Key Takeaways
- • AI adoption among venture capital and private equity-backed firms in emerging markets rose to 71% this year from 39% a year earlier, according to data released by the International Finance Corporation at the IMF-World Bank Annual Meetings.
- • Only 17% of surveyed companies have embedded AI into core business operations, and more than half of adopters confine deployments to isolated operational functions.
- • Skills shortages are the primary barrier, cited by more than 40% of non-adopters, while improved productivity is the main driver, cited by 54% of respondents.
- • Medical imaging and fintech deployments face regulatory ambiguity and cross-border payment friction, with industry calls for faster domestic approvals of diagnostic algorithms.
Summary
Artificial intelligence adoption among venture capital and private equity-backed firms has climbed to 71% this year in emerging markets, up from 39% a year earlier, according to data released by the International Finance Corporation (IFC) at the IMF-World Bank Annual Meetings. The article, published by Retail News Asia, notes that the headline figure masks narrow implementation: only 17% of surveyed companies have embedded AI into their core business operations, and more than half of adopting businesses still restrict deployments to isolated operational functions. The IFC survey sampled 1,600 private equity and venture capital firms. Improved productivity was the leading driver, cited by 54% of respondents.
Human capital constraints remain the principal obstacle. More than 40% of non-adopters identified acute skills shortages as their primary barrier, while broader institutional capacity deficits continue to stall deployments beyond basic software tools. Enterprise operators in Southeast and South Asia often find that tools built for Western data sets and cost structures perform poorly without local tuning, failing to handle regional dialects, local commercial registries and fragmented banking APIs. The article argues that practical value depends on lightweight, localized models rather than expensive enterprise subscriptions that internal teams cannot maintain.
In fintech, operators are deploying targeted AI agents to address transaction delays for small and medium-sized enterprises. Adeola Adewede, founder and chief executive of Kredete, said autonomous agents configured for local language and cost structures can transact on behalf of regional merchants across borders, lowering operating costs and accelerating liquidity cycles. In healthcare, CARPL, led by founder and chief executive Vidur Mahajan, has deployed portable X-ray units running laptop-based AI models to screen populations in India for tuberculosis and breast cancer. Mahajan said the platform supports more than 300 imaging applications for hospitals, but ambiguous medical software regulations across emerging markets delay rollouts, prompting calls for regulators to streamline approvals of diagnostic algorithms already certified in developed economies.
On the hardware side, providers argue that long-term productivity gains depend on on-device processing rather than continuous cloud connections, which reduces latency and connectivity costs for small businesses. Veerabhan Kheterpal, co-founder and chief executive of semiconductor IP licensing company Quadric, said on-device intelligence allows systems to generate customized software adjustments in real time. The article concludes that the key commercial question is whether portfolio companies can convert functional experiments into revenue-generating workflows before early-stage investment capital is exhausted.
Human capital constraints remain the principal obstacle. More than 40% of non-adopters identified acute skills shortages as their primary barrier, while broader institutional capacity deficits continue to stall deployments beyond basic software tools. Enterprise operators in Southeast and South Asia often find that tools built for Western data sets and cost structures perform poorly without local tuning, failing to handle regional dialects, local commercial registries and fragmented banking APIs. The article argues that practical value depends on lightweight, localized models rather than expensive enterprise subscriptions that internal teams cannot maintain.
In fintech, operators are deploying targeted AI agents to address transaction delays for small and medium-sized enterprises. Adeola Adewede, founder and chief executive of Kredete, said autonomous agents configured for local language and cost structures can transact on behalf of regional merchants across borders, lowering operating costs and accelerating liquidity cycles. In healthcare, CARPL, led by founder and chief executive Vidur Mahajan, has deployed portable X-ray units running laptop-based AI models to screen populations in India for tuberculosis and breast cancer. Mahajan said the platform supports more than 300 imaging applications for hospitals, but ambiguous medical software regulations across emerging markets delay rollouts, prompting calls for regulators to streamline approvals of diagnostic algorithms already certified in developed economies.
On the hardware side, providers argue that long-term productivity gains depend on on-device processing rather than continuous cloud connections, which reduces latency and connectivity costs for small businesses. Veerabhan Kheterpal, co-founder and chief executive of semiconductor IP licensing company Quadric, said on-device intelligence allows systems to generate customized software adjustments in real time. The article concludes that the key commercial question is whether portfolio companies can convert functional experiments into revenue-generating workflows before early-stage investment capital is exhausted.
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