AI Without Policy: How Bangladesh’s Delayed AI Governance Could Impact Economic Growth, Startup Ecosystem and Technological Sovereignty

Atlas AI Institute Policy Analysis Report

August 2026

Executive Summary

Artificial intelligence has moved beyond being a technology issue. It now functions as economic infrastructure, shaping national competitiveness, investment flows, and innovation capacity (OECD, 2024, OECD.AI Policy Observatory). The United States, the European Union, China, and India established AI strategies early, and they are building competitive advantages by combining governance, infrastructure investment, skills development, and regulatory certainty within a coordinated policy architecture (Government of India, 2024, PIB; European Parliament and Council, 2024, Official Journal of the EU).

Bangladesh has an active AI policy pipeline rather than an absence of policy. A draft National AI Policy 2026 to 2030 is under public review, building on the earlier National Strategy for Artificial Intelligence (2019 to 2024), and the Personal Data Protection Ordinance 2025 and Cyber Security Ordinance 2025 have already been adopted (Government of Bangladesh, 2026, Ministry of Posts, Telecommunications and Information Technology; UNESCO, 2024, Global AI Ethics and Governance Observatory). The principal challenge is the gap between policy formulation and implementation. Enacted legal status, institutional capacity, enforcement mechanisms, and funding alignment remain underdeveloped relative to the ambition set out in these drafts.

If this implementation gap persists, Bangladesh risks positioning itself as an AI consumer rather than an AI producer by 2030, dependent on foreign models, infrastructure, and standards, and exposed to the global productivity transformation without a proportionate share of its gains (World Bank, 2024, Digital Development Blog; IMF, 2024, Working Paper 2024/065). This report presents a comparative readiness assessment against leading AI economies, examines the economic stakes sector by sector, explains how AI investors price in governance uncertainty, situates Bangladesh’s experience within the broader pattern observed across the Global South, and proposes a five-pillar framework, together with a phased policy roadmap.

The central argument of this report is that AI policy functions as infrastructure for innovation rather than a constraint on it. Well-designed governance allows innovation to scale safely, attract investment, and generate durable economic value. Bangladesh has already begun adopting AI across banking, agriculture, and manufacturing. The open question is whether the country will shape the terms of its participation in the AI economy or continue to depend on technologies, standards, and capital developed elsewhere.

Key Findings

Bangladesh maintains an active AI policy pipeline, including a draft National AI Policy 2026 to 2030, an earlier national AI strategy, and new data and cybersecurity ordinances, but none of these instruments carries the enacted legal status, institutional authority, or budget allocation seen in comparator economies (Government of Bangladesh, 2026; UNESCO, 2024).
Across eight readiness dimensions covering strategy, governance, data, compute, funding, talent, procurement, and safety evaluation, Bangladesh trails both AI-producing economies and regional peers such as India. The widest gaps appear in compute infrastructure and dedicated AI investment vehicles (Government of India, 2024, PIB; OECD, 2024).
Bangladesh’s 2025 startup funding data indicates that investors are already pricing in regulatory uncertainty. Total funding reached USD 124 million across only 12 deals, with 95 percent of capital concentrated in three transactions and 99 percent sourced from global rather than domestic investors (Startup Bangladesh, 2026, Startup Investment Report, Year in Review 2025).
Sector-level analysis across banking, healthcare, agriculture, manufacturing, education, and public services shows consistent opportunity alongside recurring governance gaps in liability, data access, and procurement standards (Bangladesh Bank, 2025; UNESCO, 2024).
Global South economies face a common pattern of AI dependency across infrastructure, data, and talent. Policy delay tends to compound this pattern rather than simply postpone it (World Bank, 2024).
A five-pillar national framework, paired with a phased implementation roadmap, offers a realistic path for closing the readiness gap without disrupting the startup activity already under way.

