Bangladesh’s AI Journey: Promise Versus Preparedness

The global economy is rapidly entering an era shaped by artificial intelligence, with profound implications for productivity, competitiveness and governance. According to estimates by leading international research institutions, AI could contribute more than 15 trillion dollars to global economic output by 2030. For developing economies, this transformation is a double-edged sword: countries that fail to adapt risk falling far behind in an increasingly technology-driven global marketplace.

For Bangladesh, the debate over AI is no longer theoretical. The central question is whether artificial intelligence will remain a fashionable buzzword, or whether it will translate into tangible improvements in productivity, public services and state capacity. The answer depends on a set of difficult but unavoidable choices related to data readiness, computing infrastructure, human capital and regulatory frameworks.

Why AI matters for Bangladesh’s economy

Bangladesh’s economic structure remains heavily reliant on labour-intensive sectors. While this has supported employment growth, overall productivity remains relatively low, and pressure on public services continues to rise. AI has the potential to play a transformative role in two critical areas: increasing productivity and enabling more efficient delivery of services with limited resources.

Empirical evidence suggests that AI applications could reduce the time and cost of government services by 20 to 30 per cent. In agriculture, yield forecasting and early disease detection could cut losses by 10 to 15 per cent. In banking and financial services, AI-driven fraud detection systems could improve efficiency by 30 to 40 per cent. However, these gains cannot be achieved simply by importing technology; AI systems must be adapted to domestic realities and local data.

Data abundance, readiness deficit

In terms of volume, Bangladesh possesses a vast reservoir of data. The country holds more than 180 million national identity records, over 170 million mobile connections and upwards of 70 million mobile financial service users. Health, education and social protection systems also generate millions of records each year. Numerically, this represents enormous potential.

Yet AI requires usable, interoperable and high-quality data—areas where significant gaps remain. Three interrelated challenges stand out. First, fragmentation: much of the public sector still operates in data silos, with limited data sharing across ministries. Various studies suggest that 60 to 70 per cent of government datasets are not interoperable. Second, data quality and updates: many datasets are not regularly updated, undermining the accuracy of AI-driven predictions. Third, weak data governance: without clear legal and institutional frameworks for data use, large-scale AI deployment carries substantial risks.

Cloud computing and infrastructure constraints

Globally, the cost of computing is one of the largest barriers to AI development. Training even a mid-sized AI model requires high-performance graphics processing units (GPUs), sustained electricity supply, advanced cooling systems and scalable cloud infrastructure. Bangladesh does have a national data centre, and several private data centres are emerging, but AI-grade GPU capacity remains limited.

As a result, large AI initiatives continue to rely heavily on foreign cloud providers. This dependence raises costs and brings questions of data sovereignty and long-term sustainability. At present, roughly 30 per cent of AI-related computing relies on domestic infrastructure, while around 70 per cent depends on overseas cloud services.

Skills, policy and regulation

Bangladesh produces between 20,000 and 25,000 IT graduates each year and hosts more than a thousand technology start-ups. However, the number of engineers with hands-on experience in applied AI remains very small. Experts estimate that the country has only one-fifth to one-quarter of the skilled AI workforce it currently needs. Advanced research capacity is still in its infancy.

At the policy level, global regulatory approaches are evolving rapidly, from risk-based AI legislation in Europe to sector-specific guidelines in the United States and national AI strategies across Asia. Bangladesh, by contrast, lacks a comprehensive AI policy. Data protection legislation awaits effective implementation, and ethical guidelines for government use of AI remain unclear. Without a coherent policy framework, AI risks becoming an unregulated technology whose costs are ultimately borne by citizens.

Where early gains are possible

Despite these challenges, targeted AI applications could deliver quick wins. In agriculture, losses could be reduced by 10 to 15 per cent. In healthcare, primary screening efficiency could improve by 25 to 30 per cent. Disaster management systems could gain longer and more accurate early-warning windows, while urban traffic congestion could be cut by 15 to 20 per cent.

Bangladesh’s AI readiness at a glance

Ultimately, the numbers tell a clear story. Bangladesh has data, but not sufficient readiness. There is enthusiasm, but infrastructure remains limited. There is manpower, but skills are incomplete. The opportunity to harness AI still exists, but the window will not remain open indefinitely. Bangladesh has begun its journey towards AI; the pressing question is whether it will proceed with strategy, evidence and realistic planning.