Help Center AI Readiness Scorecard: Grade Before Launch

The first dimension is coverage. Export recent tickets, chats, and help-center searches to find the top customer intents. Then check whether each intent has a focused, current help article that answers it directly.

Missing coverage is a launch blocker for the affected intent. Do not let the AI improvise an answer because the help center is incomplete.

Freshness is not publication date. It is the time since the article was last verified against the current product, pricing, policy, and workflow. An article published two years ago and reviewed last month can be fresh. An article published last month but skipped in the latest product launch can be stale.

Score freshness by intent risk. A stale article about office hours is low risk. A stale article about refund eligibility is high risk.

Conflict scoring checks whether the help center agrees with macros, canned responses, SOPs, and private policies. The AI may retrieve multiple sources and blend them. If they contradict, the result is a confident wrong answer.

Score conflict by intent. An intent with one clear source scores high. An intent with two or more conflicting sources scores low and should be blocked until resolved.

Machine readability asks whether an AI agent can discover, parse, and cite the page. This includes semantic structure, heading hierarchy, clear answer boundaries, and minimal formatting noise that could break retrieval.

For support teams, readability also means the article is not buried behind login walls, internal domains, or PDFs that the AI cannot parse. Public help center pages should be the canonical source for customer-facing answers.

A help article should not only answer the question. It should also tell the AI when the answer is not enough: when the customer needs human context, when the case requires verification, when the exception is outside policy, or when the topic is regulated.

Score escalation clarity by intent. Articles that say contact support for exceptions without naming the exception are weak. Articles that explicitly list the conditions that require human handoff are strong.

The scorecard should produce a launch recommendation, not only a number. Use score bands to decide whether the help center is ready for broad automation, narrow pilot, or source-fix first.

A low score is not a failure. It is a signal to shrink the launch boundary and fix sources before expanding.

The scorecard should be copyable and exportable. Support ops, legal, compliance, and vendor admins all need to see the same evidence. Export the scorecard as a shareable report with score bands, intent-level grades, conflict tables, source-fix backlog, and retest triggers.

The exported report becomes the launch boundary, the source-fix backlog, and the compliance record. Keep it updated when sources, policies, or products change.