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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.

Despite $30–40 billion already invested in GenAI, MIT NANDA reports that 95% of initiatives fail to show measurable business impact. AI has arrived, but enterprise value hasn’t. That’s where the Model Context Protocol (MCP) fits. And it’s Syntasso Kratix Enterprise that turns MCP from an unrealised opportunity into a provided-by-default reality.

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