The industry has an AI deployment problem, and it's not what vendors want you to think.
Gartner projects that more than 50% of enterprise AI initiatives will fail to reach production through 2027. The culprit isn't model performance. It's foundational architecture that was never designed to support AI at scale.
The gap is specific. In a 2024 survey, 63% of organizations lack AI-ready data practices. Another data point: 60% of agentic AI projects failed in 2026 due to data preparation issues alone. McKinsey's numbers are worse. Only 20% of organizations achieve enterprise-scale AI impact. Most pilots die on data integration.
What's actually breaking
The architecture failures cluster around predictable points. Data remains locked in silos across business units. MLOps practices, where they exist, are immature. Governance frameworks aren't designed for model drift or explainability requirements. Tool stacks are fragmented, with no clear path from experimentation to production.
For deployment specifically, the microservices versus monolithic debate matters more than most architecture discussions. Deploying ML models in microservices architecture introduces latency considerations that don't exist in traditional workloads. Distributed inference on Kubernetes requires careful orchestration. Tools like vLLM are being evaluated against traditional serving approaches for cost and throughput trade-offs.
The technical challenge extends to containerization best practices for ML models and batch processing optimization at scale. Enterprise teams are working through distributed inference latency on Kubernetes while balancing GPU utilization against infrastructure costs.
What works
Organizations that ship AI to production share common patterns. They built hybrid stacks that integrate with existing systems rather than replacing them. They invested in AI-ready data pipelines before scaling model development. They established regulatory compliance frameworks early, not as an afterthought.
The World Economic Forum reports that 95% of AI pilots fail to deliver efficiency or cost savings. The pattern is consistent: organizations that treat AI as a pure model problem hit a ceiling. Those that solve for architecture first have a path to scale.
The hard work isn't training better models. It's building systems that can actually use them.