If you’ve ever tried building something on shaky ground, you know how it ends. It’s the same with AI and machine learning (ML). No matter how smart or shiny your models are, if your data isn’t up to par, things start falling apart fast.
In 2024, McKinsey reported that 65% of companies now use generative AI, which sounds impressive. But here’s the catch—most of these tools still struggle to deliver real value because of one key issue: bad data. You can bolt AI features onto your systems, but if the data driving them is outdated, biased, or incomplete, don’t expect miracles.
Take Zillow, for example. Their AI-driven home-buying program was ambitious but folded in 2021. Why? The algorithms were trained on poor data, which led to flawed price predictions and, eventually, heavy financial losses.
Good data isn’t just about numbers. It’s about having a clean, ethical, and comprehensive dataset that mirrors real-world situations. And this doesn’t happen by accident. It takes proper collection methods, ongoing quality checks, and clear governance.
Think of it like designing a new car. You need to focus on the basics before showing off flashy features. The same goes for AI—engine performance = your data, and it better be smooth. Without good mileage (or in this case, good model output), the project stalls.
Realistically, most IT teams know this struggle well. You deploy a tool that’s supposed to be helpful, but it backfires because employees find workarounds, or the system pulls from inaccurate data. That’s why governance and explainability matter—especially as Agentic AI becomes more common in business environments.
So if you’re considering integrating AI or ML, start by asking: Is my data ready? Because if it’s not, the rest won’t matter.