AI-ML Readiness in Architecture

A focused set of 10 actionable best practices for AI-ML Readiness in Architecture — the practices that separate an architecture that can actually support production models from one that only works for a demo because the data pipeline, governance, and monitoring were never built out. Built for technology leaders under pressure to show AI progress fast.

What's Inside

• 10 Best Practices — covering data pipeline maturity, model governance, drift monitoring, and the gap between a working pilot and a supportable production model
• Introduction — why most "AI readiness" gaps are architecture gaps, not model gaps, and where they typically surface first
• PowerPoint Format — Clean slides ready for team presentations, workshops, or leadership briefings

How Teams Use It

• Architects use it to assess whether existing data and platform foundations can actually support AI initiatives before committing budget
• Use as a checklist to benchmark current architecture against AI-ML readiness practices
• Incorporate into training materials, onboarding decks, or strategy presentations