Fixing the Patent Paradox: Guiding Principles for AI in IP

Patents are vital to protecting innovation and driving competitive advantage, yet IP teams managing them are often seen as cost centers rather than growth drivers.

Drawing on his experience as CEO and co-founder of Amplified, Sam Davis explores how AI — when implemented thoughtfully with people and process at the core — can resolve the Patent Paradox and transform IP from administrative burden into strategic engine.

Patents are among the most valuable assets companies own. They protect innovation, secure competitive advantage, and often define market leadership. Yet the teams responsible for creating and managing these assets are almost universally treated as cost centers — buried in deadlines, manual work, and administrative overhead.

This is what I call the Patent Paradox. I firmly believe that AI gives IP teams a real opportunity to solve it. And this isn’t just me riding the hype cycle. I started Amplified in 2017 and haven’t changed my tune since. There is no longer a question that the technical barriers can be overcome. IP teams can move from cost center to growth driver, but doing that successfully requires more than just technology. People and process is the key. Now that AI has enabled radically new possibilities, IP leaders must think carefully about how they implement it.

What do I mean by thoughtful implementation? I mean critically thinking about how current systems are setup and questioning our assumptions about how work must be done.

Take a common example: most companies accept that going from invention disclosure to a filed patent application takes 2 to 12 months. That timeline is driven by handoffs between inventors, attorneys, and searchers. Each step requires documentation and discussion, which inevitably slows things down.

With AI agents, the entire process can now be completed in a single day: invention captured, prior art searched, differentiation suggested, and a draft specification ready for review.

This isn’t just about speed. It’s also about empowering both R&D and IP with actionable information in real-time while freeing them from the grind of manual work so they can focus on strategy: which inventions to pursue, how to align the portfolio with business goals, and where to invest for maximum impact.

Over the past 15 years in IP and nearly a decade working with AI — specifically deep learning and transformers — I’ve seen this challenge from both sides. When we started Amplified in 2017, we saw AI as a transformational technology for IP. But to unlock its potential, we had to solve a deeper problem: how patent knowledge itself is represented.

That recognition led us to spend three years building a proprietary language model and an AI-indexed database that could make global patent information usable at scale. This was a fundamental departure from how traditional patent databases are built, and it opened up new possibilities for search, analysis, and drafting.

We also saw a pattern repeat across countless companies. Many tried to adopt AI by supplementing their main patent database with one or two AI tools on the side. On paper, it looked prudent. In practice, it almost always failed. Users had to juggle multiple tools, efficiency dropped, and because people were busy and wary of wasting time, most defaulted back to the familiar system. A few power users might stick with the new tool, but adoption at scale rarely happened and actual efficiency gains hard to quantify.

We saw this happen with all of our competitors and experienced it ourselves too. So we made an early decision to shift our approach. We built out full traditional functionality so that end users could always get their job done in one place. We partnered with customers to jointly define success criteria before buying and then worked together to ensure that we delivered.

The experience taught us that having an excellent product and great AI is just the price of entry. You can’t succeed without it, but having it isn’t enough. AI must be embedded directly into core workflows, not bolted on as an experiment. Real change requires removing friction, building trust, and making AI a natural extension of existing processes. In other words: it’s all about people and process.

From these experiences, we distilled a set of guiding principles to help leaders think about how to integrate AI meaningfully into their IP operations.

1. Build on a Solid Knowledge Foundation

AI is only as good as the data it works with. Patent data is complex and messy, and context is critical. Leaders should ensure that knowledge is captured and structured in ways that make it usable for both humans and machines.

Investments in data foundations deliver more value than algorithms and fine-tuning.

2. Reduce Friction, Don’t Add It

AI adoption fails when it adds to workload. IP teams are already under intense pressure, so switching systems or juggling an extra tool usually leads to wasted effort and partial adoption. Real change requires embedding AI into existing workflows so it feels seamless.

AI must feel like the same workflow — only faster and smarter.

3. Respect and Augment Expertise

Patent professionals bring decades of skill. AI should not replace that expertise but extend it. Sometimes that means automating parts of the work, but trust is essential. That trust comes from how the technology is implemented and from ongoing dialogue between the people building AI and the people using it.

Augmentation, not replacement, drives adoption and trust.

4. Capture and Reuse Knowledge

Insights too often vanish into static reports that are never revisited. AI can speed up analysis, but the real opportunity is creating a durable system of record: reusable knowledge tied to patent information and available for future strategy.

Think beyond speed — design AI systems to make your organization smarter over time.

5. Demand Autonomy With Accountability

In high-stakes work like IP, blind trust in AI won’t work. Leaders should insist on outputs that are auditable and reproducible so humans can evaluate and take responsibility. This is essential for building confidence and driving adoption.

AI should be both autonomous and accountable.

Transforming IP from a cost center to a growth driver won’t happen overnight, and AI alone is not the magic solution. But it is a powerful tool for the first step: freeing IP teams from the time-consuming work that dominates their current day-to-day.

There are other challenges ahead, but this is where the journey begins. The leaders who thoughtfully embed AI into their workflows today will be the ones who turn IP into a true driver of growth tomorrow.

Curious what Amplified’s new AI agents can do? Stop by our booth at IPSW to hear how they’re transforming search and drafting — and what’s next.