How AI adoption is reshaping IP practice

New research from the Clarivate Centre for IP and Innovation Research shows that AI adoption in IP has increased from 57% in 2023 to 85% in 2025, marking a shift from pilots to embedded workflows. Confidence correlates with breadth of exposure, but trust remains conditional on governance and explainability. AI is becoming an operational layer of IP practice; its absence, rather than its presence, increasingly requires justification. Viewed through the data, this pattern signals a maturing discipline.

This piece is the first in a three‑part blog series expanding on insights first published in The Patent Lawyer Magazine, exploring how AI adoption is evolving across the IP landscape.

The relationship between artificial intelligence (AI) and intellectual property (IP) has long been implicit in their shared root: intellect. This connection is more than linguistic; it reflects a deeper truth about the field. IP is one of the few systems designed to recognize, protect, and assign value to human creativity according to specific standards. AI, meanwhile, is a technology that seeks to model and extend the capacities of that creativity.

As AI settles into IP workflows, practitioners find themselves at an inflection point; deeper integration must be balanced against governance, the effort required to validate outputs and emerging questions about where efficiency gains accrue across the wider IP ecosystem.

Research from Clarivate offers a longitudinal view of how AI is reshaping practice. AI adoption in IP has increased substantially in recent years, marking a shift from pilots to embedded workflows. Confidence correlates with breadth of exposure, but trust remains conditional on governance and explainability. AI is becoming an operational layer of IP practice; its absence, rather than its presence, increasingly requires justification.

A great deal has changed in a short time. Large language models have proliferated across industries, and legal tech has attracted significant investment as models become increasingly fine tuned and domain specific. Meanwhile, IP law continues to be tested. Judicial decisions affirm the necessity of human creative contribution. PTOs and industry bodies have issued new guidance on AI use in search, drafting and prosecution, and several offices have begun integrating AI into their own examination workflows.

On the regulatory front, the EU AI Act imposes new obligations for deployment, including requirements around organizational AI literacy and training data quality. Broader governance standards continue to mature, shaping expectations around transparency and accountability.

This context matters because it clarifies what this research does not attempt. It does not address doctrinal questions of IP law or assess model capability. Instead, it examines something often missing in discussions of AI in IP: practitioner sentiment, benchmarked over time. Sentiment reveals whether AI performs as intended in real workflows. Oversight and involvement remain preconditions for trust.

Understanding these perspectives helps identify barriers, trust gaps and unintended effects early. These insights are essential for developing AI that is reliable, explainable and scalable, particularly as scrutiny intensifies around training data provenance and the quality of the patent and scientific corpora on which models depend.

AI’s role in IP is no longer speculative. It is now a material, measurable influence on how teams conduct research, analysis and operational work. Yet its advancement depends on more than technical capability — it hinges on trust, governance and responsible integration. As organizations continue exploring what AI can do, they must also remain clear eyed about when and why human judgment must prevail.

Want a deeper look at how AI adoption is reshaping IP teams globally? Explore how Clarivate supports responsible AI adoption in IP.

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