With AI at the forefront of enterprise initiatives in 2025, data readiness strategies are top of mind. According to a recent market study on Enterprise Data Transformation by the Intelligent Enterprise Leaders Alliance:
- 55% of companies are increasing their budgets for AI data readiness this year.
- 85% are doubling down on foundational investments like data governance and management, while 60% are also prioritizing data security.
- 75%—a significant majority—say aligning data initiatives with overall business goals is a top priority.
Although many are investing in AI and data readiness, the question of how to find the right quality of data to drive the biggest impact remains. But there isn’t a simple answer; to drive true business impact with GenAI, data readiness strategies must be use-case-specific and aligned with business goals.
“AI readiness is not about having all the data—it’s about having the right data tailored to the right objectives.”
In his latest article with Forbes Tech Council, CEO DryvIQ Sean Nathaniel discusses the challenges with preparing unstructured data—particularly knowledge worker content—for AI initiatives and shares strategic steps to prepare data for generative AI, advocating a use case-specific approach. You’ll learn:
- How to identify and align AI use cases with business objectives through a values assessment.
- The importance of auditing unstructured data repositories to discover enterprise content that is valuable to your AI initiatives.
- Strategies to enhance data quality and ready content for AI using the ROCS framework and intelligent data management.
Read the full article to learn more about the importance of targeted data readiness strategies for GenAI success.
