In 2025, tech startups across the continent attracted around $4.1 billion in combined equity and debt, with the fintech segment still the largest equity segment. In short, there is a significant opportunity in a market that’s far from saturated.

There are, says McKinsey, a plethora of untapped opportunities that include cross-border payments, SME lending and embedded, sector-specific solutions.

However, says Mandla Mbonambi, CEO of Africonology, fintech innovation is outpacing the sector’s ability to plug it into old systems.

“As a result, the real constraint to change has become integration,” he continues. “Legacy systems were not designed for real-time, API-driven products and data is scattered across channels and back-office systems, limiting personalisation and cross-sell opportunities, as well as governance, security and efficiencies.”

While on the one hand, the market has depth, velocity, and real-world relevance, on the other, it is entering a complex phase of growth in which its essential fintech integration is being implemented correctly. And this is where the real bottleneck now sits.

As instant payments, digital wallets, embedded finance and platform-based services accelerate, many companies are still trying to push modern experiences through ageing core banking systems, fragmented data estates and integration layers held together by workarounds.

Banks, mobile money operators, remittance companies and fintechs are increasingly offering new ways of banking and accessing funds, particularly around cross-border payments. Still, these solutions are also fragmented with high costs and delays.

“This is not a pressure unique to Africa,” says Mbonambi. “But it is particularly important here because the continent has moved so rapidly in financial innovation. If Africa wants to continue leading in this sector, integration has to become a strategic priority. Legacy system integration is a persistent obstacle, with challenges that include poorly managed data migration, incompatible system architectures, and insufficient testing protocols.”

Companies need a strategic framework that aligns technology investments with business objectives while still ensuring smooth integration across the enterprise. Innovation at the front end has become relatively easy to showcase. A new lending feature, a smarter onboarding

journey, a slicker payments interface, and an AI-driven customer layer are visible wins. The harder work happens underneath, where systems need to talk to one another cleanly and reliably.

MuleSoft’s 2025 Connectivity Benchmark shows how widespread this issue has become: the average enterprise now runs 897 applications, yet only 29% are integrated, and 90% say data silos are creating business obstacles.

With IT teams spending close to 40% of their time designing, building, and testing custom integrations, it’s clear that integration debt has become one of the biggest brakes on digital and AI initiatives.

“You need processes that make sense, clean data and people who understand both the business and the technology. If you skip these steps, you’re just building an expensive way to make mistakes,” says Mbonambi. “Solving for these incoming bottlenecks translates into four immediate priorities: legacy modernisation, API enablement, integration as a service and platform expertise.”

Core systems do not always need to be ripped out. Still, they do need to be re-architected so old and new can coexist with less friction because careful integrations can allow old and new systems to coexist while protecting prior investments.

API-led connectivity gives institutions a more flexible way to expose services, connect partners, orchestrate data flows and reduce the dependence on brittle point-to-point integrations.

“Composable enterprise architectures built on API-led connectivity can reduce integration costs by 30% while creating reusable building blocks for innovation,” says Mbonambi.

The third step is integration as a service to ensure organisations have integration capability embedded into delivery from the start, which is supported by governance, testing and visibility. Then, platform expertise wraps all the factors into a cohesive whole, providing a deep understanding of the business ecosystems and how to bring them together coherently.

“Africa has already shown that it can lead in mobile money, digital payments and financial access,” concludes Mbonambi. “The next step is less glamorous, it’s process, process, process because the winners in fintech’s next chapter will be the ones that can connect core banking, customer channels, partner ecosystems and data in ways that are secure, scalable and commercially sustainable.”

Today, however, despite a growing need for agile and efficient testing, legacy QA models are struggling to keep up with cloud-native environments, fragmented teams and rapid release cycles.

And this, says Mandla Mbonambi, CEO of Africonology, introduces a new era of software testing using AI and automation to prioritise tests and analyse vast quantities of data. It is also, he says, introducing quality, security and governance risks.

“The benefits of AI in testing automation and QA are that it allows teams to move incredibly rapidly,” he says. “Companies benefit from faster automation processes, and their productivity increases exponentially. AI is also capable of analysing the data to detect defects or coverage gaps, and it can provide teams with high-risk scenarios or recommend additional tests based on its analyses.”

The Capgemini World Quality Report 2024-2025 was quick to highlight the impact of AI on the industry, emphasising its ability to optimise test coverage, reduce human error and introduce intelligent automation.

The study found that an impressive 68% of companies are using AI, with 72% reporting faster processes as a result.

The technology is changing the testing story, moving it away from an after-the-fact process that discovers unexpected errors and frustrates teams and deadlines alike. AI enables testing in near real time as models continuously analyse code throughout the development process, and they can be integrated into development and operations from the outset.

“AI models can learn, they can predict errors that crop up regularly or where they’re most likely to occur,” says Mbonambi. “They turn testing into a smoother part of the process, making it proactive and immediate instead of reactive and defined by ticking boxes. They also allow for self-healing, where the automation can detect when a test will break, find a resolution, and then apply the update so the test still passes if the business behaviour remains valid.”

Self-healing has the potential to minimise failures caused by minor changes and reduce the monotony burden on testing teams – talented humans now have more time to prioritise exploratory testing or more complex tasks.

The technology gives people the space to become high-quality architects, defining risk models, guiding AI, and interpreting patterns, rather than just running tests.

“There are agentic platforms that now can take on a lot of the heavy testing grunt work with minimal human input, capable of acting almost like testing interns that fortunately don’t get tired or frustrated,” says Mbonambi. “It sounds too good to be true, which unfortunately it can be – while AI has immense value in the QA environment, it also introduces risks that have to stay top of mind.”

Just as AI in the workplace tends to hallucinate or overcompensate, the same risks apply in testing. Some of the most common problems include false positives and false negatives, which add to testing noise rather than minimising it, an over-reliance on automation that impacts skills and awareness, and data privacy and security.

Then there’s the reality that AI models are still black boxes – teams don’t have visibility into the AI decision-making process and don’t know why some tests are prioritised while others are not.

“Bias, security, private information, limited guardrails and governance that’s battling to keep up with the pace of change, are very real concerns when it comes to introducing AI into the testing environment,” says Mbonambi. “Right now, as teams learn how to use and optimise AI in testing better, it’s important to remember it is a tool designed to augment the process. People are needed to validate AI output and make release decisions and, very importantly, become the guardrails for data privacy and explainability.”

This doesn’t mean AI is too risky, just as it doesn’t mean AI is the perfect solution to every testing problem.

Right now, AI is as much in its infancy as its use cases, which means testing with AI models and tools should be considered and balanced. Humans are essential, but so are the tools that lift the burden of complexity and deadlines.