ISSUE № 12 · September 3, 2026 · 3 min READ
A prospect asked how this fails in six months. Neither answer was about AI.
Two failure modes, zero mentions of AI.
I'm Jeremy Hurst, VP of Autonomous GTM at Swan. I left a $450K VP Sales role to take a job with a made-up title. I did it because I got a glimpse of the 100x seller - it stopped being a thought experiment and became the actual playbook. This is a field journal for GTM and growth leaders who can feel the model changing under their feet and prefer changing with it to being left behind.
"Let's say we wake up 6 months from now and this project's been a complete failure. Why?"
A prospect asked me that yesterday during lunch. He'd already stated an intention to move forward, but wanted to pre-mortem the failure modes together.
I love the question - it's not one I get very often.
I gave him 2 reasons:
1 - Self-serve deployment. Swan in 2025 was pure PLG. No implementation or training. "Just talk to the onboarding agent," they said.
Somehow, many customers DID figure things out. But many others didn't. And when you ask why, it often boiled down to a lack of support.
No guidance on how to set things up, which use cases to tackle first, or how to integrate Swan with other tools in the stack.
We killed the gap only recently. You have 5+ users? Our forward-deployed motion's not optional.
2 - Doing too much at once. Some teams try attacking 5 or 6 use cases all at the same time.
Unsurprisingly, none move the needle. No measurable ROI on any of it, so the agents die quietly. Usage drops, and everyone defaults back to doing things the old way.
That's why we cap it - we attack 1 or 2 GTM motions max at the start of an engagement. Nail those first, then consider what comes next.
As I think about both reasons, neither has much to do with AI.
Support & change management. A disciplined focus on what matters at the expense of other things. These are very human challenges.
That's the part of AI transformation that actually decides outcomes. It's not the model or the agents. It's the people adopting them.
It's why we always talk about augmentation & never replacement - it's taking the judgement & expertise of humans and encoding it into the system, so you get 100x the output from your best people.
THE SIGNAL
Every failure mode I named traces back to people, not the model, which means the AI was never the risk in the room.
THE PLAY
Audit your support
- Count how many people in your org are touching the AI or agents daily. 5 or more means self-serve isn't enough anymore.
- Ask 3 of them how they figured out what to do first.
- If "I just tried stuff" comes up more than once, that's your gap.
- Write a one-page starting guide: which use case to tackle first, how it fits the rest of the stack, who to ask when stuck.
- Assign someone, even part time, to walk new users through it. Time to implement: 3-4 hours to set up, ongoing after that.
SIT WITH THIS
If your project failed in six months, would the postmortem mention the AI at all? If not, you already know where the real risk was sitting the whole time.
COMMUNITY NOTES
Still trying to figure out how much support is actually enough. My guess is more than most of us think.
-Jeremy
Keep going