Decide whether an AI initiative is ready for production — before you scale the wrong thing.
Last reviewed: 2026-09-10 · Original Vasiva Framework — not an industry certification
Enterprise AI readiness is the degree to which an organization can run an AI capability as a production system: clear problem, usable data, operable architecture, safe integration, governance, evaluation, human oversight, observability, and day-to-day operations — not merely a working demo.
This page is a Vasiva Framework (or checklist): an original engineering decision aid. It is not an ISO standard, academic instrument, or third-party certification.
Weak signal: AI is justified by curiosity or vendor pressure without a named outcome.
Recommended next step: Write a one-page problem statement with owner, metric, and non-goals before model work expands.
Weak signal: Teams train or retrieve against unknown, stale, or over-permissioned corpora.
Recommended next step: Inventory sources, owners, access rules, and retention before expanding retrieval or training.
Weak signal: A chatbot wrapper is treated as an enterprise system.
Recommended next step: Document components, trust boundaries, and fallbacks as an operable design, not a slide.
Weak signal: Prototype APIs hit production systems without rate limits, auth scopes, or rollback.
Recommended next step: Map every write path and require explicit approval for consequential mutations.
Weak signal: Model and tool credentials are shared and over-scoped “to make demos work.”
Recommended next step: Least-privilege credentials, content filters on untrusted input, and egress controls.
Weak signal: No named owner for model behavior after the pilot team moves on.
Recommended next step: Assign an operating owner and an approval matrix for high-impact actions.
Weak signal: Quality is judged by a few happy-path demos.
Recommended next step: Build a fixed evaluation set and block promotion when scores regress.
Weak signal: “Human in the loop” means a log file nobody reads.
Recommended next step: Put approval before irreversible actions, with clear ownership and SLA.
Weak signal: Production AI is a black box until customers complain.
Recommended next step: Instrument traces for model and tool paths; alert on error and cost anomalies.
Weak signal: Launch without runbooks, rollback, or a named operator.
Recommended next step: Write runbooks, define rollback, and test the kill switch before wider rollout.
Score each dimension from 0–10. Multiply by weight (each dimension is 10% of the total). Sum for a 0–100 readiness score. This is a Vasiva Framework for decision support — not a certification, ISO standard, or academic instrument.
0–20 · Not Ready
Foundational gaps across problem, data, or control. Do not scale automation.
21–40 · Early
Exploration can continue, but production side effects should stay blocked.
41–60 · Developing
Architecture and gates are forming. Limited pilots with human approval only.
61–80 · Production Capable
Narrow production use is plausible with monitoring and rollback.
81–100 · Production Ready
Capability can be operated with known failure modes and ownership.
Treat the score as a go / no-go aid for production exposure. A high score on demos with a low score on evaluation, HITL, or operations is still not production-ready.
Enterprise AI readiness is whether an organization can operate an AI capability in production with clear ownership, data access, architecture, security, evaluation, human gates, observability, and rollback — not only whether a prototype looks impressive.
No. This is a Vasiva Framework — an original engineering decision aid. It is not ISO, not an academic standard, and not a third-party certification.
If you want a structured review of a live initiative against this framework, talk to the engineers who ship Vasiva products.
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