Infographic: Generative AI’s Adoption by Trademark Attorneys

A snapshot of how trademark professionals are adopting GenAI

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Generative AI is quickly moving from experimentation to practical use in intellectual property. Trademark professionals are beginning to integrate AI into day-to-day workflows, driven by rising client expectations, competitive pressure, evolving regulation, and the availability of more secure, IP-focused tools.

To help trademark teams understand where the market is heading, we created this infographic based on findings from Questel’s 2026 Industry Outlook, highlighting how IP professionals are approaching GenAI adoption and where they see the most immediate value.

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3 signals trademark teams should pay attention to

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• AI adoption in IP is no longer marginal: According to Questel’s 2026 Industry Outlook, 82% of surveyed IP professionals plan to increase their use of AI in 2026. AI is no longer a side experiment: it is becoming part of how IP teams think about productivity, responsiveness, and service delivery.

• Trademark use cases are becoming concrete and workflow-driven: The most frequently cited use cases are not futuristic. They are highly practical: trademark search, office action management, IP reporting, drafting goods and services, and docketing. In other words, AI is starting to support the real operational backbone of trademark work.

• The real differentiator is no longer access to AI, but access to the right AI: As adoption grows, trademark teams need more than a general-purpose AI assistant. They need solutions that are secure, reliable, and built around actual IP workflows, with the right safeguards, the right sources, and the right level of trademark expertise.

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→Explore the infographic: Anatomy of GenAI’s Adoption by Trademark Attorneys

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This infographic highlights:

• Why GenAI adoption is rising in IP

• Which trademark workflows are seeing the most interest

• What is changing for trademark professionals in practice

• Why IP-specific, secure AI tools are becoming increasingly important.

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What these findings mean for trademark teams

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• AI is moving into day-to-day trademark work: The data suggests that AI adoption in trademark is shifting from general curiosity to workflow integration. Teams are not simply testing AI in isolation, they are starting to apply it to recurring, time-consuming tasks such as search, reporting, drafting support, office action review, and administrative work. That shift matters because these are the workflows where speed, consistency, and quality can have a direct impact on both internal efficiency and client satisfaction.

• The most valuable use cases are practical, not experimental: One of the clearest takeaways from the infographic is that trademark professionals are prioritizing use cases that help them work faster and better within existing processes. Rather than looking for abstract “AI transformation,” they are looking for support in the work they already do every day. This is an important signal for both firms and in-house teams: the value of AI in trademark will likely come less from replacing expertise and more from helping professionals accelerate analysis, reduce repetitive effort, and improve turnaround times.

• Tool selection is becoming a strategic decision: As AI adoption increases, the question is no longer whether trademark professionals will use AI, but which tools they can trust. Data confidentiality, quality of output, source reliability, professional standards, and trademark-specific relevance all become central. That is especially true in IP environments, where generic tools may not reflect the nuance of trademark work, the need for controlled data handling, or the importance of producing outputs that can be reviewed and relied upon by professionals.

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Why GenAI adoption is accelerating in trademark and IP

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The infographic points to several forces pushing adoption forward:

• Changing expectations: Corporate legal and IP teams are already seeing AI adopted across branding, R&D, and other adjacent functions. As a result, expectations are changing: trademark and IP teams are increasingly expected to explore where AI can improve efficiency, responsiveness, and collaboration.

• Evolving regulation and clearer guidance : New regulations and professional guidance are helping organizations think more concretely about how to use AI responsibly. Rather than slowing adoption entirely, this can help create the conditions for safer implementation.

• Better security options : Corporate-grade LLM environments and professional tools with tighter data controls are helping reduce one of the biggest barriers to adoption: confidentiality concerns.

• Competitive pressure : As more firms and teams experiment with AI, standing still comes at a cost. Slower turnaround times, higher production costs, and reduced perceived innovation can all become competitive disadvantages.

• The rise of IP-specific solutions : Perhaps most importantly, the market is moving beyond generic AI assistants. Solutions designed specifically for IP workflows are beginning to emerge, making it easier for trademark professionals to use AI in a way that aligns with their actual work.

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How Qthena supports trademark workflows

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Qthena was built to help IP professionals apply AI to real trademark and patent work, not just to generic text generation tasks.

Unlike general-purpose AI tools, Qthena is designed around the needs of IP teams, with a focus on workflow relevance, output quality, collaboration, and data safety.

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Qthena helps trademark professionals with:

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• Trademark-specific workflows : Qthena supports key trademark tasks and workflows, helping teams apply AI where it can create immediate operational value.

• Higher-quality, more relevant outputs : With IP-aware prompting, structured workflows, and features designed around professional use cases, Qthena helps teams produce outputs that are more aligned with trademark work than generic AI tools typically are.

• Secure, professional use of AI : Qthena is designed for professional environments where confidentiality, trust, and control matter. That includes strong attention to how data is handled and how AI can be used within a secure workspace.

• Better collaboration across teams : Qthena also helps teams work together more effectively by supporting review, collaboration, and shared workflows across contributors, stakeholders, or clients.

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See how these AI trends translate into real trademark workflows

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If your team is exploring how to apply AI to trademark work, from search and office action analysis to reporting, drafting, and docketing, we’d be happy to show you how Qthena supports those workflows in practice.

📅 →Request a demo

See how Qthena can help your team work faster, improve consistency, and adopt AI more confidently in trademark workflows.

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