Many countries have experienced “technology leapfrogging,” where populations moved directly from having no phones to widespread mobile phone usage—skipping the era of landlines entirely. For end consumers, this was a clear leap. However, for service providers, the shift was less revolutionary. While providers avoided the costly task of wiring every household, the core work of enabling large-scale communication didn’t disappear; in fact, networks had to be more robust and scalable to handle the surge in data and voice traffic. Significant effort went into strengthening foundational technologies so that the infrastructure could support this growth.

Lately, I’ve been part of conversations, organisations urging to “leapfrog” with AI technology, mirroring the mobile phone revolution. While the enthusiasm is understandable, many underestimate the critical value of foundational IT systems. For mid to large organisations, adopting AI isn’t like the mobile leapfrogging where consumers moved straight to a modern tech. Skipping essential architectural elements—like solid API design, security frameworks, and enterprise integration—is akin to skipping the main course and jumping straight to dessert.

Building a scalable, secure, and maintainable AI-enabled system still requires strong foundations. Effective AI integration demands robust data pipelines, secure access controls, and clear interoperability standards. Ignoring these will lead to challenges in scalability, security vulnerabilities, and fragmented systems.

AI adoption is transformative but must be layered on a strong technological foundation. Just as mobile networks demanded fortified infrastructure behind the scenes, AI initiatives need reliable architecture to truly deliver on their promise without risking systemic issues.