AI in Project Management: Promise vs. Practice

In this insightful podcast episode, Cornelius Fischner interviews Matt Mong from Adika about the practical application of AI in project management. They discuss the current hype around AI and the need to approach its integration into project management with a pragmatic mindset. The conversation covers the technical, organizational, and cultural challenges of adopting AI, emphasizing the importance of standardized and structured data for AI to be effective. Matt shares insights on the narrow use cases of AI in project management currently and the steps organizations need to take to prepare for broader AI integration. The discussion also touches on the potential impact of AI on the role of project managers, suggesting that while AI may automate certain tasks, it will ultimately empower project managers to focus on more strategic aspects of their work.

Takeaways

• 📊 **Data Standardization is Key**: The effectiveness of AI in project management is directly tied to the quality and structure of the data. Organizations need to focus on standardizing and systemizing their data for AI to be truly effective.
• 🛠️ **Process Systemization**: To prepare for AI, organizations should aim to systemize their processes within a unified project business system. This ensures controlled, trustworthy, and real-time data that AI can utilize.
• 🚀 **Long-term Vision**: AI implementation in project management is not a quick fix; it's a long-term strategic move that requires a comprehensive approach, including upskilling and cultural shifts within the organization.
• 💡 **AI for Decision Support**: AI can assist project managers by providing data-driven insights, enabling them to make informed decisions more efficiently and effectively.
• 🤝 **Enhanced Collaboration**: AI has the potential to improve communication within project teams, translate languages, and identify communication gaps, thus fostering better collaboration.
• 🧑‍💼 **Role of Project Managers**: While AI may take over certain tasks, project managers will likely see an increase in responsibility, focusing more on strategic aspects and leveraging AI for predictive insights.
• 👥 **Cultural Change**: For successful AI integration, organizations need to embrace cultural changes, including training and re-skilling employees to work alongside AI systems.
• ⏱️ **Realistic Expectations**: It's important to set realistic expectations about AI's capabilities and timelines. The technology is not yet ready to fully automate complex project management tasks.
• 📈 **AI and Project Success**: AI is not a guarantee for perfect projects but can aid in risk mitigation, schedule optimization, and resource allocation, thus improving the chances of success.
• 🧠 **Emotional Intelligence**: AI lacks the emotional intelligence and empathy that are crucial for certain aspects of project management. Human involvement remains indispensable.
• 🔄 **Redeployment of Resources**: As AI takes over more routine tasks, project managers and teams can be redeployed to focus on more value-added activities, such as problem-solving and relationship building.

Q & A

What is the main focus of the discussion between Cornelius and Matt in the podcast?

The main focus of the discussion is the practical application of AI in project management, addressing the technical, organizational, and cultural challenges, and the difference between the promise and practice of AI integration in this context.

Why is it difficult to use AI in project management meaningfully?

It is difficult because AI requires standardized and structured data to be effective, which most project-driven organizations lack due to the use of disconnected tools, spreadsheets, and inconsistent data management practices.

What is the current state of AI in project management according to Matt?

The current state of AI in project management is that it is mostly used in narrow use cases and is marginal at best. Companies think AI will be their savior, but it won't be until they adopt a comprehensive business system approach that standardizes and structures their data.

What does Matt suggest organizations should do to prepare for future AI integration?

Matt suggests that organizations need to invest in standardizing and structuring their data by systemizing their processes and using a controlled environment. This is a prerequisite for AI to be effective in the future.

What is the role of project business leaders in driving AI adoption?

Project business leaders, often at the executive level, should champion the effort to adopt AI. They need to drive the need for real-time, predictive insights, and they should ensure that the organization moves towards a standardized platform for data and processes.

How does Matt view the impact of AI on the role of project managers?

Matt believes that AI will be disruptive but also beneficial for project managers. It may take over some responsibilities, particularly those involving data processing and reporting. However, it will ultimately empower project managers by providing them with more data-driven insights, enabling them to make better decisions and have a greater impact on project outcomes.

What is the 'crawl, walk, run' approach mentioned by Cornelius?

The 'crawl, walk, run' approach refers to a multi-step process for organizations to adopt AI. It starts with improving data management to achieve real-time insights, then moves to adopting a business system for standardized processes and data, and finally, integrating AI for predictive analytics and enhanced decision-making.

What are the key organizational challenges in adopting AI for project management?

Key organizational challenges include training and skill development, governance, and justifying the return on investment for AI. There is also the need for project managers to set realistic expectations about AI's capabilities and limitations.

How does AI enhance communication within project teams?

AI can assist in translation between languages, analyze communication patterns to identify gaps, and help in conveying ideas more effectively among team members, thus fostering better collaboration and understanding.

What is the importance of data quality and standardization in the context of AI?

