AI has the potential to transform how organizations operate, but AI is only as effective as the data that it can access.
is a SaaS expert specializing in information management and risk mitigation for Fortune 500 companies.AI has the potential to transform how organizations operate, but the success of AI initiatives depends on one critical factor: AI is only as effective as the data that it can access.
Think of your organization’s information as a library. If the books, which are representative of emails, contracts, policies and records, are properly shelved and labeled, AI can quickly identify the relevant data needed to generate accurate results. However, if your books are scattered in piles across several libraries, AI will be unable to find the appropriate data to inform its action, leading to incomplete, misleading or risky outcomes. Most enterprises today are not AI-ready, but this has nothing to do with the efficacy of the technology itself. Rather, it has everything to do with the organizations' data, which is, in most cases, disorganized and inaccessible to AI programs. Preparing data for AI is the first use case for AI, as automating the discovery and classification of data, along with implementing policy controls, is the best way to ensure that unstructured data is usable for GenAI implementations.The answer to your organization’s data being spread across multiple, disconnected systems and file shares is not to create another data silo. Instead, organizations should pursue a unified approach to data governance, which discovers and classifies data automatically. This begins with deploying AI-powered information management tools capable of understanding unstructured data across business repositories. By automatically labeling documents, emails and file shares according to their true nature, organizations can uncover the insights and nuances hidden within. Reducing redundancy and duplication helps ensure that AI models are trained and operate on high-quality data, producing high-quality outcomes in return. It may sound counterintuitive to use AI to prepare for AI, but this recursive strategy is what enables scalable success. When governance operates as a continuous, automated process—quietly strengthening data quality in the background—it frees teams from manual strain and builds a foundation where every future AI initiative can thrive.When your data is organized and labeled, users can ask questions of it, gaining insights without having to manually search for the information. For example, a manager could ask an AI agent to find any employee contracts with a “flexible hours clause,” and pulling from the readily available information, the AI agent could immediately produce any employee contract containing those details for instantaneous reference. Similarly, GenAI programs can use organized data to produce a specific kind of document, like a request for proposal , for instance. That GenAI program can then sort through the labeled data, referencing past, finalized RFPs that have been labeled and are discoverable as RFPs, rather than random drafts or irrelevant files, and create an accurate, trustworthy document. Data governance practices also establish guardrails for sensitive data that not only prevent unauthorized access by employees but also unauthorized access by AI agents, ensuring trust in these programs. These guardrails come from AI’s analysis and classification of sensitive records, ensuring that these documents are classified appropriately and that enterprises can comply with policies that protect against the unlawful collection and storage of personal data, all while minimizing the impact and risk of potential data breaches. Organized data also ensures that any AI programs that are being transitioned from pilot programs can succeed at scale. Those organizations that have gained value from AI deployments have been aligned among their leadership and have ensured that their data governance has scaled along with their AI initiatives.It's critical that businesses organize their unstructured data so that AI can gather information from the right repositories and deliver tangible advantages in efficiency and efficacy. Whether data preparation is led internally or by a trusted third-party information manager, it is a business imperative to organize enterprise data for AI implementations, which includes using AI to prepare data for AI. Now is the best time to start organizing your data for AI. Over the next 12 to 24 months, companies that identify a specific use case, automate their data governance and label their data will prepare themselves to achieve repeatable competitive advantage.
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