Chapter 33: Further Reading
An annotated bibliography of resources for deeper exploration of project planning, estimation, and Agile methodology adaptation in the AI era. Resources are organized by topic and include a brief description of what each offers.
Project Estimation Fundamentals
1. "Software Estimation: Demystifying the Black Art" by Steve McConnell
Description: The definitive book on software estimation, covering why estimates fail, how to estimate more accurately, and how to communicate uncertainty. While written before the AI era, its core frameworks -- cone of uncertainty, PERT estimation, historical calibration -- remain foundational. Every estimation technique discussed in Chapter 33 builds on principles from this book. Essential reading for anyone who wants to understand estimation as a discipline.
2. "The Mythical Man-Month" by Frederick P. Brooks Jr.
Description: The classic text on software project management, originally published in 1975 and updated in 1995. Brooks' observation that "adding manpower to a late software project makes it later" is directly relevant to the AI era: adding AI tools to a struggling project introduces complexity that can make things worse before they get better. The chapters on conceptual integrity and the surgical team model have particular resonance for AI-augmented teams.
3. "How Big Things Get Done" by Bent Flyvbjerg and Dan Gardner (2023)
Description: A research-backed exploration of why large projects fail and what makes the successful ones different. The authors' concept of "reference class forecasting" -- estimating by analogy to similar completed projects rather than bottom-up task estimation -- provides a useful complement to the task-level estimation techniques covered in this chapter. Particularly relevant for teams trying to estimate large AI-augmented projects where historical data is limited.
Agile and Lean Methodologies
4. "Scrum: The Art of Doing Twice the Work in Half the Time" by Jeff Sutherland
Description: Written by one of Scrum's co-creators, this book explains the principles behind Scrum in accessible terms. Understanding the "why" behind sprint planning, retrospectives, and velocity tracking makes it much easier to adapt these practices for AI-augmented teams as discussed in Section 33.5. The sections on estimation, velocity, and team capacity are directly applicable.
5. "Kanban: Successful Evolutionary Change for Your Technology Business" by David J. Anderson
Description: The foundational text on Kanban for software development. Anderson's focus on flow, work-in-progress limits, and lead time metrics provides the framework needed to adapt Kanban for AI-augmented workflows. Particularly relevant to the discussion in Section 33.5 about adjusting WIP limits and analyzing cycle times when AI accelerates certain workflow stages disproportionately.
6. "Agile Estimating and Planning" by Mike Cohn
Description: A practical guide to estimation and planning within Agile frameworks. Cohn's treatment of story points, ideal days, velocity-based planning, and release planning provides the baseline methodology that Chapter 33 adapts for AI augmentation. The sections on cone of uncertainty and progressive refinement of estimates are particularly relevant for teams learning to estimate with AI tools.
AI and Software Engineering
7. "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot" by Peng et al. (2024)
Description: One of the most rigorous empirical studies of AI coding tool productivity, finding approximately 55% faster task completion with GitHub Copilot. The study's methodology for measuring productivity -- controlled experiments with specific task types -- provides a model for teams building their own acceleration baselines. The paper also discusses important confounding factors and limitations that help calibrate expectations.
8. "Measuring Developer Productivity in the Age of AI" -- Various Industry Reports (2024-2025)
Description: Several technology companies and research firms have published reports on measuring AI-assisted developer productivity, including Google's DORA (DevOps Research and Assessment) reports and Microsoft Research studies. These reports provide industry-level data on acceleration factors, quality impacts, and adoption patterns. Look for the most recent editions, as this field evolves rapidly. Useful for benchmarking your team's AI acceleration factors against industry averages.
9. "AI-Assisted Software Development: A Practitioner's Guide" -- Various Authors
Description: A growing body of practitioner-focused books and guides has emerged covering the practical aspects of integrating AI tools into software development workflows. Look for titles that specifically address project management and team coordination, not just individual coding productivity. The best of these include real-world case studies similar to the ones presented in this chapter.
Risk Management and Quality Assurance
10. "Waltzing with Bears: Managing Risk on Software Projects" by Tom DeMarco and Timothy Lister
Description: A practical guide to risk management in software projects that goes beyond traditional risk matrices. DeMarco and Lister's approach to quantifying risk, managing uncertainty, and communicating risk to stakeholders provides the foundation for the AI-specific risk assessment discussed in Section 33.6. Their treatment of "risk discovery" is particularly valuable for identifying the novel risks that AI tools introduce.
11. "Accelerate: The Science of Lean Software and DevOps" by Nicole Forsgren, Jez Humble, and Gene Kim
Description: Based on years of research through the State of DevOps Reports, this book identifies the capabilities that drive software delivery performance. The four key metrics (lead time, deployment frequency, change failure rate, and mean time to recovery) provide a rigorous framework for measuring the actual impact of AI tools on delivery performance beyond simple velocity numbers. Essential for teams that want to measure AI's impact rigorously.
Stakeholder Communication and Product Management
12. "Inspired: How to Create Tech Products Customers Love" by Marty Cagan
Description: Cagan's framework for product discovery and delivery provides the context for understanding why AI accelerates delivery but not discovery. His insistence that the hardest part of building software is figuring out what to build (not building it) directly supports the Asymmetric Acceleration Principle. The sections on stakeholder management and roadmap communication are relevant to Section 33.9.
13. "The Art of Action: How Leaders Close the Gaps Between Plans, Actions, and Results" by Stephen Bungay
Description: A management book that draws on military strategy to address the gap between plans and execution. Bungay's framework of "directed opportunism" -- providing clear intent while allowing tactical flexibility -- maps well to AI-augmented project management, where implementation can adapt rapidly while strategic direction must remain stable. Useful for leaders managing the transition to AI-augmented development.
Tools and Practical Resources
14. Anthropic Claude Code Documentation -- Project and Task Management Features
URL: https://docs.anthropic.com/en/docs/claude-code Description: Claude Code's documentation includes features relevant to project planning, such as creating project-level configuration files (CLAUDE.md), managing context across development sessions, and using extended thinking for architectural planning. Understanding these features helps integrate AI tools more effectively into the planning and estimation workflow described in this chapter.
15. CHAOS Report by the Standish Group
URL: https://www.standishgroup.com Description: The Standish Group's ongoing research into software project success and failure rates provides the empirical foundation for why project planning matters. Their data showing that only about one-third of projects finish on time and on budget is a sobering context for any discussion of AI-accelerated development. The reports also track how Agile adoption has affected success rates, providing a precedent for understanding how AI adoption might follow a similar trajectory.
How to Use These Resources
- Start with estimation fundamentals (entries 1-3) if you are new to software estimation. The principles in these books are timeless and form the foundation for AI-adapted estimation.
- Read the Agile methodology books (entries 4-6) if your team uses Scrum or Kanban and you need to understand the baseline practices before adapting them.
- Consult the AI-specific research (entries 7-9) for empirical data to calibrate your expectations and benchmark your team's performance.
- Study risk management and quality (entries 10-11) if your organization has strict quality requirements or operates in regulated industries.
- Explore stakeholder communication resources (entries 12-13) if you are responsible for communicating AI-augmented timelines to non-technical leadership.
The intersection of AI tools and project management is evolving rapidly. Prioritize resources that provide frameworks and principles over those that prescribe specific practices, as the practices will need to adapt as AI tools continue to improve.