Seth Earley CEO at Earley Information Science, Artificial Intelligence Speaker, Writer and Influencer

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• How to Navigate the 5 Stages of Organizational Maturity in Digital Transformation

  In contemplating digital transformation program investments, executives ask two things: What is the current state costing us, and does it make economic sense to fix it? One challenge is deciding where to begin tactically. A good starting point is to assess current capabilities in the context of organizational maturity and the desired future state. That exercise will help decision-makers determine whether their organization can get from where is to where it wants to be.

• Governance That Enables Iteration: Operating Models for Enterprise AI at Scale (Pt. 3 of 3)

  Most AI governance frameworks slow teams down. Seth argues that well-designed governance does the opposite—accelerating improvement cycles, sustaining user trust, and creating the feedback loops that allow enterprise AI to get better over time rather than decay.

• Removing Friction from Information Flows: Vital for a Successful Digital Transformation

  A critical step in digital transformation is to enable the free flow of information throughout the enterprise. But various forms of friction can obstruct this flow. Friction is anything that slows down information access, information retrieval or information manipulation. These sources of friction point directly to the conclusion that a solid information architecture and well-designed information management system, along with an overall vision for the digital transformation, are prerequisites for success.

• Garbage In, Confidence Out: How Information Architecture Powers Enterprise Retrieval (Pt. 2 of 3)

  RAG sounds straightforward until it meets your actual enterprise data. Seth explains why information architecture—taxonomy, metadata, content modeling, and authority structures—is the foundational layer that determines whether retrieval produces accurate answers or confidently wrong ones.

• AI-Assisted vs. Human-Curated Metadata: The Hybrid Approach That Actually Scales

  Pure manual metadata creation doesn't scale. Pure AI tagging isn't accurate enough. Seth makes the case for a hybrid model that assigns AI to volume tasks and humans to judgment calls—delivering enterprise-grade metadata quality at roughly 4% of the cost of manual-only approaches.

• Knowledge Graphs, a Tool to Support Successful Digital Transformation Programs

  Knowledge graphs are pretty hot these days. While this class of technology is getting a lot of market and vendor attention these days, it is not necessarily a new construct or approach. The core principles have been around for decades. Organizations are becoming more aware of the potential of knowledge graphs, but many digital leaders are puzzled as to how to take the next step and build business capabilities that leverage this technology.

• The Pilot Paradox: Why Enterprise AI Complexity Grows Exponentially (Pt. 1 of 3)

  A successful AI pilot creates dangerous confidence. Seth shows why scaling from one department to the enterprise isn't a linear increase in effort—it's an exponential increase in coordination complexity—and what organizations that successfully cross that gap do differently from the start.

• The Architecture of the Agentic Enterprise: Semantics, Governance & Safe Autonomy (Pt. 2 of 2)

  Better retrieval alone won't get agents to production. Seth explains how knowledge graphs, controlled vocabularies, and semantic layers give agents the precision to act reliably—and how governance and operating models must evolve to make autonomous AI trustworthy at enterprise scale.

• Why Knowledge Management Gets Cut — and How to Make It Untouchable

  KM initiatives die because they're framed as overhead. Seth shows how reframing knowledge management as AI infrastructure—a prerequisite for accuracy, risk mitigation, and ROI—changes the executive conversation entirely and keeps budgets intact when cuts come.

• No Agents Without Architecture: Why Enterprise AI Fails Before It Starts (Pt. 1 of 2)

  As enterprises move toward agentic AI, the gap between demo and production widens fast. Seth explains why agents operating in real enterprise environments—with fragmented data, competing vocabularies, and undocumented assumptions—require information architecture as a foundation, not an afterthought.

• 1. The 5-Level Content Operations Maturity Model: Where Are You on the Path to AI-Ready?

  Most organizations don't know where they stand on AI readiness—and that's the problem. Seth introduces a five-level framework for assessing content operations maturity across governance, metadata, and technology integration, giving leaders a clear baseline and a practical path forward.

• The GenAI Stakeholder Ecosystem: Navigating the People Problem

  Technology rarely kills AI initiatives—misaligned stakeholders do. Seth maps the eight stakeholder groups every GenAI program must navigate, from executive sponsors to content owners to finance, explaining what each group needs, what they fear, and how to build the alignment that separates scaled programs from perpetual pilots.

• From LLMs to Agentic AI: A Roadmap for Enterprise Readiness

  Agentic AI isn't just a technology upgrade—it's a fundamental architectural shift. Seth outlines five imperatives IT and business leaders must address before deploying multi-agent systems, including orchestration, grounding, observability, and governance. Organizations that skip these steps will scale their problems faster than their capabilities.

• The Rise of Agentic AI: Why Your AI Agent Is Clueless

  Organizations are rushing to deploy agentic AI without building the knowledge infrastructure agents require to act reliably. Seth examines why most deployments stall after the demo, what authentic agentic systems actually need, and why information architecture—not model capability—is the binding constraint.

• Stop Guessing What GenAI Needs — Your Search Logs Already Know

  Before investing in more content, look at what your users are already searching for. Seth explains how zero-result queries, low-satisfaction patterns, and click-through data in existing search logs reveal your content gaps, quality failures, and retrieval problems—before your AI surfaces them at scale.

• Leveraging Data to Improve the Customer Experience

  When you consider how customers interact with organizations these days, it quickly becomes apparent that much of that interaction is through digital channels. “CX” suggests a customer experience via laptops or mobile devices, and that digital experience is driven entirely by data. The question is, how do we make it the most relevant and seamless experience possible, given the needs and objectives of the user, and what data can we leverage to do so?

• When AI Delivers Only Velocity, Not Value

  Everyone is moving faster with AI. Few are moving in the right direction. Seth argues that semantic architecture—taxonomy, metadata, ontology, and controlled vocabulary—is what separates AI programs that scale from pilots that stall, regardless of how powerful the underlying model is.

• There's No AI Without IA

  Artificial intelligence (AI) is increasingly hyped by vendors of all shapes and sizes—from well-funded startups to the well-known software brands. Financial organizations are building AI-driven investment advisors. Chat bots provide everything from customer service to sales assistance. Although AI is receiving a lot of visibility, the fact that these technologies all require some element of knowledge engineering, information architecture, and high-quality data sources is not well known...

• Harvard Business Review: Is Your Data Infrastructure Ready for AI

  Creating an ontology is an essential investment to prepare your enterprise to realize the benefits of AI and machine learning. Gone are the days when businesses should simply allow a number of small AI projects to blossom independently: for these projects to be competitive they need to draw on data from across the company, data stored in many different forms in many different systems. Businesses will be best positioned to build ontologies if they identify and research pain points first–areas where the data connections are most needed–before beginning to set the organizing principles for the ontology itself.

• The Critical Role of Enterprise Data in Generative AI

  A flood of Gen AI-based tools and applications, often acting as wrappers for LLMs like ChatGPT, has hit the market. While they offer clever and creative solutions, LLMs alone can't solve all organizational information problems. Machine learning, integral to AI applications, is now embedded in conventional enterprise tools like ERP, data warehouses, eCommerce, and content/knowledge management systems, enhancing their core functionalities. This integration promises new efficiencies and productivity across various scenarios.