From traditional task management to workflows where AI agents become part of the team. How is AI changing project management?

AI changed the way software is written.

However, I think we have only just begun to understand how muchthe way software development teams manage their workwill change.

Until a few years ago, project management tools were primarily expected to answer simple questions such as the following:

• What are we building?
• Who is working on it?
• When is the deadline?
• What is stalled?
• What has been completed?

These are still important.

However, new questions have now emerged.

What happens when AI agents take on part of the work?

One developer may implement features in Claude Code, while another may fix bugs in Codex. AI agents may also generate tests, update documentation, refactor code, or investigate issues.

The things project managers need to keep track of will no longer be limited to developers.

Both developers and agentsmust be kept track of.

The role expected of traditional project management tools is also beginning to change.

This article introduces7 AI project management toolsthat can help software development teams organize their work in 2026. It covers everything from established platforms that have added AI features to tools designed around new workflows in which humans and agents collaborate.

What makes AI project management tools different?

Before looking at the individual tools, let's clarify what we're looking for.

Simply adding an AI chatbot to a project management app does not necessarily make it an AI project management tool.

For development teams, there are several features worth considering from a more practical perspective.

1. AI-Powered Task Management Assistance

AI can break high-level requirements down into actionable tasks, summarize discussions, identify missing information, and suggest priorities.

2. Integration with Development Workflows

Project management tools should not be completely disconnected from the development environment.

GitHub, GitLab, CI/CD systems, IDEs, and AI coding tools need to integrate and work together.

3. Support for AI Agents

This is a relatively new perspective.

Can AI agents actually participate in the workflow?

Can they receive tasks, move the work forward, report progress, and return results in a form that someone can review?

4. Human Oversight

The goal is not necessarily to remove people from the process.

In software development, people must continue to be responsible for defining requirements, reviewing changes, resolving conflicts, and deciding what to release.

5. Context

AI becomes far more useful when it can access the appropriate project context.

It is not enough to simply write the following in a task:

Fix the login.

For the person or agent doing the work, you need to include sufficient information about the problem, requirements, repository, previous discussions, and expected outcome.

With these points in mind, let's look at each tool.

1. Sharkly — Project Management Built on Collaboration Between Humans and AI Agents

Recommended teams: Development teams that want to directly involve AI agents in their work management workflow.

Sharkly is taking a somewhat different approach to AI project management.

Rather than treating AI as an assistant standing alongside the project management system, it is designed as a work management system shared by humans and AI agents

.

The idea is simple.

AI coding tools are already capable of executing development tasks.

The harder part is orchestrating the entire execution.

Teams still need to decide what to do, provide context, assign responsibilities, track progress, address issues that impede the work, and review the results.

Sharkly provides this kind of coordination mechanism, centered around work executed by AI.

Key features

• Task-based work management
• Parallel task execution by multiple AI agents
• Connected computers and AI coding runtime environments (Runtime)
• Sharing context and project information
• Reusable agents and skills
• Projects and sprints
• Agent activity and execution history
• Human review and acceptance
• Integration with existing project management and collaboration workflows

Use cases

Imagine a team developing a SaaS application.

Instead of creating one large ticket to “build a new dashboard,” the work can be divided into smaller tasks.

• Research the dashboard requirements
• Build the frontend components
• Add the necessary API endpoints
• Write automated tests
• Update the documentation

While each agent works on a clearly scoped task, developers and product managers can see the status of the project as a whole.

Importantly, Sharkly does not replace tools such as Claude Code or Codex.

Instead, it provides a management and coordination layer around those tools

. This is particularly interesting as AI coding progresses from individual experimentation to team-based development.

2. Jira — Using AI in established project management

Recommended teams: Large development organizations that already use Jira.

Jira remains one of the best-known project management platforms in software development.

Its strength is its ability to systematically manage issues, projects, workflows, planning, and development processes.

Its AI features support tasks such as summarizing work, generating content, and leveraging project information.

