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Rajib Das

Project Managers today face a critical challenge: how to remain relevant in an AI-driven world. Traditional skills like planning, reporting, and stakeholder communication are still vital—but much of the execution is increasingly being automated. The reality is clear: you won’t be replaced by AI, but by a Project Manager who knows how to use AI.

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Project Managers today face a critical challenge: how to remain relevant in an AI-driven world. Traditional skills like planning, reporting, and stakeholder communication are still vital—but much of the execution is increasingly being automated. The reality is clear: you won’t be replaced by AI, but by a Project Manager who knows how to use AI.

This course is designed specifically for mid-to-late career Project Managers who are not tech-savvy but want to confidently adopt AI in their daily workflows. You don’t need to be a data scientist or coder. Instead, you’ll learn practical, hands-on techniques that you can apply immediately to your projects.

By the end of this course, you’ll have a solid understanding of AI and ML basics, explained in plain project management language. You’ll master Prompt Engineering, learning how to write structured prompts that deliver accurate, reliable results. You’ll explore Project Management Design Patterns and their Key Automatable Tasks, gaining insight into repeatable AI workflows that save time and reduce effort.

You’ll also learn how to read and apply Project Management AI Automation Blueprint, giving you a roadmap for building AI-driven project workflows. With access to ready-to-use prompt templates and downloadable resources, you’ll be equipped to automate tasks like RAID log analysis, KPI dashboards, risk management, and stakeholder updates.

Finally, you’ll complete a capstone project, automating one of your own real-world PM tasks and demonstrating measurable impact—time saved, errors reduced, and confidence gained.

If you’re a Project Manager who wants to transition from “managing the old way” to leading as an AI-powered PM, this course is your runway clearance to the future.

Once you complete the course you can download my eBook - A Project Manager's Daily AI Handbook  - Automate your workflow for Peak Productivity, for your daily reference.

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What's inside

Learning objectives

  • Understand ai & ml fundamentals – role of ai & ml in project management in simple, non-technical terms.
  • Assess your current ai readiness – evaluate your personal ai maturity using a structured checklist.
  • Master prompt engineering basics – write clear, structured prompts to communicate effectively with ai tools.
  • Apply project management design patterns – identify recurring pm challenges and map them to ai-powered automation patterns.
  • Automate key pm tasks – use prompt templates to automate common workflows such as raid logs, risk analysis, stakeholder updates, and kpi dashboards.
  • Interpret project management ai automation blueprint – read and apply pm ai automation blueprints to design repeatable automation strategies across teams.
  • Leverage daily ai workflows – build simple, practical workflows using available ai tools to save time and reduce errors.
  • Address ethical & governance issues – apply safe ai practices by recognizing data sensitivity, bias, compliance, and human-in-the-loop requirements.
  • Develop an ai-leadership mindset – transition from traditional pm to “ai pilot,” balancing automation with human leadership.
  • Execute a capstone project – design and deliver one real-world automation of a recurring pm task, showcasing measurable impact.
  • Once you complete the course you can download my ebook - a project manager's daily ai handook - for your daily reference.
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Syllabus

Know Your Tutor

Hi, I’m Rajib Das – a seasoned Project Management professional with over 25 years of experience leading large-scale IT services projects. Over the decades, I’ve specialized in rescuing failing programs, managing global teams, and delivering high-stakes initiatives under pressure.

As the author of “A Project Manager’s Daily AI Handbook” and the creator of the Project Management AI Automation Blueprint, designed to help organizations adopt AI in a structured, scalable, and holistic way.  I’ve been helping project leaders worldwide embrace AI without needing to become tech experts. My approach is simple: translate AI into plain project management language so that mid-to-late career PMs can confidently integrate it into their daily workflows.

I bring together deep project management expertise (PMBOK, SAFe, PMP) with hands-on experimentation using Gen AI automations and AI agent platforms like CrewAI and AutoGen. Through this, I’ve developed practical strategies, PM design patterns, and prompt templates that save time, reduce errors, and enhance leadership impact.

My mission is to help Project Managers like you transition from traditional methods to becoming AI-Savvy PMs—leaders who balance human judgment with AI-powered efficiency.

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By the end of this module, you will be able to:

  • Assess whether your organization is ready to adopt AI for automation.

  • Apply the “Align Before You Automate” principle to avoid costly mistakes.

  • Recognize internal AI resources (e.g., copilots, open-source models) that you can leverage.

  • Follow the 4 Golden Rules of safe AI adoption: masking sensitive info, logging prompts, human review, and escalation paths.

By the end of this module, you will be able to:

  • Complete a self-assessment to determine your current AI literacy level.

  • Identify whether you are an AI Curious, Explorer, Integrator, Strategist, or AI-Savvy PM.

  • Build a 3-step roadmap to progress from where you are today to AI mastery.

By the end of this module, you will be able to:

  • Explain AI and ML in plain project management terms (no jargon).

  • Understand core concepts like models, training data, bias, overfitting, and inference.

  • Recognize common AI models (classification, clustering, NLP, neural networks) and their relevance to PM tasks.

