The latest PMI Pulse of the Profession 2024 Report shows that hybrid gains ground as the fit-for-purpose approach, reflecting comparative data from 2022 and 2023. While the agile methodology has seen a slight decline in usage from 27% to 24.6%, and Waterfall has also decreased from 47% to 43.9%, the hybrid model has notably risen from 26% to 31.5%.
An increasing number of organizations are encountering challenges in their agile transformations. Despite establishing agile delivery organizations with continuous agile teams and scaling frameworks like SAFe’s agile release trains, they confront obstacles in fully integrating these changes, realizing benefits, and capturing value. One critical issue is the insufficiency of merely having permanent agile teams and structures such as agile release trains. Another problem arises when there is an absence of backlogs to incorporate their initiatives, or in other words, a lack of appropriate teams to address their issues. In such cases, organizations often revert to more conventional Project Portfolio Management (PPM) methods to find solutions.
As this hybrid approach to project management becomes increasingly mainstream, organizations are seeking guidance on integrating the two distinct models. However, the relationship between a project or program manager and a product manager isn’t hierarchically direct or functional.
To elucidate how a temporary project or program organization can effectively collaborate with a permanent agile delivery organization, I employ a specific model that illustrates the interfaces and facilitates a discussion about the flow of information.
1 Point of contact program manager in ARTs: The point of contact for a program manager (PgM) is product or solution management (PdM) depending on the topology of the delivery organization and the size of the request. The program manager needs the delivery organization to develop/change specific functions needed for the success of the program.
2 Information flows PgM: from PgM to PdM requirements, functions, dates, deadlines, changes, accelerate, slow down. From PdM to PgM realization dates, impact, risks.
3 Which boundaries / preconditions? Priorities, capacity for small changes, for portfolio initiatives (swim lanes) set by portfolio board. Given the absence of a hierarchical relationship, it’s crucial for Product Managers (PdMs) or Product Owners (POs) to recognize the priority of this initiative. This understanding is necessary to appropriately factor it into the prioritization of their backlog, ensuring that this initiative is duly considered.
4 Point of contact PMO in ARTs: The PMO is an attendee of the PO/SM Sync to collect the needed information.
5 Information flows PMO: From PO/SM Sync to PMO progress (e.g. Burn-up chart), issues, risks (e.g. risk burn down chart).
6 Point of contact project manager in ARTs: The point of contact for a project manager (PjM) is product management or Product Owner. depending on the topology of the delivery organization and the size of the request. The project manager needs the delivery organization to develop/change specific functions or stories needed for the success of the project.
7 Information flows PjM from PjM to PdM or PO requirements, functions, dates, deadlines, changes, accelerate, slow down. From PdM or PO to PgM realization dates, impact, risks.
8 Point of contact for program sponsor in ARTs: The point of contact for a program sponsor is the Business owner of the ART or solution train. The business owner must be aware of the objectives of the initiative. The business owner can bring this forward during the next PI event. Ideally the business owner and the program sponsor are the same person.
9 Information flows Program Sponsor. From Program sponsor to Business Owner objectives/goals of the initiative.
10 Representative of ARTs in program steering committee. To aid in decision-making during steering committee meetings, it’s essential for the business owner of the Agile Release Trains (ARTs) to attend the program steering committee meetings. As mentioned, it would be advantageous if the program sponsor and the business owner were the same individual.
Next steps
This model is a starting point, it is absolutely not perfect. I’m seeking input to refine and enhance this model. What contact points do you see? Which information flows are important to make it work. Can the model be simplified?
These two books can be enjoyed independently, yet they share a significant connection through their central theme of principles and the fact that a PMO is part of the broader PPM environment too. It’s intriguing to explore the extent to which they also align with each other in terms of consistency.
PMO Principles and PMO Services Principles
In “PMO Principles and PMO Services Principles – How to Build a Principle-based PMO,” Robert Joslin presents principles as a toolkit for designing, implementing, and operating a PMO that is customized to the unique needs and circumstances of each organization. This book is part of a series, some of which are still in development.
The series will include the following upcoming volumes:
PMO Principles and PMO Services Principles
Organizational, Portfolio, Program, and Project Principles
From Single to Enterprise-wide PMOs (under development)
Developing and Applying Project, Program, Portfolio and PMO Competence Frameworks (under development)
PMO Management Body of Knowledge (under development).
The book unfolds across three primary sections. It begins with an in-depth exploration of the fundamental PMO principles, enriched with descriptions, insights, and illustrative examples. The subsequent part focuses on the PMO service principles, specifically designed to align with the PMO services lifecycle framework. Concluding the book is an overview of the PMO service domain principles, demonstrating their application within the service domain.
A principle is a natural law, fundamental truth, or proposition that serves as the foundation for a system of belief or behavior or a chain of reasoning.
I appreciate the concept of applying the Dreyfus model of skill acquisition to the realms of procedure- and principle-based services. The model suggests that the lower levels—novice, advanced beginner, and competent—benefit from procedural services, while the higher levels—competent, proficient, and expert—present an opportunity for principle-based services. This approach could also prove beneficial for maturity models, especially when transitioning from level 3 to 4, shifting from standard to adaptive processes.