High-quality, standardized, and structured data is crucial for AI to function effectively. Without it, AI cannot provide accurate and reliable insights, predictions, or decision support, which are essential for successful project management.

What is the role of project managers in facilitating AI adoption within their teams?

Project managers play a crucial role in leading by example, setting realistic expectations, and encouraging the organization to transform towards a systemized approach. They should also be prepared to embrace change and be open to retraining as AI takes over certain tasks.

Outlines

00:00

🎙️ Introduction to AI in Project Management

The episode begins with Cornelius Fishner welcoming the audience to the 499th episode of the project management podcast at pm-podcast.com. He introduces the topic of AI in project management, highlighting the need for a pragmatic conversation on the subject. Matt Mong from Adika is invited for an interview due to his insightful response to Cornelius's invitation, which resonated with the course AI for project managers. Matt's background as a chief revenue officer with expertise in project business automation and operational strategy is briefly discussed. The episode aims to discuss the technical, organizational, and cultural challenges of AI in project management and to distinguish between the promise and actual practice of AI in this field.

05:03

📚 Standardization of Data for Effective AI Use

Matt explains the difficulty of using AI in a meaningful way within projects, emphasizing the necessity for standardized and structured data. He discusses the common issue of data being scattered across various disconnected tools and spreadsheets in most project-driven organizations. AI requires organized and structured data to be effective, which is often not the case with current organizational data practices. The conversation touches on the marginal use of AI in narrow use cases and the need for a comprehensive business system approach to fully leverage AI's potential in project management.

10:03

🚀 Integration of AI into Project Business Systems

The discussion moves to the integration of AI into business systems, with Matt sharing his vision of a project business system where all foundational processes are managed within a single system. This systemization is crucial for the seamless connection and operation of project schedules, resource management, and financial metrics. The goal is to create a platform that not only provides insights and analytics but also serves as a foundational layer for future AI integration. Matt also addresses the challenges of transitioning from multiple disparate tools to a unified system and the importance of minimizing data management issues by centralizing processes within a single system.

15:04

🛠️ Technical Challenges and Project Business Definition

The conversation delves into the technical challenges of adopting AI, particularly the need for a plan and vision to transition to a comprehensive project business system. Matt clarifies the concept of project business, differentiating it from regular project management by the selling of projects as a service to external clients. He stresses the importance of having project data structured in a single, real-time accessible place, whether for internal or external clients, to allow AI to assist effectively.

20:04

🧑‍💼 Organizational Challenges and Leadership's Role

The focus shifts to organizational challenges, including training and skill competencies. Matt discusses the need for awareness about AI and its practical applications rather than a current skills gap. He identifies project business leaders and executives as the drivers of data standardization and systemization efforts. These leaders should champion the move towards real-time, predictive insights to improve decision-making. Matt also addresses the financial aspect, emphasizing the need to consider the entire investment in a standardized data platform as the foundation for AI, rather than viewing AI implementation in isolation.

25:05

🤝 Cultural Challenges and AI Adoption

Cultural challenges are explored, with an emphasis on the role of project managers in facilitating AI adoption within their teams. Matt advises project managers to be realistic, knowledgeable, and prepared for transformation. He suggests leading by example and considering the broader organizational perspective when advocating for AI integration. The conversation also touches on the importance of managing expectations regarding AI capabilities and the potential for AI to augment human capabilities in project management.

30:06

📉 Gartner Hype Cycle and Realistic AI Expectations

The discussion references the Gartner hype cycle to illustrate the inflated expectations surrounding AI, particularly generative AI like chat GPT. Matt advises setting exploratory and realistic expectations for AI in project management, recognizing that AI is not yet ready for full integration and automation in this field. He suggests that project managers should focus on understanding AI's potential and planning for its future integration, rather than expecting immediate transformative results.

35:07

🌟 The Impact of AI on Project Managers

Matt envisions AI as both disruptive and beneficial to the role of project managers. He suggests that while AI may take over some responsibilities, particularly those involving data analysis and reporting, it will ultimately empower project managers. By providing predictive insights and suggestions for risk mitigation and schedule optimization, AI will enable project managers to make more informed decisions and have a greater impact on project outcomes and business objectives.

40:09

🤔 Addressing the Hype and Focusing on Realistic AI Integration

The conversation concludes with a reflection on the hype surrounding AI and the responsibility of industry professionals to communicate realistic expectations. Matt advocates for a focus on realistic use cases and the steps organizations need to take to prepare for AI integration. He emphasizes the importance of data quality, process systemization, and the need for organizations to adopt a business system approach to effectively utilize AI in the future.

