Companies have a new toolkit for solving complex problems in teams that borrows its approach from computer science, according to MIT Sloan.
Getting started with a complex problem can seem daunting. Figuring out how to approach the task at hand can be one of the hardest parts of the challenge. Algorithmic business thinking, though, can help teams tackle these issues and communicate to solve problems.
MIT Sloan senior lecturer Paul McDonagh-Smith developed the concept after repeatedly observing teams approaching the same problem from different angles, failing to connect and learn from each other. He then turned to the logical, mechanical steps provided by algorithms and computer science for answers. The toolkit helps teams communicate and problem-solve using tenets borrowed from computer science by providing a framework for thinking. Here are the four key foundations of algorithmic business thinking:
- Decomposition: By breaking down the intimidating challenge into bite-sized pieces, you can begin to chip away at the problem and make it more manageable.
- Pattern recognition: Noticing patterns of success and failure across different domains can help you apply these lessons to other challenges.
- Abstraction: Contrary to common understanding, abstraction in this sense means removing noise from the signals and amplifying only the necessary information.
- Algorithmic partnership: Using the partnership between humans and machines to solve problems by uniting both capabilities.