I’m trying to separate “AI workflows that look productive” from the ones that actually help with growth.
A lot of AI use cases sound useful at first:
But not all of them actually lead to more replies, signups, demos, or useful conversations.
For me, the interesting question is not “which AI model is best?” but:
Which AI workflow actually creates growth?
Some steps need speed. Some need better reasoning. Some need cheap volume. Some need reliability.
That is also why I’ve been thinking more about model choice as part of workflow design. If the workflow changes, the right model or API may change too.
We’re exploring this direction at EvoLink model page: making it easier to compare and use different models based on the task, not just the launch hype.
Curious how others think about this:
The AI workflows that actually help growth usually have one thing in common: they start from an existing buyer pain, not from content production.
The noisy ones are usually “turn one idea into ten posts” or “draft generic cold emails.” They create activity, but not necessarily conversations.
The useful workflow is closer to:
find people already showing the pain
understand the context
write one specific reply or message
track whether it creates a real response
For early growth, I would not judge the workflow by output volume. I’d judge it by reply quality: did it create a founder conversation, a demo, a signup, or useful rejection?
Model choice matters less at the start than workflow design. A weaker model inside a sharp workflow can outperform a better model used for generic content.
Happy to put a tighter version in writing if useful. The useful part would be mapping which AI growth workflows are worth testing first and which ones are probably just noise.