AI support readiness score
Is your knowledge base ready for an AI agent?
Score the operational readiness behind your AI support rollout: knowledge freshness, policy conflicts, pre-launch testing, escalation, governance, and wrong-answer measurement.
Sample result
52 / 100
Approved intents
24
safe for limited automation
Policy conflicts
9
must be resolved before expansion
Missing sources
17
need owner or escalation
What the score checks
Six dimensions, one launch decision.
The score should produce a launch map, not a fluffy quiz result.
Knowledge freshness
Checks whether top customer intents have current, owned, customer-safe source evidence.
Policy conflict risk
Finds topics where macros, help articles, or policy docs disagree before the AI blends them.
Pre-launch testing
Scores whether the AI has been tested against historical tickets, high-risk edge cases, and adversarial prompts.
Escalation readiness
Reviews handoff triggers, loop detection, human routing, and context transfer.
Governance
Checks whether answer quality, knowledge updates, and regulated topics have clear owners.
Wrong-answer measurement
Looks beyond deflection to re-contact, wrong-answer rate, AI-only CSAT, and review cadence.
Score bands
Use the score to decide scope, not confidence.
80-100
Launch-ready
Approved intents can go live with monitoring.
60-79
Pilot-ready
Use limited rollout and restrict high-risk topics.
40-59
High-risk
Resolve gaps and conflicts before broad launch.
Under 40
Not ready
Customer-facing AI will likely create avoidable failures.
Treat Keyword Planner's low-volume readiness phrases as conversion language, not traffic guarantees. The page exists to turn testing and knowledge-base audit traffic into a concrete next step.
FAQ
Questions before scoring readiness.
What is an AI support readiness score?
It is a 0-100 score that measures whether your support operation can safely let an AI agent answer customers. It covers knowledge quality, policy conflicts, testing, escalation, governance, and measurement.
Is this the same as an AI knowledge base audit tool?
The knowledge base audit is one part of the score. Meihaku also checks testing, escalation, governance, and wrong-answer measurement because a clean help center alone does not make an AI support launch safe.
What should we do with a low score?
Do not launch AI across every topic. Clear low-risk intents first, block high-risk topics, resolve policy conflicts, and retest before expanding.