When I joined Thoughtworks, one of the first few things that I enjoyed was how the teams learnt and solved problems together. Until then, I was used to tasks getting assigned to me and taking it to completion. Asking for help was seen as a weakness, knowledge sharing was considered to be detrimental to career as you will be replaced easily. I was wrong, learning and sharing together which I came to know as ensemble learning made my work life very rewarding.

Simple things that ensured that we learnt as a group were

1. Information radiators on the wall, omnipresent in the entire office. We even used to complain that we do not have enough walls.
   • Gotchas
   • Skill/Knoweledge matrix of who knows what and how much
   • Pair rotation matrix, to ensure that no silos form
   • Story wall and Release plan, to know what are the upcoming tasks in the near term
2. Learning sessions
   • Collective code review and refactor sessions.
   • Deliberate KT based on Skill/Knowledge matrix
3. Huddles
   • No questions asked, cry for help when stuck. This meant that if someone is stuck for more than 30 minutes they have to raise an alarm and entire team stops their work and jumps in to unblock.

Seems like a simple list, but it had a profound impact and kept the working life stress free and productive. In a remote first environment, the radiators can be managed with pinned messages on group chat. Learning sessions and huddles should happen as it can in physical environments.

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.