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Macro vs help center policy audit showing source conflicts and launch blockers

Policy conflict audit

Macro vs Help Center Policy Audit: Find Conflicts Before AI Launch

A policy conflict audit to compare macros, help docs, and SOPs and find contradictions that become AI wrong answers.

Claire Bennett

Support Readiness Lead, Meihaku · May 11, 2026

The most common source of AI support wrong answers is not model failure. It is policy conflict: the macro promises one thing, the help center says another, and the SOP adds a third rule that only agents know.

This audit turns policy-drift and gap-detection review into a conflict table, a source-fix backlog, and a launch boundary that keeps contradictory intents out of AI scope until they are resolved.

Use it before launching any AI support agent that retrieves from both public help centers and private macro or SOP libraries.

What this helps decide

Turn Macro vs Help Center Audit into launch scope.

Use this guide to decide which customer intents are approved for AI, which need restrictions, which need source cleanup, and which should stay human-owned.

Evidence used

Sources, policies, and support artifacts

  • HappySupport: knowledge base AI readiness audit
  • Help.center: AI knowledge support article
  • Zendesk: preparing your help center for generative AI

Review output

Approve, restrict, block, or hand off

  • Audit setup
  • Conflict resolution
  • Launch output

How this guide was built

3 public references, 6 review areas

  • Map the three source layers
  • Find contradictions in high-risk topics
  • Build the conflict table

Map the three source layers

Start by mapping every high-volume customer intent to its sources across three layers. The public help center is what customers see and what the AI will likely retrieve first. The macro library is what agents paste into tickets, often with faster edits and less review. The SOP or policy document is the internal rulebook that may contain exceptions, approvals, and workflows not written for customers.

Each intent should have one canonical source. If it has two or more that disagree, the intent is not ready for AI automation.

  • Public help center: customer-facing articles, FAQs, and guides.
  • Macro library: canned responses, quick replies, and agent snippets.
  • SOP/policy: internal rules, exceptions, approvals, and workflows.
  • Canonical source: the one approved answer the AI should use.

Find contradictions in high-risk topics

Not every contradiction matters equally. A mismatch in branding tone is low risk. A mismatch in refund window, eligibility rule, cancellation policy, data retention, or account recovery is high risk. Focus the audit on topics where a wrong answer creates customer harm, legal exposure, or financial loss.

Common high-risk contradictions include: the macro says full refund while the help center says case-by-case; the SOP allows a manager override while the public policy does not mention it; the macro promises a credit while the billing system cannot issue one.

  • Refund and credit rules.
  • Cancellation and downgrade windows.
  • Eligibility and plan conditions.
  • Privacy, security, and account-control workflows.

Build the conflict table

The conflict table is the core audit artifact. Each row names the customer intent, the conflicting sources, the policy owner, the customer-safe answer, the resolution action, and the retest trigger. This turns a vague worry into a named, owned, timed fix.

Without a conflict table, teams often discover contradictions only after a customer complains or after the AI gives a wrong answer at scale.

  • Customer intent and risk category.
  • Conflicting source A and source B.
  • Policy owner and decision deadline.
  • Customer-safe canonical answer.
  • Resolution action: rewrite, archive, approve exception, or create new source.
  • Retest prompt and launch state after fix.

Decide which source wins

When sources conflict, someone must choose the canonical answer. This is a business decision, not a model decision. The policy owner, legal reviewer, or support lead should choose the customer-safe version and retire or rewrite the conflicting source.

Do not let the AI reconcile contradictions. The AI may average conflicting sources into a middle ground that satisfies no one and violates policy.

  • Assign a canonical source owner for each conflict.
  • Require legal or compliance review for regulated topics.
  • Archive the losing source or add a redirect to the canonical answer.
  • Retest the intent with the same customer phrasing after the fix.

Turn conflicts into launch states

The audit should end with a launch decision for every conflicting intent. Approved means the conflict is resolved and the canonical source is current. Restricted means the intent is answerable only with additional context. Blocked means the conflict is unresolved and the intent should stay out of AI scope. Source-fix-needed means a fix is in progress. Human-only means the intent requires judgement even when sources agree.

This launch map becomes the boundary for vendor configuration, test sets, and QA sampling.

  • Approved: conflict resolved, canonical source current and tested.
  • Restricted: answerable only after required context is known.
  • Blocked: unresolved conflict; keep out of AI scope.
  • Source-fix-needed: fix in progress; retest after completion.
  • Human-only: judgement-heavy or regulated even without conflict.

Feed the source-fix backlog

Every blocked or source-fix-needed intent should become a work item. The backlog should include the exact source to rewrite, the owner, the deadline, the customer-safe wording, and the retest prompt. Sort by launch impact rather than document count.

A single resolved refund contradiction can unlock a high-volume intent. Twenty fixed typos do not unlock any intent.

  • Link each backlog item to the customer intent it unlocks.
  • Assign owners and deadlines.
  • Include customer-safe canonical wording.
  • Attach the retest prompt that proves the fix.

Checklist

Use this as the working review before launch.

Audit setup

  • Export top customer intents from tickets and chats.
  • Map each intent to help center, macro, and SOP sources.
  • Flag intents with two or more sources that disagree.
  • Weight refund, billing, privacy, account, and compliance topics higher.

Conflict resolution

  • Assign a canonical source owner for each conflict.
  • Require legal or compliance review for regulated topics.
  • Archive or rewrite conflicting sources.
  • Record the customer-safe canonical answer.

Launch output

  • Mark each intent approved, restricted, blocked, source-fix-needed, or human-only.
  • Export conflict table and source-fix backlog.
  • Retest affected intents after every source change.
  • Update launch map when new conflicts are found.

How Meihaku helps

Turn the checklist into a launch audit.

Meihaku reads your sources, maps them to customer intents, drafts cited answers, and shows which topics are cleared for AI, blocked, source-fix needed, or human-only.

Related guides

Keep clearing answers before launch.

These pages connect testing, knowledge-base cleanup, and readiness scoring into one pre-launch workflow.

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FAQ

Common questions

What is a macro vs help center policy audit?

It is a comparison of macros, help center articles, and SOPs to find policy contradictions that could cause AI wrong answers, followed by a launch decision for each affected customer intent.

Why do macros conflict with help centers so often?

Macros are edited faster than help articles and often contain agent workarounds, temporary exceptions, or outdated promises that were never synchronized back to the public knowledge base.

Should the AI use macros as sources?

Only if the macro is current, customer-safe, and consistent with the canonical help article or policy. Conflicting macros should be blocked from AI retrieval until resolved.

How does Meihaku run this audit?

Meihaku maps customer intents to help articles, macros, and SOPs, flags contradictions, assigns canonical source owners, and turns conflicts into launch states and a source-fix backlog.