Engineering Enterprise AI:The Foundations That Enable Value at Scale

Seven-Part Webinar Series

 

Series Overview

Many organizations have succeeded in building generative AI prototypes. Far fewer have achieved sustained enterprise value. The gap is not model performance. It is readiness.

This seven-part webinar series examines what it truly means to engineer AI for the enterprise. Rather than focusing on tools or isolated use cases, it explores the foundational capabilities required to apply AI and large language models effectively across the organization. These capabilities span knowledge and information architecture, operational processes, technical infrastructure, data and content foundations, and governance. 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. The series introduces the concept of enterprise information metabolism: how organizations sense, interpret, and act on information, and how LLMs, when properly grounded, can dramatically improve the speed and accuracy of this flow.

This series is designed for senior leaders, architects, and practitioners who want to move beyond experimentation and understand how to build the conditions for AI-driven value at enterprise scale.

 

Series at a Glance

 

Session

Title

Focus

1

Engineering Enterprise AI: The Foundations That Enable Value at Scale

Series introduction, AI readiness framework, information metabolism

2

Governance First: Why AI Safety and Control Must Precede Scale

Policies, risk frameworks, oversight structures, escalation logic

3

There Is No AI Without IA: Information Architecture as the Semantic Foundation

Taxonomy, ontology, metadata, semantic structures for retrieval

4

From Documents to Knowledge: Engineering Content for AI Retrieval

Knowledge engineering, content transformation, SME capture, RAG readiness

5

Data and Content Foundations: Preparing the Enterprise for RAG Performance

Content readiness, data quality, metadata, version control, lifecycle management

6

From Pilot to Production: The AI Readiness Roadmap

Sequencing, dependencies, maturity assessment, transformation planning

7

Putting It Together: The AI Readiness Architecture Pilot

Pilot structure, IAD-RAG methodology, VIA platform, next steps
 
 

Sessions in this Series

Session 1: Engineering Enterprise AI: The Foundations That Enable Value at Scale

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Many organizations are experimenting with GenAI, but far fewer are achieving sustained enterprise value. This opening session introduces Earley’s AI Readiness Framework and explains why readiness, not model performance, determines whether AI scales. You will learn how knowledge, operations, technical infrastructure, data and content foundations, and governance work together to create the conditions for trustworthy, repeatable outcomes.

Session 2: Governance First: Why AI Safety and Control Must Precede Scale

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Enterprise AI introduces risk when boundaries, oversight, and accountability are missing. This session breaks down what mature AI governance looks like, from policies and standards to controls, escalation logic, and monitoring. You will learn why governance must be established before AI expands across regulated processes, safety-critical decisions, and complex organizational environments.

Session 3:  There Is No AI Without IA: Information Architecture as the Semantic Foundation

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LLMs do not understand your domain. They retrieve based on language similarity, not meaning. This session explains how information architecture creates the semantic structure AI needs for accurate and predictable retrieval. You will learn how taxonomy, ontology, and metadata work together to define terminology, relationships, and applicability logic that keep RAG outputs relevant, safe, and consistent.

Session 4: From Documents to Knowledge: Engineering Content for AI Retrieval

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AI cannot interpret ambiguity the way humans can. This session focuses on knowledge engineering, the process of transforming human-oriented documentation into structured, machine-interpretable knowledge. You will learn how to break down procedures into atomic components, capture SME expertise, and build retrieval-ready content using Earley’s IAD-RAG approach.

Session 5: Data and Content Foundations: Preparing the Enterprise for RAG Performance

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In most AI deployments, the model is the easiest part. The hardest work is content readiness. This session explains what it takes to prepare enterprise content for reliable RAG performance, including audits, metadata frameworks, lifecycle management, version control, and governance. You will learn why content quality is the largest driver of AI accuracy, safety, and scalability.

Session 6: From Pilot to Production: The AI Readiness Roadmap

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AI readiness cannot be built effectively by tackling everything in parallel. Sequencing matters. This session introduces the AI Readiness Maturity Model and shows how to assess readiness, identify dependencies, and build a phased roadmap. You will learn how to prioritize investments, reduce risk, and move from isolated pilots to scalable enterprise capability.

Session 7: Putting It Together: The AI Readiness Architecture Pilot

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The most effective path to enterprise AI is not a massive transformation program. It is an architecture-first pilot that delivers value while building the foundation for scale. This session introduces Earley’s AI Readiness Architecture Pilot and explains how it combines governance, information architecture, knowledge engineering, and RAG deployment into a structured engagement with repeatable outcomes and a clear rollout roadmap.

 

Contact Information

For questions about the series, custom briefings, or to discuss how these sessions align with your organization’s AI initiatives, contact Earley Information Science.

Website: earley.com
Email: info@earley.com

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Meet The Speaker

Seth Earley

Founder & CEO
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 Knowledge 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.