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.
Even when AI implementations are not core to customer-facing products, they warrant strategic protection. Operations-focused enterprises can leverage these innovations for competitive advantage, licensing opportunities, or as defensive assets in a broader IP strategy.
AI patent growth trends
The patent landscape tells a compelling story. The United States has experienced significant growth in AI-related patent applications. According to the US Patent and Trademark Office (USPTO) Artificial Intelligence Strategy Report, AI-related patent applications increased 33% between 2018 and 2024, indicating sustained growth in this area beyond just the recent generative AI boom.
While AI-related patent applications have faced initial higher rates of rejection based on subject matter eligibility challenges at the USPTO, patenting AI-related inventions can be successful with a proper strategy. The USPTO’s August 2025 memorandum clarifies common issues in subject matter eligibility analysis, creating a clearer path to patenting AI-related inventions by narrowing the overuse of the “mental process” rejection category and ensuring examiners only reject applications when ineligibility is more likely than not. Experienced practitioners regularly achieve success by utilizing strategic drafting and prosecution to navigate USPTO challenges, including;
- Emphasizing technical implementation details over business methods. Claims that describe specific hardware configurations are more likely to overcome abstract idea rejections than those focused on business outcomes or end results.
- Highlighting specific technical problems solved by the AI system. Articulating how the invention addresses a concrete technical challenge that were previously roadblocks demonstrates practical application rather than mere automation of an abstract idea.
- Considering strategies that may route an application to a favorable art unit. Strategic claim drafting and characterization of the invention can influence which examination group reviews the application, potentially affecting the likelihood of subject matter eligibility challenges.
Understanding competitive value
The business impact of well-implemented AI systems can be substantial. According to Netflix, approximately 80% of subscribers trust and follow the recommendations of their algorithm, demonstrating how AI-driven systems can fundamentally shape not only customer behavior but business outcomes.
While Netflix’s algorithm directly drives customer engagement, this same principle of AI-driven competitive advantage applies equally to behind-the-scenes operational systems. Manufacturing companies with sophisticated predictive-maintenance datasets could potentially license that technology or leverage it in vendor negotiations, while e-commerce businesses with effective recommendation engines or pricing algorithms possess valuable assets that differentiate them from competitors and can similarly be leveraged through licensing.
Recognizing AI in your operations
Many businesses utilize AI technologies without recognizing them as such. Common applications in companies outside the traditional tech sector include:
- Predictive maintenance systems that analyze equipment data to prevent failures. A manufacturing company developed machine learning algorithms that predict when specialized automated component placement equipment would fail, reducing downtime and extending equipment lifespan. The system learned from sensor data, environmental conditions, and maintenance history to identify failure patterns invisible to human operators.
- Quality control processes using image recognition and pattern analysis. An electronics manufacturer implemented AI-powered visual inspection systems that detect microscopic defects in circuit board assembly with greater consistency than manual inspection. The system continuously improves its accuracy by learning from each inspection cycle and integrating with programmable logic controller (PLC) environments for real-time adjustments.
- Risk assessment systems that combine multiple data sources for enhanced accuracy. A consulting firm, for instance, developed a comprehensive weather-risk analysis platform that combines machine learning and statistical modeling to predict various environmental hazards with increased precision. Because the underlying methodology is adaptable across weather-related risks—from severe storms to extreme temperature events—the system evolved into a valuable platform technology extending beyond its original use case.
Each of these applications, potentially, represents valuable intellectual property that could be protected through patents, maintained as trade secrets, or licensed.
Conclusion
The rapid growth in AI patent applications and the increasing sophistication of AI implementations across industries create both opportunities and risks for businesses. “Non-tech companies” or companies outside the traditional tech sector should seriously consider the potential value of their AI-related inventions as patents or trade secrets. With U.S. patent applications related to generative AI surging in the US and across the globe, companies must develop comprehensive strategies to protect their AI-related intellectual property while managing and mitigating risks.
