Three things change: drafts and documentation arrive faster, fewer requirements get lost between what you said and what gets built, and your documentation is ready at handover instead of promised afterwards. What does not change is your own involvement. You still make the decisions, still test the result, and still need to give the project real time.
Shorter gaps between sessions. The write-up of a discovery session comes back to you quickly rather than several days later, while the conversation is still fresh enough for you to spot what was misheard.
Requirements you can actually check. You receive your requirements structured and written down before build starts. Most clients find at least one significant misunderstanding at this stage, which is precisely the point of the exercise.
Documentation that exists. Incomplete or missing documentation is one of the most common complaints about implementation projects, because documentation is usually written last and under time pressure. When it is produced alongside the build, that pressure does not arise.
More consultant attention on decisions. Less consultant time goes into typing configuration and writing up notes, so more of it goes into the questions that actually determine whether the system works for you.
Your team still has to show up for discovery, still has to test, and still has to decide things. If your people cannot give the project time, AI does not rescue it. The most common cause of a failed implementation is an absent client, and no tooling fixes that.
Expect a faster and better-documented project, and expect to be asked for the same engagement you would owe any consultancy. If a partner tells you AI means less work for your team, treat that as a warning sign.
Bring your project to a scoping call and we will walk you through how the stages would run. Book a call.