Master Class: AI for Project ManagementAIPMMTMaster ClassMT-AIPM1.0<ul>
<li>Understand how generative AI impacts project management.</li><li>Identify AI automation opportunities within your projects.</li><li>Integrate AI tools effectively with existing project management software.</li><li>Enhance communication and reporting using AI capabilities.</li><li>Manage ethical and data privacy aspects in AI-assisted projects.</li><li>Apply AI in various stages of the project lifecycle.</li><li>Navigate AI applications in risk and stakeholder management.</li><li>Develop a strategy for AI implementation in project environments.</li><li>Utilize AI for improved decision-making and efficiency.</li><li>Implement practical AI solutions and strategies in your workplace.</li></ul><p>To get the most out of this course, it is recommended that participants have foundational knowledge of project management through formal training like our Project Management Fundamentals or Project Management Professional (PMP) courses or have relevant experience working in a project management context.</p><ul>
<li>Project Managers</li><li>IT Project Managers</li><li>Program Managers</li><li>Agile Coaches/Scrum Masters</li><li>Product Managers</li><li>Project Directors/Portfolio Managers</li><li>Project Coordinators</li><li>PMO Managers</li><li>Business Analysts</li><li>Operations/Digital Transformation Managers</li></ul><ul>
<li>Class Intro</li><li>First things first: Generative AI Enablement</li><li>AI-Enhanced Project Management Environments</li><li>Applying assistive GenAI in daily PM work</li><li>Implementing AI within a Project Management Framework</li><li>Ethical and Practical Considerations in AI-assisted projects</li><li>Developing your AI-enabled project management approach</li><li>Future trends AI-enabled project management</li><li>Conclusion and Q&A</li></ul><h5>Section 1: Class Intro </h5><ul>
<li>Expectations: What this class is, and what it isn’t</li><li>Overview of AI and Its Impact on Project Management</li></ul><h5>Section 2: First things first: Generative AI Enablement</h5><ul>
<li>GenAI metaframing</li><li>Understanding AI capabilities and limitations</li><li>What LLM-based AI can and can't do well</li><li>Common misconceptions and realistic expectations</li><li>Best practices for interacting with LLMs</li><li>A two-way language model</li><li>Dialectic rules for getting the most out of LLMs</li><li>Establishing context</li><li>Identifying Automation Use Cases</li><li>Understanding processes suitable for AI automation.</li><li>Assessing the impact and feasibility of automation.</li></ul><p>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. </p>
<h5>Section 3: AI-Enhanced Project Management Environments</h5><ul>
<li>Typical AI and Project Management Tooling</li><li>Overview of tools integrating AI (Microsoft Project, Dynamics365, ClickUp etc.)</li><li>Customizing AI tools to fit various project management environments.</li><li>Closer look: Implementing an Internal LLM</li><li>Private LLM overview</li><li>Core components of a private LLM implementation project</li></ul><p>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.</p>
<p>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. </p>
<h5>Section 4: Applying assistive GenAI in daily PM work</h5><ul>
<li>Identifying, capturing, and storing useful datasets</li><li>Content generation and management</li><li>Communication assistance and automation</li><li>Interfacing with code: Spot checking, evaluating tests, and a BDD and ATDD project primer</li><li>Using onboard or external AI in conjunction with your PMIS</li><li>Sentiment analysis for stakeholder feedback</li><li>AI-powered search and information retrieval</li><li>Text summarization for efficient reporting</li><li>Information extraction and expansion</li></ul><p>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. </p>
<h5>Section 5: Implementing AI within a Project Management Framework</h5><p>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. Our use cases will include:</p>
<ul>
<li>Defining project vision, goals, and objectives</li><li>Scoping, scheduling, budgeting, resource and capacity planning</li><li>Automated progress updates, resource optimization, decision support, progress and issue tracking, and adjustment recommendations</li><li>Performance tracking, metrics and KPIs, risk analysis and mitigation, change impact analysis, and quality control</li><li>Project documentation, formal project reporting, OKRs and success criteria, deliverable tracking, and a continuous learning journal</li><li>Project Governance: Use AI to assist in configuring clear roles, responsibilities, decision-making processes, project sponsors, stakeholders, and project managers</li><li>Management by Stages: Clearly define deliverables and milestones with the help of an AI assistant</li><li>Risk Management: AI assistance for identifying, analyzing, communicating and mitigating project risk</li><li>Stakeholder Management: Leveraging AI for stakeholder engagement and communicating project progress and updates</li><li>Change Management: How AI can help address changes to the project scope, schedule, or budget in a controlled manner</li></ul><p>Practice / Demo: Using the project life cycle framework we’ve described, walk through using your generative AI tools for keeping track of project status, managing meetings, and analyzing/visualizing data for project reporting and metrics. </p>
<h5>Section 6: Ethical and Practical Considerations in AI-assisted projects</h5><ul>
<li>Data privacy and security</li><li>Understanding AI bias</li><li>Ethical implications of AI use</li><li>Legal and compliance considerations</li></ul><h5>Section 7: Developing your AI-enabled project management approach</h5><ul>
<li>Creating an adoption roadmap</li><li>Current state assessment</li><li>AI tools evaluation</li><li>Training and team development for AI readiness.</li><li>SMART goals</li><li>Risk assessment and mitigation</li><li>Training, pilot testing, and at-scale implementation</li></ul><p>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. </p>
<h5>Section 8: Future trends AI-enabled project management</h5><ul>
<li>Emerging trends and their potential impact.</li><li>Continual Learning and Adaptation: Resources for staying updated with AI advancements in project management.</li></ul><h5>Section 9: Conclusion and Q&A</h5>- Understand how generative AI impacts project management.
