AI FOR CS OPS

THE FOUNDING COHORT

SELF-PACED CORE

Work through practical text lessons, prompt packs, templates, and exercises on your own schedule.

LIVE SESSION 1 — USE-CASE CLINIC

Choose the highest-value AI opportunity in your CS Ops environment and define your first workflow.

LIVE SESSION 2 — IMPLEMENTATION LAB

Build, test, and improve your workflow with live feedback and peer learning.

1:1 IMPLEMENTATION SESSION

A 45-minute workflow audit to turn your materials, data, and process into a focused 30-day implementation plan.

YOU LEAVE WITH

An AI opportunity map, a prioritized workflow backlog, reusable prompts and templates, and one practical workflow ready to implement.

THE AI FOR CS OPS IMPLEMENTATION COURSE

START HERE — THE OUTCOME

This is a practical implementation sprint, not an AI theory course. By the end, you will have a prioritized AI opportunity map, a workflow backlog, reusable prompts, and one workflow ready to run safely with your team.

MODULE 1 — MAP THE WORK BEFORE YOU AUTOMATE

Lesson: AI should remove repeatable judgement-light work, not replace customer ownership. Map your weekly work into four buckets: data preparation, analysis, drafting, and decision support. Circle work that is frequent, painful, manual, and low-risk.

Exercise: List ten recurring CS Ops tasks. Score each 1–5 for frequency, time cost, data quality, and customer risk. Start only with tasks scoring 12+ and customer risk below 3.

Prompt: “Act as a CS Ops analyst. Turn this task list into an opportunity map. For every task, show expected time saved, required inputs, risk, human reviewer, and the smallest useful pilot.”

MODULE 2 — BUILD A HIGH-VALUE USE CASE

Lesson: A valuable first use case has one clear input, one defined output, an accountable reviewer, and a measurable before/after. Good candidates: account-summary preparation, call-note extraction, risk-signal triage, QBR draft outlines, or renewal-plan first drafts.

Exercise: Write your use-case brief: trigger, inputs, output, user, reviewer, risk boundary, and success metric. Do not automate sending, updating records, or decisions in the first version.

Prompt: “Design an AI-assisted workflow for [use case]. Give me: trigger, inputs, five-step process, output template, quality checks, failure modes, reviewer instructions, and a two-week pilot metric.”

MODULE 3 — CREATE A TRUSTED CONTEXT PACK

Lesson: Output quality follows input quality. Build a small context pack before writing prompts: customer segment definitions, lifecycle stages, health signals, required fields, approved terminology, and examples of good work. Remove personal or sensitive data unless it is necessary and allowed.

Exercise: Create a one-page CS Ops context pack. Include what the assistant must know, what it must never infer, and what requires human confirmation.

Prompt: “Using the context pack below, create a reusable instruction set for a CS Ops assistant. Separate facts, assumptions, and recommended next actions. Flag missing data instead of inventing it.”

MODULE 4 — DESIGN PROMPTS AS OPERATING PLAYBOOKS

Lesson: A professional prompt contains a role, goal, context, constraints, output format, quality bar, and escalation rule. Vague prompts create attractive but unreliable work.

Exercise: Convert one manual SOP into a prompt. Include source fields, definition of done, output format, and the phrase: ‘If data is missing or contradictory, ask a question or flag it.’

Prompt template: “You are a CS Ops analyst. Goal: [goal]. Use only [sources]. Apply these rules: [rules]. Output: [exact format]. Quality check: [check]. Escalate when: [condition]. Do not: [boundary].”

MODULE 5 — TEST, REVIEW, AND SCALE

Lesson: Start with 10 real examples. Compare AI-assisted output with your current process for accuracy, usefulness, time saved, and rework. Keep a lightweight error log; errors are workflow design feedback, not a reason to abandon the work.

Exercise: Run ten examples and score each: correct, usable with edits, unsafe/incorrect. Record the cause: missing context, unclear prompt, poor source data, or wrong use case.

Scorecard: Aim for 80% usable-with-minor-edits before scaling. If accuracy is lower, improve the context pack and quality check before changing tools.

LIVE SESSION 1 — USE-CASE CLINIC

Bring your opportunity map and use-case brief. We will select one workflow, define its value, identify risks, and agree the smallest pilot.

LIVE SESSION 2 — IMPLEMENTATION LAB

Bring ten test outputs and your error log. We will tighten prompts, improve context, and decide what belongs in the next version.

YOUR 1:1 WORKFLOW AUDIT

Bring the workflow, sample inputs, current SOP, and pilot results. You will leave with a 30-day implementation plan: owner, cadence, review point, success metric, and next workflow to prioritise.

FINAL CHECKLIST

□ I chose one narrow, repeatable, low-risk workflow.

□ I defined the human reviewer and escalation rule.

□ I built a context pack and reusable prompt.

□ I tested ten real examples and logged errors.

□ I set a 30-day metric for time saved, quality, or consistency.