1. The Global AI Race: Policy Before Technology

The landscape of global AI governance has shifted substantially since 2017, when only a small number of countries maintained national AI strategies. By 2024, the OECD.AI Policy Observatory tracked more than 1,000 AI policy initiatives across roughly 70 jurisdictions, and more than 40 countries operated comprehensive national AI strategies (OECD, 2024, OECD.AI Policy Observatory). Public-sector AI readiness assessments published in 2026 project that more than 80 jurisdictions will maintain formal national AI strategies by the end of the year (Alice Labs, 2026, Public Sector AI Readiness Index).

This expansion reflects a shared understanding among the leading AI economies. Competitiveness depends less on model capability alone and more on integrating policy, infrastructure, investment, skills, and governance into a coherent ecosystem.

The United States relies on federally coordinated risk management layered onto a private-sector-led innovation base. Executive Order 14110, issued in October 2023, directed more than 50 federal entities toward over 100 actions across eight policy domains, including safety, security, privacy, equity, and innovation. It requires developers of the most capable AI systems to share safety test results with government and establishes an AI Safety and Security Board within the Department of Homeland Security (Government of the United States, 2023, Executive Order 14110; White House, 2023, Fact Sheet).

The European Union has adopted the first binding, horizontal AI regulation of its kind. Regulation (EU) 2024/1689, the AI Act, has been in force since August 2024 (European Parliament and Council, 2024, Official Journal of the EU). It classifies AI systems by risk tier, ranging from unacceptable to high, limited, and minimal risk, and imposes the strictest obligations on high-risk applications in areas such as critical infrastructure, employment, and law enforcement. Regulatory sandboxes allow controlled real-world testing (European Parliament, 2021, Think Tank Briefing), and penalties scale with company turnover, with proportional treatment for small and medium enterprises.

China’s approach centers on state-coordinated industrial policy. Centralized research funding, state-backed compute infrastructure, and coordinated deployment across priority sectors are oriented toward building indigenous capability and reducing dependence on foreign technology.

India’s IndiaAI Mission, approved in March 2024 with a budget of 10,371.92 crore rupees (Government of India, 2024, PIB), is the most complete national AI initiative in the Global South. It integrates seven components: compute capacity, an innovation centre for indigenous models, a datasets platform, sector-specific application development, a talent pipeline known as FutureSkills, dedicated startup financing, and a Safe and Trusted AI governance track (Government of India, 2024, PSA India). By bundling compute access, data, capital, and regulatory clarity into a single mission, India addresses directly the inputs that determine whether a domestic AI startup ecosystem can scale rather than merely exist.

No single instrument among these four models, whether an executive order, a risk-tiered statute, or a funding mission, is directly transferable to Bangladesh. The comparative lesson is that all four leading economies pair governance with deliberate infrastructure and capital allocation. Strategy documents alone, without institutional authority and budget, tend not to produce this effect.

2. Bangladesh’s AI Governance Landscape: From Strategy to Implementation

Bangladesh’s AI policy trajectory should be read carefully. This is not a governance vacuum but a policy-implementation gap, a pattern observed across many developing economies where strategic vision outpaces enacted authority and funded execution.

2.1 What exists

Bangladesh has produced a draft National AI Policy 2026 to 2030 (version 2.0), currently under public review. The draft proposes a National Data Governance Authority as a coordinating body, an Independent AI Ethics Board, and open-standards requirements for public-sector AI systems (Government of Bangladesh, 2026, AI Policy for Bangladesh). It builds on an earlier National Strategy for Artificial Intelligence (2019 to 2024), which identified agriculture, education, health, and four other sectors as AI priority areas (Government of Bangladesh, 2020, National Strategy for Artificial Intelligence).

Two general-purpose instruments are already in force: the Personal Data Protection Ordinance 2025 and the Cyber Security Ordinance 2025 (UNESCO, 2024, Global AI Ethics and Governance Observatory). At the sector level, Bangladesh Bank is preparing a dedicated AI policy for the banking sector, targeted for December 2025, including a decision to build an in-house large language model to limit cross-border data transfer risk (The Business Standard, 2025, Bangladesh Bank AI Policy Report).

