By Oluwadamilare Sadare

As Nigeria continues to confront food insecurity, post-harvest losses, and weaknesses across its agricultural value chains, Nigerian researcher Oluwadamilola Esan is advocating greater attention to the role that emerging technologies can play in strengthening the infrastructure that supports the country’s food system.

Esan’s work explores the intersection of digital technologies, sustainable infrastructure, and food systems, with a particular focus on how innovations such as Generative Artificial Intelligence (GenAI) can be adapted to address practical challenges in Nigeria and other developing economies. One area of growing interest is the application of GenAI to quality assurance and quality control (QA/QC) in food-system infrastructure.

According to Esan, conversations about food security often concentrate on agricultural production while paying less attention to the infrastructure required to preserve, process, store and move what is produced. Storage facilities, processing plants, cold-chain systems and production hubs are essential components of a functioning food system. Yet construction defects, inadequate inspection, fragmented documentation, repeated corrective work, and operational failures can compromise their reliability.

“Food security should not be considered only in terms of how much food we produce,” Esan said. “We also need to consider the quality and reliability of the infrastructure that supports food from production through processing, storage and distribution.”

Beyond ChatGPT: Putting AI to work
Esan believes Nigeria needs to broaden its understanding of artificial intelligence beyond chatbots, text generation and office productivity.

Within infrastructure development, GenAI could assist professionals with quality planning and documentation, early identification of project risks, regulatory compliance and improved traceability. Combined with other digital technologies, AI could also support defect detection, continuous monitoring of infrastructure assets and predictive maintenance, helping professionals identify potential failures before they become expensive or dangerous problems.

For Esan, the greater opportunity lies in transitioning from reactive quality control to preventive and predictive quality management.

Rather than waiting until infrastructure has deteriorated or defects have become apparent, digitally enabled quality systems could help engineers and facility managers recognise warning signs earlier and intervene before failures affect operations.

Research points to opportunity
Research examining the relationship between GenAI, quality management, and infrastructure performance in Nigeria indicates that the technology has considerable potential to strengthen quality assurance and control and to improve infrastructure performance and safety.

Esan, however, cautions against interpreting this as an argument for replacing professionals with artificial intelligence.
“The future should not be AI versus professionals,” she said. “It should be about how professionals can use AI to make better-informed decisions, identify risks earlier and improve the consistency and traceability of quality management.”

This human-centred approach is particularly important because artificial intelligence systems can themselves produce errors and potentially introduce algorithmic bias.

Professional oversight, therefore, remains essential.

Building an intelligent infrastructure ecosystem

For Nigeria to realise these benefits, Esan argues that AI cannot operate in isolation.
One promising direction is integrating GenAI with technologies such as Internet of Things (IoT) sensors and digital twins, enabling continuous monitoring of infrastructure conditions and earlier identification of emerging risks. AI-powered QA/QC assistants could also support professionals by providing real-time compliance alerts, while blockchain-backed quality records could make documentation more traceable and resistant to manipulation.

However, the deployment of technology must be accompanied by appropriate standards, professional training, data governance, and mechanisms for validating AI systems before they are relied upon for critical infrastructure decisions.

Esan believes Nigeria should begin developing sector-specific approaches to AI adoption rather than simply importing generic technological solutions.

Solving African problems with emerging technologies

The implications extend beyond food infrastructure.
For Esan, the bigger question is how Nigeria and Africa can move from being predominantly consumers of emerging technologies to becoming active participants in determining how those technologies are applied to local developmental challenges.

Food systems provide an important opportunity.

Nigeria possesses substantial agricultural potential, but increasing production alone will not resolve all the weaknesses within the food value chain. The infrastructure connecting production, processing, storage and markets must also become more reliable, efficient and resilient.

Artificial intelligence could form part of that transformation.

“The real value of emerging technologies for Africa will be determined by how effectively we connect them to our own problems,” Esan said. “We should be asking how AI can improve the systems people depend on every day from food and water to energy and infrastructure.”

The goal, she maintains, is not technological adoption for its own sake. It is about using technology to create infrastructure that is safer, more reliable, more traceable and better able to support sustainable development.

And as Nigeria searches for solutions to food insecurity and infrastructure deficits, that may be one of the most consequential applications of the country’s emerging artificial-intelligence ecosystem.