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Run 3–5 weeks

Operationalize AI Governance for CS

Establish a written Responsible AI policy for CS operations covering PII handling, human-review requirements, adoption standards, and impact measurement.

Why This Matters

Run-stage teams have CSMs experimenting with AI individually: different tools, different practices, inconsistent outcomes. Some are pasting customer data into public LLMs. Some are sending AI drafts without review. Without a governance layer, you accumulate risk as adoption grows. A clear policy isn't about slowing AI adoption; it's about scaling it safely and measuring whether it actually improves outcomes.

Action Plan

  1. 01 Audit current AI tool usage across the CS team: which tools, which use cases, how often, and what review practices CSMs follow today
  2. 02 Draft a PII policy: specify which customer data can be entered into AI tools, which tools are approved, and which are prohibited (e.g., no personal contact data into non-approved public LLMs)
  3. 03 Define human-review requirements by use case: customer-facing content requires CSM review before sending; internal summaries can be used as-is
  4. 04 Create a list of approved AI tools with their data processing terms verified by your legal or security team
  5. 05 Run a one-hour team training session covering: approved tools, PII rules, prompt hygiene, and how to catch hallucinations
  6. 06 Set an AI adoption baseline: what percentage of CSMs use AI tools weekly? Track this monthly going forward
  7. 07 Measure impact quarterly: time saved, accuracy of AI outputs, and whether AI-assisted interactions correlate with better customer outcomes

Metrics to Watch

AI-Assisted Resolution Rate CSM AI Adoption Rate Customer Health Score Escalation Rate

Related Principles

Maturity Requires Iteration Strong CS teams keep evolving as the business, customers, and product change. Context Drives Strategy What works at $5M ARR breaks at $100M. Tailor everything.

Common Pitfalls

  • Writing a policy and filing it away. Governance only works if it's enforced and reviewed as tools evolve
  • Blocking all AI use in the name of safety. This pushes usage underground where you have even less visibility
  • Measuring AI governance by tool adoption alone. Adoption without outcome measurement tells you nothing about whether AI is helping or hurting
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