Personally, id say the hardest part is customer adoption, rather than the technical implementation. I can build or integrate an AI feature successfully and still have it fail if users don’t understand when to use it or don’t trust the results. I’ve also noticed that adding AI can sometimes create a second workflow instead of improving the existing one. For me, the biggest test would be whether the AI actually makes the product feel simpler. If users need training, extra clicks, or constant correction, then the feature probably isn’t solving the right problem.

This is a really practical breakdown. The gap between building AI and integrating it into an existing product is often where the real challenges emerge especially, around user permissions, data consistency, and avoiding support overhead. The question of whether users actually change their workflow or just stick with what they know is probably the most telling metric long-term.

The technical integration can usually be solved. The harder questions are: What should the AI be allowed to do? When should it step in? When should it stay out of the way? And what happens when it gets something wrong? I would also add ongoing maintenance as an underestimated challenge. Once AI is embedded into a SaaS product, it becomes another system that needs monitoring, testing, updated context, permission controls, and continuous improvement.

I’d say the biggest issue is finding the right balance between AI and the existing product. Adding AI just because it’s trending doesn’t really make the product any better. The feature needs to feel useful, dependable and natural within the customer’s existing workflow. Otherwise, consumers may try it once and go straight back to the old way of doing things.