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👋 Hi, it’s Kyle Poyar and welcome to Growth Unhinged, your source for practical advice on marketing, pricing, and growth.
Up today: I’m teaming up again with a favorite collaborator, Brendan Short of the excellent The Signal newsletter. Brendan and I revisit our favorite automated GTM plays from last year, and show what’s changed for AI-native teams.
Outbound has been around for, well, a long time. It feels fresh again because of two forces, data and automation.
There’s so much better data on both who’s your target customer and who’s ready to buy. When asked which GTM channels B2B companies are investing the most into for 2026, intent-based outbound was #2 on the list and warm outbound took the #5 spot. Buyers may hate AI slop cannons, but (good) outbound still pays the bills.
In February 2025, Kyle and I published The best automated GTM plays you’re not running. It was a menu of 25 plays like website visitor outbound, champion tracking, closed-lost re-engagement, and micro-campaigns. That post still holds up. The plays are still good. But the way you build them has changed… a lot.
14 months ago, "automation" meant setting up a signal that gets triggered, enriching a list in Clay, and having AI attempt to write an email. Today, it means AI agents that monitor signals continuously, pull research from your CRM and the web in real-time, and draft outreach that references what the prospect actually said on their last call with you.
Four things shifted:
AI has a better context layer that understands your business.
AI agents now execute multi-step workflows autonomously.
MCP (Model Context Protocol) connects AI directly to your GTM stack.
The best teams stopped thinking in "plays" and started thinking in "systems."
So we're back. Instead of 25 plays at a surface level, we picked 5 workflows to show you how AI has changed how you set up GTM systems in 2026.
Important to notice: The workflows covered here include a combination of deterministic steps (if-then) and agentic steps (AI acts autonomously).
Play 1: Closed-lost deal re-engagement
Original version (2025): Set a CRM automation to flag opportunities closed-lost 9 months ago. Rep reviews, writes a re-engagement email. Maybe they use the OpenAI API to summarize the last call and insert that summary into the email with a line like: "To jog your memory, here's what we talked about last time."
That was a solid play, but it had two problems. The timing was arbitrary. Why 9 months? What if the right moment to re-engage was 4 months (because they got a new VP) or 14 months (because they just raised a round)? And the summary was generic. It told you what was discussed, but not why the deal died.
The AI-native version: An agent monitors your closed-lost pipeline continuously.
The trigger is a cluster of re-engagement signals at the account: leadership change, new funding, job posting for a relevant role, champion who killed the deal left the company. When enough signals stack, the agent fires.
When it fires, the agent pulls the call transcript from the last conversation. It identifies the specific objection that killed the deal. "They went with [competitor] because of [specific feature gap]." Or "budget got pulled because of [reorg]."
Then it drafts an email that references the objection and what's changed. For Tier 1 accounts, the email routes to the rep for review. For Tier 2 and below, it sends automatically.
The key shift: Timing is dynamic instead of calendar-based. And the email references the real reason the deal died, not a generic recap.
Tool chain: CRM (closed-lost pipeline) → Signal sources (job changes, funding, leadership) → Call transcript via API and/or email replies (transcript + objection extraction) → Clay (enrichment + orchestration) → Claude (email drafting) → SEP, CRM sequence, or Slack.
Pro-tip: Stack this with champion tracking. If the person who killed your deal left the company, that's a completely different play than if they're still there. Your agent should know the difference and adjust.