Staying compliant with copyright laws while maximizing research productivity can feel like walking a tightrope. Researchers now juggle AI tools, digital collaboration platforms, and an expanding web of copyright complications.
But understanding copyright doesn't have to be a barrier to innovation. And increasingly, new infrastructure is emerging to simply compliancy and reduce the burden on the path toward innovation.
Recent data paints a concerning picture: while AI adoption has reached 97% in some industries, only 77% of professionals are familiar with their organization's copyright policy. Even more worrying, just 62% understand how copyright applies to AI when using third-party data. This knowledge gap creates significant risks for researchers and their institutions.
The disconnect gets even more stark when you look at actual usage patterns. Around 76% of researchers now incorporate AI tools into their workflows and want to analyze scientific content with these platforms. They're not asking permission or waiting for clear guidance. They're already doing it, which means many organizations are operating in a risky gray area without realizing the negative consequences.
Researchers often view copyright as a roadblock rather than a framework for proper content use. You're likely dealing with some combination of these roadblocks.
Over half of surveyed researchers need to use copyrighted material for internal research purposes. Without clear understanding of usage rights and limitations, this can lead to delays, or even full stoppage of work, when it comes to sharing crucial findings with team members. The situation gets trickier when researchers want to upload journal articles into AI platforms for analysis and summarization. Most major publishers explicitly prohibit this use without express permission, even for internal research purposes, but the infrastructure for easily obtaining those permissions has been lagging behind the technology by several years.
Managing documentation and permission requirements for regulatory submissions demands meticulous attention to detail. Without proper planning, the process of securing necessary copyright permissions can create unexpected delays in regulatory timelines, potentially impacting crucial research milestones and compliance deadlines.
Sharing findings with external audiences requires careful attention to reuse rights. The shift toward digital collaboration tools (now used by 38% of researchers, up from 33% in 2022) has made this even more complex, as content sharing becomes increasingly easy to do but potentially risky from a compliance standpoint.
Researchers frequently need to adapt published content, such as graphs or images, to effectively communicate their findings. However, even minor modifications can require specific permissions, creating uncertainty about what's allowed.
While open access publishing seems to offer a solution, it comes with its own complexities. Creative Commons licenses, particularly the distinction between commercial and non-commercial use, can be confusing.
Remember: even with open access content, the law requires proper attribution. Good academic practice aside, you're legally obligated.
The emergence of AI tools has introduced fresh headaches for copyright compliance. The rising number of AI-related lawsuits highlights the importance of understanding how copyright applies when using AI tools with third-party content. Using AI doesn't circumvent copyright requirements. Derivative works created through AI may still require permission.
But it gets more complicated still. Most publishers' licenses and terms clearly state that their content can't be used in major AI applications like ChatGPT, Microsoft Copilot, Claude, Gemini, or DeepSeek without express permission. This includes scientific workflow applications like Elicit, Consensus, Readcube Papers, or SciSpace that don't often have license agreements with publishers and place the burden on users to know whether they have the required rights.
The reason for these restrictions makes sense from a publisher's perspective. Without controls, AI models can consume the content and use it for training and evaluation, often becoming permanently incorporated into the models themselves. Many corporate researchers have "enterprise" licenses for AI applications that prevent content from being used to train models, but that doesn't solve the copyright permission problem.
The Copyright Clearance Center (CCC) and Copyright Licensing Agency (CLA) have both developed licensing solutions aimed at addressing this gap. In July 2024, CCC introduced AI re-use rights within its Annual Copyright Licenses, making it the first collective licensing solution for internal use of copyrighted materials in AI systems. Similarly, the CLA expanded its business and public sector licenses to include workplace AI permissions. These programs represent genuine progress in creating structured frameworks for AI use.
However, there's a catch. Corporate licensing professionals, librarians, and STM publisher representatives report that the majority of scientific, technical, and medical (STM) content isn't covered by these collective licenses. Neither licensing program explicitly names major STM publishers as participants. So, while these collective licenses offer valuable structure, researchers still need to verify their specific coverage before integrating AI into their workflows. Organizations must confirm with CCC and CLA whether the publishers they rely on most are actually included.
Meanwhile, major publishers are engaging directly with customers to extend the rights needed for AI use, though these negotiations can be lengthy and come with specific conditions. The pace of open access publishing has also slowed, meaning most new STM content still isn't published openly. And publishers have increasingly adopted Open Access licenses with non-commercial restrictions, further limiting what commercial researchers can do.
What's become clear is that there's a significant gap between what copyright agencies currently cover and what publishers are beginning to address directly. Specialized solutions are starting to emerge that help fill this gap through direct publisher partnerships, giving researchers clear article-level visibility into their AI usage permissions and straightforward ways to acquire missing rights. These solutions work by automatically detecting existing permissions from Open Access licenses, reproduction rights organization agreements, and direct publisher relationships, then providing simple acquisition paths when rights don't already exist.
New research tools are expanding what’s possible, while simultaneously raising new questions about copyright compliance.
When researchers understand and actively manage copyright requirements, they can move forward with confidence while respecting intellectual property. Rather than a barrier, copyright becomes a framework for responsible content use that supports both ethical research practices and legal compliance.
Copyright compliance is not passive. It requires staying informed and proactive, because your research and your institution depend on it.
The goal is balance, enabling innovation while honoring copyright’s core purpose of advancing knowledge and protecting intellectual property. Encouragingly, the gap between AI capabilities and copyright infrastructure is beginning to narrow. Publishers, rights organizations, and technology providers are developing more streamlined, researcher-friendly approaches to help navigate permissions and compliance more efficiently.
By embedding copyright considerations into your research strategy from the start and leveraging emerging tools that simplify the permissions process, you can continue pushing the boundaries of discovery while maintaining ethical and legal integrity.