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Agentic AI project management: automate your PM workflow

Name: Agentic AI project management: automate your PM workflow
Rating: 4.6 (62 reviews)

ChatGPT & Claude workflows for discovery, sprint planning, release comms, and incident response - ready-to-use prompts

Created bySkliar Serhii

Last updated 3/2026

English

Czech [Auto],English [Auto],

What you'll learn

Use AI prompts to run discovery interviews, synthesize user research, and turn raw notes into structured PRDs
Plan and simulate sprints with AI model capacity, flag scope risks, and produce trade-off options before the sprint begins
Draft multi-audience release notes from a list of closed tickets in one prompt customer changelog, exec summary, and CS briefing
Reconstruct an incident timeline, identify root cause, and generate a full stakeholder communication package
Apply the CLEAR framework with AI to write transparent stakeholder communications for incidents, AI features, and UAT outcomes
Use AI as a go/no-go criteria generator and deployment checklist tool to replace subjective release decisions with objective gates

Course content

9 sections • 34 lectures • 11h 10m total length

1.1 Pre-sale pain points17:32
Identify the four pre-sale bottlenecks—ambiguous requirements, bid fatigue, siloed knowledge, and inconsistent proposals—and preview AI-augmented workflows to accelerate and clarify deal momentum.
1.2 AI workflow: from raw requirements to proposal pack23:08
Explore the integrated intelligence flow to turn raw requirements into a proposal pack. Apply a five-stage workflow—signal qualification, context harvesting, thought partner drafting, proposal automation, collaborative refinement—to cut cycle time.
1.3 Effort estimation workflow: scope slicing, assumptions, ranges19:18
1.4 Competitive scan workflow: positioning, differentiators, “why us” narrative20:01
Adopt a continuous sensing system to monitor competitors in real time, extract actionable signals from reviews, identify white space, and craft buyer-focused narratives that win deals.

2.1 Discovery pain points: messy inputs, hidden assumptions20:46
Explore the discovery paradox and how messy inputs, hidden assumptions, and information silos derail product discovery, and see how Anthropx Cloud turns that data into a usable knowledge base.
2.2 AI workflow: interview synthesis into themes, risks, and open questions21:40
Learn how to transform research with Claude's 200,000-token context window by mastering synthesis, multi-source contradiction analysis, and guardrails, reducing fifty hours to four to eight and elevating researchers to strategists.
2.3 Assumption mapping: turning “beliefs” into testable statements21:22
Turn beliefs into testable statements using the we believe framework and an assumption map, guided by AI-driven analysis, risk prioritization, and kill conditions.
2.4 Opportunity framing: problem statements, success metrics, decision log22:32
Learn how to frame opportunities with precise problems, tier two how might we statements, measurable success, and auditable decisions using leading indicators, causal chains, and decision logs.

3.1 Planning pain points: scope creep, fragile timelines, unclear ownership20:58
Identify three planning traps—scope ambiguity, optimism-biased timelines, and ownership theater—and show how AI and Monte Carlo simulations enforce clear scope, probabilistic timelines, and single accountability.
3.2 AI workflow: scope boundaries, objectives, constraints, acceptance signals15:20
3.3 Risk register generation: categories, likelihood/impact, mitigations19:57
Turn the static risk register into a predictive AI system using Monte Carlo, comprehensive risk identification, contextual mitigations, and trajectory signals to forecast project risks earlier.
3.4 Resource and milestone mapping: capacity-based plans and forecasting18:54

4.1 Analysis pain points: incomplete requirements, inconsistent stories18:22
Discover how agentic AI project management surfaces incomplete requirements, inconsistent stories, and missed dependencies, then use AI to draft complete user stories and reveal edge cases early.
4.2 AI workflow: from raw needs to requirement set (functional + non-functional)18:54
4.3 Gap analysis: what’s missing, ambiguous, or contradictory24:50
Harness AI-driven gap analysis to expose missing, ambiguous, or contradictory requirements using automated conflict detection, sparring, synthetic evaluation, risk tagging, and visual gap mapping.
4.4 Dependency mapping: cross-team and system dependencies with owners and risks17:53
Explore four-layer ai-driven dependency mapping for cross-team and system dependencies with owners and risks, from extraction to early warning, and learn to prevent combinatorial invisibility and implicit deadlocks.

