What valuation methods are used for AI assets?
Short Answer
AI assets are typically valued using the Cost Approach (replacement cost of training data and model development), the Income Approach (MPEEM or With-and-Without), or the Market Approach when comparable AI licence transactions exist.
Full Explanation
Valuing AI assets requires selecting the method best suited to the asset's characteristics and available data. The Cost Approach estimates the cost to recreate the AI asset — including data acquisition, cleaning, labelling, model architecture development, training compute, and fine-tuning. This works well for early-stage models without revenue attribution. The Income Approach is preferred for revenue-generating AI: the Multi-Period Excess Earnings Method (MPEEM) isolates the earnings attributable specifically to the AI asset after deducting contributory asset charges, while the With-and-Without method compares projected cash flows with and without the AI capability. The Market Approach uses comparable transaction data — AI model acquisitions, licensing deals, or API pricing benchmarks — but comparable data is still scarce in many AI sub-domains. In practice, valuers often use multiple methods and triangulate. Key challenges specific to AI valuation include rapid technological obsolescence (shorter useful lives), dependency on training data quality, and the difficulty of isolating AI's contribution from human expertise and other technology. In the Asset Valuator in Opagio Intangibles you choose the method, and the tool marks a recommended one for the asset type.
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Further Reading
AI Cost Reduction: How to Measure Real Operational Savings
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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