15 Topics
5 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
1. Project Managers
2. Program Managers
3. Agile Coaches
4. Scrum Masters
5. Operations Managers
6. Business Analysts
7. Product Managers
8. Freelancers
9. IT Professionals
10. Team Leads
Session Objectives
- Understand the core concepts of Workday Project Management
- Navigate the Workday interface for project-related tasks
- Create and configure new projects
- Manage project teams and resources
- Track project progress and time
- Understand project costs and financials
- Create and run project reports
- Use Workday for project-based billing
- Collaborate on projects within Workday
- Configure project security and roles
About the Course
The landscape of project management is being revolutionized by the integration of artificial intelligence, which is enabling project teams to work smarter, not harder. This "Innovate & Automate: AI in Project Management Tools Training Course" is designed to equip you with the knowledge and practical skills to harness the power of AI-driven tools. You will learn how to leverage these advanced technologies to automate routine tasks, predict project risks, and make data-driven decisions that will significantly enhance project outcomes and team productivity.
Over a dynamic 10-day period, this course will provide a hands-on exploration of how AI is transforming project management, from intelligent scheduling and resource allocation to predictive analytics and automated reporting. You will gain a deep understanding of the core AI concepts that power these tools and learn how to implement them in real-world scenarios. By the end of this training, you will be proficient in using AI to streamline your project workflows, anticipate challenges, and lead your team with greater efficiency and foresight.
Curriculum & Topics
15 Topics | 5 Days
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Subtopic 1.1:
What is AI and how does it apply to projects
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Subtopic 1.2:
The history of AI in business
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Subtopic 1.3:
Understanding the difference between ML and AI
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Subtopic 1.4:
The benefits of AI-powered project tools
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Subtopic 1.5:
A look at the current market of AI PM tools
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Subtopic 2.1:
How AI optimizes project timelines
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Subtopic 2.2:
Using AI to create project schedules
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Subtopic 2.3:
Dynamic scheduling based on real-time data
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Subtopic 2.4:
The role of dependencies and constraints
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Subtopic 2.5:
Handling unexpected delays and changes
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Subtopic 3.1:
Optimizing resource utilization with AI
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Subtopic 3.2:
Matching team members to tasks based on skills
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Subtopic 3.3:
The challenge of resource contention
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Subtopic 3.4:
Predicting resource availability
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Subtopic 3.5:
Using AI to balance workloads
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Subtopic 4.1:
What is predictive analytics
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Subtopic 4.2:
Forecasting project timelines and milestones
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Subtopic 4.3:
Predicting budget overruns
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Subtopic 4.4:
Identifying potential roadblocks
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Subtopic 4.5:
Using historical data to inform future decisions
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Subtopic 5.1:
How AI identifies project risks
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Subtopic 5.2:
Assessing risk severity and probability
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Subtopic 5.3:
Creating a risk mitigation plan
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Subtopic 5.4:
The role of sentiment analysis in risk detection
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Subtopic 5.5:
Using real-time data for risk monitoring
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Subtopic 6.1:
Generating automated status reports
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Subtopic 6.2:
Creating dashboards with real-time insights
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Subtopic 6.3:
The benefit of natural language generation (NLG)
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Subtopic 6.4:
Customizing reports for different stakeholders
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Subtopic 6.5:
The future of project reporting
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Subtopic 7.1:
Automating routine and repetitive tasks
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Subtopic 7.2:
Using AI to trigger actions
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Subtopic 7.3:
Setting up simple automations
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Subtopic 7.4:
The concept of smart workflows
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Subtopic 7.5:
The role of low-code/no-code platforms
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Subtopic 8.1:
AI assistants in team communication
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Subtopic 8.2:
Summarizing meeting notes with AI
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Subtopic 8.3:
The role of chatbots for team support
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Subtopic 8.4:
Analyzing team communication patterns
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Subtopic 8.5:
Improving collaboration with AI-powered tools
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Subtopic 9.1:
Using AI in sprint planning
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Subtopic 9.2:
The role of AI in backlog refinement
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Subtopic 9.3:
Automating sprint retrospectives
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Subtopic 9.4:
Predicting story points and velocity
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Subtopic 9.5:
The future of AI in agile methodologies
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Subtopic 10.1:
AI-driven budget planning
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Subtopic 10.2:
Predicting project costs
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Subtopic 10.3:
The importance of real-time financial data
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Subtopic 10.4:
Integrating with financial systems
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Subtopic 10.5:
The role of AI in project ROI
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Subtopic 11.1:
How to evaluate AI PM tools
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Subtopic 11.2:
Key features to look for
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Subtopic 11.3:
The importance of tool integration
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Subtopic 11.4:
A comparison of popular AI-powered tools
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Subtopic 11.5:
Making a business case for a new tool
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Subtopic 12.1:
The importance of data quality
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Subtopic 12.2:
The ethics of using AI in projects
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Subtopic 12.3:
Understanding data privacy and security
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Subtopic 12.4:
The challenge of data bias
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Subtopic 12.5:
The role of responsible AI
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Subtopic 13.1:
Using AI to optimize a project portfolio
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Subtopic 13.2:
The role of AI in portfolio selection
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Subtopic 13.3:
The importance of aligning projects with strategy
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Subtopic 13.4:
Reporting on portfolio performance
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Subtopic 13.5:
The future of PPM
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Subtopic 14.1:
A case study on a successful AI implementation
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Subtopic 14.2:
A failure analysis of a poorly implemented tool
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Subtopic 14.3:
The impact of AI on project outcomes
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Subtopic 14.4:
The benefits of a data-driven approach
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Subtopic 14.5:
Real-world applications of AI in projects
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Subtopic 15.1:
The next wave of AI in project management
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Subtopic 15.2:
The role of generative AI
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Subtopic 15.3:
The integration of AI with the metaverse
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Subtopic 15.4:
The future of the project manager
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Subtopic 15.5:
Keeping up with a rapidly changing landscape
