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.

When the entry barrier is lowered to try and create new things, there will be an explosion of people attempting to create a lot of new low effort poor quality outputs which I discussed about in my previous writings as Inverse vandalism. Gen AI arrived and pushed the outputs to slop territory. Every new tech is useful and makes lives easy, but channeling the effort to not product sloppy work is the key.

How do we know that we are not producing sloppy work? My idea is to stay away from people who knows exactly what needs to be done. Collective intelligence and learning has always been superior than individual learning and intelligence. Many people are of the opinion that with the new age AI, tools they can reduce the dependence on humans (statements like we do not need programmers, AI will write everything), while they are just moving the workload from a deterministic abstraction to a non deterministic abstraction (at least for a few years). This means your plain english is a program, that will require linting to remove sarcasm, language analysis to remove ambiguity, differentiate between idiomatic expression and literal expression. I have just started, the list will go on because you have to bring everything else that applied to programming here.

It is collaboration, not blind automation; that will transform how we work with the latest AI tools. Treating these tools solely as automation risks producing sloppy, unreliable results.