How AI Can Boost Productivity for Informal Workers-Ending the Manufacturing vs. Services Debate

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Developing economies face a false dichotomy in their pursuit of growth: whether to prioritize manufacturing-led industrialization or services-driven expansion. But a new perspective from economists and technologists suggests that the real opportunity lies in leveraging artificial intelligence to boost productivity in the informal sector—the backbone of many emerging markets. If governments and private players can democratize access to AI tools, the choice between industrialization models may become obsolete.

The debate over manufacturing-led growth—long championed by economists like Dani Rodrik—versus services-led expansion has dominated policy discussions for decades. Rodrik, a Harvard professor, has argued that manufacturing remains critical for structural transformation, citing its role in creating higher-value jobs and reducing poverty. Yet, as digital tools reshape labor markets, a growing body of research suggests that AI could unlock productivity gains in sectors traditionally overlooked by industrialization strategies, such as street vending, smallholder farming and gig-based services.

AI as the Great Equalizer for Informal Workers

In India, for instance, the informal economy accounts for nearly half of GDP, employing over 80% of the workforce. Street vendors, smallholder farmers, and micro-entrepreneurs—who lack formal sector protections—could see dramatic productivity gains if AI-driven tools were scaled to their needs. Ravi Venkatesan, former CEO of Flipkart and a vocal advocate for inclusive tech, has highlighted how AI could automate administrative burdens for informal workers, from inventory management for street vendors to predictive analytics for crop yields for farmers.

Consider the case of street vendors in Indian cities like Mumbai or Delhi. Many operate without digital records, making it difficult to access loans, insurance, or even basic market intelligence. AI-powered apps could help vendors track sales patterns, optimize pricing, and even connect with buyers directly—mirroring the efficiency gains seen in formal retail but adapted for their context. Similarly, smallholder farmers in Africa and Southeast Asia could use AI to monitor soil health, weather risks, and commodity prices, reducing waste and increasing yields without requiring large-scale infrastructure investments.

Avoiding the Manufacturing vs. Services Deadlock

The traditional framing of the growth debate assumes that developing countries must choose between two paths: either double down on manufacturing to build industrial capacity or embrace services to tap into global demand. But as Venkatesan and others argue, AI could bridge this divide by making informal-sector productivity comparable to—or even surpass—what manufacturing alone could achieve.

For example, a 2023 study by the World Bank found that AI-driven agricultural extensions in Kenya increased farmer incomes by up to 30% within two years, without requiring the farmers to relocate to urban centers or factory jobs. In Bangladesh, microfinance institutions are experimenting with AI to assess creditworthiness for informal borrowers, expanding access to capital beyond traditional collateral-based lending. These models suggest that productivity gains in the informal economy don’t require industrialization—they can be driven by digital inclusion.

Policy and Private Sector Challenges

Yet, scaling AI for informal workers isn’t without hurdles. Digital literacy remains low in many regions, and the cost of deploying AI tools at scale—especially in rural or low-income areas—can be prohibitive. Governments will need to partner with tech firms, microfinance institutions, and NGOs to subsidize access, much like India’s Digital India initiative has done with smartphone subsidies and digital payment adoption.

Private sector players also have a role. Flipkart, for instance, has invested in AI-driven logistics for small merchants, while companies like Jumia in Africa are using machine learning to connect informal sellers with online buyers. But broader adoption requires regulatory frameworks that protect data privacy for informal workers while ensuring interoperability between platforms. Without these safeguards, the risk of exploitation—such as predatory pricing or data misuse—could outweigh the benefits.

The Flip Side: Risks of Over-Optimism

Critics warn that overemphasizing AI in the informal sector could distract from deeper structural issues, such as weak labor laws, poor infrastructure, and financial exclusion. Dani Rodrik has cautioned that while AI may enhance productivity, it won’t address the root causes of informality, such as lack of social protection or access to formal credit. Without complementary policies—like portable social security systems or land reforms—AI tools may only deepen inequality by benefiting a tech-savvy elite while leaving others behind.

the environmental and ethical costs of AI deployment must be considered. Training AI models requires significant energy, and deploying data centers in developing nations could exacerbate carbon footprints. Ethical concerns also arise: Who owns the data generated by informal workers? How are they compensated if their behavior is analyzed by algorithms? These questions remain unresolved in many pilot projects.

A Path Forward: Hybrid Models

The most promising approach may lie in hybrid models that combine AI-driven productivity gains with targeted industrialization. For example, Ethiopia’s Industrial Parks initiative has attracted manufacturing investments while simultaneously rolling out digital literacy programs for nearby informal workers. Similarly, Vietnam’s textile sector—long a manufacturing powerhouse—is now integrating AI into supply chains to reduce waste, benefiting both formal factories and informal suppliers.

Economists like Venkatesan advocate for a third way: one where AI acts as a catalyst for inclusive growth, rather than a replacement for traditional development strategies. This would require:

• Public-private partnerships to subsidize AI tool adoption for informal workers, with a focus on affordability and accessibility.
• Regulatory sandboxes to test AI applications in informal sectors without stifling innovation.
• Data cooperatives where informal workers collectively own and control their digital data.
• Reskilling programs to ensure workers can adapt to AI-augmented roles without falling into precarity.

The choice confronting developing countries is no longer between manufacturing and services. It is between clinging to outdated growth models and embracing a future where technology democratizes opportunity—starting with the most vulnerable workers. The tools exist. What’s needed now is the political will to deploy them wisely.

This analysis draws on insights from Ravi Venkatesan, Dani Rodrik, and recent World Bank research on AI and informal economies. Data on informal sector employment sourced from ILO and national labor reports.