Will AI Replace Humans? Reframing the Fourth Industrial Revolution within a Human Development Paradigm - By Michael A. Sarfo-Kantanka

The growing debate about Artificial Intelligence has produced a question that appears simple but is considerably more complex than it first suggests: will AI replace human beings?

We need to distinguish between two versions of this question. The first is the popular fear that AI will render humanity as a whole obsolete. This fear dominates public discourse but finds little support in serious scholarship. The second is the more substantive academic and policy question: who will benefit from AI, who will be excluded, and under what conditions can societies govern and deploy these technologies for human development?

The International Monetary Fund, for instance, does not argue that humans will become irrelevant. It argues that employment will be restructured, that some tasks will be substituted while others will be complemented, and that the capacity to capture AI's benefits is unevenly distributed across countries. Similarly, the World Economic Forum projects both displacement and creation of jobs, producing a net increase across the trends it considers.

These are questions of distribution, capability and governance; not of human obsolescence. This article addresses the second question, and it argues that the first has diverted attention from it for too long.

The question has gained legitimacy from the speed at which contemporary AI systems are developing. The IMF estimates that almost 40 percent of global employment is exposed to AI, with exposure rising to about 60 percent in advanced economies. At the same time, the IMF distinguishes between jobs in which AI may substitute for human tasks and those in which it may complement human labor and improve productivity. For emerging markets and low-income countries, the estimated exposure is lower, but these economies also tend to be less prepared to capture the benefits of AI, raising the possibility that technological advancement could widen existing disparities between countries.

The World Economic Forum's Future of Jobs Report 2025 similarly projects substantial labor-market transformation by 2030, including the creation of approximately 170 million jobs and displacement of about 92 million, producing a projected net increase of 78 million jobs across the trends considered in the report.

These projections deserve serious attention. They demonstrate that AI will affect work, production and the organization of economies. They do not, however, establish that human beings as a whole are becoming obsolete. Indeed, I believe that the debate is being situated in the wrong parlance.

Before asking whether AI will replace humans, we ought to ask a more fundamental set of questions. Replace humans from what? Replace humans in the production of what? For whose benefit? And finally, towards what developmental end?

These questions take us back to the more fundamental relationship between production and development.

AI did not emerge from a vacuum. It is a product of human ingenuity accumulated over centuries. It is the latest expression of humanity's continuing effort to overcome limitations, improve production, process information, solve problems and expand what human beings can accomplish. To understand the Fourth Industrial Revolution properly, therefore, it is necessary to understand the three industrial transformations that preceded it.

Research on information and communication technologies in developing countries has long demonstrated that the developmental consequences of a technology depend less on the technology itself than on the institutional, political and organizational context into which it is introduced. Avgerou, for instance, argues that explaining the relationship between technology and development requires sustained attention to context—historical, institutional and political—rather than assuming that technological capability translates automatically into developmental capability. The implication for AI is direct: the same technology may produce very different outcomes depending on the institutions and capabilities that surround it.

Human societies have always been confronted by needs that exceed existing productive capacities. Food has to be produced, goods transported, information communicated, diseases treated, resources extracted and environments managed. As human needs expanded, so did the search for better ways of meeting them.

The First Industrial Revolution, conventionally associated with the period from approximately 1760 to 1840, represented a fundamental shift from predominantly human, animal and biomass-based sources of energy towards mechanized production, powered increasingly by water and steam. It transformed manufacturing, mining, textiles and transportation and established the foundations of the modern factory system.

The significance of this transformation was not simply that machines began performing tasks previously performed by human beings or animals. Its deeper significance was that human productive capacity was expanded. The steam engine allowed societies to undertake forms of production and transportation at scales and speeds that were previously impossible. The locomotive transformed mobility, while mechanized production altered the organization of labor and industry.

Human beings did not become irrelevant because the steam engine became powerful. In fact, human beings became capable of doing more.

The Second Industrial Revolution extended this productive transformation. From the late 19th century into the early 20th century, electricity, new forms of communication, the internal combustion engine, steel, chemicals and mass-production techniques fundamentally altered industrial economies. Electricity allowed production to be reorganized around increasingly sophisticated systems of power. The telegraph and telephone accelerated communication. The assembly line became an important mechanism through which standardized goods could be produced on a scale that earlier industrial methods could not easily achieve.

