As artificial intelligence spending accelerates globally, a hard reality is setting in across boardrooms and policy circles: AI is no longer constrained by ambition, models, or software talent. It is constrained by infrastructure.
That was the central message emerging from a recent Africa Hyperscalers–Vertiv webinar, which brought together enterprise technology leaders and infrastructure specialists to examine what it actually takes to run AI workloads at scale in African operating environments.
While AI discussions across the continent often center on use cases and applications, this session deliberately shifted the lens to the physical layer – power availability, thermal performance, resilience, and the ability to execute reliably under real-world conditions. The conclusion was unambiguous: without deliberate investment in infrastructure foundations, Africa’s AI ambitions risk colliding with operational reality.
Opening the session, Gary Chomse, Vertiv’s Regional Director for Central and Southern Africa, framed the moment as a structural transition. AI workloads are fundamentally different from traditional enterprise IT – more compute-dense, more power-hungry, and far less tolerant of instability. Infrastructure that was “good enough” for legacy workloads is increasingly unfit for purpose in an AI-driven environment.
That shift was explored further by Okechi Osuagwu, Regional Account Executive, Vertiv, who outlined how AI is forcing a rethink of data center design itself. Rather than incremental upgrades, organizations are moving toward modular, factory-tested infrastructure models – smart cabinets, rows, pods, and prefabricated data centers – that can scale rapidly and adapt as workloads evolve. The emphasis, he noted, is not just speed of deployment, but predictability and control.
Cooling emerged as one of the most critical bottlenecks. Luther Ogbaji, a Thermal Infrastructure Engineer highlighted how rising rack densities are pushing air cooling to its limits. As AI workloads concentrate heat in ways traditional facilities were never designed to handle, liquid cooling is moving from edge case to necessity. Preparing for hybrid cooling environments – where air and liquid systems coexist – is becoming a strategic requirement rather than a future consideration.
Power infrastructure, however, may be the defining constraint. Wilson Eigbadon, Vertiv’s Regional Account Manager, Central Africa addressed the growing mismatch between AI’s energy demands and the realities of African power grids. As compute intensity rises and tolerance for outages approaches zero, reliance on grid power alone becomes a material risk. Advanced UPS systems, battery energy storage systems, and microgrid architectures are increasingly essential – not as backup, but as core components of AI-ready infrastructure.
The discussion was grounded in execution by Olajide Aminu, who drew on Vertiv’s global and African project experience to show how these concepts translate into real deployments. Modular and prefabricated infrastructure, he argued, is not about convenience – it is about compressing delivery timelines, reducing risk, and ensuring that infrastructure can keep pace with AI-driven demand.
The session closed with a candid Question & Answer, moderated by Olajumoke Rufus, Marketing Manager, Central Africa at Vertiv, that touched on edge computing, skills constraints, regulatory readiness, and Nigeria’s preparedness for liquid cooling adoption. What emerged was a shared understanding that AI infrastructure is no longer a purely technical concern. It is a strategic, financial, and policy issue.
As enterprises move from AI experimentation to production, infrastructure decisions are becoming board-level decisions. Markets that invest early in resilient power, advanced cooling, and scalable design will be better positioned to capture AI-driven value. Those that do not may find that ambition without infrastructure creates fragility rather than advantage.
