The SIC Codes module is now live in production within a major South African financial institution — marking the country's first deployment of a semantic intelligence solution in banking. This milestone represents a fundamental shift from traditional rule-based systems to an intelligent, ontology-driven architecture that understands business context.

Artificial intelligence has moved from experimentation to imperative. Nearly all enterprises have active AI initiatives underway. Most can best be categorized as experiments, pilots, or proofs of concept. Many surveys and reports have produced banner headlines proclaiming that very few of these projects reach production, and fewer still deliver sustained business value. But this should not be surprising or incriminating. Veterans of previous technology-driven enterprise transformations know that failure rates were high, how hard they were, how long they took, and how high the costs were. But the time to pursue AI-enabled transformation is upon us. And there is a lot of work to do. 

Gartner’s latest views on semantic technologies, knowledge graphs, and intelligent data fabrics confirm what Infinity Data AI was built for: enterprises can no longer scale AI with isolated models and brittle point integrations alone.

The strong registration, attendance, and active participation at our first Infinity Data AI webcast made one thing clear: enterprises are facing a critical inflection point around their AI journeys.​ To watch, click here.

The strong registration, attendance, and active participation at our first Infinity Data AI webcast made one thing clear: enterprises are facing a critical inflection point around their AI journeys.​ To watch, click here.

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.