Juristat Blog | Patent analysis and insight

5 Practical Ways to Implement AI in Your Patent Practice

Written by admin | 2/27/26 3:54 AM

Most patent teams aren’t debating whether AI belongs in the practice anymore. They’re debating something sharper. Where does it actually work? Where does it break? And how do you use it in a way you can defend to clients, partners, and your own risk team?

In a recent panel discussion hosted by Juristat and IPWatchdog, practitioners who are already deep into AI adoption shared what’s working right now in prosecution and adjacent workflows.

What they share were some hard truths: There are no magic AI buttons. No “50% savings” fantasies.

But there are very practical ways you can start implementing this quarter.

1. Start with defined-scope tasks

If you want quick wins, stop asking AI to “be the navigator.”

Ask it to be the engine.

Ian Clouse, partner at Holland & Hart put it simply: AI performs best on problems with a defined scope. When you give clear inputs, constraints, and specific questions, you get better output and real speed.

Try it on:

  • Drafting definitions for claim terms you already drafted
  • Outlining sections of an application based on invention disclosure materials
  • Turning a set of notes into a structured first pass you can refine
  • Summarizing a single document or a small set of documents with a clear purpose

Avoid “draft me a patent” prompts.

That’s where it breaks.

Your goal is repeatable tasks with predictable structure. That’s how you build confidence and adoption across the team.

2. Use AI to upgrade drafting quality, not just reduce hours

A recurring theme from the panel: AI is already improving work product more consistently than it improves time savings.

That matters.

Clients may be asking for discounts because you’re “faster.” But the real leverage right now is higher quality.

Better issue spotting.

Better consistency.

Fewer missed details.

Clouse noted that for discrete drafting components, AI can produce output that competes with a brand-new associate’s first pass. That doesn’t replace judgment. It reduces the time you spend getting from a blank page to something usable.

The practical move:

Use AI for the first pass. Then spend your time doing what you’re paid for.

Refining, strategizing, making your work defensible, and then iterating again when it improves.

IPWatchdog’s Gene Quinn described the loop well: draft, critique, iterate, refine, iterate again. Stop when the next iteration stops improving.

That’s the human-centric use of AI that actually works.

3. Put AI in the room for calls, then turn it into deliverables

This one is low friction and immediately useful.

Instead of trying to take perfect notes in an inventor call, let AI capture the conversation.

Then turn it into a memo, follow-up email, or action list.

Clouse shared that for any email that’s hard to write, he’ll record himself saying what he needs to say, then use AI to synthesize it into a clean draft. That can remove a surprising amount of time spent on “blank screen” work.

Charles Gray, partner at Kilpatrick Townsend, took it further. He described using AI as if it’s literally in the room, telling it what to key in on, then using the transcript to extract the important pieces.

Try this workflow:

  • Record the call (within your firm’s approved tools and policies)
  • Generate a transcript
  • Prompt AI to extract key points, questions, decisions, and next steps
  • Prompt AI to draft a client-ready recap email
  • Prompt AI to generate follow-up questions for the inventor

This doesn’t replace the call.

It makes the output from the call more consistent, more usable, and easier to execute.

4. Build simple “agents” for repeatable workflows

Gray shared a real example that many in-house teams will recognize: invention intake overload.

Hundreds of ideas, multiple channels, and not enough time to triage them intelligently.

His approach was not “use AI to write more words.”

It was “use AI to build tools that organize decisions.”

He described creating lightweight agent workflows that ingest invention disclosures, transcripts, and inventor messages, then organize them into discrete ideas with recommendations like:

Yes, file.

No, don’t.

Maybe, need more info.

Then layer in scoring criteria that match the business: patentability, usefulness, detectability, novelty.

This is the key shift. You stop thinking of AI as a chat box.

You start thinking of it as a workflow component you can design.

Francesca Cruz, SVP of IP Solutions at Juristat, echoed this: build a strong template once, then reuse it with better inputs.

With Juristat’s MCP server, you can feed your chosen LLM or AI program with the industry’s best patent analytics data. Current users are implementing this solution for office action response strategies, RFP decks and more. What once took hours becomes a repeatable process that produced high-quality drafts in under an hour, with human review.

Your takeaway:

If a task repeats, it can be templated.

If it can be templated, it can be scaled.

And if you use Juristat, the outcome can be defended.

5. Make governance easy, or people will route around it

AI policy is not optional. But AI policy that blocks work will get ignored.

Quinn said the quiet part out loud: your people will use AI whether you want them to or not. If you don’t give them a safe, approved way to do it at work, they’ll do it at home on personal devices.

That’s your worst-case outcome.

The panel’s practical governance advice:

  • Give people access to an approved LLM environment
  • Put sensible guardrails around it
  • Train on repeatable use cases
  • Encourage sharing and cross-pollination
  • Retrain often, because tools change fast

Clouse framed AI adoption as a mindset and a skill. Not a one-time rollout. More like building the habit of opening your LLM alongside email each morning.

Gray added the human truth: it takes one-on-one evangelism. You have to show people. More than once. In the way they work.

If you want safe adoption, make the “blessed place” the easiest place.

AI reality check

AI does not automatically create savings.

Efficient AI workflows do. When you:

  • Define the scope
  • Choose tasks that repeat
  • Build templates and guardrails
  • Ground output in trusted data
  • Keep humans in the loop

You get a practice that’s faster and safer.

And you can actually answer the question clients are now asking:

“If AI made you faster, where are my savings?”

Want help making AI work for your team? Juristat helps patent teams ground AI-enabled workflows in defensible USPTO-backed analytics.