How to use AI for project management: 4 of the best use cases

A while back, we surveyed several tech leaders about their AI implementation strategies and their use of AI in project management. As one survey respondent said of AI project management, there is still a “lack of understanding into how AI could help at the moment.” 

This reflects the disconnect between the state of technology and what the public is aware of. For businesses, this lack of understanding regarding using AI to optimize one of the most time-consuming yet critical aspects of the business–project management–could result in a loss of time and, ultimately, money. 

In this blog, we’ll explore the various use cases of AI for project management and give examples of AI tools that facilitate those use cases so you can make the most of this new technology.

1. Documentation 

When we think of AI, documentation is generally the first use case we think of, thanks to the rise of ChatGPT’s generative AI. Documentation is, of course, one of the most critical aspects of project management and spans many core functions, including project summarization, formatting, and record-keeping. 

Examples

Acceptance criteria: Zenhub AI allows you to generate a set of acceptance criteria for software projects in behavior-driven development (BDD) format. 

Reformatting notes: Notion AI lets you reformat information in a document into a desired format, such as a checklist. For example, you could format messy meeting notes into actionable takeaways. 

Summarization: Zenhub sprint reviews provide an AI-generated summary of previously completed sprints.

2. Data integrity

Previously, we’ve discussed data integrity as one of the biggest challenges leaders face with implementing AI, considering poor data quality results in poor AI outcomes. But did you know that AI can improve data quality? AI can be trained to be an expert in data categorization, making it perfect for maintaining data quality in a system. In project management systems, this may be used to suggest data inputs to improve the quality and quantity of data available.

Examples

Zenhub AI labels: Zenhub AI-generated labels improve data quality in your PM system by helping team members select the most appropriate label for their project. This makes it more likely that the user will a) use a label and b) use the correct one. 

3. Knowledge search

One emerging use case of AI you may not have considered is searching for knowledge or other elements within a project management system. This could be a search engine or bot that can provide easier access to critical information. Considering the vastness of most project management systems, this might be used to provide details of specific projects or better navigate or provide information about the project management system.

Examples

Zenhub Assistant: The Zenhub assistant helps you and your team get quick answers to questions about Zenhub. 

JIRA AI search: JIRA’s AI search tool uses machine learning to help users find information about their existing projects faster. 

4. Scheduling and deadline prediction

Time management isn’t the easiest part of project management, especially when it comes to getting real about how long projects will take. AI, with its ability to become a subject matter expert on just about anything, can also be trained to be a subject matter expert on estimating time and scheduling. This makes it the perfect sidekick for creating realistic project schedules and estimating how long projects will take. 

Examples

Predictive roadmaps: Zenhub’s predictive roadmaps use a team’s calculated velocity (their average task completion speed) to accurately predict the project completion date based on previous sprint history. 

Calendar scheduling: Motion uses AI to optimize calendar time by automatically scheduling tasks and meetings based on priorities, deadlines, and user preferences. This ensures efficient use of time and minimizes scheduling conflicts.

Final thoughts

If your head is still spinning and you haven’t yet implemented AI for project management–don’t fret! Many tech leaders and project managers are in the same position as you. Of course, now that this tech exists, no one is expected to suddenly start to use it for everything we’ve listed above. The important thing is to think critically about your team’s biggest challenges and select AI solutions that directly address those. 

If you’d like some help getting started with AI project management, talk to an expert here.