5.1 Design pain points: unclear interfaces, “architecture by chat”20:47
Identify and fix design phase pain points, including the Figma illusion, architecture by chat, and weak non-functional coverage, with ADRs and AI-assisted friction reduction.
5.2 AI workflow: architecture brief reviewer (quality checklist + red flags)19:24
Automate architecture reviews with an AI devil's advocate to surface flaws, run structured trade-offs, and generate failure scenarios. Use ADRs and compliance validation to document decisions and stay lawful.
5.3 UI/UX requirement specs: turning screens into acceptance criteria, edge case19:48
5.4 System interfaces and network requirements25:55

6.1 Development pain points: backlog entropy, miscommunication, tech debt20:25
Diagnose backlog entropy, translation friction, and unmanaged tech debt as the three agile development pain points slowing teams, and leverage ai as the universal translator to bridge precision asymmetry.
6.2 AI workflow: sprint planning assistant (scope, trade-offs, capacity, risks)19:54
6.3 Backlog grooming: splitting tickets, adding AC, definition of done19:23
AI-driven backlog grooming uses micro WBS, five-path acceptance criteria, and a DoD linter to produce vertical, ready-to-build tickets. PMs become architects of value, boosting sprint readiness and slashing grooming time.
6.4 Tech debt tracking: categorization, impact statements, prioritization18:10

7.1 Testing pain points: inconsistent coverage, noisy bug lists, weak UAT9:59
Automate intake parsing and deduplication to fix 60 percent completeness and 20 percent duplicates. Use AI to generate UAT scripts, define roles, and establish sign-off criteria for stronger readiness.
7.2 AI workflow: generate test scenarios from requirements and edge cases18:56
AI transforms software testing from reactive to proactive by generating exhaustive test scenarios from acceptance criteria before coding, via the PM defines, engineering validates framework.
7.3 Bug triage: severity, priority, and impact framing for stakeholders21:41
Agentic AI project management reframes bug triage as critical decision making, enforcing a minimum viable bug report structure, severity and priority framing, and clear stakeholder communications to speed fixes.
7.4 UAT planning: scripts, roles, comms, sign-off criteria14:19
Learn how to turn chaotic uat feedback into a definitive quality gate using ai-generated scenarios, clear communications, and a raci framework to define roles, three-tier prioritization, and written sign-off criteria.

8.1 Deployment pain points: chaotic releases, unclear comms, missed checklists18:49
Identify deployment pain points from chaotic releases, unclear communications, and missed checklists, and learn how AI workflows can fix these bottlenecks using audience-specific notes and cross-team coordination.
8.2 AI workflow: release notes and rollout comms (internal + external)20:18
Implement an ai workflow for release notes and rollout communications that turns raw sprint data into five audience-specific messages using a structured release data set and conventional commits.
8.3 Deployment checklist and go/no-go criteria generation22:54

9.1 Maintenance pain points: incident noise, slow post-mortems, SLA gaps19:52
Identify maintenance pain points—alert fatigue, incident noise, SLA gaps—and show how AI tools like Claude and ChatGPT streamline postmortems and diagnostics.
9.2 AI workflow: incident summary, timeline reconstruction, and stakeholder comm18:25
Use AI to bridge the crisis communication gap by turning raw outage data into a reconstructed incident timeline, root cause analysis, and tailored multi-audience updates.
9.3 Retrospective facilitation: themes, actions, owners, and follow-through20:16
Leverage ai-driven retrospective facilitation to turn raw sprint data into neutral, data-first themes, assign clear owners and concrete actions, and automate tickets and a pattern library for continuous improvement.