2.2 Where the gap sits

UNESCO’s Global AI Ethics and Governance Observatory assesses that Bangladesh’s draft policy needs a stronger focus on ethics and human rights, accessibility, transparency, and accountability of AI systems, along with strategic investment and incentives for AI research and development. The Observatory recommends AI-specific procurement standards with independent vendor audits (UNESCO, 2024). Academic assessment of Bangladesh’s AI governance environment similarly identifies unresolved liability allocation as a weakness (Journal of BAUET, 2025, AI and Legal Reform in Bangladesh).

Four gaps recur across these independent assessments. First, the National AI Policy remains a draft without binding legal force (Government of Bangladesh, 2026). Second, no established mechanism allocates responsibility when an AI system causes harm (Journal of BAUET, 2025). Third, general data protection exists, but sector- and AI-specific data-sharing rules remain limited (UNESCO, 2024). Fourth, Bangladesh has no AI-specific capital allocation comparable in scale or design to India’s Startup Financing component (Government of India, 2024; Startup Bangladesh, 2026).

These gaps reflect limited execution capacity rather than a lack of policy intent, and they are the kind of gap that a coordinated implementation roadmap is designed to close, rather than a new strategy document.

3. Bangladesh AI Economy Readiness Gap

The framework below benchmarks Bangladesh against practices observed in leading AI economies, namely the United States, the European Union, China, and India, across eight readiness dimensions. These dimensions determine whether a national AI ecosystem can attract capital, generate domestic value, and scale startups.

Readiness Dimension	Leading AI Economies (Typical Practice)	Bangladesh: Current Position
AI Strategy	Enacted national strategy with funded roadmap and periodic review (OECD, 2024)	Draft National AI Policy 2026 to 2030 under public review; not yet enacted (Government of Bangladesh, 2026)
AI Governance	Statutory authority with enforcement powers, for example the EU AI Office or the U.S. AI Safety and Security Board	Proposed National Data Governance Authority and Ethics Board exist only in draft form (Government of Bangladesh, 2026)
Data Governance	AI-specific data-sharing, access, and portability rules layered on general data protection law	General Personal Data Protection Ordinance 2025 in force; AI-specific data rules remain limited (UNESCO, 2024)
AI Compute Infrastructure	Dedicated national compute capacity, for example IndiaAI Compute Capacity, reducing reliance on foreign cloud	No domestic AI compute infrastructure program identified
Startup Funding	Dedicated AI-specific risk capital, for example IndiaAI Startup Financing (Government of India, 2024)	No AI-specific fund; 2025 startup capital of USD 124 million across 12 deals, 99 percent foreign-sourced (Startup Bangladesh, 2026)
AI Talent Development	National AI curricula, research labs, and reskilling programs, for example IndiaAI FutureSkills	AI education referenced in national strategy drafts; no funded national program identified (Government of Bangladesh, 2020)
Public Sector AI Procurement	Standardized procurement rules, vendor audit requirements, interoperability mandates	Recommended but not yet codified; UNESCO identifies this as a priority gap (UNESCO, 2024)
AI Safety and Evaluation	Mandatory testing and certification for high-risk systems, per EU AI Act risk tiers and the NIST AI Risk Management Framework	No mandatory testing or certification regime identified in current drafts (Government of Bangladesh, 2026)

Two dimensions show the widest gap relative to comparator economies: AI compute infrastructure and dedicated startup funding. Both are directly linked to whether Bangladeshi AI startups can scale domestically rather than default to foreign infrastructure and capital.

4. Startup Ecosystem Impact Without AI Policy

Bangladesh can and does produce AI-adjacent startups without a fully enacted governance framework, though with limits on scale, sustainability, and competitiveness.