Key features

• Issue and task management
• Agile Boards
• Backlog and Sprints
• Workflows and Automation
• Reports
• Integration with Development Tools
• Support for Improving Productivity with AI

Use Cases

In large engineering organizations, there may be hundreds of issues spanning multiple teams.

Developers can work on their assigned Jira issues, while project managers can monitor overall progress through boards and reports.

By incorporating AI, organizations can summarize discussions and receive support with repetitive project management tasks.

This makes it especially suitable for teams whose Jira-centered processes are already established and who want to introduce AI without completely replacing their existing project management systems.

3. Linear — Streamlined Project Management for Product Teams

Recommended Teams: Modern software development teams seeking simple issue management and a robust development workflow.

Linear is popular among startups that want to manage projects without adding unnecessary complexity, as well as engineering teams that place a strong emphasis on product development.

The interface is built around issues, projects, cycles, roadmaps, and team workflows.

Linear has also incorporated AI into its product, providing AI assistance in various aspects of issue and project management.

Key Features

• Issue Management
• Projects and Roadmaps
• Cycles
• Team Workflows
• GitHub Integration
• Automation
• AI-Powered Workflow Support

Use Cases

Consider a small SaaS team of five developers.

Instead of maintaining a complex project management hierarchy, the team can organize its work through projects and cycles, while developers manage individual issues.

AI supports repetitive tasks such as creating and organizing issues, while developers remain responsible for implementation and review.

Linear is particularly well suited to teams that value speed and a clean interface.

4. GitHub Projects — Development Work and Planning in One Place

Recommended teams: Teams that already manage their source code and development workflows on GitHub.

GitHub Projects takes a different approach in that project management is closely tied to the code itself.

If you already use GitHub Issues and pull requests, managing plans close to the repository can reduce context switching while working.

Key features

• Issues
• Project boards
• Custom fields
• Roadmaps
• Pull request integration
• GitHub Actions
• AI development ecosystem

Use cases

One possible workflow is to create an issue for a new feature, assign it to a developer, link it to a pull request, run automated checks with GitHub Actions, and close the issue once the changes are merged.

Adding AI coding agents makes this workflow even more interesting.

Agents can work on repository tasks while developers continue to review code and manage their source on GitHub.

Teams already making extensive use of GitHub may not need to add another project management platform.

5. ClickUp — Leveraging AI for broad project management

Recommended teams: Teams that want to consolidate project management, documentation, tasks, and AI capabilities into a single platform.

ClickUp targets a broader scope than tools designed exclusively for developers.

It is designed to handle various types of work across an organization, including tasks, documentation, goals, project planning, and collaboration.

AI capabilities support a variety of productivity workflows, including writing, summarization, and task-related work.

Key features

• Task management
• Documentation
• Goals
• Dashboards
• Automation
• Team collaboration
• AI-powered productivity support

Use cases

Software companies can also use ClickUp to manage work outside of engineering.

You can bring product requirements, marketing tasks, documentation, design work, and engineering projects together in a single environment.

This is useful when development is part of the organization’s overall workflow.

On the other hand, developers who want engineering-specific workflows may be better served by more specialized tools.

6. Asana — AI-Assisted Work Management

Recommended teams: Organizations that manage software projects and a wide range of business work across departments.

Asana is also a well-established project management platform that incorporates AI into work management.

It is particularly useful when software development is closely connected with product, marketing, operations, design, and other departments.

Key Features

• Tasks and Projects
• Planning with Timelines
• Workflows
• Goals
• Team Collaboration
• Automation
• AI-Assisted Work Management

Use Cases

Imagine a company launching a new web application.

The engineering department advances development tasks, while the marketing department prepares a launch campaign, the design department creates assets, and the customer success department prepares onboarding support materials.

Instead of dispersing these activities across separate systems, you can share project status in Asana.

AI can also help with repetitive tasks such as summarizing information and organizing the team’s tasks.

7. Monday.com — Flexible Workflow Management Powered by AI

Recommended teams: Teams that want to manage projects and workflows with flexible customization.

