Generative AI (GenAI) has received a lot of attention in recent years. The technology can deliver impressive results: variation in text and images, creative combinations of context, and rapid prototyping. Yet GenAI is often deployed in situations where reliability, consistency, and predictability are crucial. That clashes with how GenAI fundamentally works.

The issue is not that GenAI is “immature,” but that it is used incorrectly.

What generative AI is good at

• Generating variation: creating multiple alternatives or angles.

• synthesizing language and images: combining information into new forms such as summaries, illustrations, or concept drafts.

• combining context: connecting diverse sources or ideas into a coherent whole.

What generative AI structurally cannot do

• Provide guarantees: GenAI cannot offer hard certainty about correctness.

• Deliver consistent output: every generation is probabilistic and may vary.

• Make decisions within strict boundaries: GenAI is not suited for deterministic tasks that require rules or fixed models.

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Common situations where GenAI is used incorrectly

• As a decision engine: organizations use GenAI as if it can make hard yes/no decisions, even though it is not designed for that.

• In processes where errors are invisible but impactful: for example in compliance or risk management, where mistakes may not be immediately visible but can have major consequences.

• As a replacement for deterministic rules or models: GenAI is deployed where predictable, rule‑based systems are needed, such as price calculation or logistics planning.

Why organizations do it anyway

• Low barrier to entry: GenAI produces quick results and demos look convincing

• Confusion between “sounds smart” and “is reliable”: fluent, confident output is often mistaken for correctness.

• Overestimation of context understanding: organizations assume GenAI “understands” what is happening, while it actually predicts patterns based on probability.

The important distinction

• Predictive / rule‑based AI: consistent, repeatable, and controllable. Suitable for tasks where reliability and predictability are essential.

• Generative AI: creative, flexible, probabilistic, and non‑deterministic. Suitable for tasks where variation, inspiration, or contextual synthesis is valuable.

It’s not about being better or worse, but about serving a different purpose.

Generative AI is not a universal replacement for other forms of AI. It is a specific solution for specific problems. Organizations that fail to make this distinction build fragile systems that are unreliable in practice. Successful organizations understand that GenAI is valuable where creativity and variation matter, but that predictability and consistency are better served by rule‑based or predictive AI.

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