A lot of teams get an AI proof-of-concept working quickly, but struggle to move it into production, it either breaks under real data volume, becomes too costly to maintain, or just quietly gets abandoned after the demo phase.

For people who've taken AI projects from pilot to production successfully: what technical or architectural decisions made the biggest difference? Was it data pipeline design, model evaluation/monitoring, choosing the right integration points with existing systems, or something more organizational (buy-in, ownership, maintenance planning)? Also curious whether teams found it more sustainable to build this in-house versus using existing platforms/tools as a foundation. Real examples, failure stories, and lessons learned would be genuinely useful here.