Intangible assets account for over 90% of the value of every AI company.
A robust intellectual property (IP) strategy is essential for protecting these assets, driving higher valuations, and providing leverage when commercializing technology.
To develop an IP strategy that meets these objectives, it is crucial that the focus is placed on the revenue-generating components of the business, both current and future.
Although, from an IP standpoint, an AI company shares similarities with other software businesses, there are unique challenges that require careful consideration in designing a tailored IP strategy. Without attention to these details, a company might, for example, become overly focused on patents or face gaps in IP governance, leading to costly risks.
Here are some examples of how these nuances impact the development of IP strategies for AI technology or platforms:
Data Intensity. AI companies are highly data-driven, and this is especially true when considering what will likely become the most valuable intangible assets of the company. Legal protection of data is complex and often varies by jurisdiction. For instance, in Europe, AI tools that incorporate third-party data must track the origin of that information. While AI companies often view legal issues related to data through the lens of privacy law, ensuring the legal right to exploit products—including data assets—requires a broader view. This includes understanding IP protections available for various types of data and establishing a legal chain of title that secures the company’s rights to the data needed to implement its business strategy. It’s critical to connect the company’s data governance approach with its overall IP strategy, a link often overlooked.
Contracts Rule. More than in any other technology sector, AI companies must pay close attention to the drafting and negotiation of contracts related to both the development and commercialization of intangible assets. In many cases, ensuring that contracts clearly address issues such as ownership of derivative data or technology improvements, and the right to use third-party software or data to develop and commercialize AI products, is a key part of a successful IP strategy. This process must also account for business realities, as AI is often developed collaboratively with other parties, or relies on third-party platforms or data. The terms agreed upon with these partners will have lasting consequences on the company’s value, and any contractual gaps can lead to costly corrections. Moreover, it’s important for the IP strategy to address the varying legal rules around ownership of contributions to AI tools by employees or contractors, which can differ depending on jurisdiction. For example, contributions by employees or contractors in Europe may result in ownership rights that are difficult to circumvent. An IP strategy should include a plan for negotiating these contracts or managing suboptimal terms in existing agreements.
Tough-to-Protect Innovations. Many AI companies’ most valuable intangible assets—those providing competitive advantage—are challenging to protect using traditional intellectual property mechanisms, like patents. Determining which IP tools to use for which aspects of the company’s AI innovations can be difficult, but given the crowded market and complex patent landscape, it’s crucial to dig into the specifics of the company’s technology and develop an appropriate patent strategy. For many AI companies, software, algorithms, models, UI/UX designs, and applied know-how together represent significant value, but patenting these innovations can be particularly challenging. Additionally, many AI companies rely on third-party tools or known techniques, and their innovation often lies in how these elements are combined. As a result, AI companies can find themselves in a Catch-22, disclosing key details about a differentiated implementation in a patent application, only to find that the innovation is not patentable or is only patentable in a very limited scope. Alternatively, the claims might have minimal enforceability. “Either way, competitors may end up, in a worst-case scenario, with a quasi-blueprint to practice the company’s technology without enforceable exclusive rights.” This risk has likely led some AI companies to invest less in patenting or avoid patents altogether.
However, avoiding patents altogether is often the wrong approach. There may still be opportunities to secure exclusivity by focusing patenting efforts on systems that represent an implementation use case of the company’s AI technology or by registering design patents covering innovative elements of user interfaces that encourage platform adoption. If these opportunities are not pursued, competitors are likely to stake their own claims. “Being boxed in by a competitor’s patents is a real risk that AI companies eventually face, and mitigating it is generally expensive, time-consuming, and often results in less-than-optimal outcomes.” To avoid this, a well-thought-out IP strategy should include identifying relevant patents for freedom-to-operate analysis and targeting specific aspects of AI technologies for patent filing, with associated patent prosecution tactics. Focusing on patent quality, not quantity, and distinguishing between innovations that should be patented versus those to be protected as trade secrets is crucial to success.
Trade Secrets Strategy and Management. As discussed above, protecting trade secrets is a key component of any well-managed AI company’s IP strategy. But to be effective, trade secrets protection must be intentional and supported by robust business processes. Too often, AI companies treat trade secrets as a catch-all category, lacking a clear strategy or processes for managing them. This approach becomes evident during due diligence in financing rounds or when a company must enforce its trade secrets—especially if an employee leaves to join a competitor. Conversely, by establishing processes to identify and manage valuable trade secrets, including applied know-how, companies can protect their assets efficiently. Developing a trade secrets register to categorize and safeguard these assets can help mitigate risk without stalling development or commercialization. “Most clients who take on design of a proper trade secrets management program report that it is not a ‘heavy lift’ and that it delivered improved risk management and helped them justify higher valuations with stakeholders.”
Open Source and Open Data. The use of open-source software and open data is common in AI, but not all “open” resources are as legally open as they may seem. The use of open-source software can sometimes be problematic depending on the terms and conditions of the license. For example, some open-source licenses require the disclosure of source code, limit commercial use, or restrict IP protection for derivative works. When these terms are associated with an innovation that is central to the company’s competitive advantage, the consequences can be costly and time-consuming to resolve. If these issues arise during legal due diligence, they can create major problems. However, with the right controls over which open-source software or open data is used, many of these issues can be avoided. “All too often it turns out that there was a close alternative to using an open-source library with problematic terms, one with relatively friendly terms.”
In conclusion, all of these nuances can be effectively addressed by AI companies with a disciplined commitment to building a value-driven IP strategy that also mitigates risk. With expert guidance, companies can focus their efforts and resources on developing IP assets that matter and scale their efforts at the right points in the business’s growth.