For the past two years, much of the global conversation around artificial intelligence (AI) has focused on massive, centralised platforms and powerful tools capable of answering questions within closed ecosystems. Yet, standing in the auditorium at Strathmore University during ClawCon Nairobi, surrounded by more than 400 developers and entrepreneurs, a different reality became clear: the next frontier of AI in emerging markets will be defined less by theoretical sophistication and more by practical execution, individual agency and commercial viability.
We are moving from reactive chatbots to proactive personal AI agents. The critical question is no longer, “What can AI tell me?” but rather, “What can AI help me accomplish?”
In African markets, technology succeeds when it delivers tangible and measurable value. AI is already changing the economics of software development, enabling founders and small technology teams to achieve significantly more with leaner teams.
AI-assisted development is lowering the barriers to building and testing software, allowing entrepreneurs to move from an idea to a working product faster and at lower cost. But lower development costs alone do not guarantee success. Commercial viability still requires discipline, customer validation and a clear understanding of the problem being solved.
Entrepreneurs must therefore focus on three priorities: identify a concrete operational problem rather than building technology simply because it is possible; secure paying customers early to validate genuine demand while retaining ownership of core intellectual property; and solve before scaling, ensuring the product consistently addresses problems customers are willing to pay to solve.
The winners will not necessarily be those who build the most sophisticated AI systems, but those who use available technology to solve important problems better, faster and more affordably.
For corporate executives and SME leaders, the business case for AI is often framed too narrowly around reducing headcount. The greater opportunity lies in productivity, stronger business intelligence, improved customer experiences and revenue growth.
By deploying open-source AI agents and automating repetitive workflows, organisations can enable existing teams to spend less time on routine administration and more time on customer acquisition, strategic decision-making, innovation and growth.
The objective should not simply be to replace people with machines, but to equip people with better tools.
For African businesses operating in competitive and resource-constrained environments, this productivity dividend could be transformative.
The speed of AI adoption must be matched by operational discipline. As AI systems take on deeper business processes, organisations must establish safeguards around data privacy, cybersecurity, intellectual property, access controls and human oversight.
Businesses need clear rules governing what information AI systems can access, what decisions they can make independently and where human approval remains necessary. Responsible adoption means building systems that are not only efficient, but secure, transparent and predictable.
The next chapter of AI in East Africa and other emerging markets will not belong exclusively to organisations with the largest technology budgets. It will belong to those that understand how to turn AI capability into practical outcomes.
Founders must identify real problems and build solutions customers will pay for. Business leaders must move beyond experimentation and identify where AI can improve productivity, customer experience, decision-making and growth.
Most importantly, individuals need to become AI-proficient. AI literacy should no longer be reserved for developers and technology teams. Executives, entrepreneurs, managers and professionals across industries must understand how these tools can create value and become part of everyday work.
The competitive advantage will increasingly belong to those who know how to work with AI, rather than simply those who have access to it.
The question is no longer whether AI will transform business. It is who will learn to use it effectively enough to transform their business first.
