Internal Comms & Meetings

How to write an exec brief with AI

Internal Comms & Meetings3 AI tools7 steps6 friction points

Want an agent to handle this on your live data? See the Starch version →

An exec brief is a condensed summary document — usually one to two pages — that gives a decision-maker the context they need without making them read the full report, thread, or deck underneath it. For operators, this comes up constantly: board prep, investor updates, cross-functional handoffs, weekly leadership syncs. The brief has to be accurate, clear on what action is needed, and fast to produce. Most founders write them by hand, under time pressure, often late.

The appeal of using AI for this is obvious. An exec brief is fundamentally a compression and framing task — take a lot of information, cut it to what matters, structure it for a specific reader and decision. That's exactly what large language models are trained to do. If you can get the right inputs into the prompt, the model can handle the restructuring, the tone calibration, and the first-draft prose faster than you can.

ChatGPT, Claude, and Gemini can all contribute meaningfully here. They're good at summarizing long documents, reformatting raw notes into structured sections, and adjusting reading level or tone for a specific audience. Claude in particular handles long-context source material well. Gemini has a native Google Docs connection that reduces some copy-paste. None of them are connected to your live business data by default — but if you bring the inputs, they can produce a solid first draft.

Skip the manual work → See the Starch version

Internal Comms & Meetings3 AI tools7 steps6 friction points

AI walkthrough

How to do it with AI today

A practical walkthrough using ChatGPT, Claude, and other off-the-shelf LLMs — what they're good at, what you'll have to do by hand.

Tools that work for this

ClaudeChatGPTGemini

Step-by-step

1 Gather your raw source material — meeting notes, a data export, the Slack thread, the board deck draft, whatever the brief needs to summarize — and paste it into a single document or text block.

2 Open Claude (preferred for long inputs) and paste your source material along with a prompt that specifies the audience, the decision they need to make, and the target length. Be explicit about the reader's context level.

3 Review the first draft for factual accuracy — LLMs will smooth over gaps in your inputs, so any missing number or ambiguous phrase in the source can produce a confident-sounding error in the output.

4 Paste the draft back in with a revision prompt specifying what to sharpen: cut the background section by half, lead with the recommendation, add the Q3 revenue number you forgot to include originally.

5 If the brief needs data points (burn rate, pipeline coverage, headcount), export those manually from your source systems and paste them in as a separate context block, then ask the model to weave them in accurately.

6 Run a final pass with a prompt asking the model to flag any claims in the brief that aren't directly supported by the source material you provided — a useful hallucination check.

7 Copy the output into Google Docs or Notion, do a final human read for tone and political context the model can't know, then send.

Prompts you can copy

You are drafting a 1-page exec brief for our board chair. The topic is Q2 performance. Source material is below. Lead with our recommendation, then key facts, then risks. Max 400 words. [paste notes]

Summarize this 3,000-word investor update into a 5-bullet exec brief. Audience: a Series A lead who knows our business. Cut anything that isn't a decision, a number, or a risk. [paste update]

Rewrite this draft exec brief so it leads with the ask in the first sentence. The reader has 90 seconds. Current draft: [paste]. Target length: 250 words.

Here are my raw meeting notes from today's ops review: [paste]. Extract the 3 key decisions made, the 4 open action items, and any risks flagged. Format as an exec brief I can send to the CEO tonight.

Review this exec brief draft and flag every claim that isn't directly supported by the source notes I provided. List each unsupported claim with the exact sentence and what's missing. [paste brief] [paste source]

Reality check

Where this gets hard

The walkthrough above works — until your numbers change, the LLM hallucinates, or you have to re-paste everything next month.

No connection to live business data — burn rate, pipeline, headcount all have to be manually exported and pasted in before every single brief.

Long source documents (board decks, full meeting transcripts) can push against context limits, forcing you to chunk inputs and lose cross-document coherence.

Outputs aren't consistent run to run — the structure, section order, and level of detail shift between sessions even with the same prompt, so you're re-prompting and reformatting every time.

The model has no institutional memory — it doesn't know what you told the board last quarter, what framing your CEO expects, or which risks are already known versus worth flagging.

Hallucination risk is real on numbers — if your source material is incomplete, the model fills gaps with plausible-sounding figures that can be wrong and hard to catch without a careful review.

Nothing persists between sessions — the prompt chain you built for last month's investor brief doesn't carry over, so you're reassembling context from scratch every time.

Tired of the friction?

Starch runs the whole workflow on live data — no copy-paste, no hallucinated numbers, no re-prompting next month.

See the Starch version →

Starch alternative

The same workflow on Starch

Starch is an agentic operating system — it builds and runs persistent apps against your live business data. For exec briefs, that means an agent assembles the brief from your actual connected sources, not from whatever you remembered to paste in.

Connect Gmail or Outlook once — Starch syncs your messages on a schedule, so the agent can pull the relevant thread context, prior brief, and action item history without you hunting through inboxes.

Use the Meeting Notes app to capture decisions and action items from the call itself, then describe a follow-on automation: 'After each ops review, draft an exec brief from the meeting summary and send it to my chief of staff for review.'

Use the Knowledge Management app to store prior briefs and board communications — the agent can reference your actual institutional history when drafting, so the new brief is consistent with how you've framed things before.

Describe the brief template you want in plain English — 'always lead with the recommendation, include burn and pipeline numbers from Stripe and HubSpot, flag anything red since last week' — and Starch builds an app that generates it that way every time.

Automations trigger on your schedule, not your memory — tell Starch to draft a board brief every Friday at 4pm pulling from your connected sources, dropping it into Notion for your review before you send.

The Presentation Agent (currently in development — request beta access) will extend this further, turning the approved brief directly into a polished slide deck without a separate export step.

Get closed-beta access →

Toolkit