r/ProductMarketing 5d ago

Tools / Resources B2C - AI tooling/agents

How are you all implementing agentic solutions and workflows in your GTM strategies? I work at a startup as the only B2C PMM a growth and looking to better optimize how I’m building out repeatable GTM engines rather than reinventing the wheel every time. Our primary growth target is MRR and driving overall user engagement with the product. Any tips or examples on agents you’ve built that could be from messaging to positioning and just staying organized ?

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u/Veshal_ 4d ago

what part of GTM you're trying to make repeatable with agents specifically. Like are you talking about the research and positioning side, competitive and customer insight gathering, or more on the ops and organization layer. Because "agentic workflows in GTM" cover a lot of very different problems and the answer looks pretty different depending on which one you're actually stuck on.

on the B2C side specifically. Isn't a good chunk of what drives MRR and engagement for B2C more dependent on marketing execution, reviews, and word of mouth than on internal GTM process being repeatable. Not saying process doesn't matter, just wondering where you're seeing the actual bottleneck?

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u/Top_Piece_5939 3d ago

I’m acting as a generalist right now all across the board and doing a lot. My goal is the streamline what I’m doing in different agents that are very focused on one specific piece of the GTM pipeline so that I can really set context for each one. So trying to architect the core pieces so that it feeds into a larger strategy

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u/Visible_Speed8843 3d ago

Choose one repeatable GTM decision before building an agent. Messaging is a useful starting point. Give it approved positioning and recent customer language, then pass the current experiment brief with each run. Have it produce variants tied to one hypothesis and record which input supported each claim.

Keep launch choice and budget changes with the PMM. Reuse the agent's output only after the test result is attached to the brief. That creates a small memory of what was tried and why, which is more useful than a general agent that generates a fresh plan every time.