How about connecting manufacturers to distributors’ databases and predicting sales and replenishing stock months before to stay within the market? Running root cause analysis of defects in fertiliser and initiating corrective actions, recalls and launching compliance reviews as needed, all autonomously.
Manufacturing is no stranger to automation and AI. For years, Kenyan factories have been investing in modern machinery, ERP systems, and basic robotics.
But AI represents a major leap forward — it’s not just about automating tasks, but about enabling machines to generate new insights, predict issues, recommend improvements, and even write code.
This kind of intelligence could be transformative for Kenyan industries like agro-processing, textiles, construction materials, pharmaceuticals, plastics and packaging and automotive assembly.
But there’s a catch: while the promise is clear, most manufacturers are simply not ready to harness AI at scale. A new global study by NTT DATA — Feet On The Floor, Eyes On Ai: Do You Have A Plan or A Problem?”- reveals that while 95 percent of manufacturers worldwide say GenAI is already improving efficiency and financial performance, deep challenges persist.
The study surveyed more than 500 manufacturing leaders and decision makers in 34 countries in five regions.
The findings are not just global generalities. They reflect the reality on the ground here in Kenya, where we see pockets of innovation, but widespread issues with infrastructure, skills, and responsible AI governance.
Despite the growing interest in AI, the biggest risk we face in Kenya is not so much technological, it’s strategic unpreparedness.
According to the study, 92 percent of manufacturers globally say outdated infrastructure is a major hurdle and fewer than half have conducted a full assessment of their readiness to support GenAI.
Many manufacturing plants are still grappling with legacy systems, unreliable internet connectivity, and siloed data environments. Without foundational digital infrastructure - including scalable cloud platforms and local data processing capacity - AI initiatives risk stalling before they even begin.
Another challenge is the workforce. Two-thirds of manufacturers in the study said their employees lack the skills to use AI effectively. In East Africa, the digital skills gap is a well-documented barrier to transformation.
While we are seeing progress through government programs, technical institutions, and private sector upskilling efforts, more targeted training is needed — especially at the intersection of data science, AI, and industrial operations.
Even as infrastructure and skills begin to catch up, there remains a blind spot: ethical and responsible AI governance. Only 47% of global manufacturing leaders strongly agree that they have robust frameworks in place to ensure AI is used ethically and safely. In Kenya, very few manufacturers have considered this at all.
Manufacturers must think beyond technical feasibility and consider the long-term social and reputational implications of AI-driven decisions. Whether you're automating quality checks or analysing customer feedback through AI, trust and accountability matter.
So, what should Kenyan Manufacturers do now? The path forward is not to wait, it is to plan. Manufacturers in Kenya must begin by assessing their current digital maturity and identify gaps in infrastructure, data capabilities, and workforce skills.
From there, small pilot projects can be a practical starting point. For instance, deploying AI to improve inventory forecasting or automate document processing could offer fast ROI and build internal confidence.
Inarguably, partnerships will be crucial. Collaborating with technology providers, universities, and government can help manufacturers access expertise, funding, and training programs. We also need cross-industry dialogue to share learnings, shape standards, and create ethical guidelines specific to our local context.
AI is not a silver bullet, but it is a powerful lever, a strategic pillar of business growth. For Kenya’s manufacturing sector to fully benefit, we must confront the readiness gap head-on, shifting from curiosity to capability. If we act with intention - investing in the right technologies, people, and policies - AI could help Kenya become not just a participant, but a leader, in the future of smart manufacturing.
The writer is an automation and data engineer at NTT DATA in East Africa
