Experts advocate treating AI as critical infrastructure

As artificial intelligence moves from experimental applications to a core part of modern industry, experts are urging organisations to treat it as critical infrastructure rather than a standalone tool.

Software engineer Blessing Philips highlighted the challenges of deploying AI at scale. “People tend to focus on models,” she told The PUNCH. “But in high-scale environments, the model is only one piece. The real complexity lies in the systems wrapped around it.” Building models, she said, is relatively straightforward; operating them reliably for thousands or millions of users is the true test.

Data pipelines, which feed AI systems with information, are a particular vulnerability. Financial services, healthcare, government and transport platforms process vast streams of data, and minor inconsistencies, such as changes in audio encoding or missing inputs, can quietly degrade performance. Philips described a case in which a subtle upstream data shift caused thousands of daily predictions to drop in accuracy, despite no obvious system failures.

“If the data pipeline is fragile, the entire system is fragile,” she noted. This has prompted organisations to invest heavily in real-time ingestion systems, feature stores and automated monitoring tools that flag potential quality issues before they affect operations.

Nigeria is beginning to build the infrastructure needed to support AI at scale. Government initiatives include the Nigeria Artificial Intelligence Research Scheme and the National Centre for Artificial Intelligence and Robotics, which provide funding, technical support and infrastructure in partnership with private and international collaborators.

Infrastructure is also crucial for running AI at scale. High-performance systems need distributed processing, automatically scaling computing power, caching to avoid repeating work and backup systems to prevent outages. Philips noted that handling 10 requests per second is very different from 10,000, and without strong architecture, users will notice problems immediately.

“An AI system serving 10 requests a second behaves very differently from one serving 10,000. If your architecture can’t stretch, your users will feel it immediately,” she said.

Philips stressed that monitoring and observability often matter more than raw accuracy. Long-term reliability depends on tracking metrics such as latency, accuracy drift, confidence scores and anomalies. “You can’t improve what you can’t see,” she said. “If a company can’t explain how a model behaves in the real world, it cannot claim to be operating safely.”

Engineering for failure is also crucial. In high-scale environments, system failures are inevitable. Networks go down, nodes crash, data drifts and user behaviour changes. Philips advocates a philosophy of graceful degradation, ensuring systems fail safely without collapsing entirely.

“Building for failure is just as important as building for performance,” she said.

As AI increasingly underpins critical national services, financial markets and global communications, Philips warned that organisations that neglect operational resilience will be left behind. The next decade, she said, will reward those that treat AI not as a feature but as a responsibility, requiring investment in infrastructure, data integrity and robust monitoring.

“The winners will be the ones who treat AI not as a feature, but as a responsibility,” Philips said, noting that sustainable, high-performance AI systems are as much about engineering and governance as they are about the models themselves.

In sum, as AI becomes a backbone of modern operations, its successful deployment will hinge less on the sophistication of models and more on the resilience, reliability and scalability of the systems that support them. Organisations that embrace this mindset are positioned to lead in the emerging AI-driven economy.