Unfortunately, this ain't necessarily so. Learning algos can quickly achieve dramatic improvements in benchmark tests (or performance indicators) but the selection of those tests is crucial.
Also, it is abolutely critical to understand that if the system does not behave in a smooth and consistent manner, then interpolation (prediction of results between benchmark tests) may range from being unreliable to complete garbage.
This apparent over-optimisation filter is a case in point. A learning algo can still achieve good benchmark results in the presence of such filters (esp when adjusting the filter levers), unfortunately the benchmarks themselves can become worthless.
What you would end up with is a company convinced that it is providing better and better results (because the benchmarks say so) but a public that may, in large part, disagree, either because the benchmarks are not realistic or because behaviour between those benchmark tests is unpredictable - sometimes good, sometimes not so good.