AI-Ready Support Documentation Checklist: Audit Before Launch

Every help article the AI may retrieve should pass four checks. Is it current against the latest product, pricing, policy, or workflow change? Is it focused on one primary customer intent rather than bundling setup, billing, troubleshooting, and exceptions into one page? Does it include material conditions such as plan, region, timing, or eligibility? And is the language customer-safe, with no internal-only workarounds or confidential notes?

Stale articles are launch blockers because AI agents treat retrieved text as operational truth. An outdated refund window or old plan name becomes a confident wrong answer.

Macros decay faster than help articles because agents edit them for live tickets. A macro may promise a credit, refund, or escalation path that the public help center does not support. When the AI blends both sources, the result can contradict policy.

Audit every high-volume macro against its canonical help article or policy. If they disagree, mark the intent as blocked until the policy owner chooses the customer-safe answer.

Private SOPs often contain the real workflow: who can approve an exception, when to escalate, which tier gets a workaround, and what needs legal or compliance review. AI readiness means deciding which SOP instructions are safe to expose and which must stay internal.

If the SOP is the only source for a critical exception, the customer-facing answer needs explicit approval before the AI uses it. Do not let the AI synthesize internal guidance into a customer-facing promise.

The most dangerous documentation gap is when public help articles and private sources disagree. A customer may see one answer in the help center while agents follow a different SOP or macro. The AI can retrieve both and blend them into a single, confident contradiction.

Treat public/private conflict as a launch blocker. The audit should produce a conflict table: customer intent, conflicting source A, conflicting source B, policy owner, decision deadline, and retest trigger.

Documentation decay happens when support knowledge stops matching the product, policy, or real customer workflow. Humans work around it with memory. AI agents turn it into confident wrong answers.

Decay signals include old screenshots, retired product names, outdated pricing, missing eligibility windows, policy windows that changed after publication, and articles with no owner or review date.

The audit is only useful if it becomes work. The source-fix backlog translates checklist findings into article updates, macro rewrites, SOP changes, owner review, and vendor test reruns.

Sort the backlog by launch impact, not document count. A single refund contradiction matters more than twenty low-risk stale screenshots.