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Project managers’ jobs were already complicated, but the introduction of AI features to popular project management platforms such as Asana and Jira could make effective and accurate workflows even trickier in coming years. Although these AI “teammates” promise boosted productivity, they must be managed in a controlled, transparent way, or projects could descend into chaos, putting businesses at risk.
AI Comes to Project Management Platforms
Some of the largest project management platforms have introduced AI into their products, including (but certainly not limited to):
- Asana: Asana’s AI assistant can summarize project tasks and portfolios, suggest edits to text, automate some workflows, and evaluate the practicality of certain goals.
- Atlassian: Atlassian’s AI offerings include automated documentation, summarization, customer responses and task recommendations; it also powers Trello and other Atlassian tools.
- Jira Rovo: Jira (which is owned by Atlassian) built Rovo, an “AI teammate” that searches, chats and builds customized AI agents.
- Monday.com: Monday.com’s AI extracts information from files, runs sentiment analysis and summarizes and translates text.
- Motion: Motion’s AI workspace includes AI-powered search and automatic task management.
Many project managers also rely on customized bots within OpenAI’s ChatGPT, Anthropic’s Claude and Google Gemini to help with project summaries, scheduling and other tasks.
The Pitfalls of Integrating AI Into Project Management
In theory, building AI into project management software helps eliminate many of the repetitive, annoying tasks that bedevil project managers and teams, including keeping track of progress and generating status updates. Who wouldn’t want an AI assistant cutting down on their busywork so they can focus on finishing projects? No wonder some 72% of respondents to a recent Quickbase report said their organizations planned on increasing budgets for AI tools in 2025.
However, integrating AI into project management comes with a host of company-wide issues.
For starters, AI could generate erroneous status updates or reports, which could throw a project off-track. There’s also a cybersecurity risk: Unless project managers set permissions, a platform’s AI could expose sensitive data to internal or external users. That’s why Atlassian pushes companies that work in healthcare to be aware of HIPAA compliance and patient data when using its products, for example.
Despite those security and privacy concerns, AI must digest as much data as possible to be effective, pressuring companies to expose their digital infrastructure to their new tools. This “permissions paradox” keeps project managers, business leaders and cybersecurity experts up late into the night.
Finally, an AI tool intended to save a project manager time and effort could put more pressure on other members of the team.
"When key project artifacts are built using AI tools, project managers may lose that deeper understanding of project stakeholders, needs and risks that comes from building project plans and reports,” said Jami Yazdani, founder and chief consultant of Yazdani Consulting and Facilitation, and a certified Project Management Professional. “When the project manager no longer has deep expertise in the project itself, team members may need to be more accountable for monitoring and controlling different areas of the project.”
“Many SMEs (subject matter experts) may not be comfortable playing this role, and without the more holistic view that a project manager typically brings, some teams may struggle to make decisions or mitigate risks effectively,” Yazdani added. No matter what their AI deployment, project managers must ensure project plans reflect the knowledge of their teams and environment, or risk their teams becoming overwhelmed.
How Project Managers ‘Onboard’ an AI System Effectively
When it comes to “onboarding” an AI into a company’s project management setup, the key is to start small.
“The most effective leaders I’ve seen steer AI adoption through risk-adjusted value creation; rolling out in high-value, low-risk areas first, collecting evidence of success, then scaling,” said Anthony Habayeb, an enterprise risk expert and CEO of AI governance company Monitaur. “These leaders invest in governance as a primary requirement, resulting in pilots that not only prove value but demonstrate AI can be invested in responsibly. Those pilots become proof points that make enterprise-wide adoption both faster and safer. Skipping that phased approach often means productivity gains are short-lived, as trust erodes and costly errors emerge later.”
After building a pilot with low-risk features, such as natural language search, escalate to automating well-defined project management processes such as project triage. If everything’s still fine after that, consider implementing more powerful AI workflows such as predictive risk analysis.
