Kenya’s AI Push, Big Ambition, Uneven Payoff, and a Growing Sense of Friction

Talk to enough executives in Nairobi boardrooms and a pattern emerges. Almost everyone wants to be counted among the companies using artificial intelligence as a daily tool rather than a lab experiment. Fewer have actually crossed that line. Close to 70 percent of organisations in Kenya now say they intend to fully adopt AI by the end of 2026. Only 26 percent have managed it so far. That distance between intent and reality has widened over the past 3 years, not narrowed.

The numbers come from KPMG’s Global Tech Report 2026, which surveyed 2,500 executives across 27 countries and territories. Kenya fits neatly into the global picture the report sketches. Confidence is high. Follow-through is uneven. The hardest part is no longer deciding whether AI belongs in the business. It is figuring out how to make it pay.

From pilot fatigue to operational pressure

For years, AI inside many Kenyan firms lived in side projects. A chatbot here. A forecasting tool there. Often parked within IT teams and rarely connected to revenue, costs, or accountability. That phase is ending, not because the experiments failed, but because patience has worn thin.

The report describes companies moving beyond pilots and trying to embed AI into core workflows and products. That step changes the nature of risk. A failed pilot is an inconvenience. A failed system that touches billing, customer service, or credit decisions becomes a governance problem. It explains why adoption looked slow between 2023 and 2025. Firms were not dragging their feet. They were hesitating at the edge of something harder.

Scaling introduces friction that early demos hide. Data quality issues surface. Legacy systems resist integration. Managers discover that automating decisions forces uncomfortable clarity about how those decisions are made in the first place.

Returns that refuse to behave

One of the more sobering findings in the report is how inconsistent AI returns remain. High-performing organisations report an average return on investment of nearly 5x. The industry average sits closer to 2x. Smaller firms average 3.6x. Transformation-focused organisations report about 3.2x. Companies under less cost pressure land around 2.6x.

There is no single sweet spot where spending suddenly pays off. Returns cluster in zones that correspond to maturity. Early wins come from narrow use cases. Broader value only appears once AI is woven across departments, budgets, and decision rights. Many firms never make that jump, not because the tools fail, but because the organisation does.

Only 24 percent of firms report achieving ROI across multiple AI use cases, even though 74 percent say their AI deployments are delivering some business value. That gap hints at a familiar corporate problem. Success in one corner does not automatically travel.

Agentic systems meet human reality

The enthusiasm around agentic AI is real. According to the report, 88 percent of firms are investing in building agentic capabilities into their systems. These are tools designed to act, not just advise. Schedule tasks. Trigger actions. Coordinate processes with minimal human prompting.

Yet the workforce expectations tell a different story. By 2027, organisations still expect 42 percent of their tech workforce to remain permanent human staff. That figure drops only 5 points from 2025. High-performing companies plan to retain even more people, with 50 percent of roles staying permanent.

This is not a contradiction so much as an admission. Automation creates demand for judgment. The more autonomy systems gain, the more costly their mistakes become. Someone still needs to decide where authority ends.

Talent as the stubborn bottleneck

More than half of surveyed organisations, 53 percent, say they lack the talent needed to deliver their digital transformation plans. This figure appears twice in the report, almost as if it refuses to be ignored. Tools are plentiful. Skills are not.

Kenya’s tech sector has grown quickly, but advanced AI work pulls from a narrow pool. Data engineers, model auditors, and systems architects are in short supply. Training takes time. Hiring abroad raises costs and cultural friction. The result is a dependence on vendors and consultants that can slow internal learning.

At the same time, 90 percent of organisations plan to grow partnerships and technology ecosystems over the next year. Collaboration fills gaps, but it can also mask them. When partners leave, capability often leaves with them.

Risk tolerance rises, caution stays

At least 78 percent of respondents agree they must take more risks on emerging technologies to stay relevant. It sounds bold until paired with the slower pace of execution. Risk, in this context, does not mean recklessness. It means accepting that some investments will disappoint and that waiting for certainty is its own liability.

Guy Holland, global leader of the CIO Center of Excellence at KPMG International, argues that companies are moving past what he calls AI roulette. The scattershot phase of placing bets everywhere is giving way to a tighter focus on value. That discipline, however, demands choices. Which processes deserve automation. Which decisions remain human. Which metrics matter.

Where the road narrows next

Kenya’s AI story is no longer about access to tools. It is about organisational nerve. The firms reporting stronger returns share a willingness to restructure workflows, not just software. They treat policy documents, operating manuals, and approval chains as part of the technology stack.

Over the next 18 months, the pressure will increase. Boards will ask harder questions about payback. Regulators will look more closely at automated decisions. Employees will push back where systems feel imposed rather than integrated.

The companies that move first through that narrowing road are unlikely to look flashy. They will look methodical. Less talk about potential. More attention to where AI quietly breaks, how humans compensate, and what that says about the business itself.

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