In 2025, South Africa's major banks increased their IT spending by up to 32%. Not to build new products — to replace the systems they already had. Core banking platforms that had run without significant change for ten to fifteen years were being ripped out and rebuilt. Similar exercises were underway at telecom operators, insurance groups, and large retailers across the continent.
This is not the kind of technology story that generates headlines. There is no launch, no new product, no growth metric to share. But it is arguably the most significant technology investment cycle happening in African enterprise right now — and it has implications that extend beyond the companies doing the rebuilding.
What is driving the rebuild cycle
The proximate cause is usually one of three things: a regulatory requirement that the legacy system cannot meet, a competitive pressure that requires a product capability the legacy system cannot support, or an operational problem — reliability, speed, cost to maintain — that has finally become too expensive to manage.
But the underlying cause is simpler. For a decade, the cost of staying on legacy technology was lower than the cost of changing. Migration is expensive, disruptive, and risky. As long as the legacy system worked well enough and the competitive environment did not demand more, the rational decision was to stay.
That calculus has changed. The gap between what a modern system can do and what a legacy system can do has widened to the point where it shows up in competitive outcomes. Banks on modern cores can deploy new financial products in weeks; banks on legacy cores take months or years. Retailers with modern inventory and logistics systems can operate with significantly lower working capital; those on older systems carry more stock and more cost. The competitive disadvantage of legacy technology is now visible on the income statement, not just in the engineering department.
The AI inflection point
The trigger that has accelerated the cycle in 2026 is AI. Not AI as a feature to add, but AI as an architectural requirement. The large language models and agent frameworks that are now being deployed in enterprise workflows require clean, accessible data. They require modern APIs. They require systems that can be integrated, queried, and updated programmatically.
Legacy systems often cannot provide this. Their data is locked in structures that predate modern API design. Their architectures were built for batch processing, not real-time interaction. Connecting an AI workflow to a legacy core is technically possible but operationally expensive — and often fragile. Enterprises that want to use AI at scale are discovering that they first need to solve the infrastructure problem.
This is why the rebuild cycle and the AI adoption wave are happening simultaneously. They are the same wave. Enterprises are not rebuilding their technology and then adding AI — they are recognising that rebuilding is a precondition for the kind of AI deployment that produces competitive advantage.
What this creates for builders
A large-scale enterprise technology rebuild cycle creates significant demand for the services that support it: systems integration, workflow redesign, staff training, custom tooling. It also creates the conditions for new competition. When an incumbent rebuilds its core technology, the rebuilt version often has a shorter head start on the new architecture than a new entrant starting fresh. The advantage of an established customer base remains, but the technology moat narrows.
For operators building products in Africa, the rebuild cycle is a signal worth tracking. It indicates which industries are approaching a period of disruption, and where the demand for new infrastructure — of the kind BDC builds — is growing fastest.
Speed and scale. That is what the rebuild cycle is about. The businesses that navigate it well will be faster and more scalable on the other side. The ones that defer it will find the gap between them and the field becoming difficult to close.