For business leaders who aren’t project managers, keep in mind that these tips — start small, align AI with broader priorities, and so on — are best practices for pretty much every kind of AI implementation, not just project management.
Pros and Cons of Human-in-the-Loop Workflows
Companies may attempt to sidestep the potential for AI issues by introducing Human-in-the-Loop (HITL) workflows, sometimes with help from the creators of project-management tools. For instance, Asana allows users to insert HITL checkpoints into workflows, where work won’t proceed unless a human being signs off on it. Other HITL tactics include:
- Pre-Processing: A human worker curates the data used by AI, improving accuracy for the core knowledge base.
- Human Approval: The AI needs human approval for a critical or irreversible action, such as deleting a codebase or reassigning a budget.
- Post-Project Review: A human reviews and finalizes the AI’s output, whether that’s a status update or something larger, like a project component.
But Habayeb believes there are pitfalls in HITL, particularly human review of outputs.
"If the workflow or UI is designed so that the human sees only the AI’s conclusion, without the underlying reasoning, relevant context or alternative options, then the human’s ‘approval’ is meaningless,” he said. "True HITL requires empowering the human with enough time, context and authority to question, override or escalate AI concerns. If humans are only there to speed-click ‘approve’ because of time pressure or poor system design, you’ve recreated the same risks you were trying to avoid, just with a false sense of security."
Project Management AI Demands Transparency and Planning
For business leaders, dodging these issues goes beyond training employees how to use a new tool.
“Companies should invest in AI literacy training that explains what the AI is doing, how to give it prompts and its limitations,” said Dr. Tiffany Perkins-Munn, author of “Data (De)coded” and head of data and analytics for J.P. Morgan Chase’s Connected Commerce business. "For example, you can teach them to use the AI as an assistant for summarizing meeting notes, but they’re still responsible for gathering the key takeaways."
Perkins-Munn also advises companies to use platforms that don’t operate as black boxes. “Workers should be able to ask the AI why it made a suggestion or moved a certain task,” she said. “This allows for troubleshooting and helps build trust in the system.” Ideally, any AI functionality should be modular, with the ability to set permissions, turn off features and adjust the tool’s level of autonomy.
Business leaders and project managers might feel pressure to deploy AI, but should resist sprinting in favor of a deliberate approach:
- Controlling Permissions: Before implementation, identify AI features and decide which employees will monitor and control them.
- Put an AI Policy in Place: Companies should have an overarching AI policy that dictates use.
- Start with a Pilot: Test AI features in a closed-off sandbox or on a low-risk project first.
- Implement Training: Before broader use of AI, mandate training for employees.
- Tweak as Needed: Project management and other business functions are complicated. Businesses will need to adjust their AI workflows in response to new events.
“Rather than rushing in on the one hand or waiting for an official approach to adoption on the other, organizations that seem to be handling this situation well are those that focus on creating and sharing best practices for AI use with their teams,” Yazdani said. “We’re going to be using AI, so how can we use it well? These organizations are creating AI policies aligned with core values and business strategies and supporting their staff with training.”
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With enough planning and thought, AI tools helps project managers streamline workflows and deliver projects faster. The alternative is confusion and chaos, and if you ask any project manager, they’ll probably say they face enough of that already.
Editor's Note: Read more about how people are (and aren't) using AI to improve productivity:
- The Hidden Dangers of Workplace AI Personalization — AI personalization boosts productivity — but at what cost? Explore how algorithmic silos threaten innovation and collaboration in modern workplaces.
- Is Generative AI Actually Freeing Workers From Low-Level Work? — Take AI evangelists' promises with a grain of salt. But that doesn't mean AI success is out of reach, just that you might have to switch goals.
- Google Goes Head to Head With Microsoft in AI-Enabled Productivity Software — With the launch of the new productivity capabilities announced at Google I/O, Google shifted from defense to offense, and turned up the pressure on Microsoft.
