Mind the gap
The AI revolution is changing the economics of expertise. Whether it narrows Africa's development gap or widens it, depends on what happens next
I was having a conversation this morning with two very inspiring young entrepreneurs who were complaining about the bad customer experience some major corporates are currently inflicting on their customers by their adoption of client-facing AI.
It’s a familiar conversation: people want to talk to real people, the machine doesn’t understand people, and so on.
I found myself trotting out my standard response: AI is very bad at customer relations now, but soon it won’t be. They word is now.
The mistake we keep making is assuming that today’s AI is a good guide to tomorrow’s AI. Unlike most technologies, AI isn't improving in steady, incremental steps. Its capabilities are compounding at extraordinary speed. Judging tomorrow's customer experience by today's shortcomings is therefore a bad idea.
At the same time, we assume today’s customers are a good guide to tomorrow’s customers. They aren’t either.
Soon the dominant consumers will be people who have grown up communicating through apps rather than call centres. After them will come a generation that has never known anything but AI.
As those two trends collide, the fundamental flaw may not be the technology itself, but how enterprises are designed around it.
Which brings me to a recent World Economic Forum article, 5 ways for AI-first enterprises to rebuild around intelligence. It argues that organisations need to stop treating artificial intelligence as another software tool and start rebuilding themselves around intelligence itself.
The article asks how established enterprises should adapt to abundant intelligence and, ultimately, how a corporate balance sheet adapts to AI. That is a useful question, but it is not the most urgent one.
A better question is how an under-resourced economy can use AI to overcome longstanding developmental constraints, because AI fundamentally changes the economics of expertise.
The Lesson from Mobile Phones
Developing countries have often been told that economic progress follows a fixed sequence:
Build the infrastructure.
Develop the institutions.
Accumulate expertise.
Expand capacity.
Then compete with wealthier economies.
History suggests otherwise.
Large parts of Africa bypassed extensive fixed-line telephone networks and moved directly to mobile communications. Mobile money platforms achieved widespread adoption in environments where conventional banking infrastructure remained limited. Rather than following the same path taken by Europe or North America, many developing economies skipped entire stages of technological development.
Artificial intelligence offers a similar opportunity.
South Africa, for instance enters the AI era carrying constraints that are familiar: municipal skills shortages, weak state capacity, uneven educational outcomes and a large population of small businesses operating with limited access to professional services. These weaknesses are often discussed as barriers to growth. They may also represent areas where abundant, low-cost intelligence can have the greatest impact.
These are intelligence and capability bottlenecks, not primarily technology problems.
The Economics of Capability Have Changed
The WEF article asks organisations to imagine a world where intelligence is abundant, as described recently by Open AI CEO Sam Altman.
For most of human history, analytical work was expensive because it depended on scarce, highly trained professionals. AI does not replace them, but it allows organisations to perform parts of that work at almost no marginal cost. That fundamentally changes the economics of expertise.
A municipal official can use AI tools to analyse procurement records, compare spending patterns and flag transactions that warrant closer investigation.
A small business owner can develop an export strategy informed by international market data.
A clinic manager can use predictive demand-planning tools to anticipate supply shortages and improve inventory management.
A non-profit organisation can evaluate community survey results and draft donor proposals without engaging an external consultancy.
Why Resource-Constrained Institutions Stand to Gain Most
Much of the global conversation about AI assumes that the largest benefits will accrue to the world’s largest corporations. There are obvious reasons for this assumption: capital, data and technical resources.
But the organisations that stand to gain the most from abundant intelligence are not those optimising mature operations, but those that have historically been starved of expertise.
Consider a small municipality with chronic skills shortages. A multinational corporation might use AI to improve an already sophisticated operation by 15%. A municipality that previously lacked analytical capacity entirely could use the same technology to establish a baseline of compliance monitoring, financial oversight and service-delivery analysis.
For the corporation, the gain is incremental. For the municipality, it can be transformative.
