Generative AI for Project Managers

Workshop Agenda

 

Section 1: Class Intro 

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1. Expectations: What this class is, and what it isn’t

2. Overview of AI and Its Impact on Project Management

 

Section 2: First things first: Generative AI Enablement

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1. GenAI metaframing

• Understanding AI capabilities and limitations

• What LLM-based AI can and can't do well

• Common misconceptions and realistic expectations

1. Best practices for interacting with LLMs

   • A two-way language model 

   • Dialectic rules for getting the most out of LLMs

   • Establishing context

2. Identifying Automation Use Cases

   • Understanding processes suitable for AI automation.

   • Assessing the impact and feasibility of automation.

 

Practice: We’ll break into groups and provide everyone with project management domain templates for identifying PM automation use cases. Your group will perform an affinity exercise to select the highest priority implementation candidates. 

 

Section 3: AI-Enhanced Project Management Environments

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1. Typical AI and Project Management Tooling

   • Overview of tools integrating AI (Microsoft Project, Dynamics365, ClickUp etc.)

   • Customizing AI tools to fit various project management environments.

2. Closer look: Implementing an Internal LLM

   • Private LLM overview

   • Core components of a private LLM implementation project

 

Practice 1: In this exercise we’ll explore two for the price of one: Since a successful private LLM implementation has a lot in common with any enterprise software project, we’ll use it as a scenario to start framing project work. Along the way, we’ll get more familiar with the concept of internal LLM environments.

 

Practice 2: We’ll provide you with a basic automation time savings calculator. Using the calculator with your automation use case candidates from the previous section, you will apply these use cases to the LLM project and prepare to implement them using a project management tool. 

 

Section 4: Applying assistive GenAI in daily PM work

1. Identifying, capturing, and storing useful datasets

2. Content generation and management

3. Communication assistance and automation

4. Interfacing with code: Spot checking, evaluating tests, and a BDD and ATDD project primer

5. Using onboard or external AI in conjunction with your PMIS

6. Sentiment analysis for stakeholder feedback

7. AI-powered search and information retrieval

8. Text summarization for efficient reporting

9. Information extraction and expansion

 

Practice: In our groups, we’ll take a few minutes to play with GPT-style AI for generating, manipulating, and analyzing content. This exercise will also briefly introduce the group to using the AI tool to interact with code and scripting. 

Section 5: Implementing AI within a Project Management Framework

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1. AI in the Project Lifecycle: We’ll examine AI use case opportunities in the context of a typical PM framework, using an agnostic approach compatible with PMI’s PMBOK, PRINCE2, agile PM and hybrid agile project management. 

   • Initiation: Defining project vision, goals, and objectives

   • Planning: Scoping, scheduling, budgeting, resource and capacity planning

   • Execution: Automated progress updates, resource optimization, decision support, progress and issue tracking, and adjustment recommendations

   • Monitoring and Controlling: Performance tracking, metrics and KPIs, risk analysis and mitigation, change impact analysis, and quality control

   • Closure: Project documentation, formal project reporting, OKRs and success criteria, deliverable tracking, and a continuous learning journal 

2. Project Governance: Use AI to assist in configuring clear roles, responsibilities, decision-making processes, project sponsors, stakeholders, and project managers

3. Management by Stages: Clearly define deliverables and milestones with the help of an AI assistant

4. Risk Management: AI assistance for identifying, analyzing, communicating and mitigating project risk

5. Stakeholder Management: Leveraging AI for stakeholder engagement and communicating project progress and updates

6. Change Management: How AI can help address changes to the project scope, schedule, or budget in a controlled manner

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Practice / Demo: Using the project life cycle framework we’ve described, we’ll walk through configuring our LLM Implementation test project using Microsoft Dynamics 365 Project Operations. This process will showcase Microsoft CoPilot’s assistive capabilities in Project Operations in a way that can apply to most project management frameworks and tools. We’ll consider how to leverage comparable AI assistance when using toolsets that don’t have integrated onboard AI. We’ll explore specific, practical applications for AI within the PMIS, including to generate detailed requirements documents, analyze user data, and visualize project scope.

 

Section 7: Ethical and Practical Considerations in AI-assisted projects

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1. Data privacy and security

2. Understanding AI bias

3. Ethical implications of AI use

4. Legal and compliance considerations

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Section 8: Developing your AI-enabled project management approach

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1. Creating an adoption roadmap

2. Current state assessment

3. AI tools evaluation

4. Training and team development for AI readiness.

5. SMART goals

6. Risk assessment and mitigation

7. Training, pilot testing, and at-scale implementation

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Practice: We need to leave class with an action plan for applying generative AI in your own project management work. Working in our groups, we will conduct a strategy canvas exercise to identify, capture, and prioritize your AI adoption action items. You will choose ChatGPT, BingChat, or Bard+Gemini to analyze what you come up with, sketch some user personas, and translate your priorities to intake-ready work items. You’ll finish with an organized backlog of user stories that map to your plan, and a completed canvas you can use back on the job.    

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Section 7: Future trends AI-enabled project management

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• Emerging trends and their potential impact.

• Continual Learning and Adaptation: Resources for staying updated with AI advancements in project management.

 

Section 8: Conclusion and Q&A