Again, the fundamental story was an expansion of human productive capacity.

The Third Industrial Revolution introduced another profound transformation. Beginning particularly in the middle of the 20th century, electronics, computing, digital systems and information technologies changed the manner in which human beings generated, stored, processed and communicated information. Computers, databases, statistical software, telecommunications and eventually the Internet established the digital infrastructure upon which much of today's economy depends.

The progression is therefore important. The first industrial transformation expanded what human beings could accomplish with physical power. The second expanded the scale and speed of industrial production and communication. The third dramatically expanded the capacity to process information.

The Fourth Industrial Revolution builds on this accumulated foundation. As the narratives confirm, it did not suddenly arrive from nowhere.

The Fourth Industrial Revolution is commonly associated with the convergence of digital technologies with physical and, increasingly, biological systems. Artificial Intelligence, machine learning, robotics, cloud computing, the Internet of Things, advanced analytics and cyber-physical systems are among the technologies associated with this transformation.

The distinction between the Third and Fourth Industrial Revolutions is important. The digital revolution gave humanity increasingly powerful tools for storing, transmitting and processing information. The Fourth Industrial Revolution here extends those capabilities towards systems that can identify patterns, generate prompts and predictions, automate processes and, in some circumstances, perform tasks that previously required significant human cognitive effort.

But even this extraordinary development remains deeply dependent upon what came before.

The sophisticated algorithms of today stand upon decades of computing, programming, statistics, databases, information systems and digital infrastructure. The data that AI systems process are generated through human activity and human-designed systems. The computational infrastructure is human-made. The objectives given to AI systems are all determined by people and institutions.

Even the elementary computing principle of "Garbage In, Garbage Out (GIGO)" remains relevant. The sophistication of a system does not eliminate the consequences of poor inputs, poorly structured information or flawed assumptions. Contemporary AI may process vastly larger quantities of information than earlier computing systems, but the fundamental relationship between inputs, processing and outputs remains.

This is why it is difficult to accept the assumption that the arrival of increasingly sophisticated AI automatically means the disappearance of human relevance. Technology has repeatedly changed the manner in which human beings produce. Yet the fact remains that it has not eliminated the human reason for production.

This historical context becomes particularly important when the Fourth Industrial Revolution is considered from an African perspective.

There is a tendency in global discussions about AI to speak about humanity as though all societies are approaching the technological future from the same developmental starting point. Well, they are not. We are not! Inequalities prevail!

The World Economic Forum itself acknowledges that the industrial revolutions have not unfolded uniformly across the world and that elements of earlier industrial transformations remain incomplete in some societies even as Fourth Industrial Revolution technologies emerge.

For Africa, this is not merely an academic observation.

Large sections of the continent continue to confront challenges associated with basic human needs and productive capacity. Access to potable water, sanitation, reliable energy, quality education, healthcare, decent employment, food security, adequate housing and productive infrastructure remain uneven. In many communities, the basic conditions necessary for human flourishing are still insufficient. Ghana is not exempt from this reality.

It is also important to acknowledge that Africa is not a single developmental space. The continent contains 54 countries with widely divergent levels of industrial capacity, institutional strength and technological readiness. Rwanda, for instance, has developed a national AI policy and is actively building data governance frameworks. Kenya has produced one of the world's most dynamic mobile-financial ecosystems, and Nigeria has a rapidly growing technology sector. These experiences coexist with countries in the same continent where basic electrification, connectivity and public administration remain severely constrained. The argument in this article therefore applies most directly to the continent's general condition, but it should not be read as erasing the important differences between and in fact, within—African countries. Indeed, where relevant, the discussion identifies the conditions under which the argument holds and the conditions under which it does not.

We can discuss artificial intelligence in the morning and return in the afternoon to communities struggling with inadequate sanitation, post-harvest losses, unemployment, poverty, environmental degradation and unreliable access to essential services. We can celebrate digital innovation while rivers are polluted by destructive mining practices (Galamsey) and agricultural communities continue to confront productivity constraints.

This is the paradox.
We are discussing technologies capable of analyzing enormous quantities of information while significant sections of our societies continue to struggle with challenges that previous industrial revolutions should have helped us address more effectively.

