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The questions IT and security teams should ask before an AI agent touches live systems: identity, secrets, logging, extensions and data residency.

An AI system that is wrong stays green on every dashboard. What to instrument, how failure timing points to the cause, and why models cannot audit themselves.

Fully automated, AI-powered, the AI handles it: how loose claims about your own AI create commercial risk, and the register that keeps everyone honest.

Access is not adoption. Why AI rollouts stall at occasional Q&A, and the champion-led pattern that actually changes how a team works.

A working rule from production AI systems: keep fixed rules in code, give AI the judgement work, and set human sign-off by what a mistake costs.

The six areas a proper AI readiness assessment covers, what its output should look like, and the red flags that tell you it is a pitch in disguise.

What AI search optimisation is, how ChatGPT, Perplexity and Google AI Overviews choose their sources, and the changes that actually earn citations.

The evaluation discipline that separates AI systems you can trust from demos: golden datasets of real cases, graded checks and re-testing on every change.

The pilot worked and production did not. The gaps that cause it: real volumes, messy inputs, missing error paths and costs nobody modelled at scale.

Claude API costs are set by design decisions, not the price list: model tiering, prompt caching, batch processing and instrumentation that keeps bills sane.