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Every enterprise is buying AI. Almost none of them can say what they're buying or why.
Architecture of Intellect. Part One: Language book cover

Companies are betting their futures on AI armed with little more than vendor slide decks. Most of those bets will fail, not because the technology is weak, but because it's being bolted onto enterprises designed for a past economy.

"Architecture of Intellect" is the missing layer between the hype and the decision. The first ten chapters are an honest technical primer on large language models: what they actually are, how they fail, what they cost, and what it takes to run them in production once the pilot is over. Support, monitoring, audit, compliance: the reality that has to work eighteen months from now. It's written for people accountable for a firm's integrity, not for AI researchers. No mysticism, no doom, no marketing. Engineering in plain language. Each chapter closes by converting the mechanics into what they cost you: the practical impacts, the risks, and the exact questions to put to vendors, AI teams and service providers. You won't just understand the answers; you'll know when someone is glossing over the part that matters.

It draws the line the industry blurs: an LLM and an AI agent are not the same class of thing. One is a function; the other is an action. One produces description; the other - consequences. That leap creates a class of risk your current frameworks can't see, and the answer isn't a better model, but a different architecture. 

The final chapters go to the question strategy usually skips: not which AI to buy, but what kind of firm can actually use it. The answer is the AI-Native enterprise: a blueprint for the organisation built to sense demand and respond at its tempo.

The executive summary of the AI Native Enterprise is here.

You can also buy on Amazon

Read it before your next AI decision. The decision will look different afterwards.

   Intro: The Enterprise Architect and the LLM

  1. LLM Basics (read)

  2. Inference (read)

  3. Transformer: Deep Dive

  4. Prompt Engineering & RAG

  5. Training

  6. Fine Tuning

  7. Quantisation, Distillation and Pruning

  8. Drift & Bias

  9. LLM Selection and Deployment

  10. Governance and Lifecycle

  11. LLM vs. AI Agent

  12. AI-Native Enterprise

  13. Glossary

 

What's Inside

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