Course overview
Start hereAI for Operations Analysts
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Priya's Thursday, two ways

9 lessons · 26 prompts

A day in the life of an Operations Analyst: what changes with these prompts.

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Priya, an operations analyst at a regional delivery company.

Priya starts Thursday with a spreadsheet of late deliveries. She pastes the column names and a few sample rows into ChatGPT and asks it to explain the first-attempt delivery rate and flag anything odd. It points out a missing date range and a metric that changed definition last month.

She opens Claude and uses the weekly report prompt. She gives it the week's numbers, the two biggest variances, and the note that a new depot opened. The draft comes back with clear headings and a short explanation for each variance. She edits two sentences and sends it to her manager before the morning meeting.

After lunch, Priya describes the returns process in Gemini. She lists each step from pickup request to refund. The AI maps the steps, marks two waiting points, and suggests a change to the approval step. She adds her own notes about staffing and saves the map for a process review.

At the end of the day, she uses the automation prompt to draft a formula for a recurring report. It takes her ten minutes instead of the usual hour. With the time she won back, she leaves on time, walks home, and calls her sister.

Before

  • Reports take most of Friday
  • Bottlenecks found by gut feel
  • Dashboards rebuilt from scratch
  • Weekends lost to spreadsheets

After this course

  • Reports drafted before lunch
  • Bottlenecks visible in minutes
  • Dashboards guide the team
  • Time for coaching and planning

What you'll learn

  • Read operational data: Use AI to interpret datasets, clarify metrics, and spot data quality issues before you dig deeper.
  • Draft weekly reports: Turn weekly metrics into clear updates, variance explanations, and talking points for managers.
  • Design dashboards: Choose the right measures, sketch layouts, and write guidance so stakeholders can self-serve.
  • Map processes: Describe a process in words and use AI to map steps, find bottlenecks, and draft improvements.
  • Forecast demand: Build demand and capacity scenarios, compare options, and explain uncertainty to decision makers.
  • Run cost-benefit: Structure cost-benefit models, test assumptions, and turn ROI into a clear recommendation.
  • Present findings: Shape analysis into a narrative, executive summary, and Q&A prep for stakeholder meetings.
  • Automate reporting: Use AI to draft formulas, queries, and scripts that speed up recurring operational work.

How this course works

  1. 9 lessonsOne task of your job each, from understand operational data to document ops procedures and admin.
  2. Ready-to-paste promptsCopy, fill in the parts in {{brackets}}, paste into ChatGPT, Claude or Gemini.
  3. Tick and completeTick the prompts you tried and mark each lesson complete.
  4. Get certifiedFinish and keep the prompts as your own library.