without deliberate action, Africa will fall further behind. Digital skills, infrastructure, and governance remain unevenly distributed across the continent, compounding the risk.

Mbang explicitly states that Africa risks a wider Industry 4.0 gap because 'the speed is very, very fast' and that 'if we don't take care, then the gap will still grow' . He also notes that 'digital skills and infrastructure and governance remain uneven' .

Mbang points out that when searching for information using AI, the results are based on data from other continents, not Africa, meaning African realities and use cases are absent from AI outputs. He argues that true digital inclusion must involve feeding AI models with real African data so that the outputs are relevant and accurate for the continent.

Mbang explains that 'almost all the information you will get will be based on data that are not coming from the continent, from the Africa continent' and that 'the AI are trained regarding use case data, but from other continent, other use case. They are not from Africa' . He concludes that 'inclusion means in this case, also feed the AI models with real data from the continent so that we will get also concrete reply' .

Mbang argues that Africa's primary challenge is building the capacity to manufacture and transform raw materials locally, and that AI should only be considered an additional layer on top of this foundational manufacturing capability. Without mastering how to build machines and equipment, importing all technology will prevent any meaningful progress.

Mbang states that 'Africa needs to have the capability to manufacture, to transform locally - it's about manufacturing before even talking about AI' . He emphasises that 'you cannot industrialize without machine, without equipment' and that 'if you still import all the equipment, all the technology, you will not move a step forward' . He concludes that 'AI should bring additional value' on top of manufacturing capabilities .

Mbang highlights a stark disparity whereby Africa supplies the vast majority of the world's critical minerals but captures almost none of the economic value generated from them. He draws a parallel with the data economy, where the most economically valuable activities — storage, analysis, and monetisation — all occur outside Africa.

Mbang states that 'Africa has around 68% of critical minerals in the world, but gets only less than 1% value addition' and calls this 'unfair' . He also describes a data value chain diagram showing that 'more than 80 percent of the economic value is done outside to the scope where data are provided' , linking both examples to the broader issue of sovereignty.

Mbang points out that Africa is almost entirely dependent on imported medicines, even though roughly half of the raw materials used in pharmaceutical production originate from Africa itself. This represents another example of the continent failing to capture value from its own natural resources.

Mbang notes that 'Africa is importing almost all the medicine' and that 'almost 50% of these raw materials for medicine, for pharmacy, is coming from the African nature' , describing this as 'a very big shift' .

Mbang describes ASMA as an initiative designed to bring together diverse stakeholders — governments, industry, academia, and international partners — around six concrete pillars covering industry applications, digitalisation, human capital, standards, policy, and research. The emphasis is on practical, field-based expertise from people who have real industry experience.

Mbang explains that 'iSmart intends to connect government, industry, academia, experts, and partners and acts as a strategic hub based on real experts from the field' . He outlines the six pillars including industry applications, digitalisation technologies, skills, standards, legal and ethical frameworks, and scientific research . He also references his own background implementing Industry 4.0 at Mercedes-Benz as an example of the practical expertise underpinning the initiative .

Mbang frames Africa's industrialisation not as a competitive threat to other regions but as a contribution to global prosperity. He argues that ASMA is open to collaboration with partners from all continents and that Africa's development benefits the entire world.

Mbang states that 'if Africa grows, this is for global prosperity point of view' and notes that ASMA is working with international organisations including UNIDO, the United Nations, and the African Union . He also mentions that organisations from other continents have expressed interest in replicating the ASMA model .

Mbang argues that applying GDPR-style data regulations to African contexts is misguided because a large proportion of economic activity in many African countries is informal, meaning the assumptions underpinning such regulations simply do not apply. Effective policies must be grounded in the actual behaviour and working practices of African populations.

Mbang notes that in his home country 'almost 80% of the economy is driven informal' and argues that applying GDPR in such a context is 'in the wrong direction' because 'in a formal economy, people behave different' and 'the data are different' . He stresses that ASMA takes into account 'the environment, taking the traditional and the main aspects of how the people behave and work' .

Mbang contends that debates about GDPR-style data protection are not Africa's most pressing concern at this stage of development. The immediate priority should be building the capacity to create value locally from raw materials and establishing fair trade conditions, rather than focusing on regulatory frameworks designed for more advanced economies.

Mbang explicitly states that 'topics like GDPR and some other topics are not our fight now' and that 'other continents did the same to get at the state where they are now' . He argues that 'our fight is to be able to create value addition locally, to not send our raw materials to order and export and import value added products' and to have 'a fair trade where we have also a lot of value addition locally' .

Zeqiri frames the session's central question as a strategic shift: rather than focusing solely on digital access and inclusion, the more important challenge is how African countries can transition from passive consumers of digital and industrial technologies to producers, owners, and governors of the value generated. This shift is being accelerated by Industry 4.0 and AI.

