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Hi everyone, here is Ricardo Vargas, and this is the 5 Minutes Podcast. Today, I want to talk about a growing and concerning trend in artificial intelligence projects. It's called AI washing. You may have heard about greenwashing when companies exaggerate their environmental efforts. AI washing is pretty much the same thing. It's when organizations and projects claim to use AI in a way that is misleading and not entirely true. Sometimes this happens to impress investors, to attract clients, or simply to appear innovative. But in reality, the so-called AI system might just be a simple automation or a rule-based algorithm. For example, I saw people using Microsoft Excel with Solver and telling everybody they were using AI. Look, this is a very nice feature of Excel, but it has nothing to do with what we are talking about when we are discussing AI. So, it's a bit like scope creep, but without you ever doing even the scope. Instead of expanding the project during execution, like the concept of scope creep, you just inflate the perceived scope before the project even begins. You announce to everyone that your project is AI-driven, but deep down, there is no actual intelligence behind it. It can create a lot of serious risks. First, you may lose credibility when stakeholders realize that expectations were inflated. Second, in regulated industries like finance and healthcare, this can even lead to legal or reputational consequences. And finally, even a technically successful project can be labeled a failure if it does not live up to the AI promise. And this is a very serious so how can you detect if your project is at risk of AI washing? The number one. The story is louder than the science. If your team spends more time preparing presentations and marketing than cleaning data or building models, that is a big red flag. Number two, the promises sound magical. If someone says AI will revolutionize this process, but can't explain how, that is another sign. The number three: there are no true AI experts involved. I'm not saying that every single person needs to be an AI expert, but when buzzwords replace real expertise, you are walking straight into the AI Washing. The number four data is treated as an afterthought. Look, without quality data, there is no AI. There is no AI. This is one of the biggest challenges. It's trying to make AI in Project Management when we do not have proper data. And number five, governance is ignored. Real AI projects have validation, they have metrics, and they have ethics. AI washing projects only have marketing slogans. And as project managers, we must resist the temptation to overpromise. And one of the terms we hear a lot is the concept of overpromising and under-delivering. So you promise a lot, and you do not deliver on your promises. And we have a venture lab, and it's a very challenging environment for us many times to understand in every single project we are doing what is real promise is and what is just over promise. And the line is not a clear magic line. There is a very big gray zone on that. And so we need to be very mindful that artificial intelligence is powerful, but it's not magic. Transparency, data, discipline, and humility will always be more valuable than hype. Many times, we need to recognize that we had an expectation that we will not realize, or that our project will not solve all the project problems. We need to be mindful of that and think every time we are doing an AI project. By the way, every time we are doing that project. But in AI today, because of the hype, this is happening at a frequency that is pretty much unbelievable. So think about that. I hope you enjoyed this episode, and see you next week with another 5 Minutes Podcast.