The same applies across the economy. A large company uses AI to refine an existing corporate strategy. A township entrepreneur gains access to strategic planning, financial forecasting and market intelligence that would previously have been unaffordable.
This capability is the essence of leapfrogging: the greatest impact occurs where the starting point is lowest.
A Global Race with an African Opportunity
Investment programmes worth hundreds of billions of dollars, restrictions on advanced semiconductor exports and competition over compute infrastructure all point to one conclusion: artificial intelligence has become a matter of national capability. Around the world, governments increasingly describe AI in strategic terms because they regard it as central to future economic competitiveness and national security.
For countries whose greatest constraint is limited access to expertise rather than limited access to technology, AI represents an opportunity to expand institutional capability, strengthen public administration and accelerate economic development.
Africa is unlikely to outspend the United States or China on AI infrastructure. It does not need to build the world’s largest data centres or produce the most advanced semiconductors. But this does not mean infrastructure is irrelevant.
Leapfrogging worked for mobile phones because cell towers are cheaper and faster to deploy than copper landlines. AI is different. Running large language models at scale requires reliable electricity, stable internet connectivity and, critically, compute capacity that, for now, remains heavily concentrated in a handful of foreign cloud providers.
The continent’s opportunity lies elsewhere: not in winning a race to build the biggest supercomputer, but in becoming the world’s most sophisticated user of openly available and open-source models.
That approach creates a dependency. If African governments and businesses rely entirely on foreign AI platforms, they risk becoming consumers of foreign intellectual property rather than participants in the intelligence economy. The strategic question is not whether Africa can out-build the United States, but whether it can adapt and deploy AI in ways that preserve data sovereignty and local relevance.
That question leads to another, more uncomfortable one: even if the infrastructure were in place, would the institutions be ready?
From AI-First Enterprises to AI-First Development
For decades, policymakers, economists and development practitioners have wrestled with the challenge of overcoming shortages of skills, expertise and institutional capacity. Artificial intelligence does not solve these structural problems automatically. It does, however, alter the economics of capability in ways that would have been difficult to imagine only a few years ago.
But there is a risk in assuming that under-resourced institutions will adopt new technology quickly simply because they have the most to gain. Historically, the opposite is true. Weak institutions tend to adopt new tools slowly, not rapidly (see paper embedded below. A municipal official who is overworked, lacks digital literacy and works within a bureaucracy that actively resists change is unlikely to become an AI power-user overnight, no matter how powerful the underlying models. The bottleneck is not merely access to intelligence; it is what economists call absorptive capacity, the ability of an organisation to identify, acquire, adapt and apply new knowledge (Cohen & Levinthal, 1990).
This is where the leapfrog analogy requires caution. Mobile phones were adopted rapidly because they required minimal training and delivered immediate, tangible value at the individual level. AI, by contrast, demands new workflows, new skills and, often, new forms of trust. The organisations that stand to gain most from abundant intelligence are not necessarily those that will adopt it fastest. They are those that invest simultaneously in digital literacy, change management and institutional reform. Technology alone is never enough; culture and capability must move with it.
Africa should not view AI merely as another technology trend, nor solely as a corporate productivity tool. It should be viewed as a national leapfrog opportunity.
But the real risk is not that Africa adopts AI badly.
It is that much of the continent adopts it too slowly.
While governments elsewhere are investing hundreds of billions of dollars in compute infrastructure, AI research, education and public-sector adoption, much of Africa is still treating AI as an interesting technology rather than an economic strategy. Policy debates continue while other economies are redesigning education, public administration and industry around AI.
Leapfrogging is never automatic. It demands deliberate investment, political leadership and institutions capable of change. The real danger for Africa is that by the time it decides to act, the frontier will already have moved several years ahead.
The debate over whether AI is overhyped is dead; the rest of the world has already moved past the question. The only question that now matters is whether Africa intends to build its own capability, or spend the next decade importing everyone else's.