Companies outside traditional tech sectors may be sitting on IP goldmines without realizing it. Manufacturing, e-commerce, and consumer products businesses routinely use AI technologies from predictive maintenance to automated pricing. These technologies represent valuable intellectual property assets. While these companies may not self-identify as “tech companies” in the Silicon Valley sense, their AI implementations are every bit as technically sophisticated and legally protectable.
AI patent growth trends
The United States has seen significant growth in AI-related patent applications, with a 33% increase between 2018 and 2024. While AI patents initially faced higher rejection rates due to subject matter eligibility, a proper strategy can lead to success. The USPTO's August 2025 memorandum clarified subject matter eligibility, helping to navigate these challenges. Successful strategies include emphasizing technical implementation over business methods, highlighting specific technical problems solved by the AI system, and considering approaches that direct applications to favorable examination units.
Understanding competitive value
Well-implemented AI systems can have a substantial business impact and create competitive advantage. For example, Netflix's algorithm significantly influences subscriber behavior and business outcomes. This principle extends to internal operational AI systems in 'non-tech' companies. Manufacturing firms with advanced predictive-maintenance datasets could license this technology or use it for negotiation leverage. Similarly, e-commerce businesses with effective recommendation engines or pricing algorithms hold valuable assets that differentiate them and can be leveraged through licensing.
Recognizing AI in your operations
Many businesses use AI without realizing its intellectual property potential. Common applications outside the traditional tech sector include predictive maintenance systems that analyze equipment data to prevent failures (e.g., machine learning algorithms predicting equipment failure in manufacturing), quality control processes using image recognition and pattern analysis (e.g., AI-powered visual inspection for microscopic defects in electronics), and risk assessment systems that combine multiple data sources for enhanced accuracy (e.g., a weather-risk analysis platform using machine learning). Each of these applications can represent valuable intellectual property protectable through patents, trade secrets, or licensing.
Conclusion
The rapid growth in AI patent applications and the increasing sophistication of AI implementations across industries present both opportunities and risks. Companies, particularly those not traditionally identified as 'tech companies,' should assess the potential value of their AI-related inventions for patent or trade secret protection. With the surge in generative AI patent applications globally, it is crucial for businesses to develop comprehensive strategies to protect their AI-related intellectual property and manage associated risks, considering patent protection, trade secret strategies, or licensing opportunities based on specific AI implementations and business goals.
