As foreign aid to African health systems keeps shrinking, a new industry report argues that artificial intelligence has quietly become one of the few tools capable of absorbing the shock, and it arrives armed with figures precise enough to catch the attention of health ministers across the continent.
The report, titled AI Applications in African Health Supply Chains, comes from Salient Advisory, a healthcare consulting firm that has spent years mapping Africa’s digital health landscape with backing from the Gates Foundation. It wastes little time on the funding crisis before turning to results: close to 38 million dollars shaved off procurement spending in Ethiopia, procurement planning time in Kenya slashed from three days to under an hour, and a 20 percent drop in pharmacy stock levels in Morocco.
The timing tracks a broader crisis. Official development assistance to African health systems has been contracting sharply through 2025 and 2026, a slide that health ministries and global health funders have spent much of that period sounding alarms over. Salient’s underlying argument is straightforward. If a ministry cannot secure more money, extracting more value from the money already in hand becomes the only lever left to pull, and AI, in this telling, is that lever.
The report’s central claim is structural rather than anecdotal. Drawing on interviews with supply chain leaders at global health institutions, it identifies seven recurring problems across African health supply chains where AI tools have matured enough to help, and maps 20 solutions already deployed on the continent that are addressing five of them. These are not pilots parked on a slide deck, the report insists, but systems a pharmacist in Addis Ababa or Nairobi is actually relying on day to day.
Ethiopia supplies the most striking number. Opian Technologies’ ForLab Plus platform, deployed alongside the country’s Ministry of Health and the Ethiopian Pharmaceutical Supply Service, is credited with roughly 38 million dollars in reduced planned procurement expenditure through facility level consumption forecasting. Launched in January 2025 with 435,000 dollars in Gates Foundation funding, it had reached more than 5,000 public health facilities by late 2025, with close to 9,000 registered users.
Kenya offers a comparable case. InSupply Health’s SMArT tool, built in partnership with the Ministry of Health, is reported to have compressed facility level procurement planning from two or three days down to under an hour. In Morocco, an AI inventory planning model developed by Distripha is credited with a 20 percent reduction in stock levels at private pharmacies. A smaller pilot in Nigeria, run with the Global Fund and AI firm V7 Labs, reportedly cut a fourth party logistics provider’s document processing time by 87 percent, saving close to 960 hours and shrinking invoice to pay cycles from 13 days to three.
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Deji Ogunye, director of supply chain at Salient Advisory, tempered the headline figures with a note of caution. He said early evidence points to AI solutions delivering measurable results in specific contexts, but cautioned that self reported outcomes from a limited number of deployments are not yet sufficient to justify adoption at scale on their own. What the sector needs now, he said, is coordinated action, from credible impact evidence to policy frameworks, to shift the work from isolated deployments toward system wide transformation.
The Gates Foundation, which has funded much of Salient’s earlier research in this space, is amplifying that same message. Ann Allen, a senior programme officer at the foundation, described the report as a useful evidence base, while flagging the need for African innovators to secure equitable access to the underlying AI infrastructure that makes such tools possible in the first place.
Salient’s recommendations are aimed at three audiences at once. It wants global health funders and institutions to sustain investment in African based AI capacity, finance independent cost impact studies, and guarantee equitable access to AI infrastructure. It wants governments to establish supply chain cost baselines before any AI procurement begins, adopt formal AI policy frameworks, build in house technical capacity, and redesign procurement processes so that AI generated insights feed directly into planning decisions rather than sitting idle in a dashboard.
For vendors, the message is the sharpest of all. Business cases, the report argues, should rest on verified, cost linked impact data rather than headline percentages, a standard its authors apply just as rigorously to their own findings as to the companies they profile.
