# Meetings, email, and admin: reclaiming hours each week
You just left a 45-minute meeting with seven people talking over each other. The transcript is 4,000 words of "um," tangents, and half-decisions. In 30 seconds, AI can turn that mess into a clean summary with decisions, action items, and who owns what. That single trick saves most people 2 to 4 hours a week.
This lesson shows you exactly how, with prompts you can copy today.
A transcript is just the raw text of what was said, usually captured automatically by a meeting tool. It is long and disorganized. Your job is to compress it into something useful.
Paste the transcript into ChatGPT, Claude, or Gemini and use this prompt:
Here is a raw meeting transcript. Summarize it as:
1. TL;DR (3 bullets max)
2. Key decisions made
3. Action items as a table: Task | Owner | Due date
4. Open questions still unresolved
If an owner or due date wasn't stated, write "unassigned."
Keep it factual. Do not invent anything.
[paste transcript here]That last line matters. AI tools sometimes "fill gaps" with plausible-sounding details. Telling it not to invent anything keeps your notes honest.
Raw snippet:
> "...so yeah I think we should probably push the launch, Sarah can you look at the numbers, and uh someone needs to email the vendor before Friday, maybe Tom?"
Cleaned output:
| Task | Owner | Due date |
|------|-------|----------|
| Review launch numbers | Sarah | unassigned |
| Email the vendor | Tom (tentative) | Friday |
Decision: Launch likely delayed, pending Sarah's review.
Notice how AI flagged "Tom (tentative)" because the transcript said "maybe." Good promptingpromptingPrompt engineering is the practice of designing and refining text inputs to guide large language models toward accurate, relevant, and reliable outputs.View full definition → preserves that uncertainty instead of hiding it.
You do not always have to paste transcripts by hand. Several tools join your call, record it, and produce notes automatically.
A practical workflow: let the tool capture the transcript, then run your own summary prompt on it for the exact format your team wants. The built-in summaries are fine, but a custom prompt gives you consistent structure every time.
One etiquette note: tell people they are being recorded. In many places it is legally required, and it is always good manners.
The meeting ends. Now someone has to send the recap email. That someone is usually you, and it usually takes 20 minutes. Cut it to two.
Feed your cleaned notes into this prompt:
Write a follow-up email based on these meeting notes.
Tone: warm but concise. Under 150 words.
Structure: one-line thank you, bulleted decisions,
bulleted action items with owners, a clear next step.
[paste your cleaned notes]You will get something like:
> Hi team, thanks for a productive session. Quick recap:
>
> Decisions: Launch delayed pending Sarah's number review.
> Action items: Sarah reviews launch metrics; Tom emails the vendor by Friday.
> Next step: We reconvene Thursday to confirm the new date.
Always read before sending. AI gets you 90% of the way; you supply the judgment.
The same content can sound different depending on who reads it. Just ask:
You are not rewriting from scratch each time. You are nudging.
Good meetings start with a good agenda. AI is great at turning a vague goal into a tight plan.
I'm running a 30-minute meeting about our Q2 marketing budget.
Attendees: head of marketing, finance lead, two campaign managers.
Goal: decide how to reallocate $20k from paid ads to content.
Create an agenda with time blocks, a clear decision to be made,
and 3 questions we must answer before we leave the room.The output gives you timed sections (for example, "5 min: review current spend") and forces the meeting toward an actual decision. Sharing an agenda in advance is one of the simplest ways to make meetings shorter.
For more on running meetings that are actually worth the time, Atlassian's free Team Playbook has practical, no-cost templates.
Knowledge check
1. Why does the recommended summary prompt include the instruction "Do not invent anything"?
2. In the before-and-after example, why did the AI label the vendor email owner as 'Tom (tentative)'?
3. What is the main purpose of running your own summary prompt on a transcript that a notetaker tool already captured?
4. Select ALL sections that the recommended summary prompt asks the AI to produce.
Select all the correct answers.
5. Select ALL statements that correctly describe how to handle meeting transcripts and notetaker tools in this lesson.
Select all the correct answers.
Email is the quiet time-thief. AI helps in three ways: triage, drafting replies, and summarizing long threads.
Copy a batch of subject lines and senders, then ask:
Here are 15 unread emails (sender + subject).
Sort them into: Urgent, Reply this week, FYI only, Can ignore.
Add a one-line reason for anything you mark Urgent.
[paste list]This turns a wall of unread bold text into a clear plan of attack.
We have all opened a 40-message thread and felt our soul leave our body. Paste it in:
Summarize this email thread. What was decided, what is still
open, and is there anything I specifically need to respond to?
[paste thread]For a single email, paste it and say:
Draft a reply to this email. I want to: politely decline the
meeting, suggest async updates instead, and stay friendly.
Keep it under 80 words.
[paste email]Do not paste confidential or sensitive information (customer data, legal matters, anything you would not want leaked) into a free consumer chatbot. The safer path: use your company's approved AI tool, or use the enterprise versions of ChatGPT, Claude, and Gemini, which by default do not train on your data. When in doubt, ask your IT team what is approved.
Here is how these pieces fit together in one real morning:
1. Your notetaker (Otter or a built-in recap) captures the 9am call.
2. You run the summary prompt to get clean notes with owners.
3. You run the email prompt to send the recap before 9:30.
4. At 10am, you triage your inbox with the sorting prompt.
5. You batch-draft three replies, edit, and hit send.
What used to eat your whole morning now takes 40 minutes.
If you are slightly technical and want to summarize transcripts without copy-pasting, here is a short script using Claude's APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →. An APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → is just a way for your code to talk to an AI model directly.
import anthropic
client = anthropic.Anthropic() # uses your API key
transcript = open("meeting.txt").read()
prompt = f"""Summarize this transcript as:
1. TL;DR (3 bullets)
2. Decisions
3. Action items: Task | Owner | Due date
Do not invent details.
{transcript}"""
message = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=1000,
messages=[{"role": "user", "content": prompt}],
)
print(message.content[0].text)Run it once and you have a reusable note-maker. You do not need this to benefit from the lesson, but it shows where the manual workflow can go.
These actions are compiled in the role's Playbook.