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AI is impressive. You type a prompt and something comes back that sounds surprisingly good. That’s why AI pilots are usually quick to build — and sometimes also quick to “sell” internally. But the real question is: Would you actually publish that answer, use it in a business process, or send it to a customer? Production has different rules than a demo. In production, “mostly right” is often simply: wrong.
The problem: AI sounds smart, but it’s (still) not a system
Most teams don’t get stuck on “the model”, but on everything around it. Common breaking points:
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Hallucinations (confidently wrong)
The answer sounds plausible, but can’t be traced back to a source or policy. -
No source of truth
AI knows general information, but your organization has authoritative documents, policies, exceptions, and definitions. -
Invisible context failures
One missing detail or a wrongly retrieved passage can flip the outcome — while the user only sees the text. -
Privacy & confidentiality
“Quickly summarizing a document” can suddenly mean: personal data, contract details, or internal strategy in the wrong place. -
Cost and latency
What takes 2 seconds in a demo becomes unpredictable in production: long context, multiple tool calls, peak load.
In short: a standalone AI pilot is often an answer generator.
But organizations need a decision and publication chain.
The solution: AI as a chain with guardrails (and evidence)
Once AI becomes part of operations, you want to guarantee three things:
- Traceability: where does this come from?
- Reliability: how often is it wrong — and when?
- Control: who can do what, and what happens on uncertainty or incidents?
You won’t get that with “a better prompt”. You get it with design.
1) Use an explicit source layer (RAG — but grown up)
AI can generate — but not from thin air.
- Treat internal sources as the primary truth (documents, policies, knowledge base)
- Show sources in the output (citations / references)
- Enforce: no source = no claim
2) Make validation a hard step in the workflow
If something can be checked, check it:
- schemas / constraints / business rules
- sanity checks on dates, amounts, names, versions, identifiers
- “when in doubt”: block, ask a follow-up, or use human-in-the-loop
3) Measure quality with a fixed test set (AI Quality Gate)
If you can’t measure it, you can’t improve it:
- a small, realistic golden set of questions/cases
- scores for groundedness/source coverage, consistency, error types
- regression tests for every change (prompts, data, model)
4) Logging, audit trail, and role-based access
In production you want to reconstruct:
- who asked what
- which sources were retrieved
- which steps were executed
- what answer was returned (and why it was allowed)
That’s how AI stops being a black box and becomes part of your delivery chain.
Where this is especially valuable
AI as a controlled chain matters when “mostly right” is not good enough, for example:
- internal knowledge search with role-based access and sensitive content
- document intelligence that must be verifiable (with sources)
- assistants in workflows with reputation, financial, or compliance risk
What I offer
I help teams go beyond “it works” and make AI production-ready:
- AI Readiness Scan: reference architecture + risks + evaluation plan + roadmap
- Pilot with quality thresholds: small scope, measurable, expandable
- Productionize: logging, security, governance, cost/latency, and operations
Want to discuss an AI use case, or understand what it takes to ship this safely to production? Get in touch — and we’ll focus on the fastest path to real value.