Africa therefore has a peculiar developmental challenge. We are attempting to enter the Fourth Industrial Revolution while substantial elements of the first three remain incompletely harnessed.

That does not mean Africa should wait. It means Africa must be strategic in our embrace of the Fourth Industrial Revolution.

Development is not a single event. It is a process of acquiring capabilities. A society that has not developed reliable infrastructure, quality education, productive industries, strong institutions, functioning data systems and technological skills cannot simply purchase an AI system and expect the developmental consequences to resemble those of a technologically advanced economy.

The empirical literature on technological displacement supports this reading. Acemoglu and Restrepo's analysis of industrial robots in the United States found that robot adoption reduced employment and wages in affected local labour markets, but the effects were unevenly distributed across regions and industries. Their findings do not support the claim that technology eliminates the human role in production. They support the more precise claim that technology redistributes the gains and losses of production, and that the capacity to adapt to those shifts determines who benefits and who is displaced. This distinction matters for Africa, where the capacity to adapt is unevenly developed.

This is why the AI debate must be connected to development policy. The introduction of a more sophisticated analytical tool—whether statistical software or an AI system—does not automatically eliminate the relevance of the people whose activities constitute the underlying production system. The tool may improve the researcher's ability to analyze agricultural data, identify patterns in rainfall, crop yields, disease incidence or market prices, and contribute to better decisions. But the farmer remains central to the production of food. Technology improves the productivity of those who use it; it does not replace the reason their production exists.

The same principle applies to AI. The introduction of a more sophisticated technological tool does not automatically eliminate the relevance of the people whose activities constitute the underlying production system.

Indeed, in many circumstances, the greatest value of technology is precisely its ability to improve the productivity of those people.

The United Nations Development Programme's Human Development Report 2025 makes a similar conceptual argument from a human development perspective. Rather than treating AI as an autonomous force whose consequences are predetermined, the report places emphasis on people's choices and on how societies decide to deploy AI to expand human capabilities. That distinction is fundamental.

There is nevertheless a serious concern that cannot be dismissed. It is the fact that AI, like all advancements, can deepen inequality.

The IMF observes that advanced economies are both more exposed to AI and better positioned to benefit from it, while many emerging and developing economies face constraints in infrastructure, skills and institutional readiness. This means that countries that are already ahead technologically may have greater capacity to capture the productivity gains associated with AI. The result could be a widening gap between those who produce AI and those who merely consume it.

For Africa, this is perhaps more important than the fear that machines will simply take away jobs. The greater danger may be that African economies fail to develop the human and institutional capacity necessary to participate meaningfully in the AI economy. If that happens, Africa could once again become primarily a consumer of technologies developed elsewhere, just as it has remained heavily dependent on external technologies, capital and expertise across many areas of production.

This concern is not unique to Africa, but it takes a particular form on the continent. Mohamed, Png and Isaac argue that AI systems can reproduce and even intensify historical patterns of marginalization when the perspectives, priorities and knowledge systems of affected communities are excluded from their design and governance. They describe this as a form of "algorithmic coloniality"—a dynamic in which technologies developed elsewhere encode assumptions and priorities that may not serve the communities they are deployed upon. For Africa, the risk is not only that the continent may consume technologies it did not produce, but that those technologies may embed institutional logics and dogma that are not aligned with African development priorities. This is why the capacity to understand, adapt, regulate and govern AI is not some secondary concern. It ought to be the central development question.

The inequality risk is not only international but manifests hugely domestically. Within African countries like my motherland Ghana, the benefits of AI are likely to concentrate among those who already have access to reliable electricity, connectivity, education and digital skills. Rural populations, informal-sector workers, women and marginalized communities may be excluded from the gains unless deliberate measures are taken to ensure equitable access. The same logic that applies between countries applies within them: technology amplifies existing capabilities, and where capabilities are unevenly distributed, the benefits will be unevenly distributed as well.

