Use Case 1:
The main challenges that organizations face with AI adoption involve poor data quality, which stems from a lack of data management. However, organizations can proactively work to employ AI to find, enrich, organize, and de-risk enterprise data, protecting it against possible vulnerabilities.
In these cases, AI can sort through an organization’s data, quickly and accurately de-risking and properly categorizing it. This includes finding the relevant information across repositories, organizing this iran whatsapp number data data into a singular, normalized repository, applying data governance policies at scale, and feeding the enriched data into AI systems. In other words, it takes applying AI to intelligently prepare your data to use AI for other business objectives.
The now high-quality data can be used to either train or supplement AI models that the organization will employ, enabling swift progression from AI pilots to production. It also enables responsible AI deployment by identifying and protecting sensitive data and leveraging previously untapped value that was lost amidst unstructured data. This means your organization can feel secure in future AI initiatives because of the transparency, auditability, and accuracy stemming from adopting AI the right way.