10 posts by this author
AI Knowledge Management vs. Traditional Knowledge Management: What's Actually Different?
Traditional KM asks the employee to search, browse, and interpret. AI knowledge management lets them ask and receive a sourced answer — eight operational differences, from the primary verb to the quality benchmark, and what semantic retrieval actually changes.
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GEO vs SEO: Getting Cited by AI, Not Just Ranked by Google
SEO optimizes to rank in a list of links. GEO — generative engine optimization — optimizes to be cited in an AI-generated answer. As more people ask AI instead of searching, the second goal starts to matter as much as the first.
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Enterprise RAG Glossary: 50 Terms Every AI Leader Needs to Know
RAG vocabulary sits awkwardly between engineering and the boardroom. Fifty terms defined in plain language, so a CISO, a CFO, and an ML engineer stop meaning three different things by "accuracy" and "grounding."
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llms.txt and Answer-First Content: Writing for the Models That Read You
Your content now has two readers: people and language models. Writing for the second one means being answer-first and machine-legible — and llms.txt is the emerging convention for telling models what matters on your site.
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What Is Institutional Memory? A Business Leader's Guide
Institutional memory is the layer between your company's data and its decisions. This guide defines it, separates explicit, tacit, and embedded knowledge, explains why it determines enterprise performance, and shows how AI turns it into a queryable operating asset.
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Building an AI System Registry That Actually Satisfies Regulators
The EU AI Act requires a live, auditable AI system registry — not a spreadsheet last updated in January. Here is what a compliant registry must contain, how to surface the AI systems you do not know you are using, and how to produce the regulator-ready export on demand.
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The Context Tax: How Enterprise AI Costs You 50 Hours Per Employee Per Year
Your employees spend 12 minutes every day re-establishing context with their AI. That is 50 hours per person per year — more than a full working week — of pure overhead that generates zero new output. Persistent memory eliminates it entirely.
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AI Governance vs AI Compliance: The Difference That Determines Your Risk
Compliance documents what you did. Governance controls what happens. Building one without the other leaves you with paperwork that cannot prevent the problem it describes — and a regulator who will use that paperwork against you.
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Enterprise AI Content Policies: Why Per-Team Governance Outperforms Platform-Wide Controls
A FINRA-compliant AI policy that protects your trading desk will break your engineering team's workflow. Precision governance — applied per team, per regulation — delivers both compliance and adoption. Here is how it works.
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Why Enterprise AI Gets Your Company-Specific Questions Wrong
GPT-4o and Claude are trained on essentially all of human knowledge. On questions about your own organisation, they will fail the majority of the time. The problem is not the model. The problem is context — and there are two ways to provide it.
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