Recent analysis argues that the reliability of AI tools used in legal work depends critically on the quality of their underlying source material, highlighting a growing risk for firms relying on automated systems. When legal-AI applications draw from poorly maintained or unverified data, their outputs can be unreliable, misleading or even dangerously inaccurate – a weakness that can undermine decision-making and compliance.
According to the report, two interlinked weaknesses often emerge. First, outdated documents, misfiled case law, or incomplete legislative records can produce gaps or contradictions in the AI’s knowledge base. Second, inconsistent data formatting and lack of standardisation can prevent accurate cross-referencing and undermine the integrity of search results. These problems are especially acute when AI tools attempt to summarise complex legal issues, draft contracts, or provide interpretive guidance.
For legal teams, the message is clear: embracing AI does not eliminate the need for rigorous data governance and expert oversight. Without high-quality source data, AI-assisted outputs remain provisional at best and risky at worst. Legal professionals must therefore maintain robust data-quality controls – including regular audits, metadata management, version tracking and human review of critical outputs – to preserve the accuracy and reliability of AI-augmented workflows.
This emphasis on source integrity also affects how firms evaluate AI vendors and internal tool deployment. When selecting AI products, organisations are advised to scrutinise the provenance, maintenance processes and update frequency of the vendor’s data library — on top of the tool’s algorithmic design. The quality of input data, the analysis argues, ultimately shapes the quality of AI’s legal reasoning.
