Category: AI
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Your AI Can Predict. But Can It Explain Why?
Today’s, Generative, AI can predict with impressive accuracy, but 74% of the time its stated reasoning doesn’t reflect how it actually reached its conclusion. As organisations push AI into higher-stakes decisions, that explanation gap is becoming a board-level governance liability. Causal AI offers a way to close it.
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Your Employees Are Already Using AI
Most companies are spending billions on AI programmes that aren’t delivering. Meanwhile, their own employees have quietly found AI tools that work and are using them without permission. The smartest organisations aren’t trying to stop this. They’re learning from it.
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Is Enterprise AI Actually Working?
While enterprise AI spending is skyrocketing, 95% of pilots fail to deliver measurable financial returns. The issue isn’t the technology, it’s organisational strategy. Success belongs to the “5%” who prioritize back-office automation, favour specialist vendors over in-house builds, and treat AI adoption as a change management challenge rather than a technical one.
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Your ‘AI’ Probably Isn’t AI
Many companies mistake Robotic Process Automation (RPA) for AI, a confusion that risks future competitiveness. While RPA follows rigid scripts for repetitive tasks, true AI adapts to new data and makes independent judgments. Distinguishing between ‘agent-washed’ marketing and genuine reasoning capabilities is crucial for building a scalable business foundation.
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THE ETHICS ILLUSION
AI ethics has become a box-ticking exercise. Over 200 guidelines published worldwide, yet harms keep growing. Why? Principles without enforcement, ethics boards without power, and companies using ethical language to avoid real change. This analysis explains what’s broken and what business leaders must do differently.
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The AI Boardroom Playbook – Approve Thoughtfully, Avoid Disaster
Boards can’t blame the algorithm when AI goes wrong. Courts want human accountability. This guide shows how to govern AI projects without killing innovation—fix accountability, make oversight real, and distinguish between recoverable mistakes and catastrophic failures.
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The Emperor’s New Algorithm
Many vendors exaggerate or fabricate their use of AI, putting buyers at legal and operational risk. From false automation claims to failed “AI” safety systems, the costs are real. Regulators are cracking down, so buyers must demand technical evidence, measurable performance, and contracts that clearly assign liability and exit rights.
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AI’s Causal Illusion: A Hidden Threat to Business Decisions
LLMs simulate causal reasoning by recalling patterns from their training data, not by understanding cause and effect. This leads to a significant business risk: AI recommendations may seem confident but are often flawed, particularly in novel situations.
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The Great AI Delusion
Most companies are wasting billions on AI without clear business cases. The current frenzy mirrors previous tech bubbles like dotcom and blockchain. Smart leaders start with problems, not technology: evaluate data readiness, consider total costs, and focus on foundational capabilities while competitors chase headlines. The AI revolution may happen, but not as quickly as promised.
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AI for Business Leaders: A Straightforward Guide
Most companies are making expensive AI mistakes because they don’t understand what they’re buying. AI isn’t magic – it’s pattern-matching software that’s brilliant at specific tasks but useless outside them. Stop falling for vendor hype. Ask hard questions, start with boring problems, keep humans involved, and treat AI like any other technology purchase.
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