What is the impact of AI on traditional intangible asset categories?

Full Explanation

Artificial intelligence is reshaping every category of intangible asset, creating new valuation challenges and opportunities. Technology assets: AI models represent a new sub-class of technology intangible that depreciates differently from traditional software. Models degrade as underlying data patterns shift (concept drift), require continuous retraining, and may be rendered obsolete by architectural advances. Useful lives are typically 2-4 years, shorter than traditional enterprise software. Customer relationships: AI-powered personalisation, predictive churn models, and automated customer service are changing how customer relationships are maintained and measured. Companies with AI-driven customer engagement may show higher retention rates, but the value attribution becomes complex — is the retention driven by the customer relationship asset or the technology asset? Brand assets: AI is changing how brands are built and perceived. Companies known for AI capabilities (perceived as innovative, efficient) may command brand premiums, while AI-related controversies (bias, job displacement) can damage brand equity. Data assets: AI has dramatically increased the value of proprietary data because high-quality training data is a critical bottleneck for model development. Companies with unique, curated datasets have an intangible advantage that was less significant in the pre-AI era. Assembled workforce: AI skills command premium compensation, making the assembled workforce more expensive to replace. Simultaneously, AI automation reduces the workforce needed for routine tasks, potentially lowering the total assembled workforce value. For PPAs involving AI-intensive businesses, valuers must consider these dynamics when selecting methods, determining useful lives, and assessing discount rates for each intangible asset category.

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