Zeqiri states that 'the real strategy question for Africa is more broader' and asks 'how African countries can move from being just consumers of digital and industrial technologies to become producers' , specifically 'producers, owners, and governors of the value that they create' . She notes that 'Industry 4.0, particularly AI, they are accelerating this question' .

Jean Bosco argues that Africa's challenge is not a lack of people but a lack of skilled personnel, and that the critical question is determining which specific skills are needed given that Africa's industrial problems differ fundamentally from those in Europe or Asia. In particular, Africa lacks the machines needed to teach people the full value chain.

Jean Bosco explains that 'we have people, we have numbers, but we don't have skilled personnel' and that 'the problem in Europe and US, in Asia is different from the problem we have in Africa - in Asia or in Europe you have machines you can be able to produce what you want to produce, but for us we don't have machines so we cannot teach our people the value chain' .

Jean Bosco argues that importing AI models designed for other contexts is insufficient for Africa because the continent's challenges — particularly in areas like critical minerals and agriculture — are fundamentally different. Africa must instead build its own AI models trained on African knowledge and value chains.

Jean Bosco states that 'what we need is not really to look at the model and import the model' but rather 'to build our own model in the Africa context' . He gives the example of critical minerals, asking 'if you have minerals what do you do to get a microchip' and arguing that AI should be trained on the back end to understand the full value chain .

Jean Bosco insists that building human capital is the most fundamental prerequisite for Africa's industrialisation, and that discussions about protecting data or assets are secondary to first creating the people capable of generating and using those assets. Without skilled people, no industry will invest in Africa.

Jean Bosco states that 'nobody will bring industry to Africa if we don't have the human capital' and argues that 'we need to be rude - we need the people who can be able to do things. Then when we get them, we try to figure out how can we protect them' . He repeatedly emphasises that 'human capital is what we need to keep repeating for Africa' .

Jean Bosco argues that African farmers need to understand the entire value chain from farm to final product, using coffee as an illustrative example. He advocates for building digital tools — including virtual representations of physical machines — that are grounded in African agricultural knowledge and practices rather than simply importing foreign solutions.

Jean Bosco uses the example of coffee, asking 'how this person can be able to understand the whole process from the coffee from the farm up to the cup' . He describes a solution where physical machines are made digital so that farmers can access them via mobile phone and understand the full production process . He also notes that his organisation has already deployed this approach, with 'more than 2,000 people now in industry' benefiting from it .

Jean Bosco argues that AI's most valuable contribution in African agriculture is in solving heuristic, experience-based problems — those that depend on accumulated local knowledge. Rather than focusing on front-end AI applications, Africa should train the back end of AI systems on African knowledge systems and agricultural practices.

Jean Bosco explains that 'AI we need to use it for the job which we find is complex - all the job we call heuristic' and that 'in African agriculture, there are things African knows. So these things need to be converted in a language or we need to make like a constitution' . He argues that 'the model I have to import, I have to work on the back end, not the front end of the AI' .

In response to a question about what to do when data is scarce, Jean Bosco argues that the starting point must be building human capacity — creating people who are able to generate and use data — rather than focusing prematurely on data protection frameworks. Protection of assets should follow capability-building.

Jean Bosco responds to the question about data scarcity by reiterating that 'we don't start by protecting things, we first of all create people to be able to do things, we protect what we have' . He argues that 'we need the people who can be able to do things' before worrying about protection , and reaffirms that 'human capital is what we need to keep repeating for Africa' .

Jean Bosco argues that ASMA's role is not just technical but also about building dialogue and consensus around Africa's industrialisation path. He contends that failure to agree on how Africa should develop contributes to the conflicts seen across the continent.

Jean Bosco states that 'if we don't agree on how Africa should be industrialized, how Africa should be developed, we will not solve the issue of the conflict we have everywhere' . He describes the conference in Senegal as an opportunity to 'build a conversation' and 'have a dialogue' , and emphasises that 'listening is part of conversation' .

Adel Ben Youssef argues that Africa has the potential to become a net provider of data centre infrastructure for the world, rather than simply consuming infrastructure built elsewhere. This is because Africa has abundant renewable energy resources — hydro, solar — and water, which are essential for running data centres sustainably.

Ben Youssef argues that 'Africa can net provide us for the data centers in the future because it's the continent that can be the first continent totally renewable in the next 20 years' . He cites Ethiopia as producing '200% of its electricity needs with hydro' and references hydro, solar, and submarine resources . He also notes that the main obstacle is political stability rather than resource availability .