The PMO principles
Principles are elucidated with definitions, tips, and examples provided. The seven PMO principles, along with their specific areas of focus, are detailed as follows:
Sponsorship. Senior management sponsorship and engagement.
Alignment. Governance alignment.
Transparency. Consistent, accurate, timely, and transparent information.
Challenge. Trusted challenge partner to drive value.
Adjustment. Adaptive services and capabilities.
Exemplar. Leads by example.
Improvement. Continuous improvement mindset
When choosing a set of principles, it’s crucial to perform an assurance check to verify that the selected principles deliver the anticipated impact. The book introduces a distinctive method for selecting the most appropriate principles for each PMO. For each principle, you assess whether its impact on the PMO objectives, the challenges faced by the PMO, and the systemic organizational issues is high, medium, or low. Principles that demonstrate the highest impact are then selected.
The significance of PMO principles and the extent to which they are integrated and maintained should be directly proportional to the PMO’s impact factor, acknowledging that other elements also contribute to the PMO’s overall impact.
PMO services principles
Part II elaborates on the PMO services principles, categorizing them according to each phase of the PMO services lifecycle. These phases include service strategy, service design, service pilot and implementation, service operation, and service transformation or retirement.
For each phase, you are presented with a collection of principles accompanied by their descriptions. To identify the most crucial principles, the same selection method used for the PMO principles is applied.
PMO service domain principles
The final section outlines all 22 PMO service domain principles, which are organized into four clusters: conceptualization, planning, execution, and safeguarding the future. For each service domain, you receive tips to master it.
In the appendix, you will find an overview of AIPMO’s PMO Strategic Lifecycle Framework, along with a list of acronyms, a glossary, and lists of the PMO, PMO service lifecycle, and PMO service domain principles.
Organizational, Portfolio, Program, and Project Principles
The book “Organizational, Portfolio, Program, and Project Principles – How to Build a Principle-based PPM Environment,” authored by Robert Joslin and Rälf Müller, offers a comprehensive overview of principles, illustrating their pivotal role within an organization. It delves into how principles govern the organization and lay the groundwork for portfolio, program, and project management, providing a holistic perspective on the subject.
The book begins by introducing principles, ethics, and values, exploring the causal relationships between principles, values, and behavior. It distinguishes between management and governance principles and discusses how principles can be applied throughout the organization.
Governance principles
The Organization for Economic Co-operation and Development (OECD) has outlined five key principles for effective governance: transparency, accountability, responsibility, fairness, and sustainability.
Shared principles across a portfolio, program, and project
Shared principles, which are applied across portfolio, program, and project management, come with an explanation, definitions, tips, and a case study for each. The principles discussed and their respective focuses include:
Sponsorship. The effectiveness of every initiative is linked to the degree of senior management interest and engagement.
Governance alignment. The portfolio, program, and project governance structures are aligned within and to organizational governance.
Strategic alignment. Ensuring the right initiatives are selected, and checked against the then and current strategy throughout their respective lifecycles.
Leadership. Demonstrate a balance of leadership and management to embed and uphold the PPM principles to maximize situational impact.
Insight. Reflective learning and insight.
Improvement. Continuous improvement mindset.
PMO alignment. Aligned portfolio, program, and project PMOs.
Sustainability. Taking decisions based on the three pillars of sustainability, that is, economic, environmental, and social.
Portfolio management principles
A range of principles applicable to portfolio management is thoroughly explained, complete with tips and examples on their application. These principles are designed to tackle the primary challenges encountered in portfolio management. Additional principles can be incorporated to meet the specific needs of a portfolio. The principles discussed and their respective focuses include:
Value orientation. Value creation within an organization is to exploit opportunities to create value.
Selection. Select the right projects for the right reasons.
Adjustment. Continuous adjustments to optimize cost/value, resources, work type, and schedule.
Kill early, kill fast. A clinical non-biased approach of a component deselection.
Program management principles
A variety of principles relevant to program management are comprehensively detailed, accompanied by practical tips and illustrative examples for their implementation. These principles are crafted to address the core challenges faced in program management. To cater to the unique requirements of a specific program, additional principles can be integrated. The principles discussed, along with their specific areas of focus, include:
Benefits orientation. Leading change to create sustainable benefits, as a direct and indirect result of the program.
Balance expectation/impact. Balancing the management of stakeholders and management forstakeholders to adjust expectations and impact.
Risk optimization. Develop and execute risk strategies to maximize the probability of delivering the expected benefits.
Capability. Design, build, and deliver a sustainable program capability.
Project management principles
A selection of principles pertinent to project management is elaborately presented, along with actionable tips and vivid examples for their application. These principles are designed to confront the fundamental challenges inherent in project management. To accommodate the distinct demands of a particular project, extra principles may be added. The principles discussed, each with its dedicated focus, include:
Feasibility. The continuous validation of the feasibility of project objectives and their potential adjustment.
Guidance. Guidance through management and leadership.
Be prepared. Planning and risk management as one.
The vital few (success factors). Determine what makes the difference in achieving the project goals.
Tailoring. Tailor the methodology and approach to increase the likelihood of project success.
Customer/client orientation. Providing value to your customer/client while balancing the interests of your organization and your customer/client.
International principle-based methodologies
In a dedicated chapter, we receive a comprehensive overview of the principles from various international principle-based methodologies, including PMBOK (PMI), IPMA ICB, PRINCE2 (Axelos), Agile principles, ISO principles, Half Double principles, and AIPMO PMO Principles.
Selecting principles
Considering the array of principles, it’s essential to determine which are the most crucial. Begin with the five OECD principles as a foundation, outline the entity’s objectives, major problems or challenges, and systemic issues. Compile a list of potential principles and evaluate them based on their effectiveness in supporting the objectives and mitigating the challenges and systemic issues faced by the entity. Finally, conduct tests on the selected principles to ensure their efficacy.
Decision-making
The final chapter provides insights on how to apply principles to minimize the risk of suboptimal decision-making. It addresses the following risks: bias, incomplete and/or ambiguous information, inexperience or lack of competence, self-interest, and ill-fitting or poorly designed governance structures.
Conclusion
I can highly recommend these books. The two books offer a thorough overview of principle-based PMO and PPM environments. The binding, though acceptable, has flaws affecting durability. This is likely a result of using Amazon. Adequate typography is marred by occasional inconsistencies hindering readability (e.g. the table titles are difficult to read). Effective use of color enhances visual appeal. The text is generally clear. Language usage is engaging, but occasional dull moments occur. The content is highly relevant, delivering actionable guidance that enhances practical utility and substantially enriches professional practice with innovative solutions. It also aids decision-making with clear, applicable advice and provides robust solutions for relevant problems. It presents a new perspective e.g. how to build a PMO based on principles, the usage of shared principles and the method to select the right principles, with relevant examples. Case studies vary in relevance and integration, with some offering deep insight while others lack relevance. The highly qualified authors engage with the community, demonstrating significant impact. While the book provides insight, it lacks depth and thorough coverage in certain areas, limiting its effectiveness, especially in personal growth advice. But as stated this book is part of a family of PMO related books.
Upon reviewing the chapter on international principle-based methodologies, it seems to me that agile methodologies are somewhat underrated. With over 100 different agile frameworks and approaches available, the initial explanation of the Agile Manifesto and its twelve underlying principles appears somewhat cursory. This is referred to as Agile 1, noting that there is a revised version, Agile 2. However, to my knowledge, Agile 2 is not widely recognized internationally. It’s curious why the chapter does not consider the frameworks from the Agile Business Consortium, which include AgilePfM (with 5 principles), AgilePgM (with 5 principles), and AgilePM (with 5 principles), or other well-established frameworks like SAFe or LeSS with their own principles.
On the flip side, it’s acknowledged that agile methodologies, much like prescriptive methods, are not a one-size-fits-all solution. A more hybrid approach, favoring a blend of frameworks and techniques tailored to the project’s specific needs, is often more advantageous. This adaptability underscores the benefits of adopting a principles-based approach, allowing for flexibility and customization in project management practices.
Consistency
Both books employ a consistent methodology. Each principle is succinctly defined, typically in one or two words, along with a focused explanation. This same method underlies the selection of the appropriate principles.
Bent Flyvbjerg and Dan Gardner show with the book How Big Things Get Done – The Surprising Factors Behind Every Successful Project, from Home Renovations to Space Exploration, what distinguishes the triumphs from the failures. Flyvbjer identifies the errors in judgement and decision-making that lead projects to fail, and the research-based principles that will make you succeed with your projects.
In nine chapters we get a good idea why projects fail or are successful. In the first chapter we see, based on extensive study of 16,000 projects, the iron law of project management – Over budget, over time, under benefits, over and over again: 47.90% on budget (or better), 8.50% on budget and on time (or better), 0.50% on budget and on time and on benefits (or better). Several case studies are given to show that “think fast, act slow” will not result in success. Why start projects so prematurely?
A reason is the rush to commit. Purposes and goals are not carefully considered. Alternatives are not explored. Difficulties and risks are not investigated. Solutions are not found. Instead, shallow analysis is followed by quick lock-in to a decision (commitment fallacy). Another reason is unchecked, optimism leading to unrealistic forecasts (planning fallacy, Hofstadter’s law, poorly defined goals, better options ignored, problems not spotted and dealt with, and no contingencies to counteract the inevitable surprises. Or strategic misrepresentation. A budget that was made because it could be accepted politically. If people knew the real cost from the start, nothing would ever be approved and use the “start digging in hole” strategy and make progress so no one would stop it (e.g. the Sydney Opera House). And when the project is in progress they keep going because they already spent so much (sunk cost fallacy or Concorde fallacy).
“Think fast, act slow” is the answer to the previous part but it is not enough. Why are you doing the project, what is the goal to achieve? Or with other words “think from right to left”.
If we know the goal, how are we going to achieve it. What’s the plan? A good plan is one that meticulously applies experimentation or experience. A great plan is one that rigorously applies both (e.g. the Guggenheim Bilbao). Pixar movie usually goes through the cycle from script to audience feedback eight times. This is an extremely detailed and rigorously tested proof of concept and the next, ninth step is the real development. This is an iterative process that corrects the “illusion of explanatory depth.” Whatever can be done in planning should be, and planning should be slow and rigorously iterative, based on experience. Compare Eric Ries’ MVP.
Every Olympic Games (since 1960) has gone over budget. The average overrun is 157%. “New” or “unique” is treated as a selling point, not something to avoid. This is a big mistake. It’s a main reason that projects underperform. Planners should prefer highly experienced technology (frozen experience). Olympics are forever planned and delivered by beginners (Eternal beginners’ syndrome). Besides the “frozen experience” to get projects right, the lived experience (unfrozen) of people is as important too. In both planning and delivery, there is no better asset for a big project than an experienced leader with an experienced team using more than only explicit knowledge (e.g. the Empire State Building project).
Once we frame the problem as one of time and money overruns, it may never occur to us to consider that the real source of the problem is not the overruns at all; it is underestimation. To create a successful project estimate, you must get the anchor right. To produce a reliable forecast, you need the outside view. Don’t assume your project is unique. You have to look at a project as part of a class of projects, as “one of those”. This is what the author called “reference-class forecasting (RCF)”. Reference-class forecasting is better on biases. It’s better on unknown unknowns. If you face a fat-tail distribution, the tail outcomes – the “black swans” – cover about 20% of the distribution you must do risk mitigation or “black swan management”.
You can put reference-class forecasting and risk management into your toolbox, along with experience, Pixar planning, and thinking from right to left as essential tools for thinking slow in planning before acting fast in delivery.
Some say, planning ruins your projects. Just get going! Trust your ingenuity! It’s a wonderful sentiment backed by superb stories like electric lady (recording studio Jimi Hendrix), and Sydney Opera House. But if you look at the data, we only remember the success stories, we forget the ones, and those are the majority, that failed.
To take the final, critical step, you need a capable, determined delivery team – a single, determined organism – to act fast and deliver on time. A team that is proud to work for the project, where psychological safety is the norm. Where from the suits to the hard hats, the spirit is the same. Where onsite management and workers worked together. E.g. the Hoover Dam or the Heathrow Terminal 5 (T5).
There are five project types that are not fat-tailed. That means they may come in somewhat late or over budget but it’s very unlikely that they will go disastrously wrong (black swan outcomes, 400 percent or worse over budget). These fortunate ones are solar power, wind power, fossil thermal power, electricity transmission, and roads. Most extreme fat-tailed project types are nuclear storage, Olympics, nuclear power, IT, hydroelectric dam, and airport.
In the final chapter the author discusses why these project types are exceptional? What makes them a safer bet than all the rest and why are wind and solar power the most reliable projects of all to be delivered successfully?
Modularity delivers faster, cheaper, and better, making it valuable for all project types and sizes. The core of modularity is repetition. Put down one Lego block. Snap on another. And another. Repeat, repeat, repeat. Click, click, click. Repetition enables experimentation. If something works, you keep it in the plan. If it doesn’t, you “fail fast” and adjust the plan. Repetition also generates experience, making your performance better (positive learning curve). When you can build modules and deliver them to the site, the building isn’t constructed; it’s assembled, like Lego. Modularity radically reduces risk. Modular projects are in much less danger of turning into fat-tailed disasters (e.g. the small modular reactors (SMRs) for nuclear plants).
Conclusion. This book offers a crucial guide for enhancing the success of mega projects, drawing on extensive research by Bent Flyvbjerg, a leading expert on mega projects from the University of Oxford. It addresses the high failure rates of projects worldwide, advocating for significant changes in project management approaches. Highlighting the importance of reference-class forecasting (coined by Bent Flyvbjerg), risk management, and a modular approach, the book integrates innovative strategies like Pixar planning and thinking from right to left. Through a clear, logical presentation and the use of many real-life case studies, it makes complex theories accessible and applicable. Despite its minimal use of graphs and tables, the book effectively communicates key concepts, such as fat-tail distribution in project overruns. It is an essential tool for anyone involved in mega project management. I can highly recommend this book.
The book Project Portfolio Management in Theory and Practice – Thirty Case Studies from around the World by Jamal Moustafaev briefly explains project portfolio management by looking at the three pillars of project portfolio management:
Project value. Projects selected must maximize the value for the company (Scoring matrix, e.g. strategic fit, resources required, technical feasibility, financial value, riskiness, joker project concept).
Portfolio balance. Projects selected must constitute a balanced portfolio (e.g. value vs. risk).
Strategic alignment. The final portfolio of projects must be strategically aligned with the company’s overall business strategy (top-down: product road map, strategic buckets model, bottom-up: individual departments generate initiatives, combined approach).
Case studies. The main part of the book is dedicated to 30 case studies. These case studies show how the three pillars of portfolio management – project value, portfolio balance and strategic alignment are integrated to maximize resource usage and overall portfolio value. The case studies are clustered around the following types of industry: pharmaceutical industry (3 cases), product development industry (7 cases), financial industry (4 cases), energy and logistics industry (5 cases), telecommunications industry (4 cases), Government and not-for-profit sector (4 cases), and professional services industry (3 cases).
The last part of the book concentrates on high level advice for implementing project portfolio management.
Conclusion. The offered portfolio management theory is very superficial. The 30 cases focus only on the three pillars – project value, portfolio balance and strategic alignment and are so anonymized that the 30 cases are very similar. I would have liked the cases to also show how the governance per case was set up, what is steered, how the process looks like, what’s the horizon of the portfolio plan. In the book I miss the steering on benefits or value. It seems that it is explicitly assumed that if the project is finished, the contribution to the strategic objectives will be realized, and that is questionable in many projects. I further miss a vision of the agility of the portfolio process itself. All the examples fall into the category of plan-based portfolio management, none move toward more discovery-based portfolio management. If you want to get a complete picture of theory and practice of project portfolio management, I can’t recommend this book.
To order Project Portfolio Management in Theory and Practice: managementboek, bol.
The AI Revolution in Project Management by Vijay Kanabar and Jason Wong provide prompts to assist you in the following project manager related tasks: stakeholder management, building and managing teams, choosing a development approach, planning for predictive project, adaptive projects, monitoring project work performance, risk management, and finalizing projects.
Every topic or chapter starts with a (fictional) case study, followed by an introduction and the usage of many well-crafted example prompts (ChatGPT) to support you, tips for how to improve these prompts to fit your needs, and elaborates on related ethical considerations and professional responsibility. At the end of each chapter, you get a technical guide with the practical implementation of AI (ChatGPT, Bard, Claude.ai). the last two chapters focusses on AI tools for project management and looking ahead.
Ethical considerations and professional responsibility
The authors use a list of ethical considerations and professional responsibility in AI and highlight these during discussing the different PM related topics: transparency, data privacy, bias mitigation, accountability, environmental considerations, regulatory oversight, human augmentation, hallucinations and data accuracy, and data ownership and training implications.
Stakeholder management
Projects succeed if you as a project leader successfully identify and engage stakeholders, constantly communicating with them and meeting their expectations. AI can help to identify or update stakeholder lists by reviewing email threads or by explaining the project and compare that with similar projects. AI can perform a stakeholder analysis, understanding their interests and needs and generate a power versus interest matrix. AI can analyze stakeholder interactions to determine communication preferences and channels and help to draft personalized memos, progress reports to and answer queries from stakeholders and perform stakeholder sentiment analysis and predict stakeholder behavior.
Building and managing teams
AI reshapes recruiting onboarding, providing a swift, fair, and personalized experience. AI can perform automated screening, communicate with candidates, schedule interviews, and provide feedback, fair hiring due to elimination of bias and automate skill evaluations. AI can tailor onboarding and training initiatives to match your individual employees’ unique needs, skills, and learning styles. AI can augment leadership, facilitating communication, and providing early warning of issues. AI can be used for setting and communicating vision and direction and motivating teams. AI can help in fostering collaboration and can support conflict resolution and decision-making.
Choosing a development approach
AI can help determine the approach to optimize the project management life cycle (predictive, adaptive, and hybrid). AI can help to create a questionnaire to decide which approach. It can provide more helpful insights when specific project management documents and artifacts are uploaded (be aware of confidentiality). You could see AI as the consultant.
Planning for predictive projects
AI can, in an incremental and iterative way, support during project initiation and planning. It can assist with a needs assessment, business case creation and can draft a project charter. It can help in defining the scope, requirements, work breakdown structure and formulate schedules, cost estimation and budgeting.
Adaptive projects
AI can act as a consultant if you want to run an adaptive (agile) project. It can assist with the articulation of a vision statement, the creation and prioritization of a product backlog. It can identify customer personas. It can break the product backlog into iterations, a release plan, showing the main features. It can give examples of user stories including acceptance criteria, a story map and walking skeleton. AI can build burnup or burndown charts and analyze them.
Monitoring project work performance
AI tools can process vast amounts of data, make predictions, generate reports and converse using natural human language. It can join meetings to take notes, transcribes the conversation, and summarizes key points, action items, and decisions. It can be used for task allocation, resource management, monitoring scope (creep) and schedules including EVA, controlling costs, and maintaining quality.
Risk management
AI can identify and analyze risks as well as plan responses and monitor progress. It can generate (and answer) questionnaires to gather expert opinions. It can construct a risk register. It can perform what-if scenarios in qualitative risk analysis, quantitative risk analysis, predictive modelling using data-driven forecasting, expected monetary value analysis, Monte Carlo analysis and decision tree analysis. AI can plan and develop risk response strategies, monitor risk responses and generate comprehensive risk reports and status summaries.
Finalizing projects
AI can help or act as a consultant during project verification, validation, creating test plans, release (deployment), and closure (building the final project report and presentation, extract key lessons learned).
AI tools
A separate chapter focusses on AI tools for project management. It offers factors needs to be considered when evaluating AI tools. The tools are clustered around several categories: project management systems (task allocation and tracking: Monday.com, Wrike, Asana, OnePlan, PMOtto), scheduling tools (Clockwise), communication and meeting tools (Slack GPT, Microsoft Teams Premium, Zoom AI companion), productivity and documentation tools (Microsoft 265 Copilot, Google Duet), collaboration and brainstorming tools (Miro).
Conclusion
The authors demonstrate in their book “The AI Revolution in Project Management” how generative AI tools, particularly ChatGPT, can significantly aid a project manager. By using the appropriate prompts – and the book provides numerous examples – one can greatly enhance their effectiveness and efficiency in daily tasks. This book is highly recommended for project managers.
Generative AI has the potential to unlock significant productivity benefits. This framework, created by the Central Digital and Data Office of UK Government, aims to help readers understand generative AI, to guide anyone building generative AI solutions, and, most importantly, to lay out what must be taken into account to use generative AI safely and responsibly.
It is based on a set of ten principles which should be borne in mind in all generative AI projects.
Ten principles
You know what generative AI is and what its limitations are.
You use generative AI lawfully, ethically and responsibly.
You know how to keep generative AI tools secure.
You have meaningful human control at the right stage.
You understand how to manage the full generative AI lifecycle.
You use the right tool for the job.
You are open and collaborative.
You work with commercial colleagues from the start.
You have the skills and expertise that you need to build and use generative AI.
You use these principles alongside your organization’s policies and have the right assurance in place.
Applications of generative AI in government could be used to: speed up delivery of services (retrieving information faster), reduce staff workload (drafts of routine email responses or computer code), perform complicated tasks (review and summarize information), improve accessibility of government information (improving the readability), perform specialist tasks more cost-effectively (summarizing documentation or translating).
Building generative AI solutions
In a next section you get some practical steps you’ll need to take in building generative AI solutions, including defining the goal (identification of use cases and use cases you must avoid), building the team (multi-disciplinary, right skills), creating the generative AI support structure (AI strategy and adoption plan, AI principles, AI governance board, communication strategy, AI sourcing and partnership strategy), buying generative AI and building the solution (core concepts), patterns (public generative AI applications and web services, embedded generative AI applications, public generative AI APIs, local development, cloud solutions), picking your tools (decisions on your development stack), getting reliable results, testing generative AI solutions, and data management. For each step you get some practical recommendations too.
Using generative AI safely and responsibly
This section outlines the steps you’ll need to ensure that you build generative AI solutions in a safe and responsible way, taking account of legal considerations (issues, human rights, legislation), ethics (transparency and explainability, accountability and responsibility, fairness, bias and discrimination, Information quality and misinformation, keeping a human-in-the-loop), sustainability and environmental considerations, data protection and privacy (accountability, lawfulness, purpose limitation, transparency and individual rights, fairness, data minimization, storage limitation, human oversight, accuracy), security (prompt injection threats, data leakage, hallucinations), and governance (AI governance board or have AI representation on a governance board and an ethics committee, AI/ML systems inventory, program governance). For each step you get some practical recommendations too.
Conclusion. Even though the GenAI Framework is intended for the UK government, I believe that many organizations can benefit from this framework in developing a strategy on how to deal with generative AI applications.
Mastering Project Uncertainty – A Systems Thinking Approach by Paul Cuypers offers a theoretical framework and practical models, tools, techniques, and guidelines to systematically minimize uncertainty, thereby increasing the chances of project success.
The book is divided in two parts. The first part offers the theoretical foundation and the second part the strategies. It starts with explanations of the relationship between the standing organization and temporary organization and why the author uses systems thinking approach to model project uncertainty.
Project uncertainty: The uncertainty matrix as a generic model to define uncertainty as a lack of information awareness or availability and a system thinking model of a project consisting of the main elements assignment, context, decisions, method, and scenarios to analyze the known-known, known-unknown, unknown-known and unknown-unknowns (the uncertainty matrix quadrants) aspects of a project. Each element will be broken down into components:
Assignment uncertainty: Stakeholders, benefits, deliverables, activities, and resources. Possible techniques to use are project definition matrix, stakeholder mapping, player cards, benefits realization matrix, fit criterion, and budgeting.
Context uncertainty: prerequisites, constraints, inter-dependencies, threats, and opportunities. Possible techniques to use are strategic planning, SWOT, context analysis, integrated resource planning, and business financial planning.
Decisions uncertainty: qualifiers, alternatives, rationale, effects, confirmation. Possible techniques to use are rolling wave planning, decision trees and networks, stage gate transition, responsibility assignment matrix, and pre-mortem.
Method uncertainty: approach, techniques, tools, communication, and coordination. Possible techniques to use are logical project control model, student-mentor relationships, one-breath challenge, switching thinking patterns, left- and right-hand questions, ground rules, and tribal spirit.
Scenarios uncertainty: plan, risk, problem, crisis, and measures. Possible techniques to use are risk checklist, risk response routine, Ishikawa diagram, Monte Carlo simulations, and problem-solving techniques.
For each uncertainty all related components are examined, examples of implicit and explicit uncertainty are given, techniques are explained, artifact are mentioned and ends with a set of principles, concepts and questions.
The second part of the book focusses on the reduction of uncertainty (or with other words expanding the known-known quadrant in the uncertainty matrix) using the following strategies:
Raising information awareness: step 1: create a project meta-data model, step 2: create a working log, Step 3: follow the SLACK cycle (scan, log, analyze, change, keep), and step 4: linking and filtering.
Increasing information availability: Possible techniques to use are Ikigai-coherence, the four-phase model – strategic direction (effectiveness, efficiency, flexibility, creativity), core capabilities – project nature (R&D, innovation, transformation, operational, development, support, project), project master planning – relations, elemental value grid – value (products, services, opinions, capabilities), technology-capability matrix – technological challenge, diamond of innovation – novelty (novelty, technology, complexity, pace), Cynefin framework – domain nature (obvious, complicated, complex, chaotic, disorder), the project footprint – degree of change (organization, value chain, production systems, processes, ICT, regulatory bodies, suppliers, customers, fifth parties), VUCA grid – stakeholder attitude, budgeting – financial perspectives (business, planning, cash flow, realization), stage gate transition – conditions, strategic governance decisions – operating limits (continue as planned, re-plan, put on hold, terminate).
Improving the effective use of information: Possible techniques to use are OODA loop (observations, orient, decisions/hypothesis, actions/tests), reviews (peer review, structured review)
Maximizing information efficiency: Possible techniques to use are functional organization design, RACI matrix, AORA matrix (author, owner, reviewer, approver), information system design, RBAC matrix (role-based access control), and PMD matrix (project meta-data document).
Conclusion. This book shows that problems creating project failure cannot only be found and solved by risk management only. Risk is but one of many sources of problems. Mastering uncertainty by using the uncertainty matrix and a system thinking model of a project consisting of the main elements assignment, context, decisions, method, and scenarios will help to analyze the known-known, known-unknown, unknown-known and unknown-unknowns aspects of a project and helps to increase the successful delivery of the project. This book offers a fresh perspective on project management, and I highly recommend it.
To order Mastering Project Uncertainty: bol., Amazon
The book Projects: Methods: Outcomes – The new PMO Model for True Project and Change Success by Peter Talor offers the author’s view of building a global PMO he is running. His Global PMO is build around three teams:
Projects Team focused on onboarding, education, certification, support, community, and project manager career.
Methods Team focused on the “how”, the framework for common project delivery whilst offering flexibility on approach, depending on project scale, partnership, and service offering.
Outcomes Team focused on being the direct project management and service management interaction and support. The bridge between the projects and method teams and the users of the output from those teams. The bridge between the projects and methods teams and the users of the output from those teams. A proactive, two-way communications channel to share the PMO strategy and to listen to, and react to, local tactical needs.
The author states that this book is likely his last on PMOs and that we shouldn’t hold it against him for frequently referring to his other books. With its structure of nine chapters and many short paragraphs containing references or text copied from his own books, it has become quite a superficial book in which the common thread is hard to discern. However, I must make an exception for some paragraphs written by others, which do provide more depth.
I would also expect a portfolio management function. There is only mention of a portfolio dashboard and a few KPIs. You can have the best project managers, the best method, the best support but if your organization is running too many projects in parallel the result is dramatic! You can finish fantastic projects but if they aren’t delivering value to your strategic objectives, it is of no use.
I wonder if this superficial book really adds value to the PMO community.
To order Projects: Methods: Outcomes: bol., Amazon
In Unlocking Business Agility with Evidence-Based Management – Satisfy customers and improve organizational effectiveness, authors Patricia Kong, Todd Miller, Kurt Bittner, and Ryan Ripley use the framework Evidence-Based Management (EBM). EBM is an empirical approach that helps organizations use experimentation and rapid feedback to progress towards goals.
According to the authors, the purpose of this book is to help organizations find their true purpose, improve their ability to reach their goals, and build a culture of trust and transparency that allows them to learn from their experiences.
EBM
The book starts with a brief introduction to EBM. EBM talks about three levels of goals: strategic, intermediate, and immediate tactical goals. Organizations must run experiments that involve forming hypotheses intended to advance them toward their current intermediate goal and use the evidence they obtain to evaluate their goals and determine their next steps to advance toward these goals. Along with goals and the experiment loop, EBM introduces four key value areas (current value, unrealized value, time-to-market, ability to innovate) that organizations use to consider what value they are delivering and could pursue, as well as their ability to do so.
Purpose
The first chapter shows how organizations must find purpose by looking through the eyes of their customers, why strategic goals should be focused on improving customer outcomes, and how measurement provides feedback that organizations need to adapt their goals and strategies. Understanding and bridging the customer’s satisfaction gap – the difference between the customer’s current experience and their desired experience – is key. The cases as provided in all chapters help to understand.
Value
The second chapter shows that having strategic goals isn’t enough. A company that does not measure the value it delivers to its customers simply flies blind, hoping that it guesses and preconceptions lead to business success. You need an experiment loop (hypothesis, experiment & measure, inspect and adapt) to move from your current state towards your next immediate tactical goal while aligning with the strategic goal. Goals can change because of outside events, and you may need to reconsider and revise your tactics to reach your goals.
Customer feedback
In the following chapter the focus is on improving time to market by getting better information faster about what customers need. Faster delivery means more frequent and relevant customer feedback. Besides speed, the effectiveness of an organization to deliver new capabilities that might better meet customer needs, is important too (ability to innovate).
Expectations
Next managing and overcoming expectations are in the spotlights. It is about teams or people being unable to speak to their reality without fear of retribution or negative judgement. Some common expectations that you run into all the time, but you must let go of, are:
Maximizing output maximizes value.
Maximizing efficiency maximizes value.
Internal stakeholders know what customers want.
The ‘business’ is the customer.
Going fast is all that matters.
Adding more people saves a project.
Predictability is a paramount goal.
Sunk cost matters.
Noise
It is not enough to identify measures and goals once and move on. You must constantly evaluate the usefulness of both your goals and what you are measuring. Your goals, what and how you measure will change and evolve as your products and customers change and evolve. You must look for the right signals and damp the noise. Noise is data that is irrelevant to your decision. Some areas of focus that add to the noise rather than dampen it, are utilization, capacity, velocity, productivity, misguided quality metrics, budget, and time. Bias is another source that creates noise. E.g. confirmation bias, anchoring bias, availability bias, overconfidence bias, groupthink, and sunk cost fallacy.
Apply EBM
The final chapters show how to apply EBM at product (one product), portfolio (multiple products), and organizational Level. Replace false certainty with experimentation and use strategic goal mapping to form experiments. Strategic goal mapping is a technique that helps organizations connect a strategic goal with intermediate goals, personas, desired outcome, immediate tactical goals to discover experiments.
Organizations could narrow down their options by considering the smallest possible experiments they could run to determine if an initiative is worth pursuing. For an organization, a hypothesis is a bet on value. To manage a basis quarterly cycle of portfolio-level experiments: define & refine goals, examine & abandon excess WIP, propose experiments, evaluate proposals, run experiments, evaluate progress towards goals – define & refine goals – …
If an organization wants to change it needs to consider if it wants to truly morph into a version of itself that is more likely to sustain in the future. It must understand where they are today and give their people a “why” and empower their teams by inverting the organization.
In summary, this book is excellent for gaining insights into business agility. By examining various examples and understanding the lessons derived from applying the Evidence-Based Management framework, it becomes a crucial read.
To order Unlocking Business Agility with Evidence-Based Management: managementboek, bol.
P5.express uses a cyclical system to make the portfolio management activities more straightforward and regular. There are biannual, monthly, and daily cycles, each focusing on one aspect of portfolio management activities.
In addition to the process, there are 5 portfolio management documents (portfolio description, value generation matrix, global follow-up register, global health register, and business cases) and two roles (portfolio board and portfolio manager).
Biannual Cycle
X1 — Evaluate the generated strategic value
X2 — Optimize and balance the value generation strategy
X3 — Conduct a focused communication
Monthly Cycle
Y1 — Evaluate portfolio stakeholder satisfaction
Y2 — Evaluate the ongoing programs and projects
Y3 — Plan improvements
Y4 — Conduct a focused communication
Daily Cycle
Z1 — Manage follow-up items
Z2 — Start, stop, or pause programs and projects
Z3 — Balance resources
Processes
I would make it more explicit that an initiative in the portfolio contributes to the strategic objectives and that’s more than adding projects with a strategic value (benefit ÷ investment).
Unclear for me is what is meant by “Each domain should have a relative target, and the sum of the strategic values from all programs and projects in a balancing horizon should more or less match that target. By default, the balancing horizon is the upcoming cycle plus three previous cycles.” What if a finished project didn’t deliver the projected benefit?
I don’t understand why the satisfaction surveys to the portfolio board members the program and project managers must be anonymous. This makes follow-up discussions much more difficult.
On several places an exceptional Biannual Cycle is mentioned. A process to facilitate a periodic escalation possibility would make sense.
I don’t believe that balance resources daily make sense. This will facilitate multi-tasking, firefighting, suggests dynamic resourcing, and will slow down portfolio delivery.
Management products
Looking at the five artifacts the guide is in my opinion too superficial. The value generation matrix sounds promising, but the guide explains that it lists programs and standalone projects on one dimension and their information on the other. Nothing more. I would expect at least some information regarding prioritization, value definition, contribution to strategic objectives, et cetera. In the process paragraphs you can find some attributes.
The global follow-up register is a list of risks, issues, change requests, improvement plans, and lessons learned that impact multiple programs or projects.
The global health register stores the results of the stakeholder satisfaction evaluations. But what are we asking? Status project, delivered value, contribution the strategic objectives, et cetera?
Comparison P5.express with Portfolio management agility model
If I compare the guide with the model in the book Agile Portfolio Management – The bridge to strategic agility, I see this guide as an example of plan-based portfolio management. (Bi)annual portfolio horizon, resource allocation, output-driven (%-complete, time, cost forecast), fixed strategy, validation mostly after finishing initiatives, hierarchical decision-making structure, fixed execution, some focus on growth, and annual evaluation.
Henny Portman's Blog 