Expert thinking on productivity, intangible asset valuation, growth strategy, and building more valuable businesses.
AI investment has crossed $500 billion annually, yet measured productivity growth remains flat. This article explores why conventional metrics fail to capture AI-driven value creation and what alternative measurement approaches reveal.
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Most organisations cannot quantify the return on their AI investments. This practical framework breaks AI ROI into four measurable dimensions and provides concrete methods for calculating returns across cost reduction, revenue growth, risk mitigation, and strategic optionality.
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AI washing inflates valuations and distorts capital allocation. This guide provides a structured due diligence methodology for detecting exaggerated or fabricated AI claims during investment evaluation, with specific technical and commercial red flags.
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IAS 38 sets six criteria for capitalising development costs, but applying them to AI projects is fraught with ambiguity. This article maps each criterion to AI development realities and provides practical guidance for CFOs and accountants navigating the recognition question.
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AI-enabled companies trade at 25-40% higher multiples than peers, but not all AI capability is created equal. This article examines what drives the AI valuation premium, how to assess whether it is justified, and what happens when the premium unravels.
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Private equity firms are acquiring AI-enabled targets at record rates, but traditional due diligence frameworks miss critical AI-specific risks. This article provides a comprehensive AI due diligence methodology covering technical, commercial, regulatory, and talent dimensions.
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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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