  • Apply the CPMAI Framework to manage AI projects effectively.

  • Use AI for daily PM use cases such as risk analysis, RAID logs, and status updates.

By the end of this module, you will be able to:

  • Understand what prompts are and why they matter in project management.

  • Write structured prompts to get accurate and reliable outputs.

  • Differentiate between prompt types (instruction, role-based, chain-of-thought, etc.).

  • Avoid common mistakes in prompt writing.

  • Apply a “Daily AI Diet” — integrating one AI tool into your PM routine.

By the end of this module, you will be able to:

  • Identify 11 core Project Management Design Patterns and their recurring challenges.

  • Map these patterns to Key Automatable Tasks in real-world projects.

  • Think “Pattern First” before plugging in AI solutions.

  • Leverage AI Automation Blueprints to streamline PM workflows.

By the end of this module, you will be able to:

  • Apply structured prompts to build automated workflows.

  • Generate and test synthetic project data for AI-driven analysis.

  • Create practical outputs like KPI dashboards, RAID logs, or stakeholder reports.

  • Iteratively improve prompts for better results.

  • Adopt a sustainable “AI Daily Practice” to boost productivity.

In this example, you will learn how to

  • Write a prompt using my 4-Part Prompt Structure -> Role, Goal, Context and Format.

  • The example automates writing an Executive Summary Report for the board meeting.

  • In this example, I have provided Minutes of Meetings and Project Status updates as input to the AI. You can add more details to generate a more accurate Executive Summary.

By the end of this module, you will be able to:

  • Apply ethical principles for safe AI use in projects.

  • Recognize and mitigate risks related to data privacy, bias, and security.

  • Understand the human-in-the-loop principle for decision-making.

  • Explore the rise of AI Agents and their role in project management.

  • Create your 7-step AI Leadership Plan to future-proof your PM career.

By the end of this project, you will be able to:

  • Select and automate one recurring project management task using AI tools.

  • Document your workflow with before & after diagrams.

  • Justify your tool selection and automation approach.

  • Measure and present the impact (e.g., time saved, reduced errors).

  • Showcase a real-world AI application that proves your transformation into an AI-Savvy PM.

