AI APPLICATIONS IN AFRICAN HEALTH SUPPLY CHAINS: LANDSCAPE OF SOLUTIONS & EMERGING IMPACTS

Health supply chains across Africa face several persistent challenges, including frequent stockouts leaving facilities unable to meet patient demand, manual processes slowing down planning, and fragmented data preventing planners from seeing across the supply chains they manage. However, AI-powered solutions that address these problems are beginning to emerge.

Our new report, AI Applications in African Health Supply Chains, identifies key supply chain problems where AI appears most amenable to delivering impact today, maps the deployed AI solutions addressing them, and profiles real-world examples of AI solutions from across the continent.

Key Findings

• 7 priority supply chain problems were identified in which AI appears most amenable to delivering impact today: suboptimal inventory levels, inaccurate demand forecasting, data management and report automation, fragmented data for decision-making, inefficient and manual procurement, high workforce costs and skill dependency, and lack of serialization and track-and-trace.
• At least 20 AI solutions addressing these problems are being deployed across public and private sector contexts in global and African health supply chains.
• Early evidence suggests these AI solutions are beginning to deliver results in specific contexts, with solution providers reporting self-reported impacts across 5 problem areas:
  • Inaccurate demand forecasting: ~$38M saved in planned procurement expenditure in Ethiopia — Opian Technologies (ForLabPlus)
  • Inefficient and manual procurement: procurement planning time reduced from 2–3 days to under 1 hour per facility in Kenya — InSupply Health (SMArT Tool)
  • Data management and report automation: 87% reduction in document processing time for a logistics provider in Nigeria — V7 Labs
  • Fragmented data for decision-making: 80% improvement (projected) in visibility of national supply chain KPIs, benchmarked against the measured baseline— S4D Consulting (Cognivo)
  • Suboptimal inventory levels: 20% reduction in overall stock levels at private pharmacies in Morocco — Distripha

While these early signals are promising, the evidence base remains nascent, and the impact on cost savings appears thin, representing a critical information gap. 

As AI rapidly evolves globally and official development assistance declines coordinated action is required to accelerate the adoption of AI-powered solutions that can sustainably improve supply chain management. We recommend 3 key actions: First, Solution providers should ground their business cases in verified, cost-linked impact data and embed change management as a core implementation requirement. Second, Global health organizations should leverage their influence to advocate for equitable access to AI infrastructure, invest in Africa-based AI capabilities, and fund independent cost-impact studies to build the evidence base for broader adoption. Finally, governments should establish supply chain cost baselines before procuring AI solutions; develop AI policy frameworks, invest in internal technical capacity, and update procurement processes to ensure AI outputs support effective supply chain planning. 

To learn more, explore the full report.

Project Team

This work was led by Joshua Uka, Zillah Waminaje, Yomi Kazeem, Abdullah Yusuf, Deji Ogunye, and Tosin Oshinubi. Our gratitude goes to Ann Allen (Gates Foundation), who provided invaluable guidance and partnership. Thanks also to the AI solution providers who contributed through key informant interviews: Causal Foundry, Distripha, InSupply Health, Opian Technologies, PharmaSecure, PBR Life Sciences, S4D Consulting, and Viebeg.