Why Most Enterprise AI Programs Fail Before They Start
The failure mode isn't technical. It's organizational. Most enterprises skip the operating model and jump straight to tooling.
ReadArchitect · Builder · Leader
Vice President, Cloud & Platform Architecture at Deutsche Bank. I architect and build production GenAI, NLP, and data systems — and I lead the strategy, governance, and teams that make them ship in regulated financial services.
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Ideas on AI governance, data architecture, and enterprise transformation.
The failure mode isn't technical. It's organizational. Most enterprises skip the operating model and jump straight to tooling.
ReadWhy insert-only modeling, hash keys, and full lineage aren't academic exercises — they're survival patterns in regulated environments.
ReadCenters of Excellence centralize capability. Operating models distribute it. The difference determines whether AI scales or stalls.
ReadOpen Source
Selected projects.
RAG patterns benchmarked for enterprise. Five chunking strategies, five retrieval patterns, evaluation harness, guardrails, and observability — built for regulated environments.
Reference architecture for LLM applications in banking. Chain routing, prompt registry, guardrails, evaluation pipelines, and output drift monitoring.
Production NLP from pre-LLM to post-LLM era. Capsule networks, BiLSTM-CRF for NER, transformer fine-tuning, and event-driven stream processing in PyTorch.
AI governance for regulated industries. Risk classification, model lifecycle controls, EU AI Act mapping, and worked examples for credit scoring and fraud detection.
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Open to leadership conversations.
If you're shaping the next stage of enterprise AI capability, data platform modernization, or technology transformation — I'd welcome the conversation.