How does AI training data qualify as an intangible asset?

Full Explanation

Training data can qualify as a separately identifiable intangible asset under IAS 38 if it meets three conditions: identifiability (it can be separated from the entity through sale or licence), control (the entity can restrict others from accessing the benefits), and future economic benefits (the data enables revenue generation or cost reduction). Proprietary datasets that have been cleaned, labelled, structured, and curated for AI training typically meet all three criteria. Raw, publicly available data generally does not — it lacks the control element. The value of training data as an asset depends on several factors: uniqueness (is the data available elsewhere?), scale (larger datasets often produce better models), quality (accuracy, completeness, consistency of labels), and domain specificity (niche datasets command premium value). In purchase price allocations, training datasets are valued using the Cost Approach (cost to replicate the data collection, cleaning, and labelling process) or the Income Approach (incremental earnings attributable to models trained on this data versus generic alternatives). Some of the largest AI acquisitions in recent years have been primarily motivated by access to proprietary training datasets.

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