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5 Brutal Truths About GenAI vs ML (Why Most Data Scientists Are Using the Wrong Tool)

If your models aren’t working, it might not be the algorithm—it’s the way you’re using it.

Author

Glenda Carnate
April 09, 2026

Read time: 2.5 minutes

Let’s face it, the majority of data science failures come from using the wrong tools, not from failing to deliver effective ML models.

Take a team that replaces a perfectly good ML model based on the fact that they think the new GenAI model is “better." The new model generates nice-looking results, but it begins to reduce accuracy, inconsistently predicts results, and loses trust in the model.

Eventually, they will discover that their failure was caused by abandoning a correct tool to solve the correct problem, rather than by the inaccuracy of the original ML model.

Proper Use of GenAI & ML:

1. No target = no model.
• Use AI" is not a strategy
• How do you identify the target variable?
• How do you define evaluation metric(AUC, RMSE, lift)?
 
2. GenAi is not for tabular predictions.
• LLMs are not designed to make structured predictions for structured data.
• Use ML for tabular data, use GenAi for text processing.
 
3. Feature Engineering is still important.
• Skipping feature engineering results in weak models.
• Use GenAi for the following: extraction of entities, classification of text, and generation of features.
 
4. Synthetic labels must be validated.
• Auto-labeling results in unintentional biases, as unseen biases are introduced.
• To validate synthetic labels, sample and validate them, measure precision and recall.
 
5. Optimize for Business Impact.
• Accuracy alone does not provide the needed measurements.
• Focus on the following: increase conversion rates, decrease cost, save time.

💡Key Takeaway: 

GenAI provides structure to large amounts of data while ML makes predictions based on that data...  however, when you confuse the two, things go wrong.

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