Summary: The skills gap is not the only obstacle holding back progress in artificial intelligence (AI). According to a recent survey by IBM, data complexity is cited as the second leading obstacle to AI success. Of the companies surveyed, 58% are not yet actively implementing AI, with data privacy and trust and transparency identified as the main inhibitors. Among companies that are already deploying AI, data-related barriers are often the key obstacles, with organizations taking steps to ensure trustworthy AI, such as tracking data provenance and reducing bias. However, industry leaders are warning that organizational data may not be ready to support growing AI ambitions. To address this challenge, technology professionals and their organizations need to focus on data security, AI decision-making ethics, and AI literacy. By actively engaging with the technology, educating employees, and implementing appropriate safeguards, organizations can realize the benefits of generative AI for data management while mitigating the risks. Additionally, companies must strike a balance and acknowledge the significant role of unstructured data in the advancement of AI. The wide variety of data that AI requires can be a vexing piece of the puzzle, causing internal strife as companies struggle to determine what is business-critical versus what can be archived or removed. Therefore, it is urgent for companies to determine cost-effective approaches and solutions that can filter out unnecessary information and make room for essential data. Ultimately, establishing a data-first approach and a robust, centralized data repository are critical to the successful adoption of AI for both corporate and internal IT processes.
The source of the article is from the blog maestropasta.cz