Turning hours into gold: How generative AI can unlock Uganda’s productivity potential

 

By Richard Sebaggala

The world is currently experiencing a quiet revolution in the way work is done. Across industries and continents, generative AI tools like ChatGPT, Claude and Copilot are changing the way people do everyday tasks. A recent global survey presented by Visual Capitalist found that workers using AI can reduce the time it takes to complete their tasks by more than 60 percent. Writing a report, for example, no longer took 80 minutes, but only 25, while fixing technical problems, which normally took almost two hours, was reduced to 28 minutes. Even solving mathematical problems was reduced from 108 minutes to just 29 minutes. These are not just marginal improvements, they represent a complete change in what a single employee can accomplish in a day.

 

The survey also found that tasks that require deeper thinking and human judgment, such as critical thinking, time management or instructing others, saw dramatic increases in productivity. Time spent on critical thinking dropped from 102 to 27 minutes. The time required to instruct and manage employees was also reduced by almost 70 percent. This shows that AI is not only useful for programming or technical analysis, but also for teaching, planning, decision-making and communicating. When people are equipped with the right tools, they are able to produce much more in much less time.

 

While these gains are impressive in advanced economies, their potential is even greater in countries like Uganda. For decades, low productivity has held back development in many African countries. In sectors such as agriculture, education, small businesses and government, workers still spend large parts of their day doing slow, manual and repetitive tasks. Productivity levels remain far below the global average, and this gap continues to fuel inequality between the global North and South.

Uganda has recognized this challenge and is responding with a bold new vision. With its 10-fold  development strategy, the country aims to increase its GDP from 50 billion to 500 billion dollars in just 15 years. The plan focuses on unlocking value in key sectors such as agriculture, tourism, minerals and oil and gas. However, for this vision to succeed, it is not enough to invest in industries alone. Uganda also needs to improve the way people work, and this is where AI can be a game changer.

 

Many people still think that AI is something reserved for big companies or tech firms. However, the most immediate impact in Africa could come from small, everyday businesses. Just recently, I had an experience in the city of Entebbe that brought this home to me. I wanted to take a photo in a small secretariat that offers passport photos, typing and printing services. While I was waiting, I observed a young man helping a woman who had come in with a handwritten pieces of paper. She was applying for a job as a teacher in a kindergarten and needed a typed CV and cover letter. The man patiently asked her questions, read through her notes, typed slowly, rephrased what she had said and tried to create a professional document.

 

As I watched, I was struck by how much time they were spending on something that generative AI could do in seconds. All the man had to do was take a photo of the handwritten pages or scanned them, upload it to ChatGPT and ask it to create a customized resume and cover letter. He could even include the name of the school to make the cover letter more specific. In less than five minutes, she would have gone home with polished, professional documents, and the man could have moved on to the next client. Instead, this one task took almost an hour.

 

This small example represents a much larger reality. Across Uganda, there are hundreds of thousands of people running small businesses like this secretarial bureau. They type, translate, write letters, prepare reports and plan budgets, often by hand or on outdated computers. Most of them don't realize that there is a faster, smarter way to do the same work. AI tools, especially chatbots and mobile-based platforms, can multiply their output without the need to hire more staff or buy expensive software. Time saved is money earned. In many cases, this means better service, more customers and more dignity at work. Personally, before I start a task, I now ask myself how much faster I could do it with AI

 

In schools, AI can help teachers create lesson plans, grade assignments and design learning materials with just a few clicks. In government agencies, it can optimize reporting, organize data and improve decision-making. In agriculture, farmers can use mobile AI tools to diagnose plant diseases, find out about the weather or call up market prices in their local language. For young entrepreneurs, AI can help write business proposals, design logos, manage inventory and automate customer messaging.

 

Uganda has one of the youngest populations in the world. Our youth are curious about technology, innovative and willing to work. What many of them lack is not ambition, but access to tools that match their energy. Generative AI could completely change Uganda's productivity curve if it is widely adopted and made accessible through training and mobile-friendly platforms. This does not require billions of dollars or complex infrastructure. In many cases, awareness and basic digital skills are enough.

 

To seize this opportunity, Uganda needs to act thoughtfully. Schools and universities should teach students how to use AI tools as part of their education. Government employees should be trained to use AI in their daily tasks. Innovators should be supported to develop localized AI solutions that work in Ugandan languages and sectors. And, perhaps most importantly, the average citizen, like the secretarial worker, needs to see that AI is not a distant or abstract technology. It is a tool they can use today to work faster, serve better, and earn more.

 

If Uganda is serious about achieving its 10-fold growth strategy, improving the way people work must be at the center of the journey. AI will not replace human labor; it will augment it. In a country where every minute counts, the difference between three hours and thirty minutes could be the difference between survival and success.

 The Vanishing Ladder: Rethinking Academic Training in the Age of AI

By Richard Sebaggala

 

An article I recently came across contains a quiet but significant observation: AI is not only replacing jobs, it is also replacing learning opportunities. The types of tasks that helped young people gain experience (entry-level administrative work, basic data tasks, support functions) are being quietly handed over to machines. And while this trend is already causing alarm in the developed countries, I couldn’t help but think how much more disruptive it could be in countries like Uganda, where graduate unemployment is already painfully high and where academic training often lacks the necessary practical immersion.

In Uganda, almost every university has relied on internships as a bridge between theory and practise. It is the only moment in a student's academic career when they can actually enter a workplace and apply what they have learnt in class. Of course, these internships are not enough (the number of opportunities is often much less than the number of students who need them), but at least they exist. In my experience supervising interns across various organizations, I've observed that these placements largely served a dual purpose: interns handled repetitive administrative tasks while also quietly observing and learning the ropes.

Imagine what will happen when the tasks that are critical to learning such as filing reports, organising data, writing summaries and composing simple letters are done faster, cheaper and more consistently by AI tools. This is not a distant scenario. It is already happening in global companies and is slowly finding its way into companies and NGOs in Uganda. If we are not careful, AI will not only destroy jobs, it will also displace the basic tasks where students can observe, try, fail, ask questions and grow. If these tasks disappear, what will happen to internships as we know them?

To me, this is the more pressing threat: not mass unemployment overnight, but the silent erosion of educational opportunities. The risk isn't that students don't find a job after graduation, but that they never get a fair chance to prepare for the labour market at all. We often talk about AI taking over "low-skilled" jobs, but these tasks, as mundane as they may seem, are where many professionals actually start.

What worries me most is how little our academic systems have adapted. Universities are still producing graduates who are trained for jobs that may no longer exist. Curricula still focus heavily on theory and make little effort to incorporate AI skills, digital skills or simulations of real-world work environments. Meanwhile, students are entering a labour market that is rapidly changing under their feet without the support of institutions to help them find a balance.

At this point, universities and faculties must now ask themselves a new question: What will your graduate look like in the age of AI? A law school should ask how a future lawyer will work with or alongside AI? An economics department should ask what tools an economist will need to stay relevant when forecasting and modelling are now supported by AI. Business departments, medical schools, engineering programmes, even social science departments - all need to think of their graduates not as a competitor to AI, but as someone who is able to lead in a world shaped by it. Once we start asking these kinds of questions, we stop seeing AI as a threat and start seeing it as a force to be harnessed. But more importantly, we start to orientate education towards a future that is already upon us.

If faculties don't start this rethink now, we risk something much more subtle but deeply damaging. We could end up with a generation that is well-educated on paper but excluded in practise: graduates who are equipped with knowledge but lack the ability to apply it. They will enter a world that demands real-world experience and digital adaptability, yet the very systems that trained them have offered neither. This is not an alarm bell for a distant future, but a call for urgent reflection. The cost of ignoring these changes now will be far greater when the full consequences unfold.

We need to completely rethink the purpose of academic education. It is no longer enough to prepare students for exams. We need to prepare them to collaborate with AI, to learn quickly, to solve problems in environments where humans and machines will work side by side. This means that we need to revise the way we teach, but also that we need to create new types of internships and apprenticeships where students are exposed to AI-powered workflows rather than being displaced by them.

If traditional internships are shrinking, then universities should start using AI to simulate work tasks. If there are fewer and fewer entry-level positions, companies should be encouraged to keep some open, not for the sake of efficiency, but for the future of human learning. We may not be able to stop automation, but we can at least make sure it doesn’t flatten the stepping stones that young people need to grow into the skilled workers we will one day depend on.