Ben Youssef highlights two structural advantages Africa possesses: its rapidly growing and young population, which will make it the only major region not facing demographic ageing, and its position as a late adopter of digital infrastructure, which allows it to learn from the mistakes of earlier adopters rather than simply replicating them.

Ben Youssef notes that Africa is 'passing from 1.3 billion people to 2.5 billion people in 2050' and will be 'the only country not facing aging in the next future' . He also describes the 'last mover advantage', arguing that 'you can learn more from the worst practices' than from best practices , and notes that infrastructure itself is changing, with people 'speaking about putting now the infrastructure in the sky, even in the moon' .

Ben Youssef argues that Africa's creative industries are a significant economic and cultural asset, but that African creative content is being systematically scraped to train AI models without the consent or compensation of the creators. This represents a major threat to both cultural sovereignty and the livelihoods of millions of people working in the creative sector.

Ben Youssef states that 'most of African creative content now are scraped to train the models, the AI, and without any consent' and 'without any compensation, any attribution' . He notes that 'cultural symbols are reproduced nowadays without any context and the artists they have not the means to control this' . He argues that this has 'economic implication of many million people working on this sector in Africa' .

Ben Youssef argues that simply copying the GDPR framework for African countries is the wrong approach, both because the GDPR itself has problems in Europe and because African economic contexts differ significantly. He advocates for flexible, sector-specific legislation that reflects the actual composition of each country's economy.

Ben Youssef states that 'copy-paste of GDPR and the GDPR on itself has problems now in Europe so copying it in another context is not the right approach' . He argues for 'not a kind of mega legislation for everything in the country but a kind of legislation depending on the sectors and the value creation' , giving the example that for countries where creative industry is key, 'we need a strong protection on this and perhaps less in other aspects' .

Ben Youssef argues that the fundamental challenge in the digital economy is not simply about taxation but about establishing fair business models that ensure equitable remuneration for all actors along the data value chain. He warns that current AI-driven models risk concentrating benefits among a small elite rather than serving the majority of the world's population.

Ben Youssef argues that 'it's not about taxation, it's about fairness - a model, a business model' and asks 'how to put a right and a fair process of remuneration of the actors along the value chain' . He states that 'the technology has designed it nowadays' in a way that is 'not for the interest of all the people' and expresses the desire that 'this technology be designed for the majority of the people' .

Ben Youssef describes ASMA's approach as that of a think tank that has spent three years working to apply Industry 4.0 technologies across multiple African value chains. Rather than focusing on technology in the abstract, ASMA examines how specific technologies can be used within specific value chains to transform and improve them for African users.

Ben Youssef explains that 'in ESMA, we work as a think tank for the moment' and that 'our approach is to see the value chain and to understand how we can use the technology in the value chain with the current users in Africa in order to transform them and to improve them' . He lists the value chains being addressed, including manufacturing, biopharma, agriculture, and creative industries .

Ben Youssef describes ASMA's ambition to build productive ecosystems across Africa modelled on successful examples such as Swiss smart manufacturing. The alliance is working with international partners, including Swiss experts, to adapt and implement these ecosystem models in the African context.

Ben Youssef states that 'we would like to create so many Switzerland in Africa' and thanks Dominique from Swiss Smart Manufacturing for working with ASMA to implement this vision . He notes that 'we are working with people from all over the world in order to build the right ecosystems in Africa around value chain' .

The participant from Kallen International argues that while digital taxation is important, the more pressing regulatory priority for Africa should be data protection. She raises the concern that Africa's data is being used as a goldmine for training AI algorithms, making a robust data protection framework analogous to the GDPR urgently necessary.

The participant states that 'in my opinion, from a regulatory point of view, the priority should be data protection' and asks whether the panellists see 'some kind of GDPR, African GDPR happening anytime soon, especially in times of AI in which we are the goldmine in matter of training the algorithms' .

The participant, a consultant to the African Union, points out that there has already been a significant proliferation of policies on data governance, AI, digital identity, and related areas across the African continent, including softer instruments resembling GDPR. She argues that the critical challenge is not the lack of policies but rather the failure to implement them effectively.

The participant notes that 'we've seen a proliferation of policies in the past few years around data governance, AI, digital ID, et cetera, on the continent, including Africa's own version of GDPR as softer instruments' but that 'where we often see the problem is implementation' , and asks how ASMA addresses this in its work .

The participant from Mauritania raises the practical challenge that in many African countries, digital activity is extremely limited and informal, meaning there is insufficient data to build AI systems. She uses the example of Mauritania, where most communication occurs via WhatsApp rather than through more structured digital channels, and asks what can be done in such contexts.

The participant explains that 'in Mauritania, a lot of us just use WhatsApp to communicate, and it's not really digitalized' and asks 'what do we do when there is, like, a lack of this to build on AI?' .