How to Generate Release Notes From Monday.com Without Hiring a Technical Writer

If you run an engineering team, you probably have a Monday.com board that looks like a work of art.

Every ticket is tagged. Every status is updated. The columns flow perfectly from "Backlog" to "In Progress" to "Ready for Release." It is a pristine, color-coded grid of operational efficiency.

And then release day arrives.

Someone (usually a product manager or an engineering lead) has to look at that beautiful board and figure out how to explain it to customers. Suddenly, a ticket titled "Fix null pointer in auth flow" needs to become a coherent sentence about improved login stability. A status change to "Done" needs to become a paragraph about a new feature.

Monday.com is excellent at tracking work. It was not designed to generate customer-facing documentation. If you want to get release notes out of Monday without hiring a dedicated technical writer, you have to bridge that gap yourself.

There are a few ways to do this. Some are easy but messy. Some are clean but require you to build software to maintain your software. Here is how the options actually break down, when each one stops working, and what to do when you've outgrown all of them.

The Spreadsheet Trap

The most common starting point is the export button.

You filter your Monday board to show everything marked "Done" in the last sprint. You click Export to Excel. You get a spreadsheet with 50 rows of data, a timestamp column, a status column, and a pile of ticket titles that only make sense to the person who wrote them.

Then the real work begins.

You delete the columns you don't need. You rewrite the ticket titles so they make sense to a human being who doesn't work at your company. You try to remember why a ticket called "Update API dependency" was actually a critical security fix. You spend two hours turning a spreadsheet into a Google Doc, and then you paste that Doc into your changelog.

This works fine when you release once a month and have a quiet Friday afternoon to spare.

When you move to weekly or daily deployments, it falls apart. The manual overhead of exporting, cleaning, and rewriting data becomes a bottleneck that slows down sprint velocity. You end up with product managers spending their Fridays doing data entry instead of talking to users. The release notes get shorter and vaguer with each sprint, because the person responsible for them has twenty other things to do.

The operational signal that you've outgrown this approach: you start skipping sprints. Release notes go out every other release, then quarterly, then only when a customer asks. The spreadsheet-to-Doc workflow isn't just slow. It's fragile. It depends on one person having the time and context to do it well, and that person is always the one who can least afford to spend two hours on documentation.

Hooking Up the Pipes

When the spreadsheet gets too painful, teams usually try to automate the export.

Monday.com has a robust GraphQL API. You can write a Python script that queries the board for items where the status column equals "Released," pulls the item name and description, and formats them into Markdown. The API uses cursor-based pagination, so you can retrieve up to 500 items per request and page through larger boards.

A minimal version of that script looks like this:

import requests

API_KEY = "your_api_key"
BOARD_ID = 1234567890
URL = "https://api.monday.com/v2"

query = """
{
  boards(ids: %d) {
    items_page {
      items {
        name
        column_values {
          id
          text
        }
      }
    }
  }
}
""" % BOARD_ID

response = requests.post(
    URL,
    json={"query": query},
    headers={"Authorization": API_KEY}
)
data = response.json()

From there, you filter for items where the status column text equals "Released," loop through the results, and write each item name and description to a Markdown file. The whole thing is maybe 50 lines of Python.

If you don't want to write code, you can use Zapier or Make instead. You set up a workflow that triggers whenever a status column changes to "Released." The workflow pushes the ticket title and description into Notion or Confluence. Monday.com also supports native automations that can post a notification or update another board when a status changes, which you can chain with a webhook to push data downstream.

This is better than the CSV export. It removes the manual copying and pasting. It runs automatically. It is, in theory, the kind of thing you set up once and forget about.

But it doesn't solve the core problem.

The Part Where Automation Breaks

Whether you use a Python script, Zapier, or a native Monday automation, moving data automatically only works if the data is good.

If your engineers write tickets like "WIP: fix thing," your automated pipeline is going to push "WIP: fix thing" into Notion. The API doesn't know what the ticket means. It just knows the string of text.

This is the fundamental limitation of using project management data for customer communication. Internal task tracking requires different context than external release notes. A ticket title that is perfectly clear to the engineer who wrote it ("Refactor auth middleware to handle token refresh edge case") is meaningless to the customer who reads it.

To make the automated pipeline work, you have to force your engineers to write customer-ready descriptions inside their Monday tickets before they close them. You have to turn your engineers into technical writers.

They will resist this. And eventually, under sprint pressure, they will stop doing it, and your automated release notes will revert to garbage.

A 2025 study on automated release note generation confirms this pattern. Language models and automated systems perform well when summarizing structured, contextualized information, but struggle significantly when working from raw, uncontextualized development artifacts. The study found that tools relying on commit messages alone failed for more than half the projects analyzed, precisely because the input quality was inconsistent. Monday.com tickets have the same problem. The tool is only as good as what people put into it.

There is also a formatting problem that compounds over time. When you push Monday data into Notion or Confluence via Zapier, you get plain text. No version history. No structured categories. No way to know whether a given release note was reviewed by anyone or just auto-published. The release notes accumulate in a doc somewhere, and six months later, no one can tell which version of the product any given entry refers to.

The CSV-export-and-rewrite workflow becomes unsustainable when your team ships more than a handful of features per sprint. The API-plus-Zapier workflow becomes unsustainable when your ticket quality is inconsistent and you have no validation layer. Both approaches break at the same point: when the volume of work exceeds the capacity of one person to review and fix the output.

A Better Way to Translate

The goal isn't to get data out of Monday.com. The goal is to translate engineering reality into customer value, consistently, at scale.

Monday.com captures what your team did. Release notes explain what changed for your users. Those are two different documents, and the gap between them is not a formatting problem. It's a comprehension problem. Someone has to understand the change well enough to explain it in plain language, and that understanding rarely lives in a Monday ticket.

This is where AI-assisted generation changes the equation, but not in the way most people expect.

The value isn't that AI replaces the human who writes release notes. The value is that AI handles the translation step at scale, so one skilled person can manage release communication across dozens of shipped features per sprint without becoming a bottleneck. When you pair Monday's structured task data with tooling that reads commit history, pull requests, and ticket metadata together, the AI can produce a coherent first draft of each release note. The human reviews that draft, corrects anything that misrepresents the change, adds context the AI couldn't infer, and approves it.

That's a fundamentally different workflow than the one where a product manager spends Friday afternoon rewriting 40 ticket titles.

The same 2025 study found that AI outperformed manual methods on completeness and organization when given sufficient context, with over 80% of human evaluators agreeing the AI-generated notes were clearer and better organized than the originals. The key phrase is "when given sufficient context." The AI is reading commits and pull requests, not just Monday ticket titles. That's what gives it enough signal to produce something useful.

The operational model that works looks like this: AI generates a draft from the engineering artifacts attached to each Monday item. A writer or PM reviews the draft in a structured dashboard, flagging anything that's inaccurate or missing context. Breaking changes and known issues get added by hand. The reviewed draft is published. The whole review cycle for a 20-feature sprint takes less time than the old approach took for five features.

The gap between Monday.com's project tracking capabilities and what customer-facing release notes actually require is real, and it doesn't close by exporting better spreadsheets or writing smarter webhooks. It closes when you have a system that reads your engineering workflows directly and generates documentation from them. Doc Holiday generates release notes, API documentation, and changelogs from those workflows, and gives the team the editorial structure to validate and scale that output without rebuilding the toolchain or staffing up a documentation team.