But the distribution of capabilities is never natural. It is the product of political choices, and those choices carry consequences that shape who can benefit from any new technology. Ghana's Free Senior High School policy illustrates this clearly. Introduced in 2017 by the NPP government under President Akufo-Addo, the policy abolished fees for all public secondary school students, making enrolment independent of a family's ability to pay. It was a difficult political choice fiercely contested by the opposition NDC, which argued for a targeted rather than universal approach, yet implemented within tight fiscal constraints that required difficult trade-offs in public spending. The policy has since benefited over 5.7 million students and significantly increased enrolment. Yet the consequences of that choice reveal the deeper structure of domestic inequality. Removing fees did not automatically equalize opportunity for all. Rural schools continue to face infrastructure deficits and teacher shortages. Hidden costs in transportation, uniforms and learning materials, for instance, still exclude the poorest households.

This situation finds expression in the introduction of AI. If it is deployed in Ghana and across Africa through politically driven, non-targeted initiatives that ignore the political roots of capability distribution, it will likely replicate or deepen the same patterns. The technology will concentrate where electricity, connectivity, skills and institutional support already exist. The response, therefore, cannot be purely technical. Addressing the inequality risk of AI requires not only investment in capabilities but sustained attention to the political structures that determine how those capabilities are distributed—and a willingness to make difficult political choices about who benefits.

This is where the Fourth Industrial Revolution could reinforce existing inequalities rather than reduce them. The response, however, cannot reasonably be to reject technological advancement. The response must be to build capacity.

The appropriate African response to AI therefore begins with human development. But not all investments are equally urgent, and not all capabilities are equally binding. Three constraints deserve priority.

First is reliable electricity (a fruit of the Second Industrial Revolution) and connectivity (from the Third Industrial Revolution). No AI system can function without stable power and digital infrastructure, and no digital economy can emerge where these remain unreliable. This is the most basic precondition.

Secondly, data governance and statistical capacity. AI systems depend on data, and data depend on institutions capable of collecting, managing, protecting and interpreting them. A country that cannot produce reliable statistics cannot evaluate whether AI is improving development outcomes or not.

Third is research and public-sector capability. The capacity to understand, adapt, regulate and deploy AI cannot be imported wholesale. It must be built domestically through universities, research institutions and public agencies capable of exercising judgment on where AI is useful and where it is not.

Education, digital literacy and scientific training remain essential, but in my well-considered view, they are the medium-term investments that rest on the three foundational constraints identified. A country that invests in AI skills without reliable electricity, without data systems and without institutional capacity will produce graduates who must emigrate to use their skills.

It is also necessary to be precise about what AI can and cannot do in a context of institutional weakness. AI can, under certain conditions, substitute for capabilities that are missing—for example, by automating routine administrative tasks where trained staff are scarce, or by detecting patterns in large datasets where statistical expertise is limited. But this substitution works only when the surrounding institutional conditions are adequate: when there is reliable data, when there is accountability for how the system is used, and when there are people capable of interpreting its outputs. Where these conditions are absent, AI does not bypass weak institutions; it amplifies them.

And perhaps most importantly, institutions must become capable of understanding technology well enough to determine where it can contribute meaningfully to development. This is particularly important because AI is not equally useful for every problem. The development question should not be, "Where can we use AI because AI is fashionable?" It should be, "Where can AI produce a measurable improvement in human welfare, productivity, efficiency or institutional performance?" That is a question the Fourth Industrial Revolution places at the heart of Monitoring and Evaluation, and ought to.

As a Monitoring and Evaluation professional, I find the AI debate particularly relevant because development work has always been concerned with evidence, learning, adaptation and results.

AI can significantly improve the ability of development practitioners to process information. It can assist in analyzing large datasets, identifying patterns, detecting anomalies, supporting forecasting, coding or classifying qualitative information and improving the speed with which evidence is made available for decision-making. But these capabilities do not eliminate the need for the M&E professional. They may actually increase our relevance.

Monitoring and Evaluation is not simply the production of tables, graphs and percentages. It involves asking whether an intervention is achieving its intended objectives, whether observed changes can reasonably be associated with the intervention, who is benefiting, who is being excluded, what unintended effects have emerged and what should be changed on the basis of evidence. An AI system can identify a statistical pattern, and it would take the development professional to determine whether that pattern is meaningful to development outcomes.

AI definitely can process datasets, yet the M&E professional must determine whether the data were collected appropriately. Same way AI can generate a prediction but would require the evaluator to examine the assumptions behind the prediction and determine whether it is useful within the programme's social and institutional context. And finally, where AI generates a report, human beings remain responsible for deciding what the evidence means and what should be done about it.

