AI for Pakistan’s governance, growth and human development

Artificial intelligence offers Pakistan an opportunity to improve public services, strengthen economic productivity and expand human capabilities. Experiences from China, the United States and Singapore suggest that success depends less on acquiring sophisticated systems than on applying technology to clearly defined problems, with accountable institutions and citizens at the center.

Pakistan’s development indicators reveal why this distinction matters. The United Nations Development Program’s 2025 Human Development Report ranks Pakistan 168th out of 193 countries, with a Human Development Index of 0.544. Adjusting for inequality reduces the index by 33.1 percent to 0.364. Meanwhile, Pakistan’s information technology and ICT exports reached approximately $4.6 billion in fiscal year 2025–26. The contrast is striking: a growing digital economy exists alongside persistent shortfalls in health, education and living standards. Technology’s economic value should therefore be judged partly by whether its benefits reach ordinary citizens.

Artificial intelligence can help governments analyze information, identify service gaps, process applications and allocate scarce resources. It can support teachers with tailored learning materials, doctors with diagnostic assistance and farmers with weather or crop information. Businesses can use it to improve production, customer service, and market access. For a country where service delivery varies sharply between districts, the central promise is not replacing frontline staff but giving them timely information and more time for complex cases. But these possibilities raise a central question: will Pakistan use AI to reform weak institutions, or introduce new technology into systems whose underlying problems remain unchanged?

Recent work reinforces the distinction. In AI and human interaction, Dr. Kanwal Rubab argues that progress should be measured through expanded human choices and capabilities, not productivity alone. Further, she warns that poorly governed automation can threaten privacy, relationships, and social inclusion. An efficient digital service is not necessarily an equitable one if vulnerable citizens cannot access it or challenge an incorrect decision. One possible scenario is fragmented adoption, where individual departments purchase new technologies without resolving coordination problems. China’s Hangzhou City Brain offers a useful contrast. Developed with Alibaba, the system links traffic information to operational decisions; local reporting says vehicles passed through intersections in Xiaoshan district 15 percent faster following intelligent traffic-light deployment. The figure is a reported outcome, not definitive evidence of causation. The practical lesson is linking information to a specific administrative task. Pakistan could introduce similar tools for congestion, pollution or municipal services, but isolated dashboards would achieve little without data sharing and officials responsible for acting on the results.

A second scenario involves rapid deployment without adequate safeguards or investment in people. The United States shows both the opportunity and the importance of institutional oversight. Its Food and Drug Administration introduced Elsa in 2025 to assist scientific reviewers and investigators. In 2026, the agency reported combining more than 40 data sources within a related platform while retaining expert verification. AI assists professionals; it does not remove responsibility for regulatory decisions. 

For Pakistan, a technology-first approach could widen inequality. UNDP’s digital development research found that almost half of districts had low digital development and that the richest population quintile scored 15 times higher than the poorest. People without reliable connectivity, digital skills, or access to services in their language may be excluded as services become more automated. Biased data could also reproduce unequal treatment in welfare targeting, recruitment or public administration. Women and remote communities could face particularly high barriers unless services are designed for their languages, devices and everyday constraints. Efficiency without meaningful appeals, privacy protections and alternatives for offline users would weaken public trust.

A third scenario places AI inside broader governance and human development reform. Singapore illustrates the possibilities of coordinated adoption. In November 2025, its government reported that about 80 percent of 150,000 public officers had used its secure Pair Chat system, and officials had created more than 20,000 task-specific bots. Its One-Service chatbot handles over 30,000 municipal cases monthly, with government estimates of roughly 2,000 staff-hours saved and resolution times shortened by up to two working days. These are service-level outcomes, not proof of economy-wide transformation. Nevertheless, they show how practical applications, staff training and clear responsibility can reinforce one another.

Pakistan has the foundations to pursue this approach. The National AI Policy, approved in July 2025, outlines ambitions in skills, innovation and responsible applications. The Digital Nation Pakistan Act 2025 provides a wider institutional framework. In June 2026, the World Bank approved $70 million for the Connected Punjab Program, which aims to improve digital services for 28.9 million people by 2031. These are commitments and projected beneficiaries, not achievements already delivered. Their value will depend on implementation, provincial coordination, and evidence of better services. The national framework should define which functions belong to federal institutions and which provinces can adapt, avoiding duplicated platforms and incompatible technical standards.

Domestic experiments demonstrate where progress might begin. International Growth Center-supported research has explored machine learning and satellite imagery to identify unassessed properties and improve tax administration. Other initiatives address smog alerts, crop-burning detection, and urban planning. In Karachi, a research project used electricity consumption records covering 2.4 million households to help map vulnerability for social protection, with reported predictive accuracy of 70 percent. Such initiatives connect data to immediate policy problems, but pilots must be evaluated before extending results nationally.

The obstacles are substantial. Administrative records remain scattered across agencies, while technical skills, broadband access, and public-sector capacity vary sharply by region. Procurement can reward software purchases rather than improvements in service delivery. Weak safeguards create risks of personal data breaches, surveillance, inaccurate outputs, and discriminatory decisions. Meanwhile, routine tasks may change faster than training systems can help workers adapt. Introducing AI without reforming these conditions risks automating bureaucracy rather than improving governance. Otherwise, sophisticated tools can produce faster decisions without producing better ones, while citizens struggle to identify who is answerable when a system makes a mistake.

The way forward should begin with a limited number of measurable public problems: school dropouts, delayed health referrals, property-tax gaps, air pollution and slow citizen complaints. Each application needs an accountable department, reliable and lawfully accessible data, a published performance measure and an independent evaluation before expansion. When decisions affect rights or benefits, officials must remain accountable, and citizens must be able to seek human review.Human development must guide the investment strategy. Practical AI training should extend beyond software engineering to teachers, clinicians, agricultural advisers and civil servants. Universities and firms should collaborate on locally relevant products, while Urdu and regional-language tools can reduce exclusion. Broadband expansion, affordable access and accessible public-service channels are prerequisites, not optional additions. Policies should also support workers whose tasks change by providing retraining and pathways into more productive employment.

Economic policy has a complementary role. Startups need research partnerships, finance, secure data access, and credible customers to build useful products. Government procurement should reward independently demonstrated outcomes, not impressive presentations. Partnerships with international firms can support technology transfer, helping domestic institutions learn and compete. Pakistan should measure success by improved learning, health access, administrative response times, productivity, employment, and public confidence, especially among excluded communities, rather than by counting chatbots or announcing investment targets.Ultimately, China’s operational experiments, America’s supervised professional applications, and Singapore’s integrated public services point toward a common principle: institutions determine whether technology delivers public value.

Pakistan does not need to reproduce their spending or infrastructure to learn from their approaches. It needs to choose problems carefully, protect citizens and measure results. The true test of AI will not be how intelligent government systems become, but whether people experience better services, wider opportunities and greater dignity.