4.1 Funding channels investors evaluate

Investors underwriting AI ventures weigh regulatory clarity, data accessibility, liability exposure, and market certainty alongside product and team quality (Startup Bangladesh, 2026). India’s IndiaAI Mission illustrates how bundling compute access, dedicated financing, and support for indigenous models reduces the barriers investors would otherwise price in as risk (Government of India, 2024). Bangladesh’s 2025 data shows a corresponding pattern of caution. Total startup funding reached USD 124 million across just 12 deals, with 95 percent of capital concentrated in the top three transactions and 92 percent in late-stage or merger and acquisition deals rather than early-stage formation. Domestic investor participation stayed below USD 1 million across three deals, while global capital supplied 99 percent of the total (Startup Bangladesh, 2026, Startup Investment Report, Year in Review 2025). Capital that is concentrated, late-stage, and foreign-sourced is consistent with a market discounting early-stage AI ventures for regulatory uncertainty, rather than one that lacks investable ideas.

4.2 The data economy problem

AI startups require lawful access to training data, clear privacy protection, and defined public-private data-sharing rules (UNESCO, 2024). In their absence, three risks tend to compound. Foreign cloud and AI platform vendors become the default technology base, creating vendor lock-in. Without support comparable to India’s indigenous-model program, Bangladeshi startups build primarily on foreign APIs, which raises operating costs and limits differentiation. The combined effect is a risk that Bangladesh imports AI services rather than exporting them, with value capture accruing to foreign firms rather than domestic entrepreneurs (World Bank, 2024).

World Bank analysis of AI’s uneven global impact notes that productivity gains are concentrating among wealthy nations and a small set of dominant technology firms, a dynamic that risks widening the income gap as leading countries capture most of the benefits, leaving developing nations behind (World Bank, 2024, Digital Development Blog). Absent deliberate policy intervention, Bangladesh’s current trajectory is consistent with this pattern rather than an exception to it.

5. Why AI Investors Need Governance Certainty

Institutional and venture investors evaluating AI opportunities in an emerging market apply a risk lens distinct from a standard product-market assessment. Six factors tend to dominate that lens. Regulatory clarity concerns whether the business model remains lawful under anticipated future regulation, not only current rules. Data access rules concern whether training and operational data can be lawfully sourced, shared, and retained. The liability framework determines who bears responsibility, and under what standard, when an AI system causes harm or error. Market opportunity reflects the addressable market once compliance costs and restrictions are priced in. Government procurement potential concerns whether the public sector is a credible, rules-based customer for AI vendors, which affects revenue predictability. AI infrastructure availability concerns the cost and reliability of compute and data infrastructure available to a startup without dependence on a single foreign vendor.

Policy uncertainty raises the effective cost of capital for AI ventures in two ways. It widens the range of plausible future regulatory outcomes an investor must underwrite, and it shifts diligence and legal costs onto the startup rather than a predictable compliance baseline. Bangladesh’s 2025 funding pattern, concentrated, late-stage, and foreign-sourced, is directionally consistent with this dynamic. Investors appear willing to fund proven, later-stage ventures but remain cautious on early-stage AI bets where regulatory outcomes are least predictable (Startup Bangladesh, 2026). By comparison, jurisdictions that reduce this uncertainty through risk-tiered regulation in the European Union, coordinated missions in India, or federally backed safety standards in the United States tend to lower the effective risk premium investors attach to AI ventures operating there (European Parliament and Council, 2024; Government of India, 2024).

6. Sectoral Economic Impact Analysis

IMF analysis projects that AI could raise total factor productivity across South Asia, though it cautions that realized gains may be modest, on the order of 0.55 percent over ten years, without complementary investment in skills, infrastructure, and governance (IMF, 2024, Working Paper 2024/065). The sector patterns below follow a consistent structure: current situation, AI opportunity, governance challenge, and policy recommendation.

6.1 Banking and Finance

Bangladesh Bank is preparing a dedicated AI policy for the banking sector, targeted for December 2025. This is the first sector-specific initiative of its kind by a Bangladeshi government institution and includes a decision to build an in-house large language model to limit cross-border data transfer risk (The Business Standard, 2025).

