Generative and agentic AI present a transformative opportunity. Despite the potential, many organizations struggle to draw the anticipated value from their AI initiatives.

For enterprises to realize the full value of their AI, they must overcome several critical, data-centric obstacles. A significant challenge lies in poor data quality and inadequate governance. Incomplete, inconsistent, or biased data directly contributes to flawed AI models, unreliable predictions, poor decision-making, compliance gaps, and exposure to both seen and unseen risks.

To make AI more reliable, companies must aim to modernize their data estates, making data easily accessible and contextually relevant at any scale.

Read this report from Futurum, on how IBM watsonx.data was built specifically to meet these critical requirements and is rapidly evolving to address emerging AI.