Developing an IP Strategy for AI in a Changing Legal Environment
Industries worldwide face fierce competition as leaders and disruptors rush to integrate artificial intelligence into products, services, and operations. Significant investment and innovation have pushed AI tools into automating complex tasks, boosting productivity, and informing decisions across sectors like healthcare, transportation, and construction.
With AI's growing role, companies must consider how to protect their AI assets through intellectual property (IP). Securing IP rights can safeguard market position, attract investors, support due diligence, shape licensing agreements, deter competitors, and defend against infringement claims. For any business leveraging AI, developing an IP strategy aligned with these goals is essential.
Challenges and Opportunities in Protecting AI IP
Unlike traditional technologies, AI protection often involves nuanced challenges. Effective IP protection requires counsel who can align legal strategies with the technical nature of AI products and business objectives. Patent applications should be based on thorough discussions about the technology, competitive environment, and intended market impact to ensure meaningful and enforceable protection.
Companies already experienced in securing IP for innovations may need to adjust their processes to address AI-specific factors. The U.S. and Europe continue to clarify patent eligibility, with courts and agencies generally recognizing AI software and workflows as patentable subject matter. Trade secrets are increasingly valuable, especially where patents may be limited or regulatory disclosure requirements apply.
The Value of AI Data and Software IP
High-quality, anonymized data has become a critical asset. When hardware is conventional or third-party, the AI software and data can define competitive advantage. Recent cases illustrate this: a health tech firm successfully enforced its AI-related patents to block imports of a competitor's smartwatches, protecting its market share.
Trade secret litigation has also highlighted the stakes, such as multi-million-dollar settlements in autonomous vehicle technology disputes. As data licensing disputes proliferate, businesses are actively licensing data sets to AI model developers, fueling a growing data marketplace.
Strategic Approaches to Maximize AI IP Value
Some AI inventions risk being dismissed as mere automation of known manual methods, especially since core AI techniques like deep learning have existed for years. Detecting infringement in back-end AI processes can be difficult, and AI models themselves often evolve quickly, making them less suitable for long-term patent protection.
Instead of focusing solely on the AI model, companies should consider protecting AI-driven workflows and processes that interact with the AI. These workflows often persist across product versions, are visible to customers, influence purchasing decisions, and can be easier to detect if copied.
Trade secrets may be appropriate for certain models, particularly when the model’s structure or training data is kept confidential. Additionally, IP protection can extend to data preparation methods—curation, preprocessing, and feature extraction—that feed AI models. These areas may be patentable or protected as trade secrets, especially when not publicly visible.
Data rights and licensing related to AI inputs and outputs are also crucial. Access to training data, especially when sourced from partners, can be extremely valuable and sometimes irreplaceable. These non-patent IP assets are particularly important when the AI replicates existing manual tasks.
Next Steps for Legal Professionals
Building an effective IP strategy for AI requires close collaboration with legal counsel to understand how AI is used internally and in client-facing solutions. This partnership helps identify which types of IP—patents, trade secrets, or data licensing—offer the strongest and most practical protection for each AI asset.
Legal teams should focus conversations on the business goals behind AI deployment, product features, and competitive positioning. This approach ensures IP efforts are efficient and aligned with long-term value rather than merely accumulating patents without strategic impact.
For those interested in further developing AI-related legal expertise, exploring specialized courses on AI, data rights, and technology IP can be valuable. Resources like Complete AI Training's latest AI courses offer practical insights tailored for legal professionals.