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AI has moved from isolated experimentation to daily use in patent practice.
In fact, a Clarivate survey claims that AI adoption among IP and R&D professionals has skyrocketed from 57% to 85% in only two years. Among law firms alone, the American Bar Association found the use of AI tools to have tripled from 2023 to 2024.
Patent drafting assistance, prosecution support, and workflow automation are no longer theoretical. Yet as more firms evaluate AI patent tools for real-world deployment, many encounter the same pattern: early enthusiasm, followed by uneven adoption.
That gap between promise and practice was the focus of a recent DeepIP webinar featuring Andy Newland, IT Director at Mewburn Ellis, who shared how the firm approached the evaluation of AI patent tools—and what determined whether a tool would endure beyond the pilot phase.
Early in the discussion, Newland challenged a common assumption shaping many evaluation processes: “The point is not about the smartest model…it’s about relying on the technology that disappears.”
Rather than treating evaluation as a feature comparison exercise, Mewburn Ellis reframed it as a question of long-term fit inside real patent workflows.
Why Evaluating AI Patent Tools Is Harder Than It Looks
Most AI patent tools perform well in demonstrations. The real test begins later, when attorneys are under deadline pressure and revert to familiar habits.
At Mewburn Ellis, “Success at scale is about adoption,” said Newland. “It’s not the year of the smartest model anymore.”
Beyond capabilities alone, tools that added friction, demanded attention, or lived outside existing systems struggled to gain traction once the novelty wore off.
As the legal AI market continues to compete on intelligence, Mewburn Ellis focused instead on whether a tool could be used consistently, quietly, and without ceremony.
Lesson 1: Workflow Fit Is the First Filter in Evaluating AI Patent Tools
Patent work follows well-established patterns. Drafting, review, and prosecution leave little room for experimentation once deadlines loom.
For Newland, choosing the right AI tool was a matter of finding the one that would allow them to “[bring] the technology where people work instead of asking them to change the way they work.”
During vendor evaluation, tools that required attorneys to leave familiar environments or maintain parallel workflows were quickly deprioritized. AI needed to operate inside the workflows attorneys already trusted.
Lesson 2: AI Patent Tool Quality Can Only Be Evaluated in Context
AI output quality varies significantly depending on technical field, jurisdiction, and use case. Benchmarks and generic test cases failed to capture those differences.
“You need to get the attorneys to verify the quality,” said Newland. At Mewburn Ellis, quality evaluation was deliberately placed in the hands of the patent attorneys working on real matters. Their judgment—not vendor metrics—determined whether AI outputs were usable.
Evaluation, in this sense, was less about accuracy in the abstract and more about reliability in daily practice.
Lesson 3: Security Eliminates More AI Patent Tools Than Performance Ever Will
Newland put it bluntly: “There’s no point looking at software that isn’t secure.”
In patent practice, security is not a sliding scale.
During evaluation, tools that could not clearly meet confidentiality, compliance, and governance requirements were removed early. This dramatically narrowed the field and reduced downstream risk.
For firms evaluating AI patent tools, security is not a differentiator—it is the price of entry.
Lesson 4: Short Pilots Rarely Capture the True Value of AI Patent Tools
Initial pilots often underestimate the time required for meaningful adoption. Attorneys need time to build confidence, adapt workflows, and identify where AI genuinely adds value.
Newland cautioned against drawing conclusions too quickly. “You might not see huge benefits in a short trial…but you will in one or two years,” he said.
At Mewburn Ellis, early pilots were treated as directional signals rather than final verdicts. Evaluation focused on trajectory, not immediate impact.
Lesson 5: Evaluating AI Patent Tools Is an Organizational Exercise
Even well-integrated, secure AI tools can fail without internal alignment.
Mewburn Ellis’ experience highlighted the importance of:
- A clear AI policy
- Focused initial use cases
- Internal champions
- Regular, hands-on training
These factors determined whether AI became part of the firm’s operating fabric—or remained an optional experiment.
Evaluation, the firm found, does not end at selection. It continues through governance and change management.
Lesson 6: The Best AI Patent Tools Ultimately Disappear
Perhaps the most revealing insight from the discussion concerned the end state of successful AI deployment. The goal was not visibility, but invisibility—technology that strengthens outcomes without demanding attention.
You know you’ve found the right tool when, as Newland explained, “AI is so much embedded into the way you work…that you don’t see it anymore.”
When AI fades into the background, it has moved from tool to infrastructure.
What This Means for Firms Evaluating AI Patent Tools in 2026
As AI becomes standard across patent practice, the differentiator will not be access to technology, but the discipline to evaluate it realistically.
Firms that succeed will be those that:
- Prioritize IP workflow fit over novelty
- Treat security and trust as foundational
- Evaluate tools over time, not just in pilots
In that environment, the most valuable AI patent tools may be the ones that no longer feel like AI at all.
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