In addition to designing, developing, and testing an algorithm, in many cases, it is important to consider certain practical aspects of starting to rely on a machine to solve a real-world problem as this makes the solution more useful. For certain algorithms, we may need to consider ways to reliably incorporate new important information that is expected to keep changing even after we have deployed our algorithm. Will incorporating this new information change the quality of our well-tested algorithm in any way? If so, how does our design handle it? And then, for some algorithms that use global patterns, we may need to keep an eye on real-time parameters that capture changes in the global geopolitical situation. Also, in some use cases, we may need to consider regulatory polices enforced at the time of use for the solution to be useful.

For example, Google's designed recommendation engine algorithms have recently faced the European Union's regulatory restrictions due to privacy concerns. These algorithms may be some of the most advanced in their field. But if banned, these algorithms may actually turn out to be useless as they cannot be used to solve the problems they were supposed to tackle.

The truth of the matter is that, unfortunately, the practical considerations of an algorithm are still afterthoughts that are not usually considered at the initial design phase. For many use cases, once an algorithm is deployed and the short-term excitement of providing the solution is over, the practical aspects and implications of using an algorithm will be discovered over time and will define the success or failure of the project. 

Let's look into a practical example where not paying attention to the practical consideration failed a high-profile project designed by one of the best IT companies in the world.