Top 3 Mistakes When Implementing AI in Business
Pedro Daniel highlights failures in data, post-sales, and process selection when adopting artificial intelligence.
In an interview on Marketing - The Podcast, Pedro Daniel highlighted the most common mistakes companies make when using artificial intelligence. He broke down the risks of fragmented data, blind customer service automation, and targeting the wrong business areas.
Pedro Daniel, creator of Escola de IA, analyzed the most frequent mistakes companies make when introducing artificial intelligence into their operations during a conversation with Bruno Gimenes on Marketing - The Podcast. Although studies like McKinsey's indicate that using AI focused on customer experience can reduce service costs by up to 40% without sacrificing perceived quality, practical execution often stumbles over poor prioritization and weak technical infrastructure.
In the video above, Daniel details the impact of automated tools on operational workflows and discusses how to balance process efficiency with the human touch throughout the customer journey.
Applying AI to low-impact areas
The first mistake Daniel highlights is allocating technology to areas where AI yields little real gain or compromises delivery standards. This happens when managers overestimate the tool's capabilities without evaluating the actual strategic value of that activity to the business.
Daniel explains that increasing productivity without technical standards leads to wasted effort: "So, that's something I see a lot—first, people overestimate artificial intelligence, so they deploy it in areas that often won't deliver much impact, or else they lose quality in that department, you know? They decide to take a senior professional, hand them AI, and the person starts delivering much more volume, but it's sloppy volume, which is what I mentioned before."
Decentralized and disorganized data
The lack of a structured, unified database is the second major operational bottleneck. Even in large corporations, crucial customer and process information is frequently scattered across local files and disconnected tools.
For Daniel, AI models only deliver accuracy if fed consistent metrics: "I always say, what will set you apart in the long run and at scale is how much data and metrics you have. Today, I don't work on guesswork. 'Oh, I think'—no, I don't think anything. We're going to test and validate. Great, it worked, now we look at data and analyze numbers. Two plus two will always equal four, you know?" He emphasizes that data centralization must be planned from the company's inception.
Excessive automation and neglecting post-sales
The third mistake involves relying entirely on AI for customer service while ignoring customer retention. Many organizations pour all their resources into initial conversion and abandon contact immediately after purchase, sacrificing customer lifetime value (LTV).
Daniel points out that customer acquisition costs require structured support and follow-up: "Look, leads are too expensive today to just throw away like that. I see this issue a lot, where people sell at all costs and discard the lead. Build a journey with your lead, set up a CS team, which goes back to what you said about post-sales. Today, you can deliver a personalized experience very simply using artificial intelligence. So focus on that personalized delivery, keep the customer engaged, and build high LTV with them."