The opportunity spans fraud detection, alternative credit scoring, automated customer service and dispute resolution, and AI-assisted macroeconomic and reserve forecasting (Bangladesh Bank, 2023, Digital Bank Guidelines; The Business Standard, 2025). The governance challenge centers on algorithmic bias in credit scoring, the risk of financial discrimination in automated decisions, and data misuse in the absence of clear governance, risks that Bangladesh Bank’s own draft policy acknowledges (The Business Standard, 2025). The recommended path is to finalize and enact the sector policy with mandatory algorithmic impact assessments and independent audit requirements for credit-scoring models, aligned with the risk-tiered approach used in the EU AI Act (European Parliament and Council, 2024).

6.2 Healthcare

Adoption of AI diagnostic and clinical decision-support tools is at an early stage, and no healthcare-specific AI governance framework is currently in force.

The opportunity includes medical imaging analysis, clinical decision support, and predictive analytics on patient records. The governance challenge involves diagnostic error risk, unresolved liability allocation among clinicians, institutions, and AI vendors, and the sensitivity of health data under weak sector-specific privacy rules, concerns consistent with the academic literature on AI-based medical diagnostics (Journal of Medical and Molecular Research, 2026). The recommended path is mandatory product-liability coverage for high-risk diagnostic AI, clarified accountability rules across the clinician-institution-vendor chain, and continuous post-deployment monitoring for performance drift and bias (Journal of Medical and Molecular Research, 2026).

6.3 Agriculture

Agriculture is named a priority sector in the 2019 to 2024 national AI strategy, though implementation guidance remains limited (Government of Bangladesh, 2020).

The opportunity includes climate and weather prediction, precision irrigation and pest detection, and yield and price forecasting. The governance challenge is the absence of agricultural data governance covering farmer privacy, data ownership, and commercial data-sharing terms. The recommended path is a dedicated agricultural data governance framework developed jointly by government research institutions, universities, and private agri-tech firms, closing the gap between strategic priority status and operational rules.

6.4 Manufacturing and RMG

Bangladesh’s ready-made garment sector and broader manufacturing base face increasing pressure to adopt Industry 4.0 technologies in order to defend export competitiveness.

Research on Bangladeshi manufacturing finds that Industry 4.0 adoption, including AI, the Internet of Things, and analytics, has a positive effect on supply chain performance through integrated digital supply chains and improved visibility (Industry 4.0 and Supply Chain Performance in Bangladesh, 2025). The governance challenge is that adoption remains uneven and capital-constrained without coordinated policy support, which risks widening the competitiveness gap against regional peers such as Vietnam and India that pair Industry 4.0 adoption with national industrial policy. The recommended path is to integrate Industry 4.0 support policies, including financing incentives and technical standards, into the broader AI governance framework rather than treating them as a separate industrial-policy track.

6.5 Education

The national AI strategy names education as a priority sector, envisioning personalized learning, intelligent tutoring, and predictive student-intervention tools. Recent research finds that Bangladesh still lacks a policy specifically addressing AI in education (EdTech Hub, 2025, AI in Education Across Bangladesh).

The opportunity includes adaptive learning tools, AI tutoring systems, and automated assessment and feedback. The governance challenge is a persistent gap between strategic vision and implementation: granular operational guidance, curriculum integration, and funding mechanisms remain underdeveloped (EdTech Hub, 2025). The recommended path is a dedicated national AI-in-education strategy with budgeted curriculum integration, teacher capacity-building, and research-lab funding, coordinated with but distinct from the national AI policy.

6.6 Public Services

The draft National AI Policy proposes open-standards and interoperability requirements for public-sector AI systems, though procurement practice has not yet caught up (Government of Bangladesh, 2026).