It is designed with the flexibility to accommodate various departments and types of work.

Instead of following fixed project management methodologies, teams can create their own boards and workflows.

AI features can be used to automate and assist with various tasks involved in work management.

Key Features

• Customizable Boards
• Task Management
• Automation
• Dashboards
• Team Collaboration
• Workflow Customization
• AI Features

Use Cases

Growing software companies can use separate boards for product development, engineering, customer feedback, and release planning.

For example, customer feedback can be automatically converted into development tasks, while project managers track progress on a dashboard.

This flexibility is useful for organizations that do not want their project management system to impose a uniform way of working on every team.

Differences Between AI Project Management and Traditional Project Management

I think one of the biggest changes underway is that project management systems are shifting from tracking human work to tracking work performed by humans and AI.

Traditional workflow:

Requirements → Tasks → Developers → Code → Review → Release

AI-enabled workflow:

Requirements → Tasks → Humans/Agents → Execution → Testing → Basis for Verification → Human Review → Release

It may look like a slight difference, but it is an important one.

AI agents may be able to operate continuously and handle multiple types of tasks.

However, this does not eliminate the project manager's role.

Rather, the importance of coordination increases.

Someone still has to make judgments about the following:

• What should be delegated to agents?
• Is there sufficient context?
• What permissions should be granted?
• How do you confirm that the results are correct?
• Who will review the changes?
• What should you do if two tasks conflict?
• Is it truly ready for release?

Therefore, I do not think the future of AI project management is simply **“letting AI manage everything.”**

What matters is creating a framework that allows AI to participate without making the workflow invisible.

How to Choose AI Project Management Tools

There is no single platform that is optimal for every team.

If I were choosing one, I would start with how the team actually gets its work done.

If your team is already making extensive use of Jira,

moving everything to a new platform may create more new problems than it solves.

First, let’s explore how AI features can be integrated into existing workflows.

For a small, developer-centric team,

a lightweight tool like Linear may be a better fit than a large enterprise platform.

If you have already centralized everything in GitHub,

GitHub Projects lets you manage plans right alongside your code and pull requests.

If you need to manage work across multiple departments,

ClickUp, Asana, and Monday.com may be a better fit because they are designed around a broad range of workflows across the entire organization.

If AI agents are actually beginning to take on development work,

this is where platforms like Sharkly become especially interesting.

The question changes from the following:

“How will AI help with project management?”

and becomes this:

“When AI agents handle part of the work, how should the team manage that work?”

This is an entirely different question.

What I think will happen from now on

I don’t think the future of AI project management is adding a chatbot to every Jira board.

The bigger shift should occur when AI agents become a standard part of software development.

Imagine opening a project’s dashboard and seeing the following:

3 developers working

5 AI agents executing tasks

2 tasks awaiting human review

1 agent blocked due to a test failure

4 tasks in a verifiable state

At this point, rather than traditional project management, we are closer to an operating system for development teams where humans and AI coexist.

New requirements will emerge regarding context, permissions, task accountability, execution history, the separation of execution environments, testing, and human approval.

Project management tools that adapt to this reality will become increasingly useful.

Conclusion

AI makes software development faster, but that speed also creates new coordination challenges.

If a single developer is using an AI assistant to write a few lines of code, conventional project management can handle it.

However, when an entire team begins using multiple AI agents to conduct research, coding, testing, documentation, and maintenance, the workflow becomes far more complex.

This is where AI project management tools become meaningful.

Some platforms add AI to existing project management workflows.

Other platforms are building collaboration experiences designed around AI from the outset.

And tools such as Sharkly are exploring a different model, one in which people and AI agents can participate in the same work management system, with people remaining responsible for direction, review, and acceptance.

I do not believe there will be a single winner that works for every team.

What you should choose will depend on how your team develops software today and how extensively you plan to adopt AI in the future.

However, one thing is becoming increasingly clear.

In the future of software development, it will not only be important to have AI that can write code. It will be important to create workflows in which people and AI can work together while maintaining visibility, context, and control.