Machine intelligence, or MI, has captured the public's fascination within the past few years. There are lots of problems that can be solved with machine intelligence, but there are some that don't fit so well into machine intelligence solutions. Let's look at a variety of the different tasks that are well suited for machine intelligence. For instance, if an organization has rote repetitive tasks that take up a lot of human employees' time, techniques such as robotic process automation can be used to automate those tasks. Tasks such as data entry or completing forms can very easily be automated using these techniques. Also, if an organization's data is largely pattern-based and requires human judgment to determine outcomes for that data, there's a good chance that supervised machine learning can be used to automate that decision making process. While that's the good news, there's plenty that machines are still not capable of fundamentally. Although machines can understand patterns in the data, they don't have a true understanding in a human sense as to the meaning behind data. As a result, machine intelligence isn't great for tasks that require complex judgment or higher order thinking. One of the weaknesses of today's machine intelligence algorithms is that the decisions that they make can't be readily explained. Finally, machine intelligence algorithms are data hungry. If you have a problem that you're trying to solve, but insufficient data or insufficient understanding of the data required to solve that problem, you may want to look for other solutions or invest time in building a larger collection of data to shape your problem space.