AI Shows Early Promise In African Health Supply Chains But Evidence Remains Thin

Artificial intelligence is beginning to tackle some of the most persistent problems in African health supply chains, from inaccurate demand forecasting to manual procurement. However, several stubborn challenges and early signs of potential that remain largely unproven paint a complicated reality, as a new report shows.

A new report maps 20 artificial intelligence solutions already deployed in African health supply chains, but its findings also note that most evidence is self-reported, impacts remain unverified, and many solutions are still confined to pilot projects that have yet to prove they can scale.

The report by healthcare consulting firm Salient Advisory, titled “AI Applications in African Health Supply Chains,” identifies seven critical supply chain problems where AI appears most amenable to delivering impact today. It highlights early wins that could grab any health minister’s attention: approximately USD 38 M in reduced procurement spending in Ethiopia, procurement planning time in Kenya cut from days to under an hour, and a 20% drop in pharmacy stock levels in Morocco.

Yet the report’s own methodology section acknowledges that findings are based on a “purposeful review” that is “neither systematic nor exhaustive”. Impacts are “self-reported by solution providers” and “have not been independently verified”. The analysis is “subject to publication bias,” meaning deployments with positive outcomes are more likely to be reported than those with neutral or negative results.

“Early evidence suggests AI solutions are delivering measurable results in specific contexts, offering health systems a promising path to do more with less,” said Deji Ogunye, Director of Supply Chain at Salient Advisory. “But self-reported results from a limited number of deployments are not yet sufficient on their own to drive adoption at scale”.

The research, which draws on input from supply chain leaders across global health institutions, including the Gates Foundation, the Global Fund and the Clinton Health Access Initiative, also highlights the significant barriers to adoption.

Legacy integration, data quality constraints and limited technical capacity remain formidable obstacles. Another report, for example, finds that 55.7 cents of every IT dollar go toward maintaining legacy systems across African banks, a constraint that mirrors challenges in health supply chains.

Across African health systems, poor-quality, incomplete and non-representative data undermine the reliability of AI systems. Many public health systems operate with limited access to stable electricity, reliable internet and compatible software platforms, making it difficult to deploy and maintain automated tools.

The findings suggest that AI could help governments do more with less, and this is a critical consideration as official development assistance to African health systems contracts sharply, but only if they build the infrastructure to support it. Governments and global health institutions, the report argues, need to stop treating AI adoption in supply chains as a pilot programme and start treating it as infrastructure.

Yet the gap between early pilot results and system-wide adoption remains wide. The report does not quantify the cost of scaling these solutions, nor does it assess whether the reported savings offset the investment required to deploy them at scale.

The evidence currently suggests AI can deliver measurable improvements in specific contexts. Whether those improvements can be replicated, scaled and sustained across African health systems remains an open question.