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Summary of the Reading
This paper seeks to help make it faster and easier for teams to find their ideal team structure. While many services allow teams to test out many different team structures to find the best one, many of those services can take a lot of time and can greatly affect the people who work on the team. Often times they have to switch structures so often that it makes it hard for the teams to concentrate on getting work done.
The method proposed in the paper seemed to be very successful. It resulted in teams that were 38-46% more effective. The system works by testing different team structures and taking automatically generated feedback information (like performance metrics) to figure out how effective each structure is. It will then base its future combinations on this feedback. Each time a new structure is tested, it varies on a five dimensions: hierarchy, interaction patterns, norms of engagement, decision-making norms, and feedback norms.
Reflections and Connections
I think that this paper has an excellent idea for a system that can help teams to work better together. One of the most important things about a team is how it is structured. The structure of a team can make or break its effectiveness, so getting the structure right is very important to making an effective team. A tool like this that can help a team figure out the best structure with minimal interruption will be very useful to everyone in the business world who needs to manage a team.
I also thought that it was a great idea to integrate the system into Slack. When I worked in industry last summer, all of the teams at my company used Slack. So, it makes a lot of sense to implement this new system in a system that people are already familiar with. The use of Slack also allows the creators to make the system more friendly. I think it is much better to get feedback from a human-like Slack bot than some other heartless computer program. It is also very cool how the team members can interact with the bot in Slack.
I also found the dimensions that they used in the team structures to be interesting. It is valuable to be able to classify teams in some concrete way based on certain dimensions of how they perform. This also has a lot of real world applications. I think that a lot of the time, one of the hardest things in any problem space is just to quantify the possible states of the system. They did this very nicely with the team dimensions and all of their values.
Questions
- Would you recommend this system to your boss at your next job as a way to figure out how to organize the team?
- Aside from the ones listed in the paper, what do you think could be some limitations of the current system?
- Do you think that the possible structures had enough dimensions and values for each dimension?
In answer to your third question, I think there is always value in having more granularity but at the same time I think there is such a thing as too much granularity. I think the paper has a good enough set of dimensions that it is specific enough for teams to work with, while also giving them the freedom to interpret the dimension and apply it the best way they see fit. I think more value would come of having more prompts within the dimension recommendation itself. For example, if the DreamTeam is recommending changing the team’s heirarchy to leader based, it would be also useful to prompt the team to vote for the leader or something like that.