Skip to content
FlowHubFluxonLab
B
AI Automationfree

Automatically Create JIRA Issues from Outlook Email Support Requests

by Jimleukadapted from n8n official workflow galleryUpdated Aug 2026
RequiresBBasic LLM ChainJira SoftwareJira SoftwareMicrosoft OutlookMicrosoft OutlookOpenAI Chat ModelOpenAI Chat ModelStructured Output ParserStructured Output Parser
Share Post Share
ScSchedule TriggerGet Recent MessagesGet Recent Mess…OpenAI Chat ModelOpenAI Chat Mod…OPStructured Output ParserStructured Outp…MaMarkdownCreate IssueRDMark as SeenCLGenerate Issue From Support RequestGenerate Issue …1234567
1/5
STEPS · 7
Runs on a schedule

This n8n template watches an outlook shared inbox for support messages and creates an equivalent issue item in JIRA. How it works A scheduled trigger fetches recent Outlook messages from an shared inbox which collects support requests. These support requests are filtered to ensure they are only processed once and their HTML body is converted to markdown for easier parsing. Each support request is then triaged via an AI Agent which adds appropriate labels, assesses priority and summarises a title and description of the original request. Finally, the AI generated values are used to create an issue in JIRA to be actioned. How to use Ensure the messages fetched are solely support requests otherwise you'll need to classify messages before processing them. Specify the labels and priorities to use in the system prompt of the AI agent. Requirements Outlook for incoming support OpenAI for LLM JIRA for issue management Customising this workflow Consider automating more steps after the issue is created such as attempting issue resolution or capacity planning.

Tags

n8nreference-onlychain-llmjiralm-chat-open-aimicrosoft-outlookoutput-parser-structured
Connects
BBasic LLM ChainjirasoftwareJira SoftwaremicrosoftoutlookMicrosoft OutlookopenaichatmodelOpenAI Chat ModelstructuredoutputparserStructured Output Parser
CategoryAI Automation
Triggermanual
Complexitymedium
Nodes8
AddedMay 6, 2025

Related workflows

See all AI Automation
autofixingoutputparserBopenaichatmodelstructuredoutputparser
free

Force AI to use a specific output format

This workflow is for anyone looking to automatically fetch, validate, and parse complex language-based queries into a structured format. Its unique capability lies in not only processing language but also fixing invalid outputs before structuring them. Note that to use this template, you need to be on n8n version 1.19.4 or later.

by n8n Team
BsheetsWopenaichatmodel
free

Scrape and score Amazon products with Apify, OpenAI, and Google Sheets

Quick overview This workflow manually scrapes product results from an Amazon search URL via the Apify Amazon Crawler actor, uses OpenAI to generate a structured product analysis (category, key feature, verdict, value score), and appends the enriched results to a Google Sheets spreadsheet. How it works Runs when you manually click “Test workflow” in n8n. Sends the configured Amazon search URL and scraping limits to the apify Amazon Crawler actor and retrieves the dataset items. Filters out any scraper items that are missing a title or contain an error. Uses OpenAI to analyze each product’s title, brand, price, rating, and review count and returns a structured category, key feature, buyer verdict, and 1–10 value score. Combines the scraped fields and AI analysis into a single row format and adds a timestamp. Appends each enriched product as a new row in Google Sheets. Setup Create an apify API token and add it as an HTTP Header Auth credential used by the Apify request. Add an OpenAI API key as an OpenAI credential and choose the model to use in the configuration. Connect a Google Sheets OAuth2 account and select the target spreadsheet, sheet tab, and columns for appending rows. Upda

by Dr. Firas
BCgmailsheets
free

Find and verify business emails with ScraperCity, OpenAI, Google Sheets, and Gmail

Quick overview This workflow reads contacts from Google Sheets, finds and verifies their business emails via the ScraperCity Email Finder API, generates a short personalized icebreaker with OpenAI, appends the enriched leads to another Google Sheet, and emails a run summary via Gmail. How it works Starts manually to run an email enrichment job. Reads contacts (first name, last name, domain) from an input Google Sheets worksheet and aggregates them into a single payload. Submits the contacts to the ScraperCity Email Finder API, then waits and repeatedly checks the job status until it succeeds or fails/times out. When the job succeeds, downloads the result CSV from ScraperCity, parses it, normalizes key fields (name, domain, email, verification status, provider), and keeps only rows with an email. For each verified contact, prompts OpenAI to generate one short, company-referencing cold-email opening line in the configured language. Appends the enriched lead (including email, status, and icebreaker) to an output Google Sheets worksheet and sends a summary email via Gmail. Setup Create a ScraperCity account with Email Finder credits and add an HTTP Header Auth credential that sends you

by Dr. Firas