AI has never tasted ice cream. What to do with the answer it gave you

We use AI tools every day: in process simulations, pasteurization calculations, regulatory reviews, and in the yogurt simulator we built for Foodlab Academy courses. They are good, and they are getting better. And when a client comes to us having already asked a model and received a tidy answer on marketing, nutrition and cost, that is an excellent starting point for the conversation.

But between a good answer and a product there is a stage with no shortcut.

What the model does very well

• Calculate P and F values for pasteurization and sterilization from temperature data.
• Propose a base formulation against nutritional targets.
• Summarize regulation and labelling for a target market.
• Estimate raw-material cost from market prices.
• Draft a first brief and organize the open questions.

All of these save days of work, and we use them ourselves.

What the model cannot do

The model has never smelled a chocolate tank before and after conching. It has never felt a texture break wrong in the mouth. It has never seen a raw material that is “available” on paper and, in practice, sold by the only supplier in Israel in tonnes only. It has never stood next to an extruder that behaves differently at 14 percent moisture than at 13. And it has never experienced failure and success the way a person does.

Food is, in the end, a human experience: taste, smell, texture, memory. Whoever develops it has to go through the senses and through experience, not only through the data.

Where the gap is

The gap between advice and execution is where developments get stuck. It is not in the presentation or in the document. It is next to the machine: facing a raw material that is not available at volume, a process that behaves differently at scale, a product that breaks in week three on the shelf. The model’s answer can be entirely correct, and the product can still fail, because it was never tested.

What to do with the answer

1. Confirm what is right. Most good answers really are good. We do not argue with the model, we test.
2. Turn it into a brief. The ten questions a brief answers, with what the model already covered and what is still open.
3. Bring it to the bench. A prototype on real process equipment, with numbers: aw, pH, texture. And a tasting.
4. Ask what happens on the line. Shear, heat transfer, residence time, raw materials at industrial volume.

What we tell clients

Got a good answer from the model? Excellent, that is the starting point. Now it has to be tasted. We begin where the model’s answer ends: in tasting, smell, texture and the production line. Not because the model got it wrong, but because that part is done by people, in the lab, next to the equipment.

Let’s put your idea on the bench.