Chapter 24 Further Reading: Project Planning and Task Management

Foundational Project Management

"A Guide to the Project Management Body of Knowledge (PMBOK Guide)" Project Management Institute The definitive reference for structured project management methodology. While comprehensive rather than prescriptive, it provides systematic frameworks for WBS development, risk management, stakeholder communication, and schedule management. Useful as a reference resource rather than a cover-to-cover read. [pmbok.pmi.org]

"Getting Things Done: The Art of Stress-Free Productivity" David Allen Not a project management book per se, but Allen's systematic approach to capturing, clarifying, and reviewing commitments provides a personal framework that complements team-level project management. The concept of "natural planning" (brain dump, outcome vision, brainstorming, organizing, next actions) mirrors the AI-assisted planning workflow described in this chapter. [davidco.com]

"Making Things Happen: Mastering Project Management" Scott Berkun A practitioner-oriented guide that emphasizes the human and organizational dimensions of project management that formal methodologies often skip. Particularly good on stakeholder management, managing ambiguity, and making decisions under uncertainty. More readable than the PMBOK and more practical about the informal realities of project work.

Risk Management and Decision-Making Under Uncertainty

"Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts" Annie Duke Duke, a professional poker player and decision researcher, provides a framework for thinking probabilistically about uncertain outcomes. Directly applicable to project risk assessment — the pre-mortem technique and buffer planning concepts in this chapter both draw on probabilistic thinking. The "resulting" concept (judging decisions by outcomes rather than quality of reasoning) is particularly valuable for project retrospectives.

"The Pre-Mortem: A Simple Technique to Save Any Project from Failure" Gary Klein, Harvard Business Review The original article introducing the pre-mortem technique. Concise and worth reading as a complement to this chapter's treatment. Klein's research on how prospective hindsight improves risk identification is the conceptual foundation for the pre-mortem prompt templates. Available online at hbr.org.

"How Big Things Get Done: The Surprising Factors That Determine the Fate of Every Project" Bent Flyvbjerg and Dan Gardner Research-based analysis of why large projects fail — covering the bias patterns, planning fallacies, and organizational dynamics that cause consistent overruns. Flyvbjerg's concept of "reference class forecasting" (estimating durations based on comparable historical projects rather than bottom-up analysis) is directly relevant to the effort estimation section of this chapter.

Agile and Modern Project Methodologies

"Scrum: The Art of Doing Twice the Work in Half the Time" Jeff Sutherland The co-creator of Scrum provides an accessible introduction to the methodology and its principles. The AI-in-agile section of this chapter presupposes basic familiarity with Scrum ceremonies and artifacts. If you're new to agile, this is the starting point.

"Shape Up: Stop Running in Circles and Ship Work That Matters" Ryan Singer (Basecamp) Basecamp's project management methodology — an alternative to standard Scrum that uses "shaping" to reduce uncertainty before work begins and fixed "appetite" (time budget) rather than estimates. Particularly relevant for product teams. Available free online at basecamp.com/shapeup.

AI and Cognitive Load in Work

"Thinking, Fast and Slow" Daniel Kahneman While not about AI or project management, Kahneman's research on cognitive biases is essential context for understanding why AI-assisted planning helps. The planning fallacy, optimism bias, and anchoring effects all appear in project planning. Understanding these biases explains both why AI can help (by surfacing what we're not thinking about) and where it can hurt (anchoring teams to plausible but wrong estimates).

"The Extended Mind: The Power of Thinking Outside the Brain" Annie Murphy Paul Paul's exploration of how we think with tools, environments, and other people provides the theoretical framework for treating AI as a cognitive extension rather than a replacement. The concept of "cognitive offloading" is directly relevant to the blank-page problem that AI solves in project planning.

Tools and Software

Asana Guide to AI in Project Management Asana Asana's documentation on its AI features, including workflow automation, status summaries, and intelligent task recommendations. Useful for understanding what AI can and cannot do within structured project data. [asana.com/guide/help/ai]

Atlassian Intelligence Documentation Atlassian Documentation for Atlassian's AI features across Jira and Confluence. Covers Atlassian Intelligence capabilities including issue summarization, work breakdown assistance, and cross-product search. [support.atlassian.com/atlassian-intelligence]

"Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation" Jez Humble and David Farley For software engineering project managers, this is the definitive guide to the practices that make software delivery predictable. The dependency mapping and critical path concepts in this chapter apply directly to continuous delivery pipelines. Particularly relevant to Raj's migration case study.

Academic Research

"Planning Fallacy" Kahneman and Tversky, Journal of Personality and Social Psychology, 1979 The foundational research on why people systematically underestimate project duration and cost. Essential reading for anyone using AI effort estimates — understanding planning fallacy helps calibrate how much to distrust optimistic AI outputs.

"The Effects of Structured Brainstorming on Creative Output" Multiple authors, organizational behavior literature A body of research showing that structured brainstorming (using categories, prompts, and systematic coverage) consistently outperforms unstructured brainstorming on both quantity and diversity of output. This is the research base for why AI-prompted risk identification works — it provides the systematic structure that human brainstorming typically lacks.