The opportunity includes AI-assisted public service delivery, fraud and leakage detection in public programs, and improved administrative efficiency. The governance challenge is the absence of a codified public procurement standard for AI systems, which creates vendor lock-in risk and limits transparency in how public-sector AI decisions are made, a gap UNESCO’s Observatory specifically identifies (UNESCO, 2024). The recommended path is to establish mandatory independent vendor audits, standardized interoperable data formats, and transparency requirements for public-sector AI procurement, building on proposals already present in the draft policy but not yet codified into procurement rule (Government of Bangladesh, 2026).

7. AI Policy Delay and Digital Sovereignty

AI sovereignty refers to a nation’s control over the data, models, infrastructure, standards, and talent that underpin its AI capability. This concept provides a useful frame for understanding how policy delay translates into longer-run economic exposure. Countries fall along a spectrum from AI producers, such as the United States, China, and India, to AI consumers, economies dependent primarily on foreign AI systems (World Bank, 2024).

Without enacted AI legislation, dedicated compute infrastructure, or coordinated talent development, Bangladesh’s current trajectory aligns more closely with the AI-consumer end of that spectrum. Data flows disproportionately to foreign servers, model development depends on foreign APIs, and standards are set externally rather than through domestic participation. World Bank analysis is direct on the stakes: AI productivity gains are concentrating among wealthy nations and dominant technology firms, a dynamic that risks widening the income gap between AI producers and AI consumers (World Bank, 2024). The same analysis notes that AI-driven automation can erode the labor-cost advantage underpinning Bangladesh’s export-led growth model, reducing the incentive for offshoring that has historically benefited the country (World Bank, 2024).

By 2030, absent policy intervention, this exposure is likely to compound across three channels: continued dependency on foreign platforms for critical applications, opportunity loss from AI-driven productivity gains realized elsewhere, and workforce disruption from automation without matched reskilling investment.

8. AI Development Challenge for the Global South

Bangladesh’s situation reflects a broader pattern shared across developing economies navigating AI adoption without comparable governance and infrastructure capacity. Three dependency risks recur across the Global South.

Infrastructure dependency arises when domestic compute capacity is absent, so AI workloads run on foreign cloud infrastructure by default, with cost and continuity terms set by external providers rather than domestic policy. Data dependency arises when domestic data governance and indigenous dataset development are limited, so AI systems trained and deployed locally rely on foreign data pipelines and foreign-trained models, constraining the ability to build AI attuned to local languages, markets, and needs. Talent dependency arises when national AI education and research programs are unfunded, so skilled talent trains and often emigrates toward AI-producing economies, compounding rather than closing the capability gap over time.

Addressing this pattern requires more than adopting AI. It requires deliberate capability building: domestic infrastructure investment, data governance that treats data as a national asset rather than an externality, and governance models that involve startups, researchers, and civil society in policy design rather than treating governance as a step that follows adoption. India’s IndiaAI Mission is instructive because it treats these three dependencies as a single, coordinated problem rather than three separate initiatives (Government of India, 2024). Global South economies that regulate without also investing in infrastructure, data, and talent are likely to reduce some risks of AI harm, but this alone will not close the gap between AI producers and AI consumers.

9. The Atlas AI Institute Framework: Bangladesh AI Governance and Economic Development Framework

Atlas AI Institute proposes a five-pillar framework to structure Bangladesh’s transition from policy formulation to implementation. The framework is sequenced to close the readiness gaps identified in Section 3 without disrupting the startup activity already under way.

The first pillar, national AI governance, calls for enacting the National AI Policy with binding legal status, establishing the National Data Governance Authority with genuine enforcement authority, and defining risk-tiered obligations for high-risk AI systems, drawing on international precedent without directly importing any single jurisdiction’s rulebook.

The second pillar, data and digital infrastructure, calls for extending the Personal Data Protection Ordinance 2025 with AI-specific data-sharing and portability rules, and for investing in domestic compute capacity to reduce reliance on foreign cloud infrastructure for AI workloads.

