AI Adoption in Manufacturing and Services in Emerging Markets

Bottom line: The available evidence points to a generally faster, more digitally concentrated adoption pattern in services than in manufacturing, but it does not provide a reliable, economy-wide quantitative comparison for emerging and developing countries. Services therefore face more immediate exposure in some occupations, while manufacturing policy must focus more on foundational digital capability, infrastructure, data quality, and small-firm readiness. These conclusions should be treated as qualified because much of the comparative evidence comes from OECD economies or advanced-economy firms.^[1][2][3]

For policymakers, the practical distinction is not simply “services versus manufacturing.” Adoption and workforce effects vary by task, skill level, firm size, and digital readiness. ICT, finance, and professional services are unlike retail or food services, just as high-skill electronics manufacturing is unlike less digitally connected production.^[4]

1. Adoption speed and sectoral exposure

In the economies covered by the research, services generally adopt AI earlier than manufacturing. ICT, information and communication, finance, and professional, scientific, and technical services are among the leading adopters. OECD evidence reports adoption rates of 44% in information and communication services and 26% in professional, scientific, and technical services in 2024, while manufacturing trailed the leading service sectors.^[5][6]

The available trend evidence also indicates faster adoption growth in ICT and professional services between 2021 and 2024. Manufacturing adoption increased substantially, by roughly 70% in the cited OECD comparison, but from a lower base and with a smaller percentage-point increase than the leading service sectors.^[7][8] This supports a services-first pattern, not a universal rule for every emerging market.

2. Capital, infrastructure, and data barriers

The research does not establish that manufacturing or services systematically faces greater financing barriers across emerging markets. It does show that small firms generally have less investment capacity and that financial constraints can delay long-term AI and digital-transformation investment, without proving a sector-specific lending gap.^[19][20][21]

The nature of the investment need differs. For services, the evidence more clearly identifies shortages of computing capacity, data centres, connectivity, reliable power, and cooling capacity as constraints on AI workloads. Financing and regulatory reform must make these infrastructure projects bankable.^[22][23] For manufacturing, the strongest evidence concerns smaller firms’ weak digital readiness, poor-quality data, difficulty finding suitable vendors, and difficulty adapting business models.^[24][25]

This means that “capital barriers” should be interpreted broadly. Services may require investment in shared digital infrastructure and data capacity, while manufacturing firms may need finance and technical assistance for basic digitalisation, usable data, and complementary capabilities. The sources do not support a claim that one sector is universally more capital-intensive or financially constrained than the other.^[26][27]

3. Workforce effects

Services face more immediate and uneven workforce exposure because some activities are routine, language-based, or administrative. The IZA evidence specifically identifies the Philippines’ business-process-outsourcing sector as exposed to chatbot substitution, despite its relatively small employment share and substantial economic contribution.^[28] At the same time, high-skill services such as medicine may benefit from AI-enabled worker augmentation rather than straightforward replacement.^[29]

For manufacturing, the sources do not establish a distinct manufacturing-specific employment effect in developing economies. They do indicate that less digitally connected employment is likely to face slower initial generative-AI disruption because fewer tasks are currently exposed. Evidence from manufacturing firms outside the developing-economy context also cautions against equating AI adoption with immediate factory-floor job loss: reported use was concentrated mainly in functions such as sales and marketing rather than directly in production.^[30][31]

The appropriate workforce concern is therefore different by sector. In exposed service industries, the risk is concentrated displacement or rapid changes in work practices. In manufacturing and other lower-exposure activities, the nearer-term challenge may be unequal access to productivity-enhancing technology, alongside the need to build workers’ digital and technical capabilities. These are evidence-based interpretations, not precise forecasts of employment change for all emerging economies.^[32][33]

4. Distinct policy priorities

A common national AI strategy is unlikely to be sufficient. The sectoral evidence supports differentiated measures that address both adoption capacity and worker transition.

Conclusion and evidence boundary

The best-supported comparison is qualified: services generally adopt AI earlier and expose more digitally intensive, language-based, and administrative work, while manufacturing adoption is slower overall and more uneven, with especially important capability gaps among small firms. Capital constraints affect both sectors, but the evidence points to different bottlenecks: computing, connectivity, data-centre capacity, energy, and data governance are more clearly documented for services, while manufacturing evidence centres on digital readiness, data quality, vendors, and business-model adaptation.^{[43][44][45][46]}

The evidence does not justify a precise emerging-market estimate of the manufacturing-services adoption gap, nor a universal claim that AI will reduce manufacturing employment or replace service workers. Policy should therefore combine sector-specific support with better country-level measurement, especially of small firms, informal work, task exposure, and worker transitions.