PMI + Generative AI + Project Management

Data Landscape of GenAI for Project Managers

Build a practical understanding of the data landscape behind Generative AI and explore how project managers can work with AI, project data, information, prompts, outputs, and insights to support better project decisions.

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GenAI Data LandscapeUnderstand the data behind AI

Project ManagementConnect AI concepts with project work

Data to InsightTurn information into useful project context

Responsible AIUse data and AI with appropriate oversight

Understand the data landscape behind Generative AI.

Generative AI is powered by data, models, prompts, context, and information-processing systems. For project managers, understanding this landscape helps clarify what AI can do, where project data fits, how AI-generated outputs should be interpreted, and where human judgment remains essential.

Understand AI Data

Explore the relationship between data, information, models, context, prompts, and Generative AI outputs.

Work With Project Information

Understand how project data and information can be structured, contextualized, summarized, and used with AI.

Make Better AI-Assisted Decisions

Learn to evaluate AI outputs, identify limitations, and combine AI-generated insights with professional project judgment.

Designed for project professionals navigating the AI era.

The learning experience is intended for project professionals who want to understand the relationship between Generative AI, data, and project management.

Project Professionals

Project Managers
Program Managers
Portfolio Professionals
PMO Professionals
Project Coordinators
Business Analysts
Delivery Professionals

Professionals Exploring AI

Project Leaders
Transformation Professionals
Digital Project Teams
Data-aware Project Professionals
AI Transformation Teams
Professionals adopting GenAI tools
Managers responsible for AI-enabled workflows

Understand the pieces that connect data with AI-generated output.

Build a project-manager-friendly view of the AI ecosystem without requiring a deep technical data science background.

Data

The information that provides the foundation for analysis, context, learning, and project intelligence.

Information

Structured and contextualized information that can support project understanding and decision-making.

Models

AI models that process inputs and generate responses or predictions based on their underlying design and data.

Prompts

Instructions and contextual information provided to an AI system to guide the desired output.

Context

Project-specific information that helps AI systems produce more relevant and useful responses.

AI Output

Generated text, summaries, analysis, ideas, classifications, or other forms of AI-assisted information.

Validation

Human review and verification that determines whether an AI output is suitable for the intended project purpose.

Decision

The professional judgment and action taken after considering relevant information and AI-assisted insights.

Connect project data with responsible AI-assisted decision-making.

Data → AI → Decision

Data

Identify relevant project information.

Context

Provide the right project context.

Prompt

Structure the request and desired outcome.

Output

Review and interpret the AI response.

Decision

Apply professional judgment and act.

From GenAI fundamentals to project data application.

Explore how data, information, prompts, models, outputs, and human judgment interact in AI-enabled project management.

Module 1: Introduction to Generative AI

What is Generative AI?
AI and project management
Generative AI capabilities
Common AI terminology
AI opportunities and limitations

Module 2: Understanding the AI data landscape

Data and information
Structured and unstructured information
Data quality and relevance
Context and metadata
Relationship between data and AI systems

Module 3: AI models and Generative AI

Understanding AI models
Large language models
Model inputs and outputs
AI capabilities and limitations
Understanding model-generated information

Module 4: Project data and information

Project documentation
Requirements and project information
Schedules and status information
Risk and issue information
Stakeholder and communication data
Using project context with AI

Module 5: Prompting and context engineering

Writing effective prompts
Providing project context
Role and objective definition
Constraints and assumptions
Desired output structures
Iterative prompt refinement

Module 6: Working with AI-generated outputs

Interpreting AI-generated information
Checking accuracy
Identifying assumptions
Recognizing hallucinations
Comparing AI output with source information
Human-in-the-loop review

Module 7: GenAI applications for project managers

Project planning support
Risk and issue analysis
Stakeholder communication
Meeting summaries
Project reporting
Decision support
Knowledge management

Module 8: Data quality and AI reliability

Data quality considerations
Incomplete information
Inconsistent information
Source validation
Bias and reliability
AI output verification

Module 9: Responsible AI and project data

Data privacy
Confidential project information
Security considerations
Responsible AI use
Governance and accountability
Human oversight

Module 10: Building AI-enabled project workflows

Identifying suitable project use cases
Mapping project information flows
Designing AI-assisted workflows
Defining human review points
Creating reusable AI workflows
Measuring AI-enabled productivity improvements

Build the capabilities project managers need for an AI-enabled future.

AI Literacy

Understand core GenAI concepts without requiring deep technical expertise.

Data Literacy

Understand the role of data and information in AI-enabled workflows.

Prompt Capability

Create structured prompts that provide relevant project context.

Critical Evaluation

Evaluate AI outputs instead of accepting generated information automatically.

Decision Support

Use AI to explore information and alternatives while retaining human judgment.

Workflow Design

Identify opportunities for responsible AI integration into project workflows.

Governance Awareness

Recognize privacy, security, accountability, and responsible-use considerations.

Continuous Learning

Adapt project practices as AI capabilities and organizational needs evolve.

What you can take back to your project environment.

✓Explain the relationship between Generative AI, data, information, and project management.

✓Understand key components of the GenAI data landscape.

✓Recognize the importance of project context when working with AI.

✓Create more structured prompts for project management activities.

✓Evaluate AI-generated information critically.

✓Identify practical GenAI use cases across project workflows.

✓Recognize data quality, privacy, security, and governance considerations.

✓Design responsible AI-assisted workflows for project environments.

Better AI starts with better questions, better data, and better oversight.

AI-generated information should be treated as an input to professional work rather than an automatic source of truth. Project professionals need to understand data quality, confidentiality, validation, and accountability when using AI.

Data Quality

Understand how incomplete or poor-quality information can affect AI-assisted work.

Privacy

Consider confidentiality and appropriate handling of project information.

Validation

Verify generated information against reliable project sources.

Accountability

Keep qualified professionals responsible for consequential project decisions.

Turn AI awareness into project management capability.

Project-Centric Learning

Explore Generative AI through the lens of project management rather than treating data and AI as purely technical subjects.

Data + Context

Understand why project context and information quality matter when using AI-assisted workflows.

Practical AI Application

Connect concepts with planning, reporting, communication, analysis, knowledge management, and decision-support scenarios.

Responsible Adoption

Enroll now

For groups larger than 10, contact us for a quote.

Frequently asked questions

Is this a technical data science course?

The learning experience is positioned for project professionals. It focuses on understanding the GenAI data landscape and its relevance to project management rather than requiring advanced data science or programming expertise.

Why does a project manager need to understand the GenAI data landscape?

Project managers increasingly work with large amounts of project information. Understanding how data, context, prompts, models, and AI outputs relate to one another helps professionals use AI more effectively and critically.

Will I learn prompt engineering?

Yes. Prompting and context are included as practical capabilities, with emphasis on structuring project information and desired outputs.

Can AI-generated project information be trusted automatically?

No. AI-generated information should be reviewed and validated against appropriate project sources before being used for important decisions. The course emphasizes critical evaluation and human oversight.

Is this useful for PMO professionals?

Yes. PMO professionals can use the concepts to understand how project information, AI-assisted reporting, knowledge management, and workflow automation can interact within project environments.

Build your GenAI data literacy for project management.

Understand the relationship between project data, Generative AI, information, prompts, AI outputs, and professional decision-making.

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