This approach to evaluating AI is consistent with a broader lesson from the study of technology and development. Avgerou argues that the relationship between technology and development cannot be adequately explained through general models alone; it requires contextual explanation that accounts for the specific institutional, historical and political conditions in which technology is deployed. Gagliardone's work on technology policy in Africa similarly demonstrates that the political and institutional context shapes not only how technology is used but whether it contributes to development at all. For M&E practitioners, this means that evaluating AI requires not only assessing whether the system functions, but whether it functions within the specific institutional conditions that make its outputs actionable and accountable.

This is why the future of M&E should not necessarily be understood as a competition between AI and the evaluator. It may instead become a more productive relationship in which AI reduces routine analytical burdens while the human professional concentrates increasingly on evaluation design, interpretation, contextual analysis, ethics, judgment and the translation of evidence into development decisions. For instance, it would take an M&E framework to define indicators for AI prompts. What counts as a good prompt? How would we know? These are evaluative questions, not technical ones, and they require the judgment of professionals trained in assessment.

The UNDP's 2025 report similarly identifies the potential for complementarity between people and AI, and cautions against treating technological change as an inevitable process in which humans simply give way to machines.

This has implications for how AI is evaluated in development contexts. The appropriate question is not "Does the AI system work?" but "Does the AI system improve development outcomes, and for whom?" A system that increases the speed of analysis but produces results that no one acts upon has not contributed to development. The same is true of systems whose outputs appear authoritative but are in fact unreliable. The albatross of AI hallucinations—confidently generated responses that are factually incorrect or entirely fabricated—is a clear example. An AI system that generates false or misleading results is therefore not merely technically flawed but developmentally counterproductive, because it introduces error into the very evidence upon which decisions are supposed to rest.

Furthermore, a system that reduces the cost of data processing but excludes the communities whose data are being processed has raised ethical questions rather than resolved them. Monitoring and Evaluation must therefore extend beyond technical performance to assess whether AI strengthens accountability, improves equity and expands human capabilities. This progressively embraces AI and demands that AI be held to the same evidentiary standards as any other development intervention.

This perspective becomes particularly important when AI is applied to Africa's real development challenges.

Consider agriculture for instance. AI-assisted systems can support weather prediction, crop monitoring, disease detection, yield estimation and market analysis. The farmer does not disappear. The farmer becomes better informed.

Healthcare is no different. AI can support diagnosis, disease surveillance, medical research and health-system management. Health professionals remain necessary because healthcare is not merely the processing of information. It involves clinical judgment, ethical responsibility, communication, trust and care.

Even in biodiversity conservation and environmental management, Artificial Intelligence combined with satellite imagery, geographic information systems and remote sensing can help identify changes in land use, monitor environmental degradation and improve the detection of patterns that would be difficult to identify manually. This has particular relevance to Ghana's environmental challenges, including illegal mining and its consequences for land and water resources.

The use of AI in mineral exploration offers another example. Companies such as KoBold Metals, a Silicon Valley company, have developed approaches that combine geological science, large datasets, computational modeling and machine learning to improve mineral exploration. The significance for Africa should not simply be that AI can make mineral discoveries more efficient. The larger question is whether advanced technology can be deployed within responsible regulatory systems to improve resource management while reducing environmental damage and strengthening public value. This is where technological advancement becomes a development question. The issue is no longer simply what the machine can do. The issue becomes what society chooses to do with what the machine can do.

The human development tradition provides a useful foundation for understanding this relationship. Development has never been adequately defined by production alone. Economic growth is important, but development ultimately concerns the expansion of people's capabilities and opportunities to live lives they have reason to value. This means that technology cannot be the final measure of development.

A country may possess sophisticated AI systems and still experience unacceptable inequality.

It may have advanced digital infrastructure while large numbers of citizens lack access to quality healthcare. It may have highly automated industries while significant sections of its population lack decent employment. Technological sophistication and human development are related, but they are not synonymous. The question must therefore remain: What does this technology do for people?

That question becomes particularly important as AI becomes more powerful. If AI increases productivity while excluding large populations from its benefits, the development question remains unresolved. If it reduces costs but compromises human dignity, the development question remains unresolved. If it creates efficiency but weakens accountability, the development question remains unresolved. Finally, if it generates economic growth without expanding human capabilities, we should be cautious about declaring the process development.

