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Using IG to Reduce Generative AI Inaccuracy!




44% of the respondents to the latest McKinsey generative AI adoption survey reported negative consequences arising from generative AI and, leading the pack, nearly a quarter of these respondents cited inaccuracy as the largest negative issue.

We believe that by investing in robust information governance practices, organizations can mitigate these risks and unlock significant generative AI potential.



Effective information governance not only reduces AI inaccuracies but also enhances overall organizational efficiency. In an environment where information governance methods are “baked into” organizational systems (aka, IG by Design), employees spend less time searching for data, version control minimizes errors, and automation ensures consistent adherence to best practices. Collectively, these benefits contribute to a more agile and responsive business environment, capable of leveraging AI for competitive advantage.


AI models rely heavily on the quality and relevance of the data they are trained on. Information governance best practices facilitate efficient data retrieval by establishing comprehensive data catalogs and metadata management. This structured approach allows employees to quickly locate the most relevant and accurate records, ensuring that AI systems are fed with high-quality data.


As businesses strive to leverage AI effectively, robust information governance practices emerge as a critical solution to mitigate these risks and enhance AI accuracy.

For example, for users of large language models (LLMs), having immediate access to the correct data sources is crucial. Information governance best practices such as metadata management, version control, and the regular removal of “junk” data or obsolete records help ensure that the right records are not only available but also up-to-date. This reduces the risk of feeding outdated or incorrect information into AI models as well as the likelihood (or, more accurately, frequency), of outdated data versions leading to erroneous AI outputs.


Additionally, version control mechanisms enable better audit trails, allowing organizations to trace back the sources of data used in AI training. This traceability is vital for addressing any inaccuracies that may arise, facilitating prompt corrective actions and enhancing the reliability of AI models.


Finally, given the size and complexity of a typical LLM, automation plays a pivotal role in information governance by streamlining processes and reducing the potential for human error. Automated data classification, for example, can ensure that records are accurately categorized and stored, making them easier to retrieve when needed. And, automated version control systems enable real-time document change tracking, ensuring that AI models always have access to the latest data.


Automation also enhances compliance with regulatory requirements by ensuring that data handling practices adhere to established standards. This compliance is particularly crucial in industries with stringent data privacy and security regulations, where inaccuracies can lead to severe legal and financial repercussions.


As generative AI continues to shape the future of business, addressing the risks associated with its use becomes increasingly critical to its success and continued adoption. And, information governance best practices offer a proven framework for reducing AI inaccuracies by ensuring access to the right records, implementing rigorous version control, and leveraging automation.

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