Luck, Probability, and Risk: What Is Really Under Your Control in Projects
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.
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5:34
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5:34
Executives Who Don’t Understand Projects Can’t Deliver Results
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.
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3:31
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3:31
The Invisible Loop: Why Your Schedule Never Balances Out
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.
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5:24
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5:24
The Project Didn’t Fail — It Just Revealed the Truth
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.
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4:21
The Truth Behind Fake AI Projects: Understanding AI Washing
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.
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Since 2007, Ricardo Vargas publishes the 5 Minutes Podcast where he addresses in a quick and practical way the main topics on project, portfolio and risk management.
Brasil