Complete Project Manager's Practical Guide to Using AI 8 PDU

Explore how AI accelerates change in the exponential age, transforming project management by generating and managing scope, schedule, and budget with AI tools across business processes.

Discover the three core elements of AI—perception, prediction, and planning—and how they process environment data to make informed business decisions.

Explore the two types of artificial intelligence—narrow AI and artificial general intelligence (AGI)—and understand why today most AI applications are narrow, from chatbots to facial recognition.

Explore the technology underpinning ai, including large language models, deep learning, neural networks, natural language processing and generation, computer vision, and augmented intelligence for human ai collaboration in project management.

Explore the difference between automation and intelligence, showing how automation handles repetitive tasks while intelligent systems learn, adapt, and fix issues through machine learning for autonomous decisions in project management.

Identify the goals of AI and apply it to solve project problems, automate repetitive tasks, and enhance data analytics with intelligent automation and personalized content.

Discover seven AI patterns—autonomous systems, conversational interfaces, goal-driven systems, hyper-personalization, anomaly detection, predictive analytics, and image recognition—and how projects fit these patterns.

Explore generative ai, a machine-learning approach creating text, images, and video from prompts. Discover how large language models and gpts enable content generation, summarization, translation, and information retrieval through prompts.

Learn how to implement human in the loop with AI as a co-pilot, ensuring oversight, empathy, and ethical balance while iteratively turning AI outputs into actionable project artifacts.

Learn how prompt engineering refines prompts to improve AI responses by adding regional context and detail for language models like ChatGPT.

Explore the limitations of GenAI, including intellectual property, data security, accuracy issues, biases, hallucinations, and the need for expert verification, to use AI as a strategic project management tool.

Predictive AI uses historical data to forecast future events, enabling classification, forecasting, anomaly detection, clustering, and time-series analysis; it relies on clean data and models like regression and neural networks.

Integrate ai into project management by establishing governance, ethics, and risk controls while coordinating cross-functional teams. Monitor, evaluate, and refine ai systems through continuous learning and appropriate hardware and processes.

Leverage artificial intelligence to plan projects by analyzing data, predicting resource needs, prioritizing tasks, mitigating risks, automating scheduling, establishing baselines, and building a business case.

Leverage AI to base decisions on good data, automate testing, and analyze trends, reducing delays and guiding hiring and project strategy.

Monitor projects with ai by generating automated reports and executive summaries from meeting minutes, tracking baselines, milestones, budget, and risk to keep work on plan.

Leverage AI to enhance project communications by generating clear reports, simplifying technical jargon, tailoring updates, and automating meeting notes, data aggregation, to-do lists, and chatbot status inquiries.

Manage project documents with AI by generating user guides and regulatory documents, analyzing content views, and deriving insights to identify documentation needs for software.

Use AI to monitor expenses and generate real-time budgets, spreadsheets, and reports, analyze revenue and costs, and perform cost-benefit analysis for efficient resource allocation.

Learn how to use generative AI to train project teams with simulated environments for coding, risk assessment, and stakeholder engagement, reducing risk while noting AI limits.

Identify business problems and define KPIs before applying AI, treat AI as a tool, then start with small, data-driven projects, conduct data analysis to ensure quality data and better outcomes.

Explore the shift from off-the-shelf ai to customized ai using retrieval augmented generation with your proprietary data, enabling domain-specific insights, up-to-date results, and minimizing prompt engineering.

Tune large language models with domain-specific data by collecting, training, and monitoring specialized datasets, adjusting parameters, and guarding against overfitting to improve decision quality.

Explore the limitations of off-the-shelf AI products like ChatGPT, noting strengths in prompt-based tasks and weaknesses in anomaly detection, predictive analytics, autonomous systems, and align use cases with pre-trained models.

Assess AI risks and ensure compliance with GDPR by conducting full risk assessments, anonymizing data, encrypting uploads, and auditing third-party vendors. Mitigate privacy threats and avoid surveillance and profiling.

Explore data lifecycle, retention, and ownership for AI-driven projects, and identify biases, hallucinations, and misinformation, then apply validation, explainable AI, and human oversight to mitigate risks.

Secure AI systems by encrypting data, enforcing access controls, auditing systems, and conducting regular security assessments; train employees to detect threats and ensure GDPR and HIPAA compliance through industry collaboration.

Navigate how governments lag the rapid evolution of ai, sparking agile regulatory frameworks that demand ongoing compliance, cross-jurisdiction governance, and close collaboration with legal teams.

Foster a culture of AI experimentation by starting small, testing common tools, and embracing failures to drive innovation, scale across projects with data analytics and automation.

Explore predictive project management, or waterfall, with fixed scope and upfront planning, including PMI PMBoK concepts, project management plans, baselines, and change or assumption logs.

