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Hello, everyone. Here is Ricardo Vargas, and this is the 5 Minutes podcast. And today, I want to talk about three very practical uses of AI agents in projects. And I'm not talking about ChatGPT, Copilot, or tools that simply answer questions. I'm talking about agents that actually perform the work. There is an important difference between asking an AI for something and having an AI agent continuously working in the background. The first waits for instructions, and the second observes, analyzes, takes actions, and produces results. And that is starting to fundamentally change how projects are managed. The first example I want to share with all of you is risk agents. Imagine someone sending an e-mail with the subject line Risk, Delay, Supplier Issue, or any other warning signal. Normally, someone, a human being, has to read the e-mail, interpret the content, decide whether it's a true risk or not, register it in the appropriate system, and communicate it to stakeholders. But an AI agent can do all of that automatically. It can read the messages, identify the nature of the issue, classify its severity, update the risk register, link it to similar events from previous projects, and even suggest response actions. More than simply recording the risk, it helps structure the response to the risk. The second example I want to share with you is a status and reporting agent. Think about how much project information is scattered across meetings, emails, team channels, Slack conversations, shared documents, and project management tools. A large portion of a project manager's time is spent gathering all this information just to understand what is actually happening. An agent can monitor all these sources continuously. It identifies completed deliverables, detects delays, updates performance indicators, prepares dashboards, and generates executive reports automatically. So instead of spending hours producing status reports, the project manager spends time interpreting what the report is saying. This is what matters. That may sound like a small difference, but it completely changes the nature of the job. The third example I want to share with you is one I find most interesting: the planning and forecasting agent. Traditionally, a schedule is a snapshot of what we believe will happen. But an agent can transform that schedule into something alive. It monitors actual project progress, compares performance against historical data, identifies the late trends, runs simulations, and makes recommendations for adjustments before problems officially appear in the metrics. In other words, it's not simply recording the past. It's actively trying to anticipate the future. And perhaps that is where the most significant transformation lies. When people ask about artificial intelligence in projects, they usually think about automation. But I believe the most important change is something else. For decades, we have become accustomed to spending an enormous amount of time collecting information, consolidating data, updating registers, preparing presentations, and generating reports. We accepted these activities as a natural part of project management. But perhaps they were never project management in the first place. Perhaps they were simply the administrative work required to make project management possible. And if an agent takes over those activities, an interesting question remains. How much of our daily tasks will actually be spent making decisions? Because perhaps the biggest impact of AI agents is not that they automate work. Perhaps it's that they reveal how much of our work was never really management at all. And that is the question worth thinking about. I hope you enjoyed this episode, and see you next week with another 5 Minutes podcast.
