ROI on AI workflow tools feels fake right now and i want to be wrong

Reddit r/artificial News

Summary

A bootstrapped SaaS founder shares their experience with the high upfront costs and uncertain benefits of using AI workflow tools for automating customer onboarding, questioning if others find similar tools practically useful.

been trying to automate a chunk of my customer onboarding process for my saas. nothing fancy, just reducing the back and forth emails that eat up like 2 hours a week. spent probably 6 hours across three evenings testing different AIassisted tools and prompt setups to make it work smoothly. the math on that is not great. and i keep running into this thing where the setup cost is real and upfront but the payoff is theoretical and later. which is fine in principle but when you're bootstrapped and wearing every hat, "later" feels very abstract. what i actually want to know is whether other people building small products are finding a point where AI genuinely clicks for operational stuff, or if we're all just in a weird middle period where the tools are impressive in demos and annoying in practice. not talking about coding assistants, those seem to work. more like the workflow automation layer where you're trying to get AI to handle judgment calls that are almost routine but not quite. curious if the costtosetup ratio has ever actually flipped positive for anyone doing real small business ops, or if i just keep picking the wrong tools.
Original Article

Similar Articles

Everyone builds AI workflows. Almost no one sticks with them. Here’s why.

Reddit r/AI_Agents

A founder shares his experience with AI tool adoption, noting that most people collect tools without achieving real results. He advocates focusing on one critical business problem and iterating until the workflow genuinely works, citing his own success reducing client reporting time from 4-5 hours to under 45 minutes.

The AI productivity numbers don't match what I actually see on my team

Reddit r/artificial

The author, running a small dev team, shares mixed real-world results from using AI coding tools: they speed up boilerplate and onboarding, but produce confident wrong answers on complex problems and increase code review workload, yielding modest net gains far below the often-cited 10x improvement.