45:09

📌 Final Takeaways and Preparing for AI

In wrapping up the discussion, Matt provides key takeaways: the importance of quality data for effective AI use, the need to systemize and standardize processes and data, and the preparation for AI integration by adopting a comprehensive business system. He stresses that while the journey to AI integration may be gradual, setting a clear vision and taking incremental steps towards a standardized data platform is crucial for organizations to harness the power of AI in project management.

Keywords

AI in Project Management

Artificial Intelligence (AI) in Project Management refers to the application of AI technologies to assist in various aspects of project management, such as risk assessment, resource allocation, and predictive analytics. In the video, the discussion revolves around the current capabilities and limitations of AI in project management, emphasizing the need for structured data to effectively utilize AI.

Standardized and Structured Data

Standardized and structured data refers to information that is formatted and organized in a consistent manner, which is crucial for AI to function effectively. The video emphasizes that most project-driven organizations lack this kind of data, which hinders the meaningful use of AI in their projects.

Project Business Automation

Project Business Automation is a term used to describe the automation of processes within a company that sells projects as a service. In the context of the video, it is suggested that for AI to be effectively utilized, organizations need to adopt a comprehensive business system approach that standardizes and structures their data, aligning with the concept of project business automation.

Technical Challenges

Technical challenges in the context of the video pertain to the difficulties in implementing AI in project management due to the lack of standardized data and disconnected tools. These challenges are a major focus of the discussion, highlighting the need for a systematic approach to data management as a prerequisite for effective AI use.

Organizational Challenges

Organizational challenges discussed in the video involve the skills gap, training, and competencies required for AI deployment in project management. It also touches on the need for leadership buy-in and a strategic vision to adopt a comprehensive project business system that can support AI integration.

Cultural Challenges

Cultural challenges refer to the resistance to change and the need for a shift in mindset within organizations when adopting AI. The video discusses the importance of setting realistic expectations and the role of project managers in facilitating AI adoption within their teams.

Generative AI

Generative AI is a type of AI that can generate content based on given prompts. In the video, it is mentioned in the context of tools like chat GPT, which are used for summarizing status reports and meetings. However, the speakers note that these tools represent a narrow use case and are not indicative of the broader potential of AI in project management.

Data Literacy

Data literacy is the ability to understand, analyze, and argue with data. In the video, it is highlighted as an essential component of AI literacy for project managers, emphasizing that a deep understanding of data is key to leveraging AI effectively in project management.

ROI (Return on Investment)

Return on Investment (ROI) is a measure of the profit or loss made on an investment relative to the amount of money invested. The video discusses the difficulty in calculating ROI for AI in project management, suggesting that organizations should consider the broader benefits of adopting a standardized project business system that can support future AI integration.

Gartner Hype Cycle

The Gartner Hype Cycle is a graphical representation that describes the life cycle of a technology's growth and acceptance. In the video, it is used to illustrate the current state of generative AI, noting that it is at the peak of inflated expectations and that a period of disillusionment is likely to follow before the technology becomes truly productive for organizations.

Decision Support

Decision support in the context of AI refers to the use of AI to analyze data and provide insights that assist in making informed decisions. The video suggests that AI can be particularly useful in project management for decision support by predicting risks and suggesting solutions, allowing project managers to make data-driven decisions.

Highlights

AI in project management is currently overhyped, and organizations need a pragmatic approach to effectively use AI in the future.

Standardized and structured data is essential for AI to be effective, which most project-driven organizations lack.

Matt Mong from Adika emphasizes the need for a comprehensive business system approach to standardize and structure data for AI.

Current AI applications in project management are limited to narrow use cases like summarizing reports and automating workflows.

Adopting AI requires organizations to invest in processes and systems that can centralize and standardize data.

Project managers play a crucial role in facilitating AI adoption by leading the transformation towards a standardized system.

A realistic approach to AI involves understanding its current capabilities and limitations in project management.

The Gartner Hype Cycle suggests that generative AI is at the peak of inflated expectations and is several years away from being productive in organizations.

AI is expected to automate busy work, allowing project managers to focus more on team development and stakeholder engagement.

Project managers should set exploratory expectations with AI, understanding the steps needed to make AI a useful tool for their organizations.

AI's impact on the role of project managers will likely be disruptive, but over time, it could lead to more efficiency and empowerment in their roles.

The field of project management is not at risk due to AI, as AI lacks human qualities like emotional intelligence and empathy.

AI can assist in decision support, communication, and training, augmenting human capabilities in project management.

To integrate AI effectively, focus on realistic use cases and the steps organizations need to take to prepare for AI implementation.

AI can help with cross-functional cooperation, enhancing communication, transparency, and fostering innovation within project teams.

The key takeaway is that better data leads to better use of AI, and organizations should focus on systemizing processes and data to prepare for AI.