AI-Powered Data Preparation:
Generative AI (GenAI), specifically as it pertains to the public availability of large language models (LLMs), is a relatively new business tool, so it’s understandable that some might be skeptical of a technology that can generate professional documents or organize data instantly across multiple repositories. On the one hand, AI-powered data preparation sounds too good to be true. On the other hand, can an organization really trust the work that AI produces, and that AI will protect its sensitive data?
These doubts are valid – without proper data preparation, AI can produce inconsistent, unreliable results that do not mitigate risk or encourage trust. Unfortunately, hearing concerns about the potential risks of AI iran whatsapp number data is all too common. However, there is an immediate and real-world solution to achieving responsible and reliable AI adoption: using AI to meticulously prepare your organizational data. Yes, I understand the irony of using AI to prepare your enterprise for a wider AI rollout, but there are AI-powered tools that can make the wider integration more successful.
In my experience, understanding, organizing and de-risking an organization’s data are the first steps in preparing it for other GenAI use cases. Before an organization can confidently leverage its data, it must know where this information resides across enterprise repositories, understand what the information is, be able to identify its business purpose, and identify any applicable access controls.
That’s where AI-powered data preparation capabilities come into play. Adopting AI early on in this process can deliver significant advantages, as AI can organize and de-risk an organization’s enterprise data at a scale that humans can’t.