Blueprints and engineering strategies for scaling enterprise AI from pilot to production.
48% of AI agents in production have no real monitoring, and providers ship silent model updates without warning. Here is what breaks first, and the regression testing safety net Quality Engineering teams need before the next one hits in 2026.
Most employees already use AI tools IT never approved. Here is how enterprise leaders build the visibility and governance layer that keeps pace with shadow AI adoption in 2026.
73% of enterprise AI projects that fail weren't prioritized against a consistent framework. Here's the five-dimension scorecard that helps enterprise teams rank AI use cases before committing a single engineering sprint.
94% of Indian GCCs say they have an AI strategy, but 51% remain stuck at early maturity. Here is the three-stage framework that separates AI adoption from measurable enterprise advantage in 2026.
77% of enterprise LLM deployments go live without a formal evaluation process. Here's the pre-deployment evaluation framework that separates teams who catch quality regressions before users do from teams who don't.
Most enterprise teams that fine-tune a model later discover RAG would have done the job at lower cost. The choice between fine-tuning, RAG, and prompt engineering comes down to five questions you can answer before writing a line of code.
Most AI pipeline incidents don't start in the model. They start in the data — in schema changes, quality drift, and freshness violations that nobody agreed to monitor. Data contracts are how teams stop debugging production by guesswork.
88% of enterprise AI agent pilots never reach production, and Gartner traces most of the failures back to how they were tested and evaluated, not to the model underneath. Here's what a real testing discipline for agentic AI looks like in 2026.
83% of India's Global Capability Centers are now mission-critical to their parent enterprise, yet only 17% hold full ownership of strategic decisions and budget. Here's what the HFS Generative GCC Index 2026 found, and what closing that gap actually takes this year.
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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