Editorial collection
Applied AI in Production
Build AI on governed knowledge, controlled access, measurable quality and explicit operational ownership.
Applied AI
Who should own enterprise AI governance?
Security, legal, data, technology and business leaders each own part of AI risk. Governance works when accountability and decision rights are explicit.
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Private enterprise AI: when local models make sense—and when they do not
Running a model locally can improve control, latency or offline capability. It does not automatically make the complete AI system private, secure or economical.
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Enterprise RAG fails at ingestion before it fails at the model
A knowledge assistant cannot recover structure, context, permissions and freshness that its ingestion pipeline discarded.
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Enterprise RAG security starts with retrieval permissions
A knowledge assistant must never retrieve evidence the user could not access at source. That boundary belongs inside retrieval, not after generation.
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Why company knowledge assistants hallucinate—and what to test
A wrong knowledge-assistant answer may begin in ingestion, retrieval, context, generation or citation. Testing only the final response hides the cause.
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Prompt management is not enough
A prompt only has meaning beside the model, tools, retrieval settings, evaluations and production traces that determined its behaviour.
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The dangerous journey from AI proof of concept to production
A successful AI demo proves possibility under controlled conditions. Production requires evidence about risk, reliability, operations, cost and real user outcomes.
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AI adoption without engineering discipline increases the blast radius
AI can accelerate useful work and also scale insecure access, untested decisions and invisible dependencies. The answer is a safe paved road, not blanket prohibition.
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