04/24/2026
Most AI integration projects stall in the same place: the whiteboard.
Teams spend months debating architecture and future-proofing a system that hasn't been built. Meanwhile, the actual integration work never begins.
The assumption driving this pattern is that adding AI to existing software means tearing everything down and starting over. It rarely does. Your existing platform already has what AI needs most. Real production data. Established workflows. Users who understand how the system works.
The real decision is architectural: where does intelligence happen relative to your application? API-based integration gives you flexibility, at the cost of latency. Embedded models eliminate network calls, but shift operational burden onto your team. Event-driven architectures scale well for workflows where AI augments the critical path rather than controlling it.
Each pattern has a place. Picking the wrong one is how organizations end up with lasting technical debt instead of lasting value.
Read full blog here: https://hubs.ly/Q04dkQmx0
