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Can GenAI solve the platform engineer’s biggest pains? Can it accelerate platform delivery, improve developer experience, and make infrastructure more secure and efficient? The short answer is yes, but only if your platform is designed to provide AI with a solid foundation to work with. The missing ingredient is structure.

Enterprises racing to adopt AI often create more risk than value. To mitigate these risks, AI needs the same treatment as every other capability in the internal developer platform (IDP). Provide it through the platform, complete with budgets, policies, and observability from day one. That is how adoption scales without chaos.

Independent research reinforces this. MIT’s 2025 State of AI in Business report found that despite tens of billions in spend, only 5% of custom enterprise AI tools make it to production. Enterprises lead in pilot volume yet lag in scale. The findings point to approach, not model quality, as the real limiter. This article explores three platform-engineering principles that consistently turn AI from hype into working enterprise systems.

Cloud-native engineering (and platform engineering ) is evolving quickly, and AI is at the centre of that shift. In the past, when a developer needed something as simple as a new database for testing, the process was slow. They had to raise a ticket, wait for approvals, and depend on manual provisioning, which sometimes took days before they could even start working.          Now picture the same request in 2025. Instead of waiting, an AI assistant provisions the database ins

The future of platform engineering isn’t just cloud-native; it’s now AI-native. Would you agree with that? Whether your answer is yes or...

The Syntasso team attended last month's FINOS Open Source in Finance Forum in London and learned a lot!

The Syntasso team have finally unpacked our bags from an amazing KubeCon EU in Paris, and we’re keen to share our learnings!