Intelligence
Designing AI That Does Actual Work
AI features that solve real problems look different from AI features that demonstrate a model. Here is how we approach applied intelligence at Studio.
There are two kinds of AI features in digital products. The first kind is designed to demonstrate that the product uses AI. The second kind is designed to solve a problem that could not be solved without AI.
The first kind is everywhere. It includes chatbots that answer questions worse than a FAQ page, recommendation engines that suggest things nobody wants, and search features that are less accurate than keyword matching.
The second kind is rare. It includes systems that can actually understand unstructured data, run workflows that require judgment, and provide capabilities that were not possible before.
How to tell the difference
The test is simple. Does the AI feature solve a problem that the user has? Or does it solve a problem that the product team has, which is that they want to use AI?
If the feature is there because users asked for it, or because it makes a useful task faster or easier, it is the second kind. If it is there because the competitor has it, or because it looks impressive in a demo, it is the first kind.
Where AI actually helps
AI is genuinely useful in specific contexts. Processing large volumes of unstructured text. Extracting structured data from documents. Handling repetitive decisions that require pattern recognition. Providing search over knowledge bases that are too large for manual organization.
In each of these cases, the AI is doing something that would be impossible or prohibitively expensive without it. That is the test.
At Studio, we apply intelligence where it solves a real problem. Through Tangison Labs, we connect digital experiences to the agents, integrations, and infrastructure working behind them. But we do not add AI features unless they earn their place.
