By Veerakumar Natarajan, Country Head, Zoho Kenya
Over the past decade, Kenyan businesses have modernised through cloud-based software—from HR systems to CRM platforms to digital payment tools. Software-as-a-Service (SaaS) became the default route to digital transformation. The logic was simple: buy a subscription, gain access to features, adopt new workflows, and scale. For a while, this worked. But as Artificial Intelligence takes centre stage, the limits of a feature-first mindset are becoming clear. In Kenya today, AI’s real value will not be found in how many models, prompts, menus or dashboards a vendor can showcase, but in the business outcomes the technology delivers.
AI is not merely an upgrade to software. It is a capability layer that sits on top of systems, decisions and workflows. While a SaaS tool’s value depends on what users can click or configure, AI’s value lies in what it can do. It completes tasks, reduces errors, frees human time, and improves outcomes. Kenyan organisations that treat AI like another software licence risk overspending, under-realising value, and creating complexity without impact.
Consider a microfinance institution evaluating an AI solution to support loan officers. A traditional SaaS approach would involve comparing features—onboarding modules, case management dashboards, and reporting tools. An AI-first approach asks different questions: How many staff hours will this system save each month? How much faster will loan decisions be made? How will the accuracy or fairness of decisions be improved? These outcomes—not interfaces—determine the real value.
Kenya is particularly primed for this shift. The economy is highly mobile-first, with deep penetration of digital financial services, and a large proportion of businesses operating informally or semi-informally. Their workflows are often repetitive, document-heavy and time-consuming—precisely the kinds of tasks AI can automate. Yet adoption must be done responsibly. Under Kenya’s Data Protection Act, AI systems require transparency and clear governance. In sectors such as finance, insurance, healthcare and public services, AI-driven decisions must be explainable and traceable. This means organisations need more than just models—they need guardrails, audit trails and well-designed human-in-the-loop systems.
Pricing models are also evolving. Traditional SaaS charges per seat or per licence, regardless of performance. AI, by contrast, lends itself naturally to outcome-based pricing. A call-centre automation solution, for example, might charge based on successfully resolved customer queries, while a fraud-detection system could price itself relative to losses prevented. This alignment of incentives ensures that organisations pay only when the technology performs. For Kenyan CFOs and CIOs, this demands a shift in mindset. AI budgets should be tied to measurable business outputs, efficiency gains, revenue expansion or service quality improvements, rather than software procurement line items.
However, this journey carries risks. One common pitfall is “agent sprawl”, where different departments deploy isolated AI assistants, causing inconsistent performance and governance challenges. Another is over-reliance on AI for critical decisions using messy, siloed data, leading to unpredictable outcomes. The solution lies in deliberate, incremental deployment. Start small—optimise customer onboarding in a fintech, invoice processing in a Nairobi SME, or data collection for an agricultural programme. Build end-to-end, prove value, then scale.
Ultimately, AI is not replacing SaaS—it is reframing how value is measured. The winners in Kenya’s AI era will be the organisations that focus on outcomes, not the ones dazzled by features. They will be the firms that free people from low-value administrative work, deliver better-quality services to customers, and can clearly explain how decisions are made. As businesses and government agencies across Kenya consider their next digital transformation steps, the question should not be, “Which AI platform has the most features?” but rather, “Which solution will remove visible work, improve outcomes, and leave a clear, auditable trail?”
That is where AI’s real promise lies, not in showcasing intelligence, but in delivering results.
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