Requirements

No coding or technical background required – all prompts are written in plain language
Access to ChatGPT (free or paid) or Claude - a free account is sufficient to follow along
Basic familiarity with product or project management concepts (sprints, backlogs, user stories) is helpful but not mandatory

Description

This course contains the use of artificial intelligence.

What you will learn:

This is a practical, workflow-first course for product managers who want to use AI as a daily co-pilot - not as a novelty tool, but as a reliable system that reduces the administrative load of PM work at every stage of the product lifecycle.

Across 9 modules and more than 30 lessons, you will cover every phase of product development - from discovery and requirements through development, testing, deployment, and maintenance - and learn exactly how to apply agentic AI at each stage. Nearly every lesson includes ready-to-use prompt templates you can copy, adapt, and run immediately in ChatGPT or Claude.

What is inside each lesson:

Every lesson in this course is built around a practical learning stack:

Audio podcast lesson: Narrated lesson you can consume on the go - commuting, walking, or between meetings

Infographic: Visual summary of the key frameworks, tables, and concepts from the lesson

Mind map: Structured visual overview of how the lesson fits into the broader module and course

Lesson notes database: Annotated reference notes for each lesson - searchable, scannable, ready to revisit

Slide deck: Presentation-ready slides for each lesson - useful for sharing frameworks with your team

Quiz (Q&A document): A comprehensive question bank covering all modules so you can test and reinforce your learning

Who this course is for:

Product managers who spend too much time writing, organizing, and documenting instead of thinking
PMs new to AI tools who want a structured, practical starting point
Experienced PMs who have experimented with ChatGPT but want a systematic workflow approach
Scrum Masters, product owners, and project leads who run ceremonies and manage stakeholder communications

No engineering background required. All prompts are written in plain language and explained with context.

What you will be able to do after this course:

Run an AI-assisted discovery session and convert raw interview notes into structured requirements in under 30 minutes
Generate a full backlog with acceptance criteria, story splits, and a definition of done from a feature brief
Use AI to plan sprints, simulate scope trade-offs, and manage capacity without spreadsheets
Generate multi-audience release notes from a list of closed tickets - in one prompt
Reconstruct an incident timeline, identify root cause, and draft a stakeholder communication package within an hour of resolution
Facilitate retrospectives that produce tracked, completed action items instead of forgotten sticky notes
Build and maintain a cross-sprint pattern library so recurring problems become visible before they become normalized

Module overview:

1. Discovery and research

2. Requirements and documentation

3. Roadmapping and prioritization

4. Stakeholder communication

5. Design and UX collaboration

6. Development and coding support

7. Testing

8. Deployment

9. Maintenance and retrospectives

A note on tools:

The course is tool-agnostic at the prompt level - the frameworks and prompt templates work in ChatGPT, Claude, Gemini, or any capable LLM. Where a specific tool is recommended, it is because it is the best fit for a specific task (Claude for log-heavy incident summaries, ChatGPT for structured communications), and the rationale is always explained.

Who this course is for:

Project managers who spend too much time writing and organizing and want AI to handle the administrative layer
Scrum Masters and product owners looking to run faster ceremonies with better-quality outputs
PMs new to AI tools who want a structured, workflow-first starting point rather than a general AI overview

Agentic AI project management: automate your PM workflow

What you'll learn

Explore related topics

Course content

Module 1: pre-sale4 lectures • 1hr 20min

Module 2: discovery4 lectures • 1hr 26min

Module 3: planning4 lectures • 1hr 15min

Module 4: analysis4 lectures • 1hr 20min

Module 5: design4 lectures • 1hr 26min

Module 6: development and coding4 lectures • 1hr 18min

Module 7: testing4 lectures • 1hr 5min

Module 8: deployment3 lectures • 1hr 2min

Module 9: maintenance3 lectures • 59min

Requirements

Description

Who this course is for: