Milestones, Baselines, and the Power of December 31 in Projects

In this episode, Ricardo highlights the importance of milestones, baselines, and control points in project management, using December 31st as a powerful example of a milestone, both personally and organizationally. Just as individuals reflect on decisions and plan the future at the end of the year, projects and organizations use milestones to review budgets, compare goals, and consolidate results. Although the calendar is a human convention, milestones provide essential reference points for comparison and control. Without a clear baseline, it is impossible to assess real progress. Projects without milestones rely on perception, while projects with milestones rely on facts. Milestones are not bureaucracy; they are moments of reflection, decision-making, and adjustment that help prevent gradual and unnoticed project deviation. Listen to the podcast to learn more!

In this episode, Ricardo wraps up the discussion on the PMBOK Guide 8th Edition by highlighting the role of artificial intelligence in project management. PMI included AI in Appendix X3, presenting three adoption strategies: automation (making tasks faster), assistance (AI as a partner helping with scheduling and resources), and augmentation (expanding managers’ capabilities and decision-making). The appendix provides practical use cases for governance, risks, resources, scheduling, and other areas. Ricardo emphasizes that AI evolves rapidly, so some examples may soon become outdated, but project managers must understand and leverage AI to remain competitive. Recent research indicates that organizations are already saving significant money by utilizing AI. He encourages readers to study the appendix carefully and stay adaptable. Listen to the podcast to learn more!

In this episode, Ricardo explains that in the PMBOK® 8th Edition, you do not need to memorize all 40 processes. Many of them are very similar, especially in the planning phase, which alone contains 19 processes. He shows that processes like Plan Scope Management, Plan Schedule Management, Plan Financial Management, and Plan Risk Management follow the same logic: they define the “rules of the game” for each performance domain. If you understand one, you know the others. Ricardo advises candidates for CAPM or PMP to focus on understanding the logic and flow of the processes rather than memorizing them, which is less effective for real-world project management. Listen to the podcast to learn more!

In this episode, Ricardo discusses a key change in the PMBOK® Guide 8th edition: the relationship between stakeholders and communication. In previous editions, communication was a separate knowledge area, but now it is considered part of stakeholder management. This shift is significant because communication only exists when there are stakeholders with different needs. If a project had no stakeholders besides yourself, communication would be unnecessary. Therefore, communication is a tool to support stakeholder engagement. In the new PMBOK® structure, stakeholders remain a performance domain that includes planning, execution, and control activities. Ricardo encourages PMI members to download the PMBOK® Guide PDF and explore these updates to improve project value and delivery. Listen to the podcast to learn more!

In this episode, Ricardo discusses the new edition of PMBOK 8, which brings important changes more aligned with the real work of project managers. Based on nearly 48,000 data points and two rounds of global feedback, it has become more practical, clear, and value-oriented. The old 12 principles have been condensed into six more focused ones, while maintaining good project practices. The traditional five process groups return and now apply to predictive, agile, and hybrid projects. The old knowledge areas have evolved into seven performance domains: governance, scope, schedule, finance, stakeholders, resources, and risks. This edition also features 40 updated processes with integrated ITTOs and reinforces tailoring with practical examples, making the guide more applicable and balanced. Listen to the podcast to learn more!

In this episode, Ricardo discusses the role of luck and probability in project management. He explains that while luck can influence outcomes, it favors those who are prepared. Probability, he says, is not a prediction but a decision-making tool that helps manage uncertainty. Effective project managers turn randomness into results through preparation: identifying risks, creating contingency plans, defining triggers, and building buffers. Ricardo also warns against hindsight bias, which makes us underestimate luck after success. He recommends modeling uncertainty with scenarios, using simulations for high-risk decisions, protecting the critical path with buffers, and designing flexibility into projects. True management, he concludes, is not about eliminating luck but shaping how it affects outcomes—turning uncertainty into smarter choices and opportunities. Listen to the podcast to learn more!

In this episode, Ricardo explains why executives need to understand the logic of project management to make informed strategic decisions. Projects drive organizational changes, such as digital transformation, new products, entry into new markets, and mergers. Without understanding how projects add value and manage risk, leaders may fail to connect strategy to execution. Many focus only on "normal functioning," but the future depends on "business as change." By understanding the dynamics of projects, executives ask better questions, support teams effectively, and build a results-oriented culture. This knowledge helps them keep pace with the organization, prioritize efficiently, and see failures as learning opportunities. True leadership requires learning to think like a project, not like tools, but like governance, critical thinking, and value creation. Listen to the podcast to learn more!

In this episode, Ricardo discusses activity loops, which occur when tasks become predecessors and successors to each other, creating cycles that make schedule calculations difficult. Although schedules are designed for linear flows, engineering and innovation projects are often iterative, with constant revisions and feedback. Looping isn't a mistake, but it needs to be represented correctly. Ricardo suggests some ways to avoid this problem, such as creating successive versions (elaboration 1, 2, final), using intermediate milestones, or delayed start-start relationships. When interdependence is unavoidable, he recommends using the Design Structure Matrix (DSM), which maps circular relationships and helps plan blocks of iterative activities. The important thing is to choose the model that best represents the project's reality. Listen to the podcast to learn more!

In this episode, Ricardo explains that projects don’t really fail — they reveal the truth about an organization. Projects act as mirrors, exposing hidden cultural flaws like poor alignment, weak leadership, and political decisions. When pressure from deadlines and budgets increases, the organization’s true nature surfaces: silos, egos, and fear replacing collaboration. A troubled project is not a failure but an X-ray showing what is broken and who has the courage to fix it. Crises test maturity and trust, revealing whether teams can speak honestly or stay silent. The real mistake is ignoring these lessons and repeating errors. Ricardo explains that learning from failing projects leads to real growth and invites listeners to explore his new course on recovering troubled projects. Listen to the podcast to learn more!

In this episode, Ricardo discusses AI washing, a growing trend where organizations falsely claim to use artificial intelligence. Similar to greenwashing, AI washing occurs when companies exaggerate their AI capabilities to attract investors or appear innovative. In reality, many so-called AI systems are just basic automation or rule-based tools. This practice creates serious risks, including loss of credibility, legal issues, and project failure. Vargas highlights warning signs: flashy storytelling over science, unrealistic promises, lack of true AI experts, neglect of data quality, and poor governance. He explains that real AI projects require transparency, solid data, ethics, and humility—reminding project managers to avoid overpromising and to focus on genuine, data-driven value instead of hype. Listen to the podcast to learn more!