The best strategy is clarity and simplicity. Before writing a line of TensorFlow or PyTorch, step back and ask: “What problem are we actually trying to solve, and is AI the best way to solve it?” Sometimes a straightforward algorithm or even a spreadsheet model is enough. ML guru Valdarrama advises teams to start with simple heuristics or rules before leaping into AI. “You’ll learn much more about the problem you need to solve,” he says, and you’ll establish a baseline for future ML solutions.
Garbage in, garbage out
Even a well-chosen AI problem will falter if it’s fed the wrong data. Enterprise teams often underestimate the critical-but-unexciting task of data preparation: curating the right data sets, cleaning and labeling them, and ensuring they actually represent the problem space. It’s no surprise that according to Gartner research, nearly 85% of AI projects fail due to poor data quality or lack of relevant data. If your training data is garbage (biased, incomplete, outdated), your model’s outputs will be garbage as well—no matter how advanced your algorithms.
Data-related issues are cited as a top cause of failure for AI initiatives. Enterprises frequently discover their data is siloed across departments, rife with errors, or simply not relevant to the problem at hand. A model trained on idealized or irrelevant data sets will crumble against real-world input. Successful AI/ML efforts, by contrast, treat data as a first-class citizen. That means investing in data engineering pipelines, data governance, and domain expertise before spending money on fancy algorithms. As one observer puts it, data engineering is the “unsung hero” of AI. Without clean, well-curated data, “even the most advanced AI algorithms are rendered powerless.”
