Practical writing on AI, machine learning, NLP, and data science. What works, what doesn't, and how to think about applying it to real business problems.
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Only around 15% of enterprise AI use cases generate measurable ROI. The reasons are almost always organisational rather than technical. This piece looks at why, how to spot the warning signs, and how to think about doing better.
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OpenAI's GDPval benchmark shows that top AI models now match expert-level quality on nearly half of complex professional tasks. Here's what the data actually shows and what it means.
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MIT's study found 95% of enterprise AI pilots fail to deliver measurable ROI. After 20+ years in AI, Paul Ferguson explains why this isn't surprising — and what organisations can do about it.
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A step-by-step framework for technical leaders choosing between RAG and fine-tuning for enterprise LLMs, including when to combine both in a hybrid approach.
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AI projects follow a fundamentally different development process than traditional software. Understanding the difference is the first step to getting it right.
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DeepSeek-R1 matches the performance of leading proprietary models at a fraction of the cost. Here's why it matters and what it means for businesses using AI.
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Small Language Models offer focused, efficient AI that runs on standard hardware. For many business use cases, smaller genuinely is smarter, faster, and more cost-effective.
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Explainable AI (XAI) is becoming essential as AI takes on high-stakes decisions in healthcare, finance, and law. This piece covers what XAI is, the main techniques, and why it matters.
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A plain-English guide to the EU AI Act: what it covers, the risk-based framework, what it means for your organisation, and what steps to take to prepare.
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LLM hallucinations — where AI generates confident but false information — are a real risk in high-stakes settings. Here's what they are, why they happen, and how to manage them.
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