Two different machines
Traditional AI, meaning classic machine learning, learns patterns from historical data to make predictions: will this customer churn, is this transaction fraudulent, what will demand be next month. Generative AI produces new content: text, answers, summaries, code, images, based on patterns learned from vast corpora.
The distinction sounds academic until you procure the wrong one. A surprising share of stalled AI projects trace back to using a generative model for a prediction problem, or forcing classic ML onto a language problem.
Where traditional ML remains the right tool
- Structured prediction. Churn, credit risk, demand forecasting, predictive maintenance. Tabular data in, probability out. Classic models are cheaper, faster, more accurate on this class of problem, and their behaviour can be explained to a regulator.
- High-volume, low-latency decisions. Scoring millions of events per hour is what these models were built for.
- Regulated decisions. When a decision needs a documented, auditable rationale, the interpretability of classical models is a feature no LLM currently matches.
Where generative AI changes the game
- Language in, language out. Summarising documents, answering questions over a knowledge base, drafting responses, extracting structure from messy text. This entire problem class was effectively unsolvable before LLMs.
- Unstructured data. Most company data is documents, emails and tickets. Generative models make it computable.
- Interfaces. Natural-language access to systems that previously required training and forms.
The costs differ too
A classic model is cheap to run and expensive to build: it needs labelled data, feature engineering and training. A generative system inverts this: fast to prototype against an API, but with a real per-call cost that scales with usage and requires ongoing evaluation, because the failure modes, hallucination chief among them, are subtler than a wrong probability.
Most real systems use both
The strongest production architectures combine them: an LLM reads and structures incoming documents, a classical model scores the risk, an LLM drafts the response for human review. Treating the two as rivals misses how well they compose.
Choosing for a concrete problem
Start with the output. If it is a number, a class or a ranking over structured data, start with classic ML. If it is text, an answer or a transformation of language, start generative. If your problem has both halves, design the seam deliberately. That seam is where we spend a lot of our engineering time at Digital Colliers, and we are glad to think through your case with you.