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Learners who complete AI-Powered Project Management Mastery will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AI-Powered Project Management Mastery.
The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.
A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
A monumental foundational text establishing the mathematical, algorithmic, and statistical principles of modern deep neural networks, from linear algebra and optimization to convolutional and sequence modeling architectures.
The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
Offers a comprehensive overview of Business Process Management (BPM), which is foundational to workflow automation. It covers essential concepts, methods, and techniques suitable for both beginners and advanced students. It is widely considered a standard textbook in academic settings.
This comprehensive guide provides practical advice on how to use AI to enhance decision-making, improve operational efficiency, and drive innovation. It is written by experts in the field and includes case studies from leading companies.
Provides a critical and material perspective on AI, examining its environmental costs, labor exploitation, and the power structures it reinforces. It is essential reading for understanding the broader societal and political context of AI development and is particularly valuable for those interested in the real-world impacts beyond technical discussions.
Provides a comprehensive guide to prompt engineering, covering techniques for crafting effective inputs to generative AI models. It's particularly useful for understanding how to obtain reliable and predictable results, which is crucial for both beginners and those looking to deepen their practical skills. This book is valuable as a current reference for anyone working with generative AI.
A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Guides engineers step-by-step through implementing, training, and fine-tuning complete generative transformer architectures in pure PyTorch, providing crystal-clear low-level insight into modern foundation models.
Delves into the contemporary topic of Intelligent Automation and Hyperautomation, exploring the combination of RPA, AI, and other technologies. It provides a forward-looking perspective on the future of automation and its impact on businesses and society. It is essential for understanding the current trends in the field.
Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
A definitive guide to financial risk management, this handbook provides in-depth analysis of methods for measuring and managing risk in financial institutions. It covers various types of financial risk and crucial reference for professionals and advanced students in finance.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment workflows.
This influential book challenges conventional notions of risk and probability by focusing on the impact of rare and unpredictable events ("black swans"). It argues that these events have a disproportionate impact on the world and that we are often ill-equipped to deal with them. It's a provocative read that encourages a different way of thinking about risk and uncertainty.
Introduces a new way of thinking about data science and AI, drawing on the principles of intersectional feminism. It highlights how power imbalances are embedded in data and algorithms and offers a framework for building more equitable and just data systems. It is highly relevant for understanding bias and fairness in AI.
Explores the impact of AI on leadership and management. It provides a framework for understanding the challenges and opportunities of AI and offers advice on how to lead in this new era.
Comprehensive guide to the use of AI in leadership. It covers the benefits, challenges, and risks of AI and offers advice on how to use AI to make better decisions.
Provides a foundational understanding of risk management and insurance principles, making it ideal for those new to the topic or seeking a broad overview. It covers basic concepts, traditional insurance, and introductory enterprise risk management, serving as a valuable textbook for undergraduate programs and a solid starting point for professionals.
Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Demystifies practical transformer model implementation through the Hugging Face ecosystem, demonstrating how to fine-tune, optimize, and deploy attention architectures across diverse natural language processing and understanding tasks.
Focusing on the practical aspects of implementing BPM, this book provides real-world examples and guidance for successful projects. It valuable resource for practitioners looking to apply BPM principles to workflow automation initiatives. It complements theoretical knowledge with actionable strategies.
Explores the evolution of RPA to include cognitive automation, highlighting real-world implementations and the risks and benefits involved. It provides a deeper understanding of the advanced forms of automation and their impact on enterprises. It's a good resource for those looking beyond basic RPA.
Presents a framework for centering the voices and needs of marginalized communities in the design of technology and AI. It offers practical guidance and case studies for creating more equitable and just technological systems, making it valuable for designers, developers, and policymakers.
Focused specifically on operational risk in the financial services industry, this book provides a detailed guide to managing this critical area. It covers frameworks, techniques, and best practices for identifying, assessing, and mitigating operational risks. It's essential for professionals working in risk management within banks and financial institutions.
This textbook provides a comprehensive treatment of financial risk management, covering market, credit, and operational risks within financial institutions. It's widely used in undergraduate and graduate finance programs and solid resource for understanding the principles and practices of financial risk management.
Offers a practical, hands-on approach to prompt engineering specifically with ChatGPT. It's an excellent resource for high school and undergraduate students getting started, providing clear examples and exercises. It serves as a useful introductory guide and additional reading to complement foundational AI courses.
Provides a hands-on approach to prompt engineering with a focus on using the OpenAI API. It's particularly relevant for undergraduate students, graduate students, and working professionals who want to programmatically interact with LLMs. It serves as a practical guide for implementing prompt engineering techniques in applications.
Delivers battle-tested operational patterns for serving foundation models, addressing GPU cluster throughput, semantic caching, automated evaluation benchmarks, and continuous latency minimization.
Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Aligned with international standards like ISO 31000 and the COSO ERM Framework, this book offers a comprehensive introduction to enterprise risk management. It covers identification, analysis, and treatment of various risks, including emerging trends. It's suitable for students and risk professionals seeking a thorough grounding in ERM principles and practices.
Delves into the technical aspects of designing algorithms that are fair, private, and transparent. It is particularly useful for students and professionals with a technical background who want to understand the computational challenges and solutions related to ethical AI. It can serve as a valuable reference for those building AI systems.
Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
Offers a critical look at common risk management practices and argues for more quantitative approaches. It challenges conventional wisdom and provides insights into improving risk assessment and decision-making. It's valuable for both students and professionals looking to refine their risk management methodologies.
Examines the increasing power of algorithms in finance, search, and reputation, highlighting the lack of transparency and accountability. It provides crucial context for understanding the societal implications of opaque algorithmic systems and is valuable for students and professionals across various disciplines.
Focuses on the creative aspects of prompt engineering and generating diverse language outputs. It's a good fit for students and professionals looking to go beyond basic prompting and explore more advanced techniques for creative content generation. It adds breadth by covering applications in areas like creative writing and podcasting.
Focuses on leveraging process automation technology, including workflow engines, to orchestrate various components like software, humans, and bots. It provides practical advice and explores how process automation fits into modern IT architectures. It is suitable for developers and architects.
Details an applied architectural methodology for running high-performance open-weights models on local workstations, tackling quantization fidelity, memory bandwidth limits, and prompt assembly trade-offs.
Provides real-world examples and case studies of how organizations have implemented enterprise risk management. It offers practical insights into the challenges and successes of ERM implementation, making it valuable for those involved in establishing or improving an ERM program.
Offers a rigorous theoretical foundation for reasoning under uncertainty, combining graph theory and probability distributions to model complex high-dimensional dependencies across autonomous intelligent systems.
Delves into practical issues and presents case studies related to the execution of Enterprise Risk Management programs. It provides insights from senior risk management professionals and explores how organizations measure the impact of ERM. It is particularly useful for understanding real-world challenges and best practices in ERM implementation.
Explores the art and science of prediction in various fields, from weather forecasting to finance. It examines why some predictions succeed while many fail, emphasizing the importance of probability and understanding uncertainty. It offers valuable lessons for risk managers who rely on forecasting and data analysis.
Co-authored by a co-founder of DeepMind, this book examines the transformative potential of AI and other frontier technologies while also highlighting the immense challenges of controlling them for the benefit of humanity. It provides a recent and insightful perspective on the opportunities and risks ahead.
This handbook provides a comprehensive guide to the interconnected areas of governance, risk management, and compliance (GRC). It covers frameworks, best practices, and practical guidance for implementing an integrated GRC program. It's a valuable reference for professionals responsible for GRC in their organizations.
Focused specifically on risk management within the context of projects, this book provides practical guidance and techniques for identifying, analyzing, and responding to project risks. It's a valuable resource for project managers and teams, often used as a reference or textbook in project management programs.
Written by a leading AI researcher, this book explores the long-term challenges of ensuring that advanced AI systems remain beneficial to humanity. It provides a comprehensive overview of the control problem and potential solutions, making it relevant for those interested in the future trajectory of AI and its ethical implications.

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