Artificial intelligence is entering our lives at remarkable speed. Governments are developing strategies, businesses are investing heavily and international organisations are exploring how AI can make work faster and more efficient.
Much of the discussion focuses on what AI can do: automate tasks, process information, produce reports, and deliver services more quickly. All of this has value, but perhaps we are starting the conversation from the wrong end. The more important question is not how AI can help us act faster but how it can help us understand better. This distinction matters greatly for development, particularly in a country like Pakistan, which has struggled for decades to manage population growth and its consequences.
The backbone of a successful development agenda is the understanding of population dynamics and people’s behaviours and choices. We collect data, analyse it and use it to design policies and programmes. Yet societies are not static; they change continuously with multiple transitions often taking place simultaneously. Pakistan illustrates this well; it has a very young population, rapidly growing cities, changing fertility patterns and significant migration patterns. On the one hand, climate change is affecting livelihoods and mobility; on the other, young people’s expectations about education, employment, marriage and family formation are changing.
These trends influence one another. Employment, for instance, affects decisions about marriage and family; education shapes aspirations; climate pressures influence migration. Consequently, all these change the needs of cities and communities. A young population creates opportunity, but also new demands on education, health, employment and institutions. We have data on many of these issues, but we still lack the ability to see the connections between them clearly enough and early enough.
The greater value of AI may lie in helping us bring together different sources of information, identify patterns and detect emerging signals that might otherwise remain hidden. Instead of asking only where a problem exists, we could ask what is driving it. Instead of looking only at what happened, we could better understand what is changing. And instead of waiting for a problem to become a crisis, we could identify pressures earlier and anticipate where they may lead.
Equally important is understanding why development interventions do not always lead to their intended results. A fertility rate is often treated as a demographic statistic, but behind that number are millions of individual and family decisions. Why do people marry early or late, and why do some couples choose to have fewer or more children? How do employment, housing, education, access to health services and perceptions about the future influence these choices? If we look only at the number, we see the outcome. If we understand what lies behind the number, we begin to understand society.
The same applies to Pakistan’s demographic dividend. A large young population is an enormous potential asset, but a demographic dividend does not happen simply because there are many young people. It depends on whether young people can turn their potential into opportunity. Do they have quality education? Can they find decent work? Can young women participate fully in economic and social life? Are institutions prepared for their aspirations? The size of the young population signals potential. Understanding young people’s lives, perceptions and intentions tell us whether that potential can become a dividend. That is why we should be cautious about measuring AI’s success primarily by speed.
Development is not an assembly line; the objective is not simply to produce more activities. It is to make better decisions that improve people’s lives. An algorithm can identify a pattern, but it cannot tell us whether a response is fair. It can identify a correlation, but it cannot understand dignity. It can predict demand for a health service, but it cannot decide what a community needs or what response is ethically appropriate. AI systems are also often developed around commercial objectives and measures of user engagement or satisfaction; these should not be confused with the public-interest principles that must guide development decisions.
Therefore, those remain human responsibilities. For UNFPA, this distinction is particularly important; our work is fundamentally about people, population dynamics and the choices people are able, or unable, to make about their lives and reproduction. The next generation of demographic intelligence should not simply tell us how populations are changing. It should help us understand why they are changing, where change is occurring, who is affected and what it may mean for the future.
AI is data-driven, and the quality of its insights ultimately depends on the quality, representativeness and governance of the data behind it. Poor data can produce poor conclusions faster. Biased data can reproduce existing inequalities. People who are poorly represented in official statistics can become even more invisible when algorithms depend on the same weaknesses. Nor does the ability to analyse more information mean that we should collect everything.
Privacy, consent, accountability and fairness must remain central. This makes investment in strong national data systems and data governance as important as investment in AI itself. Interoperable data systems, common standards, clear institutional responsibilities, appropriate safeguards and mechanisms for secure data sharing are essential if AI is to support credible and responsible public decision-making. AI cannot substitute for a functioning data ecosystem; it can only amplify what that ecosystem makes possible. For Pakistan, therefore, the AI conversation should be bigger than automation. The question should be whether AI can help the country understand its demographic and social future before it arrives.
Can we identify emerging pressures on health and education earlier? Can we better understand the relationship between climate change and migration? Can we see how employment, education and changing aspirations are influencing marriage and family formation? Can we identify where young people’s potential is being lost before it becomes a national problem? AI will not answer these questions for us. But it may help us ask better questions and see the answers more clearly.
We should certainly use AI to make our work faster. But we should not allow speed to become the measure of progress. The real opportunity is to use AI to understand better, anticipate better and ultimately decide better. The countries that benefit most from artificial intelligence may not be those that automate the fastest but those that learn and adapt the fastest. And learning begins not with doing more but with understanding better.
The writer is the UNFPA representative in Pakistan.
Disclaimer: The viewpoints expressed in this piece are the writer’s own and don’t necessarily reflect Geo.tv’s editorial policy.
Originally published in The News
2026-09-20 08:57:00
