Project Challenges

In this podcast, Ricardo explores the importance of identifying the real problem before implementing a project solution. Using the example of a slow elevator, he demonstrates how installing mirrors can reduce complaints without expensive technical improvements. He introduces the Double Diamond model from design thinking, emphasizing the need to understand and define problems before developing solutions.

How much effort should a project invest in planning? Too little causes rework, while too much wastes resources. This paper develops a quantitative model to identify the optimal balance, incorporating diminishing returns to derive the optimal planning effort and its limits. Two illustrative cases, a software development project and an electrical substation, use deterministic calculations, sensitivity analysis and Monte Carlo simulation. Results show that optimal planning represents 7% to 10% of baseline effort, reducing labour costs by 7% to 11%, while excessive planning can destroy value.

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In this episode, Ricardo challenges the belief that project outcomes depend mostly on luck. While predicting the future is difficult, controlling a project means recognizing early signals and making timely decisions. Based on his experience, Ricardo believes that 60 to 70 percent of project events are within reach of a well-structured team, while only around 30 percent depend on chance.

In this episode, Ricardo explores Albert Hirschman’s Hiding Hand Principle, which suggests that we often underestimate both the difficulties of a project and our ability to develop creative solutions when problems arise. In contrast, Bent Flyvbjerg’s research identified the Malevolent Hiding Hand, where costs are underestimated, and benefits overestimated, without creativity being enough to rescue the project.

In this episode, Ricardo discusses the common rush to start acting before fully understanding a project’s problem. Action creates a sense of progress, while thinking may seem like wasted time. However, teams can work intensely and still move quickly in the wrong direction. Ricardo emphasizes that speed should not be confused with impulsiveness.

In this episode, Ricardo compares a project to a disorganized email inbox, full of messages, decisions, and pending tasks without proper handling. He explains that many projects don't face difficulties due to a lack of resources or schedule flaws, but because of the accumulation of actions, risks, requests, and decisions without follow-up.

In this episode, Ricardo presents Brooks' Law, created over 50 years ago and still very relevant. The law states that adding people to a software project that is behind schedule tends to delay it even further. This is because new members need to be trained and mentored by more experienced members, reducing team productivity.

In this episode, Ricardo questions the effectiveness of traditional project planning tools, based on static plans. Although fundamental for decades, these plans quickly become obsolete in dynamic environments. He highlights that Artificial Intelligence transforms this scenario by allowing continuous forecasting and real-time adjustments, replacing fixed estimates with dynamic, data-driven analyses.

In this episode, Ricardo discusses anxiety in project management, a subtle yet pervasive risk that undermines performance. Constant urgency creates pressure-driven cultures in which clarity fades, and teams react rather than think. Under anxiety, decision quality declines: people choose speed over sound judgment, avoid difficult conversations, hesitate to escalate issues, and mistake activity for real progress.

In this episode, Ricardo discusses the importance of maintaining rhythm, not hysteria, for projects to be sustainable. He explains that many organizations confuse productivity with a chaotic environment full of emergencies, constant meetings, and changing priorities. This scenario only creates the sensation of movement but doesn't guarantee real progress.

In this episode, Ricardo explains that many projects fail not because of technical issues, but because the global context changes during execution. Elections, wars, sanctions, and trade tensions can shift priorities, block suppliers, and unexpectedly increase costs. Geopolitics goes beyond armed conflicts; it includes global supply chains, interest rates, exchange rates, and environmental regulations.

In this episode, Ricardo explains the difference between Generative AI, AI Agents, and Agentic AI—topics that are widely discussed but often misunderstood. He draws on a clear explanation by Filipa Peleja, presented during the O’Reilly Super Stream on Generative AI. Generative AI, based on large language models, responds to prompts and produces text, ideas, and analysis, but it has no initiative, goals, or independent decision-making.

In the first episode of 2026, Ricardo warns about the biggest mistake that ruins projects early in the year: saying yes to everything. January brings optimism, pressure for fast results, and a belief that everything is possible, leading to overloaded portfolios and teams working far beyond capacity. Projects are planned under unrealistic assumptions, confusing hope with real capacity.