By 2030, it is predicted that 80% of project management tasks will be run on AI. For project managers, this makes a deep understanding of AI—its capabilities, limitations, and ethical implications—a strategic necessity, as well as a key competitive advantage. While many project managers feel confident in their ability to harness AI to improve efficiency and precision, there's reason to believe that knowledge gaps persist. Fewer than half of recently surveyed project managers expressed any skepticism about the technology, which could suggest that either vendors or internal AI champions are under-communicating the potential risks.
This places the onus on project managers to approach AI deployment from a critical and informed perspective, accounting for challenges that rarely arise with traditional software tools. Whether they commit to formal upskilling programs or seek to learn on the job through SME collaboration, project managers that invest in AI literacy will be better positioned to minimize friction, and maximize results, as they set AI-driven projects in motion.
The challenges of AI in project management
AI presents a valuable opportunity to improve project-related processes, but project managers hoping to leverage that potential fully may need to refine certain skill sets. On an operational level, gaining a deeper understanding of AI-generated reporting and task assignment can bring greater precision and efficiency to project routines. From a broader, more strategic perspective, the ability to translate AI outputs into actionable insights for stakeholders, or to leverage AI-driven predictions for improved risk management, can lead to more desirable project outcomes.
However, despite these potential benefits, AI also introduces complex challenges to those responsible for steering teams toward strategic goals. As AI is increasingly deployed for task planning and execution, it's both important and challenging to determine who's ultimately accountable for the outcomes of its suggestions. This issue is compounded by the "black box" nature of AI systems, which makes it difficult to unpack AI decision-making processes and understand the factors driving recommendations. As a result, project managers may struggle not only to trust the recommended course of action but to justify it to stakeholders as well.
There is also a risk of bias influencing the decision-making process, particularly if the team training the AI system relies on long-established routines and policies. Because AI systems generate recommendations based on the data they are trained on, they may inadvertently reinforce existing issues (such as unbalanced task assignment) rather than help teams move beyond them. Modern project managers need to be wary of the potential for bias, or they risk relying on skewed information, resulting in ineffective resource allocation, inaccurate project timelines, and eroded stakeholder confidence, among other consequences.
How project managers can improve AI literacy
Training and upskilling programs focused on areas such as machine learning fundamentals and prompt engineering to optimize LLM outputs can help project managers enhance their AI skills at the strategic and operational level. It may also be beneficial to explore the privacy, compliance, and ethical implications associated with AI system deployment, which can vary drastically depending on the goals and scope of a project.
To gain more practical and hands-on learning experiences, it may be helpful to collaborate with AI or data science SMEs on projects that involve new AI tools or strategies. Ideally, cultivating relationships with employees who routinely work with AI will lead to a more comprehensive and nuanced understanding of AI's strengths in project development and execution, as well as more informed strategies for addressing its limitations.
Even as project managers build confidence in their AI skills, it can be wise to integrate AI into project tasks gradually. This will enable managers to evaluate AI’s impact on core project objectives and sharpen their instincts about which tools and strategies deliver the most value. For example, a manager might find that automated task assignments and reminders boost team-wide productivity, but determine that in-person meetings are still crucial for developing high-impact project goals and fostering collaboration.
Championing AI to project teams
In the early stages of AI adoption, project managers should focus on deploying tools for low-risk, high-impact use cases. Tasks such as scheduling or meeting transcription can deliver quick wins, showcasing AI's potential to the team without introducing unnecessary technological complexity or frustration. At this stage, it's a good idea to collect feedback from employees regarding their comfort and confidence in the AI tools that have been introduced.
Project managers may also consider involving team members in selecting AI tools that align with their specific project responsibilities and individual goals. Stakeholders may, for instance, want a say in the AI-enabled low-code tools they use to build custom project dashboards or the AI-powered analytics tools used to perform risk assessments on project strategies. This collaborative approach not only encourages enthusiasm and buy-in, but creates an opportunity to define how success with AI will be measured. Establishing a clear understanding of the tool’s intended purpose (such as to accelerate project delivery, enhance reporting, or improve cross-functional communication) along with project-relevant KPIs helps ensure more strategic and effective AI deployment.
It’s important to recognize that some team members may already be using AI tools independently to enhance their work. To ensure proper governance and compliant use of these technologies, project managers might consider conducting informal surveys to understand how AI is currently being leveraged by their teams. It may be worthwhile to invest in these tools and incorporate them into a team-wide strategy, enabling continued access to essential capabilities while improving centralized visibility, control, and security.
Remaining flexible in AI deployment
The early stages of AI deployment within project teams can—and arguably should—be largely exploratory. This is an ideal opportunity to experiment with various tools, evaluate their impact on project execution and outcomes, and assess how well they align with the project’s broader objectives. A healthy level of skepticism is wise—and it’s entirely acceptable to pivot away from tools that fail to deliver the expected results. A project manager with the expertise to evaluate AI critically may even find that the technology isn't the right fit for their use case at all. Flexibility and continuous feedback are key to making that determination.
Project managers who remain agile in their approach to AI—resisting the urge to seek permanent or one-size-fits-all solutions—will be best positioned to capitalize on this rapidly evolving technology. In many ways, this should be reassuring for seasoned project managers, as success in the role has long depended on the ability to iterate, respond to change, and refine strategies based on emerging information. The integration of AI simply extends this familiar mindset, requiring managers to continuously adopt, evaluate, and adapt tools and practices to drive optimal project outcomes.
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