I’ve been digging through the 2025 State of B2B GTM Report from Growth Unhinged, and while most of it focuses on channel strategy and GTM execution, two findings stood out for their direct relevance to product work.

These aren’t prescriptions—they’re observations from one dataset that might be useful as you think about your own product decisions.

Your pricing tier predicts your GTM motion (not the other way around)

The survey shows clear patterns between product pricing and which GTM motions actually work:

  • PLG dominates for products under $5k/year and companies under $1M ARR
  • Account-based motions work best for expensive products (above $25k ACV)
  • Mid-range products ($5-25k) see more success with paid acquisition

What caught my attention: this suggests that pricing isn’t just a revenue decision—it’s a GTM architecture decision.

When you’re setting pricing tiers or deciding on packaging, you’re also making a bet on how the product will go to market. A $2k/year product architected for sales-assisted conversion is fighting uphill. A $30k/year product expecting viral PLG growth faces the same problem.

This doesn’t mean you can’t defy these patterns. But it does mean your pricing strategy should be informed by the GTM motion you’re willing and able to execute or vice versa.

For PMs: the next time you’re in a pricing discussion, it’s worth asking explicitly: “Which GTM motions does this pricing strategy enable or constrain?”

AI features work better as an augmentation than a replacement

The report shows high AI adoption across GTM teams, but 53% see limited or no impact from those investments.

The specifics are telling. AI SDRs (full replacement plays) are particularly disappointing. One team reported “six months, zero opportunities.” Meanwhile, AI that augments human workflows—intent-driven outbound, market intelligence, content support—shows better results.

This maps to a broader product principle: automation that eliminates steps in an existing workflow tends to work better than automation that tries to replace the entire workflow. I’ve explored this pattern before—AI agents grow work rather than replace it.

For product teams building AI features, this suggests focusing on making humans more effective rather than eliminating them. AI that surfaces insights, automates tedious parts of a process, or handles high-volume, low-stakes tasks seems to land better than AI that tries to own an entire job function.

The nuance: this is one survey of B2B GTM teams, not a universal law. But it’s consistent with what I’m seeing across other domains—the “copilot” framing works, the “autopilot” framing struggles. For now.

What this means in practice

These aren’t definitive answers—they’re data points worth considering as you make product decisions.

On pricing: think through the GTM implications before you lock in that tier structure. Your pricing model is also a distribution model.

On AI: consider whether your AI feature is designed to augment a human workflow or replace it entirely. The former seems to be landing better in the market right now.

What patterns are you seeing in your own product work? Do these observations match what you’re experiencing, or are you seeing something different?