- Identify AI automation opportunities within your projects.
- Integrate AI tools effectively with existing project management software.
- Enhance communication and reporting using AI capabilities.
- Manage ethical and data privacy aspects in AI-assisted projects.
- Apply AI in various stages of the project lifecycle.
- Navigate AI applications in risk and stakeholder management.
- Develop a strategy for AI implementation in project environments.
- Utilize AI for improved decision-making and efficiency.
- Implement practical AI solutions and strategies in your workplace.To get the most out of this course, it is recommended that participants have foundational knowledge of project management through formal training like our Project Management Fundamentals or Project Management Professional (PMP) courses or have relevant experience working in a project management context.- Project Managers
- IT Project Managers
- Program Managers
- Agile Coaches/Scrum Masters
- Product Managers
- Project Directors/Portfolio Managers
- Project Coordinators
- PMO Managers
- Business Analysts
- Operations/Digital Transformation Managers- Class Intro
- First things first: Generative AI Enablement
- AI-Enhanced Project Management Environments
- Applying assistive GenAI in daily PM work
- Implementing AI within a Project Management Framework
- Ethical and Practical Considerations in AI-assisted projects
- Developing your AI-enabled project management approach
- Future trends AI-enabled project management
- Conclusion and Q&ASection 1: Class Intro
- Expectations: What this class is, and what it isn’t
- Overview of AI and Its Impact on Project Management
Section 2: First things first: Generative AI Enablement
- GenAI metaframing
- Understanding AI capabilities and limitations
- What LLM-based AI can and can't do well
- Common misconceptions and realistic expectations
- Best practices for interacting with LLMs
- A two-way language model
- Dialectic rules for getting the most out of LLMs
- Establishing context
- 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
- 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.
- 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
- Identifying, capturing, and storing useful datasets
- Content generation and management
- Communication assistance and automation
- Interfacing with code: Spot checking, evaluating tests, and a BDD and ATDD project primer
- Using onboard or external AI in conjunction with your PMIS
- Sentiment analysis for stakeholder feedback
- AI-powered search and information retrieval
- Text summarization for efficient reporting
- 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
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. Our use cases will include:
- Defining project vision, goals, and objectives
- Scoping, scheduling, budgeting, resource and capacity planning
- Automated progress updates, resource optimization, decision support, progress and issue tracking, and adjustment recommendations
- Performance tracking, metrics and KPIs, risk analysis and mitigation, change impact analysis, and quality control
- Project documentation, formal project reporting, OKRs and success criteria, deliverable tracking, and a continuous learning journal
- Project Governance: Use AI to assist in configuring clear roles, responsibilities, decision-making processes, project sponsors, stakeholders, and project managers
- Management by Stages: Clearly define deliverables and milestones with the help of an AI assistant
- Risk Management: AI assistance for identifying, analyzing, communicating and mitigating project risk
- Stakeholder Management: Leveraging AI for stakeholder engagement and communicating project progress and updates
- Change Management: How AI can help address changes to the project scope, schedule, or budget in a controlled manner
Practice / Demo: Using the project life cycle framework we’ve described, walk through using your generative AI tools for keeping track of project status, managing meetings, and analyzing/visualizing data for project reporting and metrics.
Section 6: Ethical and Practical Considerations in AI-assisted projects
- Data privacy and security
- Understanding AI bias
- Ethical implications of AI use
- Legal and compliance considerations
Section 7: Developing your AI-enabled project management approach
- Creating an adoption roadmap
- Current state assessment
- AI tools evaluation
- Training and team development for AI readiness.
- SMART goals
- Risk assessment and mitigation
- Training, pilot testing, and at-scale implementation
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.
Section 8: Future trends AI-enabled project management
- Emerging trends and their potential impact.
- Continual Learning and Adaptation: Resources for staying updated with AI advancements in project management.
Section 9: Conclusion and Q&A2 Tage2400.002400.002400.002400.002400.00