The 2025 Human Development Report is instructive here. It frames AI not simply as a technological phenomenon but as a matter of human choices and possibilities, arguing that the direction of AI's impact will depend substantially on how societies choose to use it.

It is therefore difficult to accept the proposition that AI will make humanity irrelevant in any absolute sense. Human beings are not merely workers within a production system. We are also the consumers, decision-makers, innovators, citizens, communities and beneficiaries for whom production exists.

Technology changes the means of production but does not automatically eliminate the purpose of production. In fact, the steam engine did not eliminate the need for food. Electricity did not eliminate the need for housing. The assembly line did not eliminate the need for healthcare. The computer did not eliminate the need for education. The Internet did not eliminate the need for governance. And AI will not eliminate the need for human development. What it can do is alter the manner in which these needs are addressed.

Some occupations will change. Some tasks will disappear. New occupations will emerge. Some existing skills will become less valuable while other skills become more valuable. That is already evident in labour-market research. The World Economic Forum's 2025 report identifies technological skills among the fastest-growing areas of demand while also emphasizing the continuing importance of human capabilities such as cognitive skills, collaboration, resilience, flexibility and leadership.

The relevant issue, therefore, is not whether every existing job will survive unchanged. It will not. The relevant issue is whether societies will equip their people to adapt.

This is where the African development agenda must become much more deliberate. Africa should not approach AI primarily as a consumer waiting for products developed elsewhere. We must build the capacity to understand, adapt, produce and govern these technologies.

This requires investment in education, research, digital infrastructure, data systems, technical and vocational education, entrepreneurship and public-sector capability. It also requires a different understanding of digital transformation. Digitizing a form does not necessarily transform an institution. Launching an application does not necessarily reform public service delivery. Purchasing computers does not necessarily create digital capacity. And adopting AI does not necessarily produce development.

The technology must be embedded within institutions capable of using it effectively and ethically. This is particularly important in public administration, where the temptation to announce technological initiatives can sometimes be greater than the commitment to build the institutional systems required to sustain them.

The distinction between technological adoption and institutional transformation is well established in the development literature. Gagliardone shows, in the Ethiopian context, that technology policy is inseparable from the political and institutional project of nation-building, and that the introduction of new technologies without corresponding institutional capacity produces limited developmental returns. Avgerou makes a parallel argument at the conceptual level, demonstrating that information systems contribute to development only when they are embedded within organizational and institutional arrangements capable of using them effectively.

This is precisely the challenge Africa faces with AI: not the absence of the technology, but the presence of the institutions required to make it serve development. Africa therefore needs less technological spectacle and more technological strategy.

The Fourth Industrial Revolution presents Africa with both a warning and an opportunity. The warning is that technological inequality can become another dimension of the existing development divide. Countries that possess infrastructure, skills, capital, research capacity and strong institutions are better positioned to capture the benefits of AI. Those without these foundations risk becoming increasingly dependent on external technology and expertise.

The opportunity, however, is equally significant.

Africa does not necessarily have to reproduce every stage of development in exactly the same manner as the countries that industrialized earlier. Where appropriate, emerging technologies can help overcome institutional and infrastructural limitations, provided that they are introduced within coherent development strategies.

The concept of technological leapfrogging deserves scrutiny. It must not become another slogan. The idea that developing countries can bypass earlier stages of industrialization and adopt advanced technologies directly is appealing, and there are cases—such as mobile financial services in Kenya, where it has partially occurred. But leapfrogging is not automatic. It must be supported by investment in people. It must be supported by workable evidence. It must be supported by institutions. It must be evaluated against measurable development outcomes. And it must be guided by a clear understanding of the problems it is intended to solve. It requires reliable infrastructure, capable institutions, a skilled workforce and a policy environment that supports innovation and regulates risk. Where these conditions are absent, the leapfrogging narrative can become a justification for neglecting the foundational investments that development requires. The risk is that countries attempt to leap without the capacity to land.

