Great question. In my experience, it comes down to whether the AI solves a real pain point or is just added to check a box. When it’s tacked on purely to keep up with competitors, adoption stays low, and engineering resources would definitely have been better spent refining core features first.

• This reply was modified 1 week, 1 day ago by  Bobby.

I think one thing people don’t really consider is how quickly AI can change what customers expect from a product. Once users actually get used to instant answers or automation, taking that feature away later can make the product feel worse, even if AI was never the main reason they used it in the first place. So for me, adding or including AI isn’t just about whether it works now, it’s also about whether you’re ready to keep supporting and improving that expectation over time.

It certainly raises customer expectations especially in terms of speed with which they are served. But on the other hand it makes services more efficient and less tedious.

Founders who shipped AI learned adoption fades without a clear workflow fit. Some saw backlash when AI replaced tools users liked. Costs grew with every interaction. Teams that sprinkled AI onto existing features hit ceilings and had to rebuild. The winners started with one repetitive task done well, proved value, then scaled. If they could go back, most would still add AI, but only where it solved a real problem.

Adding AI now feels like an expectation rather than an advantage. Founders who shipped it learned adoption fades without a clear workflow fit. Some saw backlash when AI replaced tools users liked. Costs grew with every interaction. Teams that sprinkled AI onto existing features hit ceilings and had to rebuild. The winners started with one repetitive task done well, proved value, then scaled. If they could go back, most would still add AI, but only where it solved a real problem.

Honestly, I don’t think adding AI is the problem. The problem is adding it just because everyone else is doing it. If customers actually use it and it makes their work easier, then it’s worth it. But if it’s just another feature sitting there that nobody uses, that time could probably have been spent fixing something customers already struggle with.

think a lot of teams add AI because they feel they have to, not because they have thought through what it actually changes. The maintenance burden is real. AI is not a one time build. It needs constant tuning, monitoring, and updates as your product and customer needs evolve. I have also seen cases where AI features got ignored because they solved a problem customers did not really have. The teams that get it right seem to start with a specific pain point, not just “we need AI.” Would I add it again? Probably, but I would be much more deliberate about where and why.

Once i heard a SaaS company talk about how AI made their relationship and close rapport with their customers decline due to inorganic interactions and it makes you wonder whether adding an AI to some kind of SaaS products is really a good idea.

I think this is a really important conversation. AI can definitely add value to a SaaS product, but it shouldn’t be added just because everyone else is doing it. What matters most is whether it actually solves a real customer problem and makes the product easier, faster, or more useful. At the same time, AI can bring new challenges like accuracy issues, higher costs, privacy concerns, and the need for ongoing improvements. I’d be interested to hear from founders who have already added AI to their products what worked, what didn’t, and whether customers actually kept using the AI features. Sometimes the biggest lesson comes after launch, not during development. Overall, I think the focus should be on creating meaningful customer value rather than simply adding “AI” to the feature list.

We added it because everyone expected it. Some users love it, most barely use it. Biggest surprise was how much ongoing work it takes to keep it from sounding dumb. Wouldn’t scrap it, but I’d start way smaller next time.

One team I know added an AI assistant because every competitor had one six months later, almost nobody used it. The bigger problem wasn’t adoption though; it was that customers started expecting everything to be AI-powered, and support got messier because the AI occasionally gave wrong answers. They ended up spending more time managing expectations than building.<div>
</div><div>Another founder I talked to had the opposite experience. AI became their best retention feature, but only because it solved a real, specific pain point not because it was AI.</div><div>
</div><div>The pattern I keep noticing: adding AI isn’t the regret. Adding AI without a clear job for it is. If you can’t name the exact workflow it improves, the engineering time is probably better spent elsewhere first.</div>

The real question is not whether to add AI but whether it changes what your product can do. Many founders find adoption stays with power users while support costs rise and maintenance never ends. The ones who win treat AI as a rethink of the workflow, not a checkbox. If customers only expect it, the feature often costs more than it returns.

I’ve seen this happen where the AI feature sounds great during planning, but once it’s live, users don’t really use it as much as expected. The maintenance and constant tweaking can also become a bigger part of the workload than people expect. I’d probably start small, see if customers actually rely on it, and only then invest heavily in building it out.