Blueprints and engineering strategies for scaling enterprise AI from pilot to production.
88% of enterprises use AI. Only 8% have a comprehensive governance framework. In healthcare, finance, and insurance, that gap produces regulatory fines, compliance failures, and AI systems that cannot explain their own decisions. Here are the 5 governance engineering decisions that close it.
Gartner expects over 70 percent of 2026 mainframe exit projects to miss their goals. AI explains COBOL well, but proving migrated code behaves the same needs a different framework.
89 percent of teams running agents have some observability, but only 37.3 percent evaluate live traffic. Here is why green dashboards hide wrong answers and how to close the gap.
Only 7% of enterprises have AI-ready data. Gartner projects 60% of AI projects without it will be abandoned. The barrier is not the model. It is what feeds the model. Here are the 5 data engineering decisions that determine the outcome.
Gartner expects 60 percent of AI projects to be abandoned by 2026 over data that was never AI-ready. Most of that gap traces back to one thing: no enforced data contract between the pipeline and the agent reading it.
Most manufacturers have already run a generative AI pilot, but very few have the certified, integrated data an agent needs to act on the production line without a person checking every output first.
31% of production LLM queries have a semantically similar prior query. Without semantic caching, each one runs a full retrieval and generation cycle. Here is how to fix that without degrading answer quality.
Most enterprises running multiple GCC sites have no single owner for AI platform decisions, so each site quietly rebuilds the same vector store, model gateway, and agent pipeline on its own budget.
Vector databases retrieve whatever they're given, with no native concept of who was allowed to see the source document. That mismatch is where enterprise RAG pipelines quietly lose the access control their source systems spent years building.
No 50-page proposals. We'll tell you which level fits your situation, what a realistic engagement looks like, and what it would cost — in one direct meeting.
Thomas leads 10decoders' AI engineering practice and sits in on the scoping call himself — so the person mapping your engagement is the one who has shipped it before. His teams build and deploy agents for mid-market healthcare and fintech companies, with enterprise grade build experience for clients like IBM, Dedalus and Harris Healthcare. He'll be straight with you about what's worth doing and what isn't.
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