A Beginning-of-Year Reflection for Enterprise Architects and Technical Leaders As we step into 2026, it’s worth pausing to reflect on the seismic shifts that defined enterprise architecture in 2025—and the hard lessons learned when AI hype met production reality. What began as breathless excitement around generative AI and LLMs has matured into a more nuanced […]
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Agentic AI in Enterprise: Why Infrastructure Readiness Matters More Than Model Capability
After 20+ years in enterprise architecture, I’ve seen that infrastructure readiness matters more than model capability for agentic AI deployment. Gartner predicts 40% of projects will be cancelled by 2027 due to infrastructure gaps, not AI failures.
Read more →2025 in Review: The Infrastructure Readiness Lesson
2025 taught enterprise technology leaders a critical lesson: infrastructure readiness matters more than model capability. This year-end review explores platform engineering, data governance, healthcare AI breakthroughs, and five predictions for 2026.
Read more →The Patterns That Actually Matter: What Building Microservices at Scale Taught Me About Distributed Systems
🎓 AUTHORITY NOTE This content reflects 20+ years of hands-on enterprise software engineering and architecture experience. Recommendations are production-tested and enterprise-validated. Executive Summary The transition from monolithic architectures to microservices is often painted as a silver bullet for scalability. However, without the right distributed system patterns, it often results in a “distributed monolith”—a system that […]
Read more →Enterprise Machine Learning in Production: Healthcare and Financial Services Case Studies
Real-world enterprise ML implementations in healthcare diagnostics and financial fraud detection. Explore RAG and LLM integration patterns, ML maturity frameworks, and strategic recommendations for building ML-enabled organizations.
Read more →Machine Learning Fundamentals: A Comprehensive Guide to Enterprise AI Foundations
Discover the foundations of machine learning from an enterprise architect’s perspective. Learn core ML concepts, the ML workflow, and practical Python implementations to kickstart your AI journey.
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