Insights

Field notes from building AI in production.

Short pieces on what actually works — and what quietly breaks — when AI meets real organisational data and real teams. Written by Vikram Katyani, founder.

AI Strategy · 13 May 2026

Why I build for usability, not scope

Scope makes a product look impressive in a demo. Usability is what makes people actually use it on a Tuesday morning. The teams that win optimise for the second one — even when it means cutting features the steering group loves.

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AI Strategy · 5 May 2026

If you didn't design the human-in-the-loop, you didn't design the system

Most "human-in-the-loop" AI is really human-on-the-side — a reviewer staring at a confidence score with no real way to intervene. Designing the loop properly is what separates AI that ships from AI that quietly gets switched off.

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AI Strategy · 24 Apr 2026

AI that assists beats AI that replaces

The strongest AI systems do not remove people from the process. They help good people move faster, make better decisions, and spend less time on repetitive work while keeping human judgement where it matters.

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Data Readiness · 13 Apr 2026

Most AI strategies fail at data readiness

Integration is the easy part. Anyone can connect Salesforce to an LMS. Then real organisational data shows up and the demo breaks. AI doesn't fix messy data — it amplifies it.

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Engineering · 7 Apr 2026

AI applications aren't 'build once, run forever'

AI systems behave like living products. Models evolve, data drifts, user behaviour shifts. The teams winning aren't the ones with the best initial build — they're the ones who treat post-launch as the real work.

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AI Strategy · 2 Nov 2025

When expertise meets AI: lessons from a multi-agent system

Even the smartest agents fail without the right context. The real success of our system didn't come from clever orchestration — it came from the precision of the domain knowledge built in at every step.

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