AI is becoming one of the most valuable tools for Product Marketers, not because it replaces their work, but because it removes the friction that slows PMMs down. Across the teams we observe at Proponent, the PMMs who get the most out of AI aren’t using it for theoretical brainstorming or long generic prompts. They use it to accelerate very specific workflows — the kind of work that usually takes hours of sifting, structuring, rewriting, and analysing. The real shift in 2025 isn’t “AI-powered PMMs.” It’s PMMs who know exactly where AI fits into their workflow and where it doesn’t.

1. Using AI to analyse customer conversations the way a PMM actually needs

Most PMMs want to be closer to the customer, but they simply don’t have time to listen to every sales call. This is where AI becomes genuinely useful. Instead of giving you long summaries, AI can highlight repeated buyer phrases, objections, hesitation points and the moments where buyers sound confused. PMMs tell us things like: “AI showed me the exact parts of the demo where buyers stopped engaging,” or “It helped me realise every buyer kept mentioning the word ‘manual’ — that completely changed how we framed our messaging.” These patterns matter because they show what your actual ICP cares about, not what you assume they care about.

The strength of AI here isn’t to summarise calls; it’s to extract patterns. PMMs can feed AI batches of transcripts and ask it to pull out themes across conversations — not just what happened on one call, but what keeps happening on every call. This gives PMMs a faster, more accurate understanding of buyer pain, language, and decision criteria without drowning in hours of recordings. It’s the kind of insight that typically takes days to gather manually.

The workflow isn’t about outsourcing judgment. PMMs still decide which signals matter. AI just reduces the time it takes to get to those signals.

2. Using AI as a drafting assistant, not a writer

AI has become a natural part of the PMM drafting workflow, but not in the “write this for me” way most people imagine. Instead, PMMs use AI to turn raw notes into structured documents they can refine. One PMM described it perfectly when they said, “AI gives me the skeleton; I add the muscle.” Whether it’s a launch brief, a sales one-pager, or messaging for a new feature, AI helps PMMs get past the blank page faster.

The benefit is that PMMs no longer spend time organising scattered notes, rewriting the same sections from scratch, or formatting documents. They can focus on improving clarity, sharpening the story and adding the nuance that only a PMM can bring. AI doesn’t replace the voice — it simply structures the thinking so PMMs can spend their energy on the parts that require expertise.

This workflow shifts PMMs away from the tedious parts of writing and lets them focus on the narrative. Instead of spending three hours building a draft, they spend three hours improving it — which is where the real value is.

3. Testing messaging with AI before it reaches the field

One of the most underrated PMM workflows is using AI to pressure-test messaging internally. Instead of waiting for Sales to surface objections or confusion, PMMs are now simulating how their ICP would react. They ask AI to take the role of a skeptical buyer and point out what sounds vague, generic or unconvincing. Many PMMs tell us AI has flagged inconsistencies they didn’t even notice, like taglines that don’t tie back to the pain points, or sections that sound clever but communicate very little.

This workflow saves time later in the GTM process because messaging gets refined before it ever reaches the customer. PMMs get early detection of weak claims, missing value, or soft positioning — all without waiting for a full feedback loop from the field.

The real advantage is speed. PMMs can now iterate messaging in hours instead of weeks.

Why big instruction prompts don’t work

One thing we see often is PMMs pasting long “mega prompts” into AI, hoping the model will spit out perfect output. But LLMs don’t work well that way. The longer the instruction, the more likely the model is to generalise or misinterpret what you want. PMMs get better results when they structure AI tasks the same way they structure their own work: small inputs, clear steps, and specific outputs.

AI performs best when it receives context, a narrow task, and a simple output format. Big prompts reduce clarity. Short prompts make AI think like a PMM. This mindset shift is core to getting real value out of AI — not by overwhelming the model, but by directing it.

Final Thought

AI is becoming a core part of the PMM workflow, not because it replaces expertise, but because it removes the slowest parts of the job — the synthesis, the structuring, the first drafts, and the early message testing. The PMMs who thrive in 2025 aren’t the ones who ask AI to “write” but the ones who use AI to think faster. Judgment still belongs to the PMM. AI simply helps them get to clarity sooner.

Want more practical PMM workflows? Subscribe to our newsletter.