The wrong starting point
When a team starts with “we need to add AI,” it has already chosen a solution before describing the problem. The result is often impressive in a demo and rarely used a few weeks later.
A better starting point is friction: where a user searches for too long, repeats work, compares too many options, keeps rewriting the same information, or makes a decision with too little context.
Three questions before the model
Before choosing an LLM, vector database or agent, I prefer to answer three questions. First: which task becomes faster or more reliable? Second: what data lets us verify response quality? Third: what happens when the model is wrong?
Those questions immediately bring the discussion back to the product. They also force guardrails, traceability and failure experience to be designed alongside the happy path.
AI as a layer, not a destination
In a good product, users should not need to understand your AI architecture to get value. They want to find information, understand a situation, prepare an action or decide faster. The model is a layer behind that experience.
That is also why some features do not need AI. A deterministic rule, a well-indexed search or a better-structured interface can be faster, cheaper and more reliable.
The metric that matters
The final test is not “the answer looks smart.” It is: did we reduce time, errors, uncertainty or the number of steps? An AI feature becomes interesting when its value can be described without using the word AI.
That is the philosophy I want to apply to GuruLabs experiments: start from an observable problem, prototype quickly, then measure whether the added intelligence actually deserves to stay.