Starbucks just scrapped an AI inventory tool that most likely cost millions to build, deploy, train, support, and roll back. So you may be asking: How do you avoid an expensive AI failure like Starbucks? We help organizations with this all the time: solve the correct problem with the correct tool.
Here is the important distinction: Some business problems, like inventory, are deterministic. That means the same input should produce the same output every time. Inventory is a good example. If there are 14 cartons of oat milk in the refrigerator, the correct answer is 14. Not probably 14. Not “close enough.” Not “based on the visual context, this appears to be oat milk, but it could be almond milk instead.” 14 exactly.
Other business problems are non-deterministic. That means the system may produce a different answer depending on context, interpretation, probabilities, training data, or prompt structure. AI systems operate probabilistically, whether they are predicting language, classifying images, or interpreting real-world visual inputs. They are often making a best-fit prediction based on patterns, context, and probability.
An AI engineer should ask: “What level of certainty does this workflow require, and what system architecture will produce that certainty?” AI can predict or interpret. Automation can execute. Rules can validate. Humans can judge exceptions or provide oversight. Good systems use the right layer at the right step for the job.
That is how you avoid turning an exciting AI initiative into an expensive operational retreat.
Link to the story: https://www.reuters.com/business/starbucks-scraps-ai-inventory-tool-across-north-america-2026-05-21/