Explore agile project management with iterative, adaptive methods and scrum artifacts like product backlog, sprint backlog, and definition of done for incremental software delivery.

Explore major large language model tools for project managers, including ChatGPT, Gemini, Meta AI, Copilot, and PMI Infinity, with notes on free and paid options.

Create a ChatGPT account to explore a widely used large language model, compare paid vs. free plans, and generate a NYC two-story house plan to apply prompts in class.

Meta AI delivers surprisingly detailed planning for a two-story house, including pre-construction scheduling and a 12-month budget, with options to try without logging in.

Evaluate Google Gemini as a project management AI, comparing its detail to ChatGPT. Explore the free trial with a Gmail account and Android integration.

Explore PMI Infinity, the project management AI tool requiring an active PMI membership and login at infinity.pmi.org, with a prompt library, guided experiences, and charter creation capabilities.

Evaluate Microsoft Copilot as an AI assistant in Windows, compare its detail level to other models, and practice prompts across tools to enhance project management outcomes.

Discover Perplexity, an AI tool that exposes data sources and reputable references, helping you assess risks, timelines, and budgets for IT project management with transparent prompts and incognito mode.

Explore how AI transforms specialized project management software like Notion and Monday.com, from note-taking and task lists to collaborative planning, with hands-on prompts and AI features.

Master prompt engineering by talking to ChatGPT as a person, being specific and creative, and treating it as a vast knowledge base you can prompt across tools.

Learn to craft a compelling business case to justify a project, including research, benefits, costs, and timelines, using a website redesign scenario and ChatGPT-backed analysis.

Create a tailored project charter with ChatGPT by outlining purpose, objectives, scope, timeline, budget, stakeholders, risks, and assumptions, then secure sign-off from management.

Identify stakeholders early and build a stakeholder register using ChatGPT templates, emails, and an editable Excel sheet to capture contact details, roles, and influence.

Learn how to build a PMBoK-aligned scope management plan for a website project, detailing how to define, validate, control scope, create the WBS, and manage changes.

Learn to gather and document website requirements from stakeholders, use ChatGPT for ideas, and produce PMBoK-based requirements documentation and a traceability matrix to guide scope.

Create a detailed scope statement per PMBoK that defines a website project's deliverables, exclusions, constraints, assumptions, and acceptance criteria from gathered requirements.

Learn to create a practical work breakdown structure (WBS) from project requirements, expand with AI tools, build a WBS dictionary, and assemble a PMBoK–aligned scope baseline.

Learn how a product backlog captures all agile project requirements, is managed by the product owner, and is prioritized to guide the project team in a scrum environment.

Learn to convert a product backlog into a detailed sprint backlog using AI, aligning with agile iterations of 1–4 weeks, with weekends off, via team-driven prompts.

Decompose high-level features into user stories on an agile backlog to capture user value. Use artificial intelligence to draft stories with acceptance criteria reflecting real users and roles.

Create a detailed schedule management plan to define activities, establish the schedule baseline, and manage timing with the critical path method, WBS, and schedule risk as guided by PMBoK.

Learn to transform a WBS into a detailed activity list, break down work packages into tasks, and sequence activities, while comparing AI-generated network diagrams with Microsoft Project workflows.

Assign activity durations with input from experienced team members, using ChatGPT to estimate tasks, then build a schedule and a Gantt chart starting January 2, 2025, Monday through Friday.

Learn to estimate agile schedules by linking team velocity and story points to sprints, using prompts with ChatGPT to model tasks, adjust sprint length, and predict overall timelines.

Create a cost management plan that outlines estimation, budgeting, monitoring, and control per PMBoK, including cost baseline, EVM, variance tracking, roles, assumptions, and risks.

Create a detailed budget by defining activities, durations, and coder costs, then use AI prompts to estimate the cost and explore savings like a minimum viable product or outsourcing.

Explore creating an agile budget by estimating costs from hours, sprint length, and location-based wages for a team of four coders per sprint.

Create a quality management plan per PMBoK to ensure defect-free deliverables through quality assurance, testing, and controls while defining requirements, standards like W3C and web accessibility, and peer review.

Distinguish quality requirements from scope, focusing on performance, reliability, and correct functionality like fast load times and accurate emails; use ChatGPT to generate a detailed quality requirements list.

Learn how to manage agile quality by defining sprint-level requirements and integrating code reviews, functionality, performance, and security testing into every sprint to ensure release-ready quality.

Leverage ai to predict and manage resources, including human resources and physical resources like consumables, equipment, machinery, and facilities, and build a PMBoK-aligned resource management plan with a RACI chart.

Learn to manage people and resource conflicts using an AI assistant, with practical steps for open discussion, assessing project fit, private conversations, clear communication, and motivation activities.