The third pillar, AI innovation ecosystem, calls for establishing a regulatory sandbox for controlled real-world testing, creating a dedicated AI startup financing vehicle, and supporting indigenous model development to reduce foreign-API dependency for domestic ventures.

The fourth pillar, sectoral AI transformation, calls for translating the sector-level recommendations in Section 6 into funded, sector-specific governance instruments across banking, healthcare, agriculture, manufacturing, education, and public services, rather than relying on a single horizontal policy to cover all sectoral risk.

The fifth pillar, human capital and inclusion, calls for funding a national AI skills program spanning curriculum integration, university research labs, teacher capacity-building, and reskilling for workers displaced by automation, addressing the talent-dependency risk identified in Section 8.

The five pillars are intended to function as an integrated system. Governance without infrastructure investment produces compliance burden without capability. Infrastructure without governance produces uncoordinated risk. Neither is sustainable beyond an initial policy cycle without investment in human capital.

10. Policy Recommendations

Short Term (0 to 12 months)

In the short term, Bangladesh should enact the National AI Policy 2026 to 2030 with clear legal status and establish the National Data Governance Authority as the central coordination body (Government of Bangladesh, 2026). An inter-ministerial AI governance committee spanning ICT, Finance, Health, Education, Agriculture, and Industry should be formed. Mandatory algorithmic impact assessments should be introduced for high-risk AI systems in government procurement, and AI-specific public procurement standards, including independent vendor audits, should be codified, as recommended by UNESCO’s Observatory (UNESCO, 2024).

Medium Term (1 to 3 years)

Over the medium term, priorities include launching a regulatory sandbox for controlled testing of novel AI applications and establishing a dedicated AI startup financing vehicle, informed by though not modeled directly on IndiaAI’s Startup Financing component (Government of India, 2024). A national AI skills program should be funded and integrated into secondary and tertiary curricula, with accompanying university research-lab funding. Sector-specific governance instruments for banking, healthcare, agriculture, manufacturing, education, and public services should be finalized, following the recommendations in Section 6.

Long Term (3 to 10 years)

Over the longer term, Bangladesh should invest in domestic AI compute infrastructure to reduce structural dependence on foreign cloud providers, and support indigenous AI model development through research funding and public-private partnership. Bangladesh is well placed to position itself as a regional AI services hub within South Asia, building on its demographic and digital-economy strengths, and to contribute to Global South AI governance cooperation by sharing implementation lessons with peer economies facing the same infrastructure, data, and talent dependencies described in Section 8.

11. Conclusion

AI policy functions as the foundation that allows innovation to scale safely, attract investment, and create economic value, rather than as a constraint on it. The evidence across the United States, the European Union, China, and India is consistent on this point: durable AI competitiveness comes from combining governance with infrastructure, investment, and skills, rather than from any single element in isolation (OECD, 2024; Government of India, 2024; European Parliament and Council, 2024).

Bangladesh is not starting from zero. A draft national policy, an earlier strategy, new data-protection legislation, and an emerging sector-specific initiative from Bangladesh Bank all represent genuine progress. What remains is the harder task of implementation: enactment, institutional authority, dedicated infrastructure and capital, and a funded talent pipeline. Left unaddressed, this implementation gap risks the outcome World Bank analysis warns against, in which productivity gains are captured disproportionately by wealthy nations and dominant technology firms, while developing economies absorb disruption without a proportionate share of the benefit (World Bank, 2024).

Bangladesh has already begun adopting AI across its banking, agricultural, and manufacturing sectors. The remaining question is whether the country will shape the terms of its participation in the AI economy or continue to depend on technologies, standards, and capital developed elsewhere.

References

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Government of Bangladesh, Ministry of Posts, Telecommunications and Information Technology. (2020). National Strategy for Artificial Intelligence.
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Prepared by Atlas AI Institute for policymakers, government officials, startup founders, investors, researchers, and development organizations. This analysis draws on publicly available policy documents, academic research, and international organization reports as of August 2026.