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Nearly every law firm markets some version of AI: “AI-First,” “AI-Enabled,” “AI-Native.” In a byline for LJN Cybersecurity Law & Strategy, Landfall IP co-founders Michael Drapkin and Nathan Mutter describe what actually separates these firms.

Most firms are adding AI to a workflow built decades ago for human labor and hourly billing. A smaller group built the workflow around the technology from the start. The difference shows up in where the work happens, who orchestrates and reviews it, and how that translates into cost and quality.

The article gives legal departments and founders five questions to ask: how AI is integrated across the full patent lifecycle, where senior attorney judgment stays central, how confidential invention data is secured, whether the firm can document that security, and how the work is priced.

Read the full article in LJN Cybersecurity Law & Strategy (subscription required) (or see the text of the article below): AI Native vs. AI Enabled: Questions Every Legal Team Should be Asking their Patent Firms

Michael Drapkin and Nathan Mutter, “AI Native vs. AI Enabled: Questions Every Legal Team Should be Asking their Patent Firms,” LJN Cybersecurity Law & Strategy, September 2026.

Reprinted with permission from the LJN Cybersecurity Law & Strategy, September 2026 © 2026 ALM Global Properties, LLC. All rights reserved. Further duplication without permission is prohibited, contact 877-256-2472 or [email protected].

AI has become the largest technology initiative shaping the legal market with both vendors and law firms marketing the next revolutionary AI capability or service. Firms now promise to protect innovations faster and cheaper and are touting a variety of labels to prove it. “AI-First.” “AI-Enabled.” “AI-Native.”

The challenge is that much of the messaging has become marketing shorthand. Labels such as AI-enabled and AI-first often describe the adoption of new technology, but they reveal very little about how the services are delivered or how they will produce better business outcomes for clients. Two firms may both claim to have AI built into their patent processes but operate in fundamentally different ways. For start-ups and established organizations, protecting innovations is core to long-term business advantage, which is why understanding how a firm operates matters more than how it markets itself.

AI-Enabled vs. AI-Native: What’s the Difference?

All law firms serving the patent industry have the same goal: help inventors and businesses secure, prosecute, and defend patent rights using attorney-led processes to navigate complex intellectual property laws and USPTO requirements. However, firms are beginning to divide into distinct operating models:

• AI-Enabled. AI-enabled (or AI-first) firms treat AI as a strategic asset to enhance traditional workflows without fundamentally altering them. Attorneys incorporate AI into selected stages of a traditional workflow, such as summarizing invention disclosures, performing prior art searches, generating patent text, or automating administrative work. These investments are designed to improve efficiency, reduce repetitive tasks, and enable attorneys to move more quickly through familiar workflows. For many organizations, this model provides an appropriate balance of innovation and familiarity. The tradeoff is that improvements tend to be bound by the underlying process, which was built around outdated assumptions about how to deliver legal services.
• AI-Native. A new class of AI-native firms is emerging. Rather than adding AI to an existing process, these firms are building the process around it, embedding AI throughout the patent lifecycle, from invention capture and inventor collaboration to drafting, prosecution support, workflow management, portfolio analytics and client reporting. This approach can accelerate the patent filing process often from months to days or weeks, with senior-level attorneys providing expert counsel on the most strategic aspects of the patent process.

Why Do Labels Matter Less Than the Underlying Operating Model?

Whether a firm describes itself as AI-enabled, AI-first, or AI-native, the terminology alone tells legal teams very little. Marketing labels don’t explain how AI is used, where attorney judgment enters the workflow, how confidential information is protected, or whether the technology actually improves the quality and efficiency of patent prosecution.

For legal departments, a more useful exercise is to move beyond the labels and evaluate the firm’s operating model. How does AI change the way inventions are captured? Does it improve collaboration with inventors? Does it shorten filing timelines without sacrificing patent quality? Can the firm demonstrate better consistency, greater transparency, or more predictable costs?

How Do You Navigate Through the Fog?

The following five questions can help legal teams distinguish marketing claims from meaningful operational differences:

1. How Has AI Been Integrated Into Your Patent Process?

It’s important to understand whether the entire process is just faster or it’s fundamentally different. Firms should be able to demonstrate whether AI has been layered onto isolated tasks or integrated across the entire lifecycle. Can the firm explain how their workflow design choices improve patent quality, enhance client visibility, deliver consistency, as well as support faster turnaround times?

