Engineering Enterprise AI:

The Foundations That Enable Value at Scale

 

RECORDED WEBINAR

Many organizations are experimenting with generative AI and large language models, but far fewer are achieving sustained enterprise value. The gap is not model performance. It is readiness.

This opening webinar explores what it truly means to engineer AI for the enterprise. Rather than focusing on tools or isolated use cases, it examines the foundational capabilities required to apply AI and LLMs effectively across the organization. These capabilities span governance, information architecture, knowledge engineering, data and content foundations, and operational readiness. Together, they determine whether AI accelerates decision-making and execution or simply amplifies existing fragmentation.

Participants will learn how AI readiness depends on connecting knowledge, operations, and governance into a coherent system. Particular attention is given to the role of semantic architecture in creating shared context, enabling trustworthy AI outputs, and increasing the speed and accuracy of information flow. The session introduces the idea of enterprise information metabolism and how LLMs, when properly grounded, can dramatically improve how organizations sense, interpret, and act on information.

This webinar establishes the conceptual foundation for the entire series and introduces the AI Readiness Framework that will be explored in depth across subsequent sessions.

This webinar is part of Earley’s 7-part AI Readiness Webinar Series. View the full series overview →

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Session Outline

Opening: The Pilot-to-Production Gap

  • Why 80% of enterprise AI initiatives fail to scale beyond proof of concept
  • The uncomfortable truth: Most failures are not technology problems
  • What organizations get wrong when they lead with models instead of foundations

The AI Readiness Framework: Foundational Capabilities

  • Introducing the foundational capabilities for enterprise AI: governance, information architecture, knowledge engineering, data and content foundations, and operational readiness
  • Why each capability has dependencies on the others
  • The sequencing imperative: Building capabilities in the right order

There Is No AI Without IA: The Foundational Principle

  • Why information architecture determines AI success
  • How semantic structures create shared context across the enterprise
  • The role of taxonomy, ontology, and metadata in retrieval accuracy

Enterprise Information Metabolism

  • How organizations sense, interpret, and act on information
  • The role of LLMs in accelerating information flow when properly grounded
  • Why fragmented content amplifies AI confusion rather than resolving it

The Stakes: Why This Matters Now

  • The competitive risk of falling behind in AI adoption
  • The operational risk of scaling AI on unstable foundations
  • The compliance risk of ungoverned AI in regulated environments

Series Preview and Takeaways

  • What participants will learn across the seven sessions
  • How to assess current readiness and identify gaps
  • The path from experimentation to enterprise-scale AI value

Target Audience

Senior leaders, enterprise architects, data and knowledge management professionals, and practitioners responsible for AI strategy and implementation in knowledge-intensive organizations.

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Seth-Earley-CEO-Earley-Information-Science

Meet The Speaker

Seth Earley

CEO & Founder
Earley Information Science

An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions.

Seth has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance by making information more findable, usable and valuable through integrated enterprise architectures supporting analytics, e-commerce and customer experience applications.

heather-eisenbraun-headshot

Meet The Speaker

Heather Eisenbraun

Chief Information Architect
Earley Information Science

With a remarkable track record spanning over two decades, Heather has transformed information retrieval accuracy by as much as 70% and slashed content search times by 60% for Fortune 500 companies, all through the clever application of strategic taxonomies, metadata schemas, and modular RAG implementations.

A Certified Knowledge Specialist in Business Taxonomy & Ontology, Heather is passionate about reimagining how organizations prepare content for success in the GenAI landscape—shifting the focus from traditional OCR to evolving documents into structured information resources. With her depth of expertise and visionary approach, Heather inspires audiences to rethink the future of enterprise knowledge management.