AI systems run on infrastructure. In Africa, that infrastructure — connectivity, data centres, digital identity, cloud access, and edge networks — is uneven and in many contexts a binding constraint on what AI can actually do. This strand maps what exists, what is missing, and what investment or policy change is required to close the gap.
Infrastructure research maps the physical and digital layer that AI operates across in Africa — identifying where gaps are binding, where investment is most leveraged, and what policy change unlocks the most deployment potential.
Active and planned briefs mapping the infrastructure layer that determines where and how AI can be deployed across Africa — including the current grid-readiness study for AI infrastructure.
Assessing whether African electricity grids can support AI-grade data centres, cloud regions, and compute-intensive digital infrastructure — with focus on reliability, generation adequacy, transmission constraints, tariff structure, and site readiness.
Mapping Africa's subsea cable network against the bandwidth and latency requirements of large-scale AI inference workloads — identifying which markets are bottlenecked by cable capacity and which will remain constrained even as domestic data centres expand.
A comparative site-selection framework for AI-grade data centre development in Africa — scoring markets against power reliability, cooling cost (climate and water stress), latency to population centres, and regulatory maturity across 12 candidate countries.
Examining how AI-driven network planning tools — using population density, terrain data, and demand forecasting — can accelerate cost-effective last-mile connectivity rollout in the rural African markets where the majority of the continent's population still lives.
Modelling the latency, throughput, and device density improvements that 5G will deliver across African markets against the timeline of actual deployment — and assessing which AI use cases become viable only with 5G-level connectivity, and when.
Evaluating edge computing deployment models — on-premise inference, micro data centres, and smart device AI — as strategies for delivering AI capability where cloud connectivity is insufficient, expensive, or unreliable, with implementation evidence from agricultural, health, and financial services contexts.
Assessing how hyperscaler data localisation commitments (AWS, Azure, Google, Huawei) match up against African countries' data residency regulations — and what gaps remain that prevent regulated sectors like banking and health from moving AI workloads to the cloud.
Examining how the coverage, reliability, and interoperability of national digital ID systems in Africa determines the deployment ceiling for AI in public services — health, social protection, taxation, and financial inclusion — across different infrastructure maturity levels.
Evaluating Starlink and other LEO satellite internet deployments as a bridge connectivity solution for AI in off-grid African communities — assessing coverage, affordability, latency performance, and what AI application classes become newly viable at satellite speeds.
Mapping the tensions between national data localisation requirements and the cross-border infrastructure sharing that would make African AI deployment more cost-effective — and evaluating whether AfCFTA's digital trade provisions provide a viable governance path for resolving them.
Developing a structured assessment framework for measuring AI infrastructure readiness at the national level — covering power reliability, connectivity, cloud access, digital skills, and data governance — and applying it to score and rank 30 African countries.
Evaluating the AI components embedded within Africa's three most advanced smart city programmes — traffic management, utility monitoring, surveillance, and public service delivery — assessing what infrastructure investment enabled them and what is replicable at lower cost elsewhere.
Assessing the cybersecurity infrastructure gap that emerges when African financial institutions deploy AI systems — quantifying exposure from adversarial attacks, model poisoning, and data breach risk, and evaluating the regulatory frameworks that are beginning to govern it.
Mapping the electronic health record coverage, data quality standards, and interoperability frameworks across African health systems — and modelling which AI diagnostic applications are deployable given current data infrastructure, and which require infrastructure investment before they become viable.
Drawing on India's Aadhaar-UPI-ONDC digital public infrastructure stack to evaluate which components are transferable to African contexts, which require modification, and what AI layers can be built on top to extend public service reach cost-effectively across the continent.
Examining how spectrum licensing decisions — allocation methods, pricing, and reserved bands — affect the rollout speed and cost structure of the mobile networks that AI-dependent services in Africa run on, with comparative analysis across regulatory regimes.
Identifying the connectivity, device, and data infrastructure preconditions for deploying AI-based agricultural advisory tools at scale across rural Africa — assessing current coverage against the smallholder population distribution and modelling what targeted infrastructure investment would unlock.
Evaluating the infrastructure gaps in cross-border payment systems across Africa's two largest regional blocs — assessing where AI fraud detection and settlement systems fail to interoperate, and what standardisation or infrastructure investment is required for regional financial AI to function effectively.
A comparative study of infrastructure investment trajectories in AI-enabling assets — data centres, connectivity, digital ID, cloud capacity — contrasting Africa's current pace and composition against the investment patterns that preceded AI adoption inflection points in Vietnam, Indonesia, and the Philippines.
Assessing whether Open RAN's disaggregated, vendor-neutral network architecture can accelerate affordable connectivity rollout in African markets — and whether it produces the quality and consistency of connectivity that AI-dependent services require, or whether its cost advantages come with performance trade-offs that matter for AI use cases.
Evaluating the cost, durability, connectivity, and data quality requirements for IoT sensor networks deployed in African smallholder agricultural settings — and assessing which AI monitoring applications (soil health, crop stress, weather, pest detection) are within reach of current sensor cost curves, and which require further hardware price reductions.