I’m excited to share that my latest Pluralsight course, Applying GenAI to Planning and Requirements, is now live!
Generative AI is changing how project managers, business analysts, product owners, developers, and other technology professionals approach project planning and requirements gathering. Teams can now use GenAI to organize scattered information, accelerate documentation, identify missing details, and create stronger project artifacts.
However, generating content is only part of the process. Teams also need to validate the output, maintain traceability, involve the right stakeholders, and apply human judgment before AI-generated artifacts are used to guide development.
In this intermediate-level course, I focus on practical and responsible ways to apply GenAI across project planning, requirements gathering, documentation, validation, and governance. The course is approximately 75 minutes and is part of Pluralsight’s Generative AI Across the Software Development Lifecycle learning path.
What You’ll Learn
Modern project teams are expected to move quickly while still producing clear plans, accurate requirements, and documentation that stakeholders can trust.
This course explores how GenAI can help teams meet those expectations without removing people from the decision-making process.
Throughout the course, you’ll learn how to:
- Generate initial project charters and define project scope
- Identify assumptions, constraints, dependencies, and scope boundaries
- Support Agile planning, backlog refinement, estimation, and release planning
- Create risk registers and develop potential mitigation strategies
- Transform meeting notes, interviews, emails, and informal briefs into structured requirements
- Generate user stories, acceptance criteria, and definitions of done
- Identify missing non-functional requirements related to security, performance, accessibility, and compliance
- Detect gaps, ambiguities, and contradictions in requirements
- Generate clarifying questions for stakeholder review
- Establish human review and stakeholder sign-off workflows
- Maintain traceability between requirements, backlog items, testing, and supporting evidence
- Apply governance practices to AI-generated project artifacts
The course is intentionally tool-agnostic, with an emphasis on concepts and transferable practices that can be applied across different GenAI platforms and tools.
Applying GenAI to Project Planning, Scoping, and Risk Management
The first module explores how GenAI can support early project planning activities.
I demonstrate how to use GenAI to create an initial project charter, establish scope boundaries, document assumptions and constraints, and support Agile planning activities.
The module also covers how GenAI can assist with backlog refinement, effort estimation, release planning, risk identification, and mitigation planning.
For example, GenAI can help a project team identify common risks, organize them into a risk register, and propose possible contingency approaches. This can give the team a stronger starting point for planning and stakeholder discussions.
Just as importantly, I discuss the limitations of AI-generated planning artifacts.
GenAI may not understand internal politics, team dynamics, organizational history, available resources, unstated constraints, or business context that was not included in the prompt. Its output may sound complete while still missing important details.
Human judgment remains essential.
Accelerating Requirements Gathering and Documentation
The second module focuses on using GenAI to improve requirements gathering and documentation.
Requirements are often distributed across stakeholder conversations, meeting transcripts, emails, notes, whiteboards, documents, research findings, and informal requests. Important decisions may also exist only as verbal agreements or undocumented assumptions.
GenAI can help organize these inputs and transform them into structured artifacts that a project team can review and refine.
In this section, I show how GenAI can assist with:
- Requirements workshops
- Stakeholder discussion prompts
- Meeting summaries and action items
- Structured requirements documentation
- User stories and acceptance criteria
- Non-functional requirements
- Definitions of done
- Gap and ambiguity detection
- Clarifying questions
- Audience-adapted requirements documentation
The goal is not to have AI make final product or project decisions.
The goal is to help teams build a stronger starting point, organize information faster, ask better questions, and spend more time validating requirements with stakeholders.
Hands-On Demos with GitHub Copilot App
One of the highlights of the course is the set of practical demos.
I used GitHub Copilot App, OpenAI’s Codex, and GitHub Projects througout the demos so that you have an example with real tools you can start using today. In one of the demos GitHub Copilot App was used to move from unstructured project inputs to usable planning and requirements artifacts in GitHub Project. I show learners how GenAI can move beyond producing isolated text and become part of a real project workflow.
Across the demos, I show how to use GitHub Copilot to:
- Review raw project inputs and stakeholder information
- Generate structured requirements
- Define project scope
- Document assumptions and constraints
- Create user stories and acceptance criteria
- Identify gaps, risks, and unanswered questions
- Generate a prioritized backlog
- Create and organize backlog items in GitHub Projects
- Connect requirements to the work needed to deliver them
- Review AI-generated artifacts for quality and completeness
These demos show how a team can move from scattered project information to a structured plan, documented requirements, and an actionable backlog inside GitHub Project.
I also demonstrate how GenAI can support traceability by mapping requirements to backlog items, acceptance criteria, validation activities, and evidence.
The demos are designed to give learners practical examples they can adapt to their own projects, workflows, and tools.
Responsible Validation, Governance, and Traceability
The final module focuses on an area that is sometimes overlooked: responsibly managing AI-generated project artifacts.
AI-generated requirements can sound polished and complete even when they contain assumptions, inaccuracies, contradictions, missing perspectives, or compliance concerns.
Because of this, teams need structured review and approval processes before using AI-generated content to guide development.
This module covers:
- Human review workflows
- Requirements validation techniques
- Stakeholder review and sign-off
- Requirements traceability
- Change tracking
- Audit trails
- Ethical considerations
- Compliance concerns
- Maintaining diverse stakeholder perspectives
- Preserving trust in AI-assisted project workflows
The course concludes with a hands-on demonstration showing how GenAI can be used alongside human review processes to validate requirements, create a traceability matrix, identify potential compliance concerns, and preserve evidence throughout the project lifecycle.
A key message throughout the course is that GenAI should support human decision-making, not replace it.
Project managers, business analysts, product owners, developers, and stakeholders remain responsible for reviewing, correcting, approving, and governing the final output.
Who This Course Is For
This course is a great fit for:
- Project managers
- Business analysts
- Product owners
- Product managers
- Scrum masters
- Agile practitioners
- Software developers
- Engineering leaders
- Technology leaders
- Professionals responsible for project documentation
- Anyone exploring practical applications of GenAI across the software development lifecycle
Some familiarity with project planning, Agile practices, or requirements gathering will be helpful, but you do not need to be an AI expert to follow the course.
Why This Matters
Many teams already experiment with GenAI by asking it to summarize notes or draft a document.
The larger opportunity is to connect those capabilities to real project workflows.
GenAI can help teams move from:
- Unstructured notes to documented requirements
- Stakeholder discussions to user stories
- Informal requests to a prioritized backlog
- Assumptions to identified risks
- Requirements to validation activities
- Scattered artifacts to a traceable system of record
When used responsibly, GenAI can help teams move faster, improve consistency, surface missing information, and create better alignment across business and technical stakeholders.
The value is not simply that AI can write content quickly. The value is that it can help teams structure information, identify questions, improve documentation, and support better project decisions.
Check Out the Course
You can view the course here:
https://www.pluralsight.com/courses/applying-genai-planning-requirements
I hope this course serves as a valuable resource as you explore how GenAI can improve project planning, requirements gathering, documentation, backlog creation, validation, and traceability.
Thank you for your continued support. Be sure to follow my Pluralsight profile so you’ll be notified as I release new courses. You can find my profile:
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