Many marketing teams are treating AI tools as though they think, learn, and create just like human colleagues — but the reality is different. According to the article, although generative AI systems mimic human-like fluency, they operate on statistical pattern-matching and predictions, not true cognition or invention.
The article lays out four fundamental truths about how large-language models (LLMs) actually work. First, they recombine existing patterns rather than invent new ideas. Second, they do not learn from experience once trained; they operate with fixed weights unless explicitly updated. Third, each output is the product of probabilistic prediction, meaning the same prompt may produce different results. Fourth, their knowledge is rooted in historical training data and may not reflect up-to-date reality unless connected to live systems.
Building on these truths, the piece offers seven practical guidelines for marketers (and other professionals) working with AI. For example, it advises using AI for well-structured, pattern-rich tasks rather than open-ended strategy; always providing context; combining AI’s outputs with human expertise; ensuring transparency and auditability; and maintaining human oversight throughout the process.
In conclusion, the article urges organisations to move away from metaphors that portray AI as “colleagues” or “partners” with agency, and instead to treat them as highly capable tools with defined constraints. By doing so, teams can better harness AI’s strengths, set realistic expectations, mitigate risk, and design workflows that amplify human judgment rather than replace it.
