PMO | How to Apply AI to Project Management

Artificial Intelligence (AI) has impacted many areas of work and daily life, and project management is no exception. AI will change how we manage projects. Let's delve into this topic today.

What does it mean to apply AI to project management? 
AI can take over the administrative tasks in project management, such as planning, tracking, and reporting, allowing project managers to focus more on communication, influence, and leadership. This includes selecting projects for mobilization, generating project plans, prioritizing tasks, allocating resources, resolving dependency issues, tracking project status, and forecasting potential risks. AI is attractive because it is faster, can assess more data points, and stays more rational than humans to prevent biases and cognitive limitations.

With AI, project managers have more time to build relationships with stakeholders, manage more projects, and perform strategic tasks!

Can you be specific on the areas in which AI should be applied?
Project managers can use AI to augment skills and boost efficiency and strategic decision-making. These areas include:

• Innovation: AI can contribute to brainstorming sessions for new ideas and extensive knowledge exploration. It can also source information for project inception research, including industry standard, market trend, sourcing information and solution options. 
• Prioritization: AI serves as a reliable support system, analyzing the requirements, customer feedback sentiments, and market conditions to ensure project strategic alignment for prioritization.
• Launching Readiness Check: AI can evaluate project readiness for mobilization. For example, AI can determine the possibility of success, resource readiness, technology, and support conditions. We can calculate the resource skills to ensure that they are ready to perform the necessary tasks in the project with all the required support and resource readiness. It is also a great tool to review industry standards and trends again. 
• Time Forecasting: AI can forecast the project timeline based on task and resource evaluations.
• Risk Prediction and Mitigation: AI can predict potential risks based on the existing conditions and suggest risk mitigation plans.
• Portfolio Evaluation: AI can evaluate the portfolio's project value, status, and execution capability. It can also simulate the results of serious, high-risk decisions.

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Are we there yet? How far? 
AI in project management is still in its early stages. While ChatGPT can generate a project plan with a comprehensive list of tasks and assign them to the right team, it needs more intelligence analysis to ensure project operation success. This includes dependency analysis, risk analysis, and proper task assessment.

However, using ChatGPT to generate project communications, plan meeting agendas, and summarize meeting minutes in real time is ready for prime time. For example, Grammarly can be used to check grammar and spelling errors and make word choices. The service is reliable and produces consistent quality results.

How can we adopt AI in our organization?
This is the most critical question to answer right now. We must adopt AI immediately to stay ahead of the curve and remain competitive. The challenge is that there needs to be ready-to-use ML mode and data to start training AI. The most critical missing piece is data. High-quality data would enable AI to learn in the right direction.

To adopt AI in project management, we must take the following actions:

• Change how we work on projects to ensure clean and ready-to-use project data for machine learning (ML). The data should include tasks, resources, time, priority, values, strategic alignment indication, dependencies, risks, skill set requirements, success factors, status with key performance indicators, stakeholders, and project charter summaries.
• Create tools to generate project plans with predefined structures and milestones for data collection.
• Define metadata for essential project elements, including tasks, resources, risks, priority, and strategic alignment. Collect data and assist machines in learning and improving their understanding of these elements.
• Provide reports to obtain insights for supervised ML training. 

It is crucial for any organization to strategically and urgently adopt AI in project management. The first step to preparing project data for AI analytics is implementing change management in their creation and maintenance.

References

1. Antonio Nieto-Rodriguez and Ricardo Viana Vargas, How AI Will Transform Project Management, Feb 02, 2023, HBR 
2. How AI Is Revolutionizing Project Management: Three Use Cases, March 2023

Created: 5/23/2023 Last Updated: 10/10/2024