Your product ships features every week, but customers hear about them three weeks later—if at all.
You ship a feature on Tuesday. By Friday, it's buried in Slack. By the following Tuesday, marketing is scrambling to write copy for a changelog entry that should have gone out days ago. Meanwhile, your customer success team is answering the same question: "What's new?" This workflow solves that by creating a single source of truth: when engineering tags a release in your internal docs (or posts to a release channel), an AI system immediately converts it into three outputs—a customer-facing changelog entry, a short-form email announcement, and a Slack notification to marketing. Marketing reviews and approves within 24 hours. If approved, the email goes to your customer list automatically. If rejected, it bounces back to the product team with specific feedback. The result: features get communicated consistently, adoption accelerates because customers know what's new, and your marketing team stops firefighting release announcements. This is not a "set it and forget it" system. It requires a human approval gate—marketing must sign off before any email ships. But that gate is fast (one click) and prevents the two failure modes that kill adoption: either silence (nobody hears about the feature) or noise (marketing sends five conflicting emails about the same update). The workflow works best if you already have a release process—a Slack channel, a GitHub release, or a shared doc where engineering documents what shipped. If you don't, build that first. Then plug this system in front of it.
Trigger: Engineering posts a release note to a designated Slack channel, GitHub release, or internal doc tagged with #release. The AI system detects the trigger within 5 minutes.
Receive release notes via form, email, or API. Extract version, date, and feature/fix/deprecation items. Store in structured format for downstream processing.
Use LLM to rewrite technical release notes into benefit-focused, jargon-free changelog language. Highlight user impact, not implementation details. Maintain tone consistent with brand voice. Output markdown or HTML ready for changelog tool.
Generate email subject line, preview text, and body copy from changelog entry. Include call-to-action (try new feature, read docs, provide feedback). Format for email template. Keep to 3–5 key updates per email.
Product marketing manager reviews generated changelog and email for tone, accuracy, and alignment with messaging. Edit copy, adjust CTAs, or request rewrites. Approve or send back to AI for regeneration.
Push approved changelog entry to your changelog platform (e.g., Changelog.com, Beamer, Canny, or custom CMS). Include version, date, and category tags. Make live or schedule for release date.
Deliver approved email via your ESP (Mailchimp, Klaviyo, HubSpot, etc.) to segmented audience. Schedule send for release date or immediately. Track open and click rates.
Optional: Auto-post summary to Slack, Twitter/X, in-app notification, or help center. Use approved changelog text as source. Adapt format per channel.
Collect email open/click rates, changelog views, feature adoption metrics, and customer support tickets related to release. Feed insights back to product and marketing teams.
1. Choose your release source: Slack channel, GitHub releases, or a shared doc (Notion, Confluence, Google Doc). 2. Connect that source to an automation platform (Make, Zapier, or custom webhook). 3. Set up an AI prompt that converts the release note into three formats: (a) 50-word changelog entry, (b) 100-word email body, (c) Slack summary for the marketing channel. 4. Route all three outputs to a shared Slack thread or approval tool (Airtable form, Notion database, or email). 5. Add a marketing team member as the approver. On approval, the email is sent to your customer list via your email platform (Mailchimp, Klaviyo, etc.). On rejection, it returns to the product team with feedback. 6. Test with one release before rolling out to the full team.