AI & analytics
AI on your company’s real data: where to start
How to connect AI to your company’s real data — with permissions, context and privacy — to query in natural language and generate real value.
Read articleKey takeaways
- Traditional ML predicts and classifies; generative AI creates new content.
- Each fits different problem types.
- ML dominates in forecasting, scoring and anomaly detection.
- Generative AI shines in language, assistants and content creation.
- Both depend on quality data.
"Artificial intelligence" has become an umbrella term lumping together very different technologies. Confusing traditional machine learning with generative AI leads to choosing the wrong tool and expecting from one what only the other delivers.
The essential difference
Traditional machine learning (ML) learns patterns from data to predict or classify; generative AI creates new content (text, images, code) from what it learned. They solve different problems: one estimates, the other generates.
What each is for
Traditional ML
Predict, classifyForecasting, scoring
Generative AI
Create contentAssistants, summaries
- Traditional ML: demand forecasting, risk scoring, fraud detection, churn, predictive maintenance.
- Generative AI: conversational assistants, summarisation, drafting, code, natural-language data queries.
How to choose
If you need to estimate a number or probability from history (how much will I sell? will this customer churn?), traditional ML is the tool. If you need to generate or interpret language, generative AI fits better. Many cases combine both.
What they share
Despite their differences, both depend on the same thing: quality data. An ML model trained on biased data predicts badly; a generative AI without reliable own data answers generically or invents. Whatever the approach, the clean, governed data layer is the deciding factor.
It is not about choosing between ML and generative AI, but knowing which problem each solves.
In summary
Traditional ML predicts and classifies; generative AI creates content. Each solves different problems — ML for forecasting, scoring and anomalies; generative for language and assistants — and many cases combine them. What never changes: both depend on clean, governed, quality data.
Sources & further reading
Frequently asked questions
Does generative AI replace machine learning?
No. They solve different problems: ML predicts and classifies; generative AI creates content. They are often combined.
Which do I need for demand forecasting?
Traditional machine learning, which estimates future values from history. Generative AI is not the tool for that problem.
What do both need to work?
Quality data. Without reliable, governed data, both ML and generative AI produce unreliable results.
What does generative AI excel at?
Language tasks: conversational assistants, summaries, drafting, code generation and natural-language data queries.
And traditional ML?
Estimating numbers or probabilities from history: demand forecasting, risk scoring, fraud detection, churn, predictive maintenance.
Can I use both together?
Yes, commonly. For example, ML predicts churn risk and generative AI drafts the retention proposal and lets you query the results.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.