If a company tells you they’re “using AI to predict customer churn,” are they using AI? I think not always. Not all predictive analytics is AI. And not all AI is predictive analytics. This confusion isn’t just academic. Because when we mislabel things, we misplace expectations, misallocate resources, and misjudge risks.
Predictive analytics?
It might be:
- An Excel spreadsheet with a linear trendline
- A statistical model forecasting next month’s sales
- A scoring system estimating how likely a customer is to churn
The models used in predictive analytics can range from basic to complex:
- Linear regression
- Logistic regression
- Decision trees
- Time-series models like ARIMA
These methods are decades old, well understood, and widely used in business, finance, marketing, and healthcare.
AI?
AI means prediction and therefore uses machine learning or algorithms to learn from data and improve over time.
Examples of machine learning methods:
- Random Forests
- Gradient Boosting Machines (like XGBoost)
- Support Vector Machines
- Neural Networks
The difference between ML and traditional data exploration models:
- Can handle much more complex datasets
- Doesn’t assume a predefined relationship between variables
- Often performs better on large, messy, real-world data.
Real-World Examples
- A bank uses a logistic regression model to estimate credit default risk.
- Answer: Not AI. That’s a traditional statistical model, widely used for decades.
- An e-commerce site uses a recommender engine trained on user behavior to suggest products.
- Answer: Yes, that’s AI — likely powered by machine learning.
- A marketing team segments customers using k-means clustering.
- Answer: Depends. K-means is unsupervised machine learning, so it’s technically part of the AI family. But it’s a stretch to call it intelligent in the way people think of AI.
- A CEO brags that their company uses “AI to forecast revenue,” but their analyst is just running a linear regression in Excel.
- Answer: Not AI. That’s classic predictive analytics.
How to Tell the Difference
Here are a few signs that your predictive system is not using AI:
- It’s based on clear formulas (e.g., linear regression).
- The relationship between variables is explicitly defined.
- The system doesn’t improve or adapt as it gets more data.
- You can explain exactly how the prediction was made.
- It was built in Excel, R, or basic Python without any ML libraries.
And here’s what might indicate AI is in play:
- It uses models that improve over time with more data.
- The system uses multiple layers of abstraction (e.g., deep learning).
- The model can detect patterns without explicit programming.
- It’s trained on massive datasets with unstructured data (text, images, logs).
- It was built using machine learning frameworks like TensorFlow, PyTorch, or Scikit-Learn.
In summary, if a linear regression model gets the job done, great. Call it what it is: predictive analytics. AI is a subset of predictive tools. So let’s stop confusing prediction with intelligence.
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