[Waffle2180]

I’ve seen LLMs make money most reliably when they’re embedded into an existing workflow rather than sold as “AI” itself.

One example: a small team built an internal tool for SEO/content teams that generates structured content briefs and refresh plans from search data. The value wasn’t faster writing, but fewer failed pages. Clients were willing to pay because it reduced wasted content spend and made outcomes more predictable. It ended up as a SaaS with recurring subscriptions rather than a usage-based novelty.

Another case was customer support tooling for a B2B product. LLMs were used to summarize long ticket histories, surface likely causes, and draft replies, but humans stayed in the loop. The business impact showed up as lower support headcount growth while revenue increased, which leadership cared about more than raw “productivity.”

Across cases, the pattern seems to be: - tie the model to a clear economic decision - charge for risk reduction or revenue lift, not for text generation - keep humans in the loop where mistakes are costly

Pure “LLM apps” struggled more unless they were tightly scoped or had strong distribution already.