How Is AI Used in Project Management?
Artificial intelligence (AI) is transforming how project managers work. The impact is significant, with AI changing many traditional project-management processes. While project managers have traditionally spent significant time preparing reports, maintaining documentation, analysing project data, and tracking progress, some of these routine activities can now be supported by AI, allowing them to focus more on higher-level managerial activities.
Project managers can use AI to analyse historical and current project data, identify potential schedule or cost risks, and support resource-allocation decisions based on factors such as workload, availability, and previous project data.
How Can Generative AI Enhance Jira-Based Project Management?
Jira is a work-management and coordination platform widely used by software development and technology teams to plan, track, and manage their work. It provides teams with a central workspace for planning, tracking, and managing software-related work.
Generative AI refers to AI technology that can create or transform content such as text, code, summaries, and documentation based on user instructions. It can be used to summarise text and code or generate certain procedures based on the desired outcome. It can act as an AI-assisted writer or editor, helping users summarise lengthy discussions, refine content, or identify possible next steps based on the available context.
Key capabilities of Jira and generative AI
1. Ticket and task management
Summarising information from existing tickets: AI can summarise conversations, comments, and updates in tickets, helping users quickly understand the key information without reading through the entire history, particularly for tickets that span multiple teams.
Suggesting ways to break down large tasks: This can be particularly useful when a customer ticket describes a feature in very general terms. AI can help break down a high-level requirement into smaller, more specific tasks for the team to consider.
Prioritising support requests and tickets: AI can help analyse incoming tickets and requests, highlighting urgent items and identifying tickets that may require reassignment.
2. Content creation
Summarising information for user stories/tasks: Instead of creating a ticket or user story from scratch, users can provide the AI with relevant information and use it to generate an initial story or task that can then be reviewed and refined by the team.
Adapting communication for different audiences: AI can take existing project content and adjust its tone, format, or level of detail for different recipients, such as technical teams, project stakeholders, or senior leadership.
3. Search and Automation
Searching without having to know JQL (Jira Query Language): JQL can be useful for advanced searches, but it can also be tedious to use for everyday queries. Users can describe what they are looking for in natural language and have AI translate the request into the appropriate JQL query.
Creating automations: Instead of manually configuring an automation rule, users can describe what they want to happen in natural language, such as notifying the on-call person when an issue is marked as urgent.
Conclusion
The use of AI within Jira extends beyond automation, supporting activities such as summarisation, content creation, natural-language search, and workflow configuration. One of the key advantages of using AI in project management is reducing the effort involved in repetitive administrative tasks, allowing teams to focus more on core delivery activities.
Teams that integrate AI into their workflows can experience several benefits. First, teams can work more efficiently by reducing the time required for routine project activities. Second, productivity can improve because teams can spend less time on routine administrative and maintenance activities and more time on core project work. Third, teams can reduce the time and effort spent on repetitive administrative activities and redirect capacity towards higher-value work. Finally, AI can support more consistent documentation and planning and help teams identify potential risks earlier, when its outputs are appropriately reviewed by the relevant team members.
AI and Automation in Trello for Project Management
Trello combines built-in automation capabilities with AI-related use cases that can support project planning, collaboration, and prioritisation.
Automation in Trello
Through its built-in automation capabilities, Trello allows users to create rules for managing cards. It can be used to move cards in accordance with specific conditions, assign tasks to team members automatically, and set due dates for cards. By utilising this feature, users can delegate repetitive card management operations to the application and eliminate time-consuming card transfers during the sprint.
Predictive Analytics with AI
When AI is applied to project data from tools such as Trello, it can identify patterns that may help predict potential project risks. For instance, the technology can recognise the tendency to miss deadlines, budget issues that may arise in the current project, or team members’ workloads causing delays. When these patterns are identified, predictive analytics can facilitate the generation of an action plan aimed at mitigating these risks.
Collaboration with AI
AI-assisted task assignment: AI can potentially recommend task ownership by considering factors such as workload, skills, previous assignments, and task requirements. This can help teams distribute work more effectively.
AI can also help surface relevant decisions and updates from collaboration tools, reducing the need for team members to manually search through lengthy conversations.
Intelligent Notifications
AI can help surface the project updates and notifications that are most relevant to individual team members, reducing the need to manually review every activity or update.
Task Prioritisation
AI can help teams prioritise work by considering factors such as dependencies, deadlines, blockers, team availability, and workload. This can help project managers focus on tasks that are most likely to affect overall delivery.
Conclusion
Trello’s strength lies in its simple, visual approach to work management, while automation and AI capabilities can further reduce repetitive administrative activities. Its value also comes from providing teams with a shared view of work, priorities, and dependencies, helping reduce blockers and duplicated effort.
Turn your conversations into coordinated work with Asana AI connectors
Teams often brainstorm ideas and develop plans in AI assistants such as Claude or ChatGPT, but may still need to switch to a project-management platform such as Asana to turn those ideas into structured work. This can create context switching and increase the risk of information being lost between tools.
With Asana’s AI connectors, users can initiate and manage supported Asana work directly from connected AI tools, reducing the need to switch between applications.
The AI can help users develop ideas and organise plans before turning them into structured work in Asana. Asana provides AI connectors for tools including ChatGPT, Claude, Microsoft 365 Copilot, Google Gemini, Le Chat, and Quick Suite.
Watch to learn how to use our AI connectors:
ChatGPT: Users can work with Asana tasks, subtasks, comments, due dates, and project details to create summaries, understand priorities, and prepare status updates.
Claude: Users can search Asana information, create and update tasks and projects, check progress, and manage work directly from Claude.
Microsoft 365 Copilot: Users can search, retrieve, and reference Asana tasks and project information through Microsoft Search and Copilot.
Gemini: Users can take supported Asana actions through Gemini and Google Workspace, helping them manage work without switching between tools.
Le Chat: Users can create and manage Asana projects and tasks directly through Mistral AI’s Le Chat.
Quick Suite: Connects Amazon Quick Suite with Asana so that context from supported applications can be used within AI workflows involving Asana.
Conclusion
AI-powered project-management tools can help organisations improve visibility, reduce administrative effort, and make project information more accessible across teams. Manual processes such as maintaining spreadsheets, sending reminder emails, and tracking task completion can increase the risk of missed updates, mistakes, and miscommunication. In addition, tasks or dependencies can be overlooked, while team members may spend time on administrative activities that could be automated or streamlined. Project managers must continuously monitor progress, dependencies, risks, and emerging issues to keep projects on track.
By using AI and automation for repetitive activities, project managers can reduce administrative effort and focus their attention on tasks that require human judgement, stakeholder management, and decision-making.