AI Risk & Governance

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Five Caribbean AI Use Cases Worth Funding First: Where Boards Should Begin

May 27, 2026by Dr Dawkins Brown

Edition 01 ended with a question. This one answers it. In Edition 01 of this series, I argued that the single most common reason Caribbean enterprise AI pilots stall is not the technology. It is the failure to choose the right use case in the first place. The discipline of starting where data exists, where...

From AI Ambition to Measurable Outcomes: A 90-Day Path for Caribbean Boards

May 27, 2026by Dr Dawkins Brown

Why most enterprise AI pilots stall — and how Caribbean boards can build a 90‑day path from idea to validated business value, with global partner discipline and local execution. Where boardroom AI conversations are stuck — and how to move them forward Across the Caribbean, every executive committee I sit in front of is...

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Group Audits and Cross-Border Engagements: What Caribbean Holding Companies Must Know About ISA 600 Revised

May 24, 2026by Dr Dawkins Brown

The modern Caribbean economy is, in significant measure, a network of conglomerates. A holding company headquartered in Kingston operates subsidiaries in Trinidad and the Eastern Caribbean. A Bahamian financial group runs operations through entities in Cayman and Barbados. A regional retail group consolidates results from operating subsidiaries across a dozen territories. Tourism groups, financial groups,...

Continuous Validation Protocols: Why Periodic Model Validation Cannot Govern AI.

May 23, 2026by Dr Dawkins Brown

Why periodic model validation cannot govern AI — and what replaces it in institutions that must oversee systems faster than the institution itself can convene THE ARGUMENT IN ONE PARAGRAPH Traditional model validation operates on calendar time: annual reviews, quarterly committee meetings, semi-annual revalidation cycles. Artificial intelligence operates on operational time: thousands of...

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The Three-Owner Principle: Who Is Accountable When an AI-Influenced Decision Goes Wrong?

May 23, 2026by Dr Dawkins Brown

Who is accountable when an AI-influenced decision goes wrong — and why three separate owners are required for every consequential decision class THE ARGUMENT IN ONE PARAGRAPH When a credit decision goes wrong, the institution must be able to identify, within hours, who is accountable for what. Traditional governance answers this with a single...

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Introducing AEGIS™: A Five-Pillar AI Governance Framework for Caribbean Institutions

May 23, 2026by Dr Dawkins Brown

THE ARGUMENT IN ONE PARAGRAPH Artificial intelligence does not extend the risk surface of an institution — it inverts the assumptions on which the institution’s governance was built. The Landmark Edition of Caribbean Boardroom Perspectives set out The Governance Inversion Thesis, arguing that AI requires governance itself to be re-architected, not merely extended. This...

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AI and the New Economics of Scaling: What Caribbean Businesses Must Prepare For

May 12, 2026by Dr Dawkins Brown

Executive Summary Artificial intelligence is no longer a distant technology trend. It is becoming a strategic force reshaping how businesses are built, financed, valued, governed, staffed, and scaled. For Caribbean businesses, this shift presents both a major opportunity and a serious competitive risk. A recent World Economic Forum report, produced in collaboration with the Stanford...

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AI in Audit: Opportunity, Oversight, and Executive Accountability

May 1, 2026by Dr Dawkins Brown

How leaders should think about AI in assurance without confusing technical capability with evidential reliability. AI is changing how audit and assurance work is performed, but it also raises questions about evidence quality, model governance, documentation, and accountability. Executives need a governance lens, not just a productivity lens. The promise and the temptation AI...

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Caribbean Sector Spotlights: How AI Adoption Looks Different in Tourism, Manufacturing, and the Public Sector

April 26, 2026by Dr Dawkins Brown

IN THIS ARTICLE Reading time: 22 minutes The tenth article of twelve, and the second article of Act IV — The Decision. Article 9 set out an AI governance frame in the abstract — eleven decisions, a Decision-Rights Matrix, two structural questions. Article 10 asks whether the frame holds up against three specific Caribbean...

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The Caribbean AI Adoption Imperative: A Boardroom Framework for AI Governance in the Caribbean

April 26, 2026by Dr Dawkins Brown

Foreword This consolidated programme brings together twelve articles published serially across the spring of 2026 under the title The Caribbean AI Adoption Imperative. The articles were written for a specific reader — the Caribbean board chair, lead independent director, or risk committee chair sitting in the chair of an institution of meaningful scale, in a...

Newer Posts

Older Posts

Five Caribbean AI Use Cases Worth Funding First: Where Boards Should Begin

From AI Ambition to Measurable Outcomes: A 90-Day Path for Caribbean Boards

Continuous Validation Protocols: Why Periodic Model Validation Cannot Govern AI.

The Three-Owner Principle: Who Is Accountable When an AI-Influenced Decision Goes Wrong?

Introducing AEGIS™: A Five-Pillar AI Governance Framework for Caribbean Institutions

AI and the New Economics of Scaling: What Caribbean Businesses Must Prepare For

AI in Audit: Opportunity, Oversight, and Executive Accountability

Caribbean Sector Spotlights: How AI Adoption Looks Different in Tourism, Manufacturing, and the Public Sector

The Caribbean AI Adoption Imperative: A Boardroom Framework for AI Governance in the Caribbean

July 22, 2026Internal Audit Meets Cybersecurity: A New Assurance Model for Caribbean Organisations

July 22, 2026The Cyber-Accountable Board: What Directors Must Know, Ask and Verify

July 21, 2026From Cybersecurity to Cyber Assurance: The Missing Link in Caribbean Risk Management

July 21, 2026Cybersecurity Is No Longer an IT Issue: Why Caribbean Boards Must Treat It as an Enterprise Risk

July 20, 2026Internal Audit in Public Bodies: Accountability, Compliance, and the Public Purse

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