Project Management Books

Chapter 1: Introduction – Sets out why the AIPGF exists: confidence in AI on the ground is built by practical governance, not by policy alone. It introduces the Framework’s purpose, its intended readers and how to use the book.  It positions the AIPGF alongside ISO/IEC 42001 and the wider standards and regulatory landscape.

Chapter 2: AI in Project Management – Surveys what AI can and cannot do for project delivery: the efficiency gains, the tool landscape from basic AI assistants to agentic systems, and the dilemmas AI use creates. It closes by looking into the future of AI in project management, including AGI and ASI, and makes the case for governance that holds as the technology evolves.

Chapter 3: Overview of the AIPGF – Presents the Framework’s architecture: the six Elements spanning mindset and method, and the three Life Cycle Stages of Foundation, Activation and Evaluation. It maps the AIPGF to PRINCE2Ò, PMBOKÒ and iterative delivery including Scrum, and closes with a one-page quick reference (Figure 12).

Chapter 4: Key AIPGF Roles and Responsibilities – Defines the human roles that govern AI use: the Core Roles required on every AI-assisted project, the conditional roles engaged as size, complexity and risk demand, and the Additional Roles providing strategic and specialised oversight.

Chapter 5: AIPGF Principles – Explains why the foundational Principles anchor the Framework where rules would date, and unpacks the three: Human-Centricity, Transparency and Adaptability. Each is developed with its rationale and its practical implications for AI-assisted projects.

Chapter 6: AIPGF Core Values – Translates the Principles into five Core Values that shape the mindset of AI-assisted project work: Accountability, Sensibility, Collaboration, Curiosity and Continuous Improvement. Each value is explored with its meaning in an AI context and the attitudes it asks of project professionals.

Chapter 7: AIPGF Core Behaviours – Makes the Core Values observable by suggesting three Core Behaviours for each, fifteen in all. These are offered as a flexible reference for embedding the values in daily project work rather than a rigid checklist.

Chapter 8: AIPGF Stage 1: Foundation – Unpacks the first Life Cycle Stage, which makes AI assistance a deliberate decision. It covers the Foundation Aim, Activities and Deliverables, including the AI Assistance Plan, Data Readiness Assessment and AI Assistance Risk Register.  It resolves where Foundation begins when a project is fronted with a business case.

Chapter 9: AIPGF Stage 2: Activation – Unpacks the second Stage, which turns the approved AI Assistance Plan into governed day-to-day practice throughout delivery. It covers monitoring of tools, data boundaries, risks and issues, the living updates to Foundation artefacts, and the AI Usage Report.

Chapter 10: AIPGF Stage 3: Evaluation – Unpacks the third Stage, which assesses whether AI delivered the intended value, was used within agreed boundaries and the quality of the human-AI collaboration effort. Evaluation recurs during delivery as well as at close, and its findings feed future stages, future projects and the organisation’s wider AI governance.

Chapter 11: Embracing the Spirit of the Framework – Concerns how the Framework should be applied: by implementing its intent with judgement and proportionality rather than complying with it to the letter. It shows what spirit-led adoption looks like in practice.

Chapter 12: Assessing Capability Maturity – Introduces the AIPG Capability Maturity Model (AIPG-CMM), tracing the maturity model tradition from the Software CMM to today’s AI maturity models. It sets out the AIPG-CMM’s Pillars, maturity levels and provides continuous improvement guidance for organisation-wide adoption of the Framework in their project environments.

Chapter 13: Tailoring the AIPGF to Different Contexts – Demonstrates the Framework’s adaptability through three case studies spanning different project sizes, sectors and AI maturity levels, showing how the Elements, Stages and roles scale to fit the context without losing their intent.

Chapter 14: Overcoming AI Adoption Challenges in Projects – Addresses the practical obstacles to ethical, efficient and effective human-AI collaboration in projects, from resistance and skills gaps to tool integration, capability drift, data governance and maintaining accountability, each with proportionate suggestions.

Appendix A: Positioning AIPGF alongside AI Standards and Regulation – Positions the Framework alongside ISO/IEC 42001, the NIST AI Risk Management Framework, the EU AI Act and the OECD AI Principles, and compares it with the Project Management Institute’s 2026 AI standard, explaining how the AIPGF activates these instruments at project and programme level.

Appendix B: AIPGF Certification Pathway – Describes the APMG-accredited certification scheme built on this book: the Foundation and Practitioner levels, what each examines and how to prepare.

Appendix C: Project Manager Core Skills in an AI World – Considers the enduring human skills project managers need as AI assistance grows, and how the profession’s value shifts as administration is increasingly delegated to technology.

Appendix D: References and Further Reading – Lists the external sources cited in the book, organised by chapter, with verified references for the standards, regulations, research and maturity models discussed.