Over the past two years, Generative AI (GenAI) has captured global attention, including here in Kenya, thanks to its ability to draft content, summarise reports, and offer conversational assistance. These tools provide meaningful value, especially for teams looking to boost productivity and ease administrative workloads. However, GenAI represents only one part of the broader AI ecosystem. For most Kenyan organisations, the real opportunity lies in understanding how generative and agentic technologies complement rather than replace one another, and how each can be applied at different stages of digital maturity.
The effectiveness of any AI system, whether generative or agentic, depends heavily on the quality of the data and workflows it operates on. This is where many Kenyan organisations face their greatest challenge. Manual processes, inconsistent data entry, fragmented systems, and limited integrations between various systems remain common issues across sectors. These realities make it difficult to leap directly into advanced AI use cases. Without clean, organised, and accessible data, even the most sophisticated AI systems can produce inconsistent or misguided outputs.
For this reason, the most practical starting point for many Kenyan businesses is not the immediate adoption of advanced GenAI models, but rather the digitization and automation of core processes. Tasks such as routing customer-service tickets, reconciling mobile-money transactions, managing field-officer reports, or processing sensor data may seem modest compared to futuristic AI visions. Yet these workflow-driven improvements provide immediate, tangible value. They reduce errors, improve consistency, and create a clearer picture of how information flows through an organisation. As these processes stabilize, they naturally highlight areas where AI can make a meaningful difference.
Once these foundations are in place, AI becomes especially powerful. While GenAI helps teams create and be more productive, agentic AI helps organisations act and be more efficient. It proposes actions, verifies them, and then executes based on predefined business rules. This distinction matters greatly in sectors such as BFSI or public services in Kenya, where trust, compliance and accountability are central. A loan approval system powered by agentic AI, for instance, might recommend an action but will only execute it after confirming that KYC rules have been met, thresholds respected, and documentation verified. This combination of intelligence and verifiable guardrails enables fast and reliable decision-making.
Kenyan Companies Growing More Comfortable with AI.
As Kenyan enterprises grow more comfortable with AI-enabled systems, another important layer emerges: context. Global AI LLM models, despite their power, often struggle with the nuances of local regulations, business practices, cultural norms, and sector-specific terminology. This is where contextual AI and sovereign LLMs become essential. These are models fine-tuned with local data and designed to operate within specific regulatory frameworks, ensuring that the insights and actions they generate reflect the realities of the Kenyan market. Such models do not replace global systems; rather, they complement them by adding the local intelligence required for accuracy, relevance, and regulatory alignment.
Beyond the technology itself, the rise of AI presents an exciting opportunity to strengthen what Zoho calls transnational localism, the idea that global technology can fuel local innovation and economic empowerment. No-code and low-code tools, embedded with AI capabilities, allow SMEs, NGOs, and governments in regions like Kisumu, Eldoret, or Turkana to build their own automations without needing specialised data-science expertise. A micro-insurer can automate risk assessments based on local claims patterns; a county office can streamline citizen services; an agritech startup can create workflows around farmer support. The result is a decentralisation of digital innovation that allows solutions to emerge from the communities that understand their challenges best.
For leaders charting their AI journey, the path forward becomes clearer when viewed through this practical lens. The most sustainable strategy is to begin with workflow automation, build strong data foundations, introduce GenAI where it offers productivity improvements, and gradually adopt agentic AI when the organisation is ready for secure and auditable automation. As maturity grows, contextual and sovereign AI models add the essential layer of local relevance.
Kenya’s AI future will not be defined by a race toward the most advanced model. Instead, it will be shaped by organisations that take a balanced approach. Those that invest in good data, well-designed workflows, and systems designed to act responsibly will see the greatest returns, through improved customer experience, reduced operational costs, and empowered teams who spend less time on repetitive tasks and more time on meaningful work. Ultimately, the future belongs to businesses that embrace AI not as a flashy tool, but as a dependable partner in delivering lasting impact.
By Veerakumar Natarajan, Country Head, Zoho Kenya
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