To succeed with AI, we have to identify the right datasets to work with.

This is where Information Architecture plays a key role; it is a strategic approach to data discovery that aligns business goals with user needs.

Whether you're developing AI capabilities or building a software product, Information Architecture helps to identify the right datasets and understand meaningful connections between those datasets.

Rather than throwing all kinds of data at an LLM to train a model and "see what works"; it is much more advisable to start with the end in mind: identify datasets and then pick specific datasets to train your AI models.

Here is why:

1. Identifying Datasets for AI: Information Architecture helps in creating a functional view of data, pinpointing the exact datasets needed for AI systems, ensuring relevance and accuracy.

2. Structure & Organization: It organizes data into a coherent structure, making it more understandable, accessible and user-friendly.

3. Enhancing User Experience: Ensures that users find what they need quickly in AI-driven applications or software interfaces.

4. Scalability: Allows for growth and adaptation of data, essential for AI learning and software evolution.

5. Compliance & Security: Helps to identify specific datasets that will require attention to legal and security standards in data handling.


Information Architecture not just about organizing data; it's about identifying the right datasets for AI and creating impactful experiences.

What are your thoughts about selecting the right datasets for AI?

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Credits: Tanishq Ahire for Airbnb Information Architecture Chart