This is where Human Development and Monitoring and Evaluation must meet the AI revolution. The AI system should not only be evaluated according to whether it works technically. It should also be evaluated according to whether it works developmentally and thus, addresses the following questions adequately:

1. Does it improve access?
2. Does it reduce inequality?
3. Does it improve productivity?
4. Does it reduce waste?
5. Does it strengthen public services?
6. Does it create meaningful opportunities?
7. Does it improve environmental outcomes?
8. Does it expand people's capabilities?
Does it preserve human agency?

These are the questions that should increasingly accompany the deployment of AI in development.

In conclusion, the fear that Artificial Intelligence will eventually render human beings useless is understandable in an age of rapid technological change. But when the question is examined within the history of production and development, the argument becomes less straightforward.

Humanity has repeatedly created technologies that extend our physical and intellectual capabilities. Steam power extended physical capacity. Electricity transformed production and communication. Computing extended information-processing capacity. The Internet transformed connectivity. Artificial Intelligence is extending the ability to analyze information, recognize patterns, generate content and automate increasingly complex tasks.

The history therefore suggests continuity rather than a sudden departure from the human story.

The greater concern for Africa should not be that AI exists. It should be whether Africa possesses the human capabilities, infrastructure, institutions and development strategies required to benefit from it.

We have unfinished business from previous industrial transformations. We continue to confront poverty, inadequate sanitation, food insecurity, unemployment, environmental degradation, weak productive systems and institutional limitations. These realities should not make us afraid of the Fourth Industrial Revolution. They should make us more deliberate about how we enter it.

If we have learned anything from the history of technology, it is that innovation responds to human needs and expands human productive possibilities.

The question before Africa is therefore not whether we should fear the machine. The question is whether we will acquire the knowledge and institutional capacity to use the machine. The Fourth Industrial Revolution will continue to advance, whether Africa is ready or not. The responsibility of all of us development practitioners, policymakers, researchers and citizens, is to ensure that its benefits do not remain concentrated among those who already possess the greatest technological and economic advantages.

Production remains a means, development is the broader purpose, and human beings remain the reason. The task before us is therefore not to preserve humanity from technology by resisting progress. It is to ensure that technological progress remains connected to human progress. As the biblical warning reminds us, people can perish for lack of knowledge. In the age of Artificial Intelligence, the lesson is particularly relevant. We cannot afford to fear what we have not learned to understand.

The argument presented here is not that AI is harmless, or that its risks are exaggerated. The argument is that the most consequential risks for Africa are not the ones that dominate global discourse. The risk is not that machines will become too intelligent for human beings. The risk is that the human and institutional capabilities required to govern, adapt and benefit from these technologies remain underdeveloped, and that Africa will again become a consumer rather than a producer of the technologies that shape its future. That is a development problem before it is a technological one. It requires development solutions: investment in people, in institutions, in data, in energy and in the capacity to make evidence-based decisions about where technology should and should not be deployed.

Finally, Ghana and Africa must learn. We must adapt. We must build. And, above all, we must ensure that the extraordinary intelligence we are creating remains in the service of the development of the people for whom all this production, innovation and advancement ultimately exist.

1. International Monetary Fund. (2024). Gen-AI: Artificial Intelligence and the Future of Work. IMF Staff Discussion Note SDN/2024/001. Washington, DC: International Monetary Fund.

2. World Economic Forum. (2025). The Future of Jobs Report 2025. Geneva: World Economic Forum.

3. World Economic Forum. (2016). The Fourth Industrial Revolution: What It Means, How to Respond. Geneva: World Economic Forum.

4. United Nations Development Programme. (2025). Human Development Report 2025: A Matter of Choice: People and Possibilities in the Age of AI. New York: United Nations Development Programme.

5. KoBold Metals. (2025). KoBold Metals: Using Machine Learning and Scientific Computing to Explore for Critical Minerals. KoBold Metals.

6. Gagliardone, I. (2016). The Politics of Technology in Africa: Communication, Development, and Nation-Building in Ethiopia. Cambridge: Cambridge University Press.

7. Avgerou, C. (2019). Contextual explanation: Alternative approaches and persistent challenges. MIS Quarterly, 43(3), 977–1006.

8. Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244.

9. Mohamed, S., Png, M. T., & Isaac, W. (2020). Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy & Technology, 33(4), 659–684.

Michael A. Sarfo-Kantanka
Human Development Practitioner & Monitoring and Evaluation Expert

[email protected].