“Explaining AI-Ready data” was a phrase that Gartner highlighted as a priority in 2025. It is clear that if you want your organisation’s AI tools to perform accurately, getting your data in shape is going to be essential. But what is AI Ready data?
There is a lot of high-level advice out there about the importance of “well-structured data,” “accessible data,” and “common identifiers,” but very little genuinely practical, hands-on guidance. So, I wanted to share a few practical things we’ve learned at Leading AI, where we specialise in retrieval augmented generation (that is, AI searching company documents and data).
First, when preparing ‘unstructured data’ (i.e., documents) for AI, less is often more. Formatting, like colours, fonts, and bold or italics might make sense for human readers, but they add noise for AI and can fragment text during processing. Stick to basic structure like clear headings (which help with chunking – another AI term) and convert the rest to plain text.
Images are another common stumbling block. Standard text-based AI models ignore visuals, so complex images should be processed separately, using OCR or vision models (or use our KnowledgeFlow platform which has both OCR and AI-vision). If an image contains essential information, embed its caption as metadata rather than bloating your text with raw image data.
Whitespace and unnecessary line breaks can disrupt chunking and introduce irrelevant tokens, harming retrieval accuracy. Strip out excessive blank space.
Tables need careful handling. Tables used for layout formatting (e.g., version control tables at the start of policies) can confuse AI and lose relational meaning. It’s best to exclude them and ‘flatten’ the data (i.e., write it as a long-hand sentence). For genuine data tables, extract them to markdown or CSV before ingestion to preserve accuracy and structure.
Underpinning all this is a simple principle: AI-ready data is about clarity, not complexity. Strip away what humans add for style and focus on what matters for machines – semantic content, clean structure, and relevant metadata.
As with so much in technology, the best results come from understanding and balancing the needs of both humans and machines.
The image was created using AI so please forgive the typos… Let me know if you spot it.
