Challenges of AI in Project Management: What PMs are saying in 2025
Trust and Governance: Trust issues impede AI adoption, as teams doubt the accuracy and reliability of outputs.
Skill Gaps: Skill gaps hinder project managers from effectively using AI tools, leading to misplaced confidence in results.
Comfort and Control: Project managers struggle with surrendering control to AI, affecting their willingness to innovate with new tools.
Data Accuracy: Concerns about data accuracy prevent teams from relying on AI for critical project decisions.
Tool Overload: Tool overload complicates AI adoption, as project managers face challenges evaluating which solutions provide real ROI.
AI is one of the most critical pieces to a workflow project managers are trying to get right at the moment. But adopting it successfully is far more complex than simply adding a new tool to the tech stack. While project managers are optimistic about its potential to automate, accelerate, and enhance how work gets done, they’re also discovering very real challenges in trust, governance, culture, and skills.
To understand what’s holding PMs back, we reached out to more than 30 project management and AI experts to learn what barriers they’ve encountered while implementing AI in their workflows. Their insights reveal that the greatest obstacles are not technical—they’re human.
1. Trust and Governance
Trust repeatedly surfaced as the defining challenge for adopting popular AI project management tools. Without confidence in the accuracy, ethics, and reliability of AI-driven outputs, many teams hesitate to use it beyond basic experimentation.
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“We’ve encountered several key barriers in adopting AI…AI skills gaps, company policies and access, tool overload and ROI concerns, and trust and governance,” said Kathleen Walch, Managing Partner at Cognilytica and contributor to PMI.
That lack of trust often stems from AI’s “black box” nature—teams can’t always see how decisions are made or what data influenced them. Dr. Matt Hasan of aiRESULTS, Inc. put it bluntly: “The biggest barrier isn’t the technology—it’s trust. Many PMs still see AI as an auditor instead of an ally, which keeps adoption shallow.”
Even when tools work well, governance frameworks tend to lag behind. Frank Vega of The Efficiency Group believes the root cause isn’t technological at all: “The biggest barrier isn’t technical, it’s cultural. Many PMOs still see AI as experimental or threatening. The real change comes when teams view AI as a co-pilot for visibility, not a replacement for judgment.”
This cultural hesitance extends across industries. Teams want AI to enhance project transparency, not create more uncertainty. As Walch and Vega both suggest, trust must be built not just through results but through governance, accountability, and clear human oversight.
2. Skill Gaps and Misplaced Confidence
Many project managers agree that the real challenge lies in capability and confidence. Teams are unevenly distributed across the AI learning curve—some eager to test everything, others unsure where to begin.
“The biggest barrier isn't technology—it's skill gaps and misplaced trust,” said Ravitez Dondeti, Senior Product Manager at Crestron Electronics. “Junior engineers and PMs can easily get carried away with the immense power these tools provide.”
When enthusiasm outpaces understanding, errors can multiply quickly. Suvrangsou Das of EasyPR added, “The roadblock to widespread AI adoption is that too much trust is placed in results. Another roadblock exists… skill set gaps...”
Closing these gaps requires structured AI training and consistent literacy initiatives. Without them, organizations risk both under-utilizing AI and misusing it—either of which can erode confidence across teams.
3. Comfort and Control
Even when teams understand AI, comfort levels vary widely. Many project managers struggle with the idea of surrendering control to a system they can’t fully explain.
Conner Flynn of Hylaine summarized this tension: “Two major barriers stand out: comfort and control. Employees often are uneasy and hesitate to experiment with AI tools they don’t fully understand, and enterprise governance frameworks can sometimes slow progress through lengthy approval cycles.”
That hesitance is reflected in real-world experiences. Peter Murphy Lewis of Strategic Pete and Ella Weddings shared, “Some project managers do not want to relinquish control…Training is a pain, too. It took half the team three weeks to get comfortable with ClickUp’s AI feature.”
This reluctance highlights the human side of adoption. While AI promises efficiency, it also challenges how people define value in their work. Until teams trust the process—and their own ability to oversee it—AI adoption will continue to move slower than the technology itself.
4. Trust in Data Accuracy
AI is only as reliable as the data it’s built on. If teams can’t verify that data is accurate, they won’t rely on it for critical decision-making.
“Trust stands out as a major issue. Plenty of folks still hesitate to depend on AI for key decisions in projects. They worry about the accuracy of AI data too,” said Arunkumar Thirunagalingam, AI & Data Researcher.
In project environments where deadlines, budgets, and resources are constantly shifting, a minor data error can cascade into major project risks. Olena Kuvarova from Overcode echoed this concern: “The biggest operational barrier is the lack of confidence in the accuracy of AI when estimating the completion times of complex R&D projects.”
Until AI systems can explain how they reach conclusions—and teams can validate those assumptions—data trust will remain a core adoption barrier.
5. Tool Overload and ROI Pressure
With new AI products emerging daily, project managers face another challenge: choice fatigue. Every platform promises automation and insight, but evaluating which tools deliver tangible ROI can be overwhelming.
“Tool overload is a real issue,” said Don Gregori, COO of First Factory, Inc. “It is best to stay current on trends and try to predict future leverage. Next, let the winners emerge in the market or be integrated directly into the existing tools we use. Then adopt the ones best suited for your needs. Learn, watch, adopt.”
This cautious approach reflects a growing maturity in the industry. Rather than rushing to adopt every new tool for countless AI use cases, leading PMs are learning to observe, experiment selectively, and measure ROI before scaling.
6. Governance and Approval Bottlenecks
Even when teams are ready to move fast, enterprise systems often aren’t. Many organizations are still adapting their procurement and compliance processes to handle the pace of AI innovation.
“AI is intrinsic to our business… [but] procurement and approval processes are in the process of catching up with AI speed,” said Cameron van Orman of Planview.
Lengthy review cycles, risk assessments, and security clearances can slow adoption to a crawl—especially in highly regulated industries. Balancing innovation with compliance is an ongoing challenge that requires both cultural and procedural shifts.
7. Responsible Innovation and Ethics
Finally, there’s a growing awareness that AI adoption isn’t just about productivity—it’s about responsibility. Teams are increasingly cautious about how their use of AI aligns with environmental impact, ethical data handling, and broader governance practices.
“We’ve seen a healthy wariness among teams around AI—especially on environmental impact, data ethics, and governance,” said Niamh Glennon of MIGSO-PCUBED. “That caution is not resistance; it’s maturity in responsible innovation.”
Glennon added, “For many organisations, the barrier isn’t technology, it’s trust. Building confidence in AI-assisted delivery means showing tangible ROI while keeping humans accountable.”
This mindset shift—from rushing adoption to implementing it responsibly—reflects a maturing phase in AI’s integration into project management. Teams aren’t rejecting AI; they’re learning to use it more thoughtfully.
The Takeaway
The message is clear: AI adoption in project management isn’t about mastering tools—it’s about building confidence, skills, and systems that support responsible, human-centered innovation.
The most successful PMs aren’t just automating tasks; they’re fostering trust, upskilling their teams, and creating governance frameworks that let AI enhance—not replace—the way people work.
As Glennon put it best, “For many organisations, the barrier isn’t technology, it’s trust.” The path forward lies in strengthening that trust—between people, data, and the intelligent systems that are reshaping how we deliver projects.
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