AI will continue to change the landscape. That is a fact. But how we prepare our students to traverse this changing terrain is still a choice. The challenge now is not to resist AI, but to make sure it doesn’t steal our opportunity to learn. Because no matter how powerful machines become, it is still people who ensure that institutions function, societies develop and ideas grow. And they all have to start somewhere.

The future of academic education is not about choosing between people and machines. It’s about creating space for people to grow, even if machines do more. If we get this balance right, we’ll be fine. But if not, we’ll look back and realise that we built a smart future but forgot to educate the people who will inherit it.

 The productivity dividend: No one should waste time on tasks that AI can do better

By Richard Sebaggala

 

A quiet revolution is underway in the UK civil service. More than 400,000 civil servants are being trained to integrate artificial intelligence into their daily work. This training is not just about improving performance, but also about rethinking what work should look like in the first place. The policy mantra underpinning this change is as direct as it is radical: "No person's substantive time should be spent on a task where digital or AI can do it better, quicker, and to the same high quality and standard digitally or through artificial intelligence."

 

It's not about replacing people. It's about distributing work more intelligently. It's about realising that not all hard work is productive and that not all productivity has to come at the expense of people. What we're seeing is the emergence of a productivity dividend; a return on investment that comes from considering how time is used and who or what is best suited to the task at hand.

 

For those of us who have called on students, researchers and institutions in Africa to engage early with AI, this shift is both encouraging and instructive. I have often argued that the real question is not whether AI can help, but why we are still doing things manually when it already can.

 

There is more at stake here than just efficiency. This moment forces us to reckon with something deeper. As Kai-Fu Lee has pointed out in AI Superpowers (2018), previous technologies such as electricity, steam engines or the internet have enhanced human capabilities. AI is different. It competes with our cognitive abilities. It doesn't just automate, it interferes with human thinking, decision making and problem solving. This makes it a unique disruptor, but also a unique transformer.

 

The evidence is already there. In the UK, AI tools such as "Humphrey" have been used to process public consultations faster than human analysts, with equally reliable results. The government estimates that it could save 75,000 working days per year on 500 annual consultations, which equates to around £20 million in labour costs. This is not simply a budget cut. It's a reallocation of time and talent in favour of tasks that require real human insight.

 

The pace of change continues to accelerate. OpenAI co-founder Ilya Sutskever put it bluntly at a recent University of Toronto event: "Anything I can learn, anything any of you can learn, AI could do." His message was not speculative, it was a warning. The fact that AI can't do everything yet doesn't mean that it won't do it. It just means that it can't do it yet. The real danger is standing still while AI evolves past you.

 

So, why are we still teaching students to format references by hand when tools can do it in seconds? Why are policy staff reading thousands of pages of public feedback when AI can collate and summarize it more efficiently? Why do researchers spend days cleaning data when automation can do it in minutes? For those familiar with econometric analysis, refusing automation in this way is akin to insisting we should derive beta coefficients in a regression using those abstract textbook formulas we learned in our second-year econometrics class. It's like saying we should ignore powerful software like Stata or SPSS, where a simple command produces results and your real job is to interpret them and check their validity.

 

 

In Africa, where resources are limited but creativity and adaptability are abundant, we have a rare opportunity to leapfrog. We aren't burdened by the outdated and often inefficient "legacy systems" that many developed nations are trying to transition away from. Instead of having to dismantle old, established ways of working, we can design our education, government, and economic systems with AI at the center from the start. This approach allows us to see AI not as a threat to be managed, but as a powerful tool to be mastered.

 

However, this means that we can no longer hesitate. It means that students must not only be afraid of AI plagiarism, but that they must work responsibly with AI. It means pushing government departments and universities to use AI to reduce administrative backlogs so that their staff can focus on delivering meaningful services. And it means cultivating a culture where the question "Why am I still doing this manually?" becomes second nature.

 

This is not a call for blind automation. It's a call for strategic delegation. If a machine can do a task better and just as reliably, leaving it in human hands is not care, it's inefficiency.

 

The real dividend of AI is not in what it replaces. It lies in what it unlocks: our ability to think deeper, act faster, and serve better. However, this dividend will only be paid to those who choose to collect it.

The future will not wait. And neither should we.

We need to start asking ourselves, our teams, and our institutions a difficult but necessary question: Why are we still doing this manually? And if the answer is: "Because that's how it's always been done," then perhaps it's time we let AI show us a better way.