Making Enterprise Data AI-ready for Governance, Analytics, and Generative AI Workloads
Businesses today face a new inflection point. While the promise of AI is widely acknowledged, the practice of AI, what it takes, and how to do it is still nascent and evolving as AI inevitably challenges all business functions to innovate.
Fragmented data estates, uneven governance mandates and policies, rising costs, and the potential for significant workforce disruption all conspire to slow or stall adoption. Studies suggest that early adoption AI projects have struggled and MIT’s Media Lab has reported that up to 95% of all pilots surveyed are failing.
This whitepaper introduces enterprise AI, a fourth-generation data platform framework designed to bridge these gaps standing in the way of full AI adoption. Enterprise AI is an incremental and extensible architecture that is intended to enhance rather than replace existing enterprise infrastructure and systems.
Built around four core capabilities automated classifiers, intelligent analytics, data governance, and AI semantics enterprise AI enriches generative AI by interfusing and embedding AI-ready data at the core. The result is an open, federated, and customizable data platform that is safe and secure, accelerates AI adoption, and optimizes total cost of ownership across multiple clouds.
Download this comprehensive whitepaper to learn more.
About the Author:
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John Ottman has over 30 years experience with enterprise applications and cloud infrastructure. He is currently the Executive Chairman of Solix Technologies, Inc. and Co-Founder and Chairman of Minds Inc. -
Suresh Mani As VP of Engineering & Chief AI Architect at Solix Technologies, I lead the development of enterprise AI platforms that enable organizations to become AI-native. With 20 years of experience in IT, product management, and AI, I specialize in Data-Centric AI, Agentic AI, and multi-agent systems, delivering scalable, industry-specific solutions across Pharma, Healthcare, eCommerce, Logistics, and Finance.
