The relative improvement is both an oversell and an undersell depending on the context. For many applications the correct answer may be that a reasoned set of DB queries is about as good as it gets owing to lack of data, no better algorithm exiting, or product experience being mildly impacted by changes to the DB fetching component.
When confronted with these uncertainties internal stakeholder will often swing from "we just need more scientists working on this problem" to "it works fine, why would we spend time on this?" attitudes. The former almost always leads to over-investment where 3 teams of people are working on what should be one individuals project. The latter can sometimes be right, but I've also seen Fortune 500 Search rankings that have never been tuned let alone leverage an ML model.
When confronted with these uncertainties internal stakeholder will often swing from "we just need more scientists working on this problem" to "it works fine, why would we spend time on this?" attitudes. The former almost always leads to over-investment where 3 teams of people are working on what should be one individuals project. The latter can sometimes be right, but I've also seen Fortune 500 Search rankings that have never been tuned let alone leverage an ML model.