Many enterprises recognize that an overriding barrier to scaling AI is that their data isn’t “AI-ready.” While poor data quality, governance, and semantics undeniably matter, the latest research shows that data readiness is one of five reasons enterprises fail to capture value from AI. Scaling requires a multi-dimensional strategy that balances leadership, workflows, technology, data governance, and infrastructure. This paper outlines five interlocking levers that enterprises must activate to scale AI responsibly and effectively. 

Enterprises can pilot AI with messy data; they cannot scale AI without AI-ready data. Regulatory obligations require it, performance economics reward it, and operational reliability depends on it.

This AI dialogue discusses the importance of ontologies and data agents in developing modern solutions applicable to various implementation strategies that organizations may adopt.

AI dialogue by NotebookLM. 

The concepts and methodologies outlined here represent the foundational thinking behind Infinity Data AI’s approach to enterprise data governance, management, and quality.

Our AI Use Case Identification & Implementation Planning Service helps determine the optimal starting point for applying our ontology-driven approach to enterprise data, ensuring measurable business impact and laying the foundation for broader AI adoption.

Here are some use cases that serve as excellent starting points:

Lee Dittmar (Co-Founder of Infinity Data AI) is the co-author of "The Essential Guide to AI Governance,” which is a comprehensive resource designed to help business leaders, risk managers, compliance officers, and board members navigate the complexities of Artificial Intelligence (AI) adoption within their organizations.