How AI Really Processes Data

A brief excursion into the machinery. How does AI understand your data? Why does it recognize patterns so quickly? And why can it also be so spectacularly wrong?

The Code Interpreter — AI with Access to Python

When you upload your data to ChatGPT or Claude, something interesting happens: AI gets access to a code interpreter. It's basically a hidden computer that executes Python code — a programming language specialized in math and data processing.

The steps are:

1. You upload a CSV or Excel file.
2. AI reads the file and recognizes: these are columns with numbers and categories.
3. It writes Python code (you don't see it, it runs in the background).
4. This code uses libraries like pandas (for tables), numpy (for math) and matplotlib (for graphics).
5. The code calculates: averages, sums, standard deviations, correlations.
6. The results are sent back to you as graphs and numbers.

This is elegant because AI doesn't really "think" — it orchestrates code that performs actual mathematical operations. Python does the heavy lifting, AI decides which code to write.

Statistical Methods — The Real Intelligence

The real power lies in the statistical methods embedded in this code. Let me explain three you will encounter:

1. Mean, median, standard deviation. These are the classics. Mean = average. Median = the middle number when you sort everything. Standard deviation = how spread out the data is. With these three numbers you can already say a lot: "On average you spend €500 per month, but in December it can range from €300 to €900."

2. Correlation. How much do two numbers depend on each other? When it gets warmer, your ice cream sales go up. Correlation = 0.95 (very strong). If two things have nothing to do with each other: Correlation = 0. If one thing goes up and the other goes down: Correlation = negative number.

This is powerful, but also deceptive. High correlation doesn't mean one thing causes the other. It might just be coincidence. Or a third invisible thing causes both.

3. Trend analysis. Does the data go up, down, or stay the same? A trend line is an imaginary line through your data points showing direction. It answers: "Is it going uphill or downhill?"

Why AI Finds Patterns SO Quickly

But why is AI so much faster than you?

Reason 1: Raw speed. Python performs millions of calculations per second. You wouldn't do that with paper and pencil even for 100 data points.

Reason 2: Systematicity. AI systematically examines ALL possible correlations. You might manage 10 comparisons in your head. AI automatically does 1,000. If there's a real connection, it finds it.

Reason 3: Pattern templates. AI was trained on real data. It "knows" typical patterns: seasonal fluctuations, cyclical trends, outliers. It can recognize these patterns without you describing them.

This isn't magic. It's organized power.

Why AI Can Also Be Spectacularly Wrong

But here's the flip side: this entire machinery assumes the data is clean and the statistical methods are appropriate.

Scenario 1: The data is corrupted. You forgot that in one month you entered "-500€" instead of "500€" (wrong sign). Now that month is an outlier that throws everything off. AI sees: "This month is extremely different!" That's technically true, but it's a measurement error, not a real trend.

Scenario 2: Not enough data. You have only 12 data points (one per month over a year). That's too little to detect real patterns. AI might find correlations, but they're purely random. With more data, the pattern would disappear.

Scenario 3: The wrong method. You have circular data (times of day, seasons). Regular correlation doesn't work well with that because the circle has no beginning and end. But AI doesn't see errors — it just calculates and gives you a wrong result.

Scenario 4: Large simultaneous events. Your revenue drops dramatically in 2020. Was it your business strategy? Or the pandemic? AI can't distinguish. It only sees: "Revenue fell." It doesn't see: "That was a global event outside your control."

Three Task Types for Data Analysis

To make this practical, let me show you the three tasks AI solves best:

Type 1: Exploratory. "I have data, but I don't know what's in it. Look at it." AI is perfect here. It finds anomalies, outliers, unexpected patterns. This task is low-risk because you should question the results anyway.

Type 2: Comparative. "Does the business differ in January versus December?" AI can compare groups and show you the differences. This is reliable if your data is clean.

Type 3: Visual. "Show me the data as a graph, not as numbers." AI is great at selecting the best graph for your data. It knows: time series go in line charts, comparisons in bar charts, proportions in pie charts.

What should AI NOT do? Make predictions without strong historical data. Draw cause-and-effect conclusions. Make decisions without human context. Those tasks remain yours.

The Comparison to K01-K05

In K01 (Text), I said: "AI is a drafting assistant, it does quickly what you do."

In K03 (Images) it was similar: "AI generates, you select."

In K05 (Code) it was: "AI writes code, but you must understand what it does."

With data it's different. Here you're not the primary actor. AI is. You're the control actor. You check if her results make sense.

That's a fundamental difference. With text, images and code you need creative control. With data you need analytical control — the ability to question a statement and say: "But is that really true?"

What This Means

The magic behind AI data analysis is no secret. It's math, code, and statistics — things people have been doing for 100 years. AI just does them exponentially faster.

That doesn't make the results magical. It makes them fast. And speed is a powerful tool — if you know how to control it.

Your next lesson will show how to really use AI for data analysis — not as an all-knowing oracle, but as an intelligent tool in your hands.