Artificial intelligence may dominate the headlines of today, but for William Munck and his firm, Munck Wilson Mandala, it is hardly a novelty. Long before large language models entered the public lexicon, Munck was building self-modifying algorithms – early machine-learning systems capable of adapting in real time – and considering the legal implications of technology that could evolve on its own. What many now call an AI “revolution” looks, from Munck’s vantage point, like an inflection point decades in the making.
“The risk isn’t in adopting this technology thoughtfully – it’s in becoming irrelevant because you were too cautious to adapt,” says Munck.
That long view characterizes Munck Wilson’s AI & Machine Learning practice at a moment when AI is no longer a discrete tool, but quickly becoming a core economic infrastructure. Across industries – from healthcare and defense to energy, communications and finance – artificial intelligence is raising legal questions that traditional intellectual property frameworks were never designed to answer. Matters of patentability, ownership, trade secret protection, regulation and liability are colliding with systems that learn, generate and operate at scale.
What distinguishes Munck Wilson is not simply that it advises on these challenges, but that it has been doing so for nearly three decades. Under Munck’s leadership, the firm has built one of the deepest AI-focused IP practices in the country, grounded in uncommon technical fluency. Its lawyers can read code, assess system architectures, and identify patentable innovations embedded in algorithms and training methodologies – often before clients themselves recognize their legal significance.
“These tools are making lawyers more efficient,” says Munck. “But they’re not replacing the judgment, strategy and client counseling that define good legal work.”
As companies race to integrate AI into products, services and decision-making, the risks of moving too fast – or standing still – are becoming increasingly clear. In that environment, experience matters. Few firms can pair decades of hands-on AI development with deep patent, litigation and regulatory expertise. For Munck Wilson, this moment is not about catching up to artificial intelligence. It is about guiding clients through a transformation the firm has been preparing for all along.
Lawdragon: How are you seeing the AI revolution play out in the legal industry so far?
William Munck: I have a somewhat different perspective on this than most people in the legal industry because I've been living at the intersection of AI and law for nearly 30 years. What many are calling the 'AI revolution' today actually feels more like a continuation and acceleration of trends I've been watching since the late 1980s.
Back when I was working at Axiom Systems, I was coding self-modifying algorithms for laboratory systems – early machine learning that could adapt in real-time based on data patterns. I've been patenting AI inventions for clients for close to three decades now, so I've had a front-row seat to this technology's evolution from neural networks and expert systems through today's large language models and generative AI.
What's different now is scale, accessibility and speed. The fundamental concepts aren't new, but the computational power and data availability have reached an inflection point that's making AI practical for everyday legal work in ways that weren't possible before.
What many are calling the 'AI revolution' today actually feels more like a continuation and acceleration of trends I've been watching since the late 1980s.
In our practice, we're seeing AI impact the legal industry on two fronts. As practitioners, we're seeing AI tools transform legal research, document review, contract analysis and discovery. These tools are making lawyers more efficient, but they're not replacing the judgment, strategy and client counseling that define good legal work. Secondly – and this is where my practice group focuses – we're advising clients on the legal implications of developing, deploying and protecting AI systems.
The IP landscape for AI is incredibly complex. We're dealing with questions about patentability of AI-generated inventions, copyright issues, trade secret protection for models and training data and licensing frameworks that didn't exist even five years ago.
The real revolution isn't just that AI is changing how lawyers work – it's that AI is becoming the substrate of the economy and every business needs sophisticated legal guidance to navigate that transformation. That's where nearly three decades of experience patenting these systems becomes invaluable.
LD: How is Munck Wilson using AI internally?
WM: We're taking a very deliberate and technically informed approach to AI integration at Munck Wilson. Given our background, we understand both the capabilities and limitations of these technologies. We're not just buying off-the-shelf solutions and hoping they work; we're building strategic implementations that improve how we serve clients.
One of the key decisions we made was to hire non-lawyer AI technicians specifically to help us develop workflows and tools for internal use. This is critical. Lawyers are excellent at legal reasoning, but building effective AI systems requires a different skill set – one that understands prompt engineering, system integration, data architecture and how to optimize these tools for specific use cases.
Our AI technicians work alongside our attorneys to identify bottlenecks, repetitive tasks and areas where AI can genuinely add value. They're developing custom workflows tailored to our practice areas, whether that's accelerating patent prior art searches, streamlining contract review or improving our research capabilities.
This is about augmenting our law firm’s capabilities so we can focus on higher-value strategic work for clients. We're also being very thoughtful about data security, client confidentiality and ethical considerations. Having technical staff who understand how these systems work – not just how to use them – gives us much better control over those risks. We are practicing what we preach. We advise clients on AI strategy and implementation, so we need to be sophisticated users ourselves.
LD: For law firm leaders that are hesitant to adopt AI technology, perhaps because of the chance for errors or liability issues, what would you suggest?
WM: I've been in technology long enough to recognize an inflection point and this is one. The question isn't whether AI will make errors, of course it will, just like junior associates, paralegals and even senior partners make errors. The question is whether you're building the competencies to manage those risks and leverage the efficiencies, or whether you're going to wake up in three years and find that your competitors are delivering better work faster and at lower cost while you're still doing things the old way.
Your clients are already using AI, your opposing counsel is using AI and the lawyers you're trying to recruit expect to use AI. The risk isn't in adopting this technology thoughtfully – it's in becoming irrelevant because you were too cautious to adapt. This technology isn't going away and neither is the competitive pressure it creates. You can lead this transition or be left behind by it.
The question isn't whether AI will make errors, of course it will, just like junior associates, paralegals and even senior partners make errors. The question is whether you're building the competencies to manage those risks and leverage the efficiencies.
LD: Tell us about your work for clients in your AI & Machine Learning practice. How are you helping them navigate the opportunities and the risk?
WM: Our AI practice combines innovation protection and risk mitigation. As one of the few law firms with close to three decades of patenting AI inventions, we provide a perspective most firms simply don't have. We help clients across all technology sectors secure patent protection for their AI innovations – whether that's novel algorithms, training methodologies, or AI-enabled systems – while simultaneously advising them on the IP landmines they need to avoid: who owns the output when AI generates content, how to protect proprietary models and training data as trade secrets, what happens when your AI infringes someone else's patents or copyrights and how to structure licensing agreements in an environment where the legal frameworks are still evolving.
We're also counseling clients on regulatory compliance, liability exposure when AI systems make consequential decisions and contractual protection when they're integrating third-party AI tools. Because I actually built self-modifying AI systems back at Axiom Systems, I can speak my clients' language. Clients benefit because our AI team understands this technology at a deep level, which means we can craft legal strategies that protect our clients’ innovations without constraining their ability to compete and innovate in what's becoming an AI-first economy.
LD: What do you want clients to be aware of when it comes to the still-developing regulations for AI?
WM: The most important thing clients need to