What changes compared with a fast model
A fast model produces the answer directly: perfect for summarizing, rephrasing, extracting data. A reasoning model spends part of the work “thinking” before it writes: it considers the constraints, tries different routes, notices contradictions. On problems with many variables, the difference shows.
Where they help in marketing
- Strategic choices — positioning, identity, value proposition: where trade-offs matter.
- Scenarios — what happens if the market changes, if we shift, if a competitor reacts.
- Complex syntheses — bringing together different and sometimes conflicting sources.
- Campaign strategy — from target to concept, by way of the message mix.
Where fast models are enough
- Structuring data and research results.
- Summarizing, translating, rephrasing.
- Answering specific questions about the project’s memory.
The rules that don’t change
- Reasoning well on wrong data produces wrong conclusions. Sources matter more than the model.
- Numbers are calculated, not reasoned. Counts, averages and totals should be done by code; the model comments on them.
- Reasoning should be made explicit. A strategy must say what evidence it rests on and what it ruled out.
- Judgment stays human. The model proposes alternatives and reasons; whoever knows the business decides.
