The valuation of intangible assets has become a major issue in the economic strategies of modern companies. These assets, which include trademarks, patents, software, designs & models and databases, now account for an ever-increasing proportion of corporate value. Thanks to technological advances, and in particular the emergence of artificial intelligence (AI), new valuation methods are possible, enabling more accurate, dynamic and efficient valuation of these often-underutilised exploited intangible resources. This article explores the global impact of AI in the valuation and maximisation of the value of these intangible assets, offering tools for predictive analysis, automation and legal security.

Why AI is revolutionising intangible asset valuation

The emergence of artificial intelligence is profoundly changing the way we value intangible assets, which are now at the heart of economic strategies. These assets have gone from being simple ‘positive externalities’ to becoming genuine instruments of growth. With AI, the valuation of intangible assets is becoming more objective, detailed and consistent, considering dynamic factors that were previously inaccessible.

According to the Organisation for Economic Co-operation and Development (OECD), intangible capital now accounts for a large proportion of the market capitalisation of listed companies. In this context, it is imperative to adopt tools that are equal to the challenge. AI is now established as a vector for automation, anticipation and security, contributing to the legal, financial and strategic optimisation of intangible assets.

New value drivers enabled by AI

AI as a catalyst for intellectual property development

Artificial intelligence tools can proactively identify exploitable inventions, creations or distinctive signs, facilitating their protection by intellectual property rights. Numerous technologies developed by AI support innovation while automatically tracing the authorship of assets.

In particular, semantic analysis refers to the ability developed by AI to understand the meaning of words within a text or data. For example, by simply reading a patent, AI will be able to determine key concepts and their links, such as a specific technology or a particular innovation, without needing to be explicitly programmed for each detail. AI also facilitates the recognition of technical patterns, which enables the identification of recurring motifs or structures in technical data, such as product drawings, diagrams or technical ideas. In particular, it will be able to automatically detect a technical solution similar to an existing invention in a patent.

These technologies are even more effective because they are based on self-learning models. These algorithms enable AI to learn and improve over time, without being explicitly programmed for each situation. In this way, AI will become better at predicting the novelty of a patent as data on past patents is accumulated.

This translates into an acceleration of patent, design and trademark filings, but also into an improvement in the quality of registered rights, based on objectively qualified criteria of distinctiveness, use or novelty.

Predictive analytics and scoring of intangible assets

AI enables detailed evaluations of intangible assets based on extensive datasets: social media presence, scientific citations, prior art, comparable transactions, and market trends. These analyses produce dynamic and regularly updated ratings, invaluable for fundraising, asset sales, or financial reporting.

Moreover, AI-generated scenarios predict future valuations, considering market shifts and regulatory developments crucial for M&A due diligence and IP litigation strategy.

Toward standardised AI-based valuation methods

Concrete examples: patents, databases, software

In the technology, healthcare and telecoms sectors, AI can be used to value patents based on the estimated lifetime of the titles, their potential for commercial exploitation or cross-citation mapping. In fact, AI can examine patents and identify those that have been cited in other patents or publications. These citations highlight connections between ideas and similar technologies, providing a better understanding of the evolution of innovation in a specific field. By cross-referencing this information, AI can create a “map” listing the various inventions and their relationship in a network, making it easier to assess the novelty, importance or influence of an invention in relation to overall technological development.

Similarly, databases and software can be assessed on the basis of their functional architecture, reuse rate and competitive exposure.

The critical issue of algorithm traceability

The use of AI in this context requires traceability of the valuation processes, both for reasons of legal security and regulatory compliance, particularly with regard to the European General Data Protection Regulation (GDPR) and the European Digital Services Act (DSA). Any potential biases in algorithms must be documented, particularly in terms of financial predictions or investment decisions. Algorithms must also be auditable.

Legal and regulatory framework: risks and opportunities

The European Commission, the OECD and the World Intellectual Property Organisation (WIPO) are encouraging the use of artificial intelligence in the analysis of intangible assets, while insisting on the need for open, interoperable and auditable standards. To achieve transparency, economic efficiency and legal certainty, companies must rely on partners specialising in intellectual property, AI and asset valuation.

 

The French Data Protection Authority (CNIL) also stresses that the use of personal data in predictive models must be processed lawfully, proportionately and in accordance with the principles of minimisation and purpose.

Conclusion: strengthening strategic approaches with AI

Incorporating AI into intangible asset valuation equips companies with a structural competitive edge. This transformation transcends technology, touching legal, financial, and organisational domains. The ultimate goal is to convert intangible assets into measurable, actionable, transferable, and defensible capital.

Dreyfus Law Firm works with clients in the food sector, providing specialist advice on intellectual property and regulatory issues to ensure compliance with national and European laws.

We collaborate with a global network of intellectual property attorneys.

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Nathalie Dreyfus with the support of the entire Dreyfus firm team.

FAQ

1. What is an intangible asset?

An intangible asset is a non-physical asset of value to a company, such as a brand, patent, software, database, know-how, etc.

2. Can AI be used to value a trademark and what are the legal risks?

Yes, by combining data on brand awareness, digital usage, legal protection and commercial performance. The risks mainly concern the transparency of algorithms, data protection and the traceability of decisions.

3. Is AI used in IP litigation?

Yes, in particular to estimate economic loss, analyse similarity or search for prior art. Specialist lawyers are responsible for identifying the right tools, securing usage and anticipating contractual issues.