2. What Parts of the Process Are Being Accelerated By AI Versus Managed By the Legal Team?

Strong patents begin long before drafting starts. They depend on comprehensive invention capture, thoughtful collaboration with inventors, careful claim development, strategic prosecution planning, and consistent execution across an entire portfolio. When AI is integrated throughout the process rather than applied to a single task, each of these activities can improve. The firm should be able to guide inventors through an AI-led structured disclosure intake process that ultimately provides legal teams better access to technical context and prior work for stronger filings.

3. Where Does Attorney Judgment Enter the Workflow and What Level of Expertise Is Provided? Who Is Doing the Work?

Perhaps the greatest misconception surrounding the use of AI in patent filings is that it will replace the role of attorneys and outside counsel. Patent law remains one of the most judgment-intensive areas of legal practice. Determining inventorship, developing claim strategy, evaluating patent eligibility under 35 U.S.C. §101, distinguishing prior art under §§102 and 103, responding to examiner rejections, and aligning intellectual property with business objectives all require experienced legal counsel. It’s important to understand the level of service and experience of the attorneys supporting the client. Traditional firms tend to rely heavily on junior associates and engineers to manage strategic, repeatable tasks, while senior counsel review the work after it’s been drafted. In a native-AI model, many of these repeatable tasks are streamlined with AI, enabling senior counsel to provide greater strategic insight and support throughout the process.

4. How Secure Is the AI? How Is Confidentiality, Invention Capture, Claim Strategy and Prosecution Quality Protected Throughout the Process?

AI should accelerate the patent process without compromising the security of highly confidential intellectual property. The firm should provide details on how AI is deployed, where data is stored, who has access to it, and whether the platform uses enterprise-grade security controls, such as encryption, role-based access, audit logs, and secure cloud infrastructure. Many firms rely on legacy systems. It’s important to ask whether invention disclosures, claim strategies and prosecution data remain within a private, controlled environment and are never used to train public AI models, as well as whether the firm is SOC 2 compliant. Throughout the process, AI should operate within a governed workflow that protects confidential information, maintains complete traceability of changes and ensures that every AI-assisted output is reviewed and validated before it becomes part of the official patent record.

5. What Is Your Firm’s Billing Model for Patent Prosecution? How Does This Impact Potential Risk, Costs, or Process Visibility?

A firm’s pricing model often reveals as much about its operating model as its technology. Traditional patent firms have typically kept their pricing stable despite gains in AI, and pricing often remains opaque. It’s important to have open, transparent conversations about newer billing model options and strategies for sharing the benefits of increased efficiency between clients and the firm.

Conclusion

AI in patent prosecution is quickly becoming common across the industry. Most firms are already adopting or using it in some form, and we all know that AI capabilities will continue to evolve. New labels will almost certainly replace today’s “AI-enabled” and “AI-native” marketing language. Regardless of how firms market their AI capabilities, it is the responsibility of legal departments to select the partner(s) best equipped to protect innovations and strengthen their organization’s IP portfolio. Asking thoughtful questions about how AI is built into the entire process will provide far more insight than any marketing label.

Michael Drapkin, Managing Partner and Founder of Landfall IP, an AI-Native law firm. He is widely recognized as one of the top patent attorneys in the U.S., guiding and assisting clients in the development and management of their patent portfolios. He has served as the top outside counsel at Qualcomm, Micron, and Salesforce, among many others, and practice innovation and automation have long been at the center of his work. Among his many accolades, Michael was named the U.S. National Practitioner of the Year for Patent Prosecution for 2022 by Managing IP, and was a finalist in 2021 and 2023 as well.

Nathan Mutter, Managing Partner and Founder of Landfall IP, an AI-Native law firm. He has counseled some of the world’s greatest technology companies in developing and maximizing the value of their IP. He has served as top outside counsel at Oura, Coinbase, Rubrik, and Salesforce among many others. Nathan has pioneered the creation of automation systems and tools that enhance efficiency across all aspects of legal services. He works with clients throughout their lifecycle, from startups to large public companies.