An Executive View of AI Governance
A short executive briefing on the governance questions a leadership team should be able to answer before AI touches operational decisions.
Original architectures, frameworks and applied thinking for intelligent businesses.
A continuing publication series examining business systems, enterprise architecture, structured data, AI adoption and intelligent operations.
Business Systems Enterprise Architecture Artificial Intelligence 2026
Ideas, Architectures and Frameworks for Intelligent Businesses
Featured paper
Featured Paper · INTELLIGENCE PAPER 01 / 2026
An AI-ready business system is not an application connected to a Large Language Model. It is a governed environment in which organisational knowledge, user identity, secured access, purposeful retrieval and LLM intelligence work together as one capability — independent of any industry, platform or model generation.
Intelligence Papers Library
Each paper sets out one architecture or framework in full — the reasoning, the design decisions and how to apply it. 7 publications.
A customer's Purchase Order or accepted contract creates the commitment; the Sales Order records it. This paper sets out how an ERP turns that record into governed execution — validation, ownership, controls, compliance, risk, escalation and subcontractor governance.
Connecting a Large Language Model to existing software does not make a business system AI-ready. This paper sets out the foundation beneath it: governed knowledge, verified identity, purpose-aware retrieval and bounded LLM reasoning.
A short executive briefing on the governance questions a leadership team should be able to answer before AI touches operational decisions.
On the operational boundary between assisted decisions and automated ones: where control, accountability and oversight have to sit.
An architectural study of what separates a genuinely connected business from a collection of applications joined by interfaces.
A working paper on data modelling for business systems — relationships, master data discipline and the design decisions that determine whether operational data can be reasoned about later.
The practice companion to Paper 01 — taking the same foundation from framework to working system.
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