Artificial intelligence is rapidly being integrated into products, services, and internal workflows across nearly every industry. Companies are using AI to design products, analyze data, generate content, and automate decision-making at scale. While these capabilities create real business value, they also introduce new and often misunderstood intellectual property (IP) risks.

Organizations that move quickly to deploy AI without addressing these issues can face uncertainty around patentability, ownership, training data rights, and downstream liability. Understanding these risks before releasing AI-enabled products is critical to protecting innovation and avoiding costly disputes.

How AI Impacts Patentability

One of the first questions companies ask is whether AI-related innovations can be patented. In many cases, the answer is yes, but the analysis is more complex than with traditional technologies.

Patent eligibility issues often arise when AI-related inventions are framed too abstractly or rely heavily on software-based processes. In the United States, inventions that appear to be mathematical concepts or generic data processing may face challenges under current patent eligibility standards. Successful AI patents typically focus on specific technical improvements, such as how a model is trained, how data is processed, or how AI is applied to solve a concrete technical problem.

Companies should also be mindful that AI tools themselves cannot be named as inventors under current U.S. law. Human inventorship remains a requirement, which means companies must carefully document the role of engineers, data scientists, and developers involved in the invention process.

Ownership of AI-Generated Outputs

Ownership questions become more complicated when AI systems generate content, designs, or technical solutions. Businesses often assume they automatically own what an AI tool produces, but that assumption is not always correct.

Ownership depends on several factors, including who developed the AI system, who operates it, and what contractual terms apply. If third-party AI platforms or tools are used, their terms of service may limit ownership rights or grant the provider broad licenses to outputs or underlying data.

For internally developed AI tools, companies should ensure that employment and contractor agreements clearly assign intellectual property rights related to AI development and use. Without clear agreements, disputes may arise over whether AI-generated outputs belong to the company, the individual user, or a third-party provider.

Training Data and Rights Management

Training data presents one of the most significant risk areas for AI-driven products. AI models are only as good as the data used to train them, but using data improperly can expose companies to legal and regulatory challenges.

Key questions include whether the company has the right to use the data, whether the data includes copyrighted material, and whether it contains confidential or proprietary information. Publicly available data is not necessarily free from restrictions, and scraping or ingesting content without permission can create infringement risk.

Companies should conduct a careful review of data sources used for training and fine-tuning AI models. This includes understanding licensing terms, documenting permissions, and implementing controls to prevent the inclusion of sensitive or protected information. For companies handling customer or client data, privacy and confidentiality obligations must also be addressed.

Risk Exposure Beyond Intellectual Property Ownership

AI-related IP risks extend beyond patents and ownership. Companies may face exposure related to trade secrets, regulatory compliance, and contractual obligations.

For example, deploying AI tools that rely on proprietary business information can inadvertently expose trade secrets if safeguards are not in place. Sharing prompts, datasets, or outputs with third-party platforms may result in loss of confidentiality or unintended disclosure.  AI systems that generate content can also introduce materials that may be subject to third party rights.

There is also increasing scrutiny around how AI systems make decisions and generate results. In some industries, regulatory requirements may apply to explainability, transparency, or data handling practices. While these issues are not strictly IP-related, they often intersect with how intellectual property is created, protected, and shared.

What Companies Should Do Before Releasing AI-Enabled Products

Before launching AI-driven products or workflows, companies should take proactive steps to manage intellectual property risk.

First, conduct an IP audit focused specifically on AI use. This includes reviewing patents, trade secrets, copyrights, and trademarks that may be implicated by the technology. Understanding what the company owns, what it licenses, and what it relies on from third parties is essential.

Second, review contracts with AI vendors, developers, and collaborators. Terms related to ownership, licensing, confidentiality, and data use should be clearly defined and aligned with the company’s business goals.

Third, document AI development processes. Clear records of human involvement in model design, training, and deployment can be critical for patent filings and ownership determinations.

Finally, involve legal and technical teams early. Waiting until after a product launch to address IP concerns often limits available options and increases risk.

A Strategic Approach to AI and Intellectual Property

AI offers significant opportunities for innovation, but it also challenges traditional IP frameworks. Companies that take a strategic approach to AI-related intellectual property are better positioned to protect their investments, reduce uncertainty, and move confidently into the market.

For companies developing or deploying AI-enabled products, early guidance from experienced IP counsel can help reduce risk and ensure that innovation is supported by a clear and defensible intellectual property strategy. Contact Conley Rose to discuss how your organization can navigate AI with greater clarity and confidence.