Expert thinking on productivity, intangible asset valuation, growth strategy, and building more valuable businesses.
AI is transforming how companies predict and enhance customer lifetime value, but measuring AI's specific contribution to CLV remains a challenge. This article provides a practical attribution framework for quantifying how AI interventions affect retention, expansion, and total customer value.
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The EU AI Act takes effect in August 2026, making AI governance a compliance requirement — not just a best practice. This article provides a practical governance framework covering risk classification, oversight structures, accountability mechanisms, and audit processes for organisations investing in AI.
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AI vendors promise dramatic cost reductions, but actual savings depend on total cost of ownership, implementation quality, and measurement rigour. This article provides a practical methodology for measuring real AI-driven operational savings — net of all hidden costs.
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Not all AI creates competitive advantage. This article examines the four types of AI moats — data, model, integration, and network — and provides a valuation framework for assessing the defensibility and durability of proprietary AI systems.
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Large language models have become core business assets, but valuing them is uniquely challenging. This article examines the economic characteristics of LLMs that affect valuation, the distinction between foundation and fine-tuned models, and practical approaches to LLM asset valuation.
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Total factor productivity is the ultimate measure of technology's economic contribution, yet AI's impact on TFP remains undetectable in aggregate data. This article examines why, what the macro-economic models predict, and what it means for long-term investment strategy.
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AI investments create complex financial reporting challenges across recognition, measurement, disclosure, and impairment. This practical guide helps CFOs navigate the accounting treatment of AI spending, from capitalisation decisions to board-level disclosure.
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The choice between open-source and proprietary AI has profound implications for intangible asset value, competitive defensibility, and company valuation. This article examines how each approach creates — or fails to create — durable intangible assets.
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AI investments carry risks that traditional investment frameworks do not capture: model degradation, data dependency, regulatory exposure, and concentration risk. This article provides a quantitative framework for assessing AI-specific downside in investment decisions.
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A comprehensive guide to brand valuation covering Relief-from-Royalty, income premium, and cost approaches. Includes industry royalty rates, real-world examples, and best practice for brand valuation in M&A, licensing, and strategic planning.
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IAS 38 excludes the majority of intangible capital from the balance sheet. This CFO guide explains what the standards require, where the gaps are, and how voluntary disclosure frameworks can close the information asymmetry that suppresses valuation. Financial reporting framework showing the gap between recognised intangible assets under IAS 38 and the full intangible capital base of a modern company
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The Corrado-Hulten-Sichel (CHS) framework is the standard taxonomy for classifying intangible investment. This deep dive explains all six categories, how they are measured, and why the framework changed how economists think about growth. Structured diagram of the CHS framework showing three groups and six categories of intangible investment with interconnected nodes
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