Today: narrow project assistants
Early project management AI will be a project assistant focused on a narrow area of managing a project or team. By focusing on supporting a team in one specific area rather than dealing with all the complexities involved in managing a project, project management AI will be useful to teams sooner rather than later.
stratejos, for example, has started out by focusing on assisting with estimates, budget, and sprint management. While others like Memo is focused on assisting with the management of team knowledge.
Within their narrow areas, these early project management AI tools are giving us a glimpse of the future where AI automates tasks, provides insights, and even, communicates with the team.
However, there are some challenges. These early, narrow project management AI tools rely on people to input data correctly, update tools in a timely manner, and make corrections. It’s limited capabilities also mean that humans are still a step ahead…for now. In order to provide even more value, project management AI needs to evolve.
Second generation: expanding project understanding
The next step for these narrow assistants is to start expanding their understanding of projects and teams.
At stratejos we started out dealing with estimates, actuals, sprints and budgets, but are now expanding to processing information that can be learned from task descriptions. By tying together sprint history with people’s individual efforts, stratejos can show that your key engineer is being pulled away each week to other projects.
As the assistants expand their understanding, new metrics will be revealed that weren’t previously possible, such as quality, performance, learning, change, and effort.
For example, AI will know the changes made to source code and link those changes to people and tasks performed. This will allow AI to link bugs reported to a line of code, the person that wrote it, and the tasks that relate to it. This will allow for real, actionable indicators of team and project performance.
With more data points about projects, predictions will become more reliable, more appropriate, and easier for people to understand. But even this enhanced understanding will still require one thing: usable data.
Third generation: Filling in the data gaps
The often unmentioned challenge with AI and the internal facing systems in organisations such as project management tools is the quality and suitability of the data.
Some teams enter minimal to no data into their project management tools. And even the most disciplined teams have issues with their data being interpreted by machines – maybe they inconsistently name their tasks, or enter minimal information. Whatever the reasons or the maturity of the team, it’s almost a given on that any project management system or toolset, there is missing data or messy, unstructured data.
Data size is certainly a challenge but not an insurmountable one. Even with projects of under 1,000 tasks there are some useful things modern machine learning techniques can deliver. Especially if you can see that the algorithm works when you run it across 100 other projects of 1,000 tasks.
Project management AI can deal with the data challenge by:
- Filling in the blanks – AI can make good enough assumptions about the data that is missing and enter that data.
- Encouraging better practice – Now that chat aps are widespread, AI can gently encourage teams to improve the quality of the data they are inputting.
- Creating new layers of metadata – In order to really understand the state of projects and the performance of teams AI will need to create metadata to represent additional concepts that aren’t currently represented. This meta-data can then feed into machine learning algorithms as features that will enhance the ability of AI to provide meaningful advice.
Full article available here: https://www.atlassian.com/blog/software-teams/3-ways-ai-will-change-project-management-better
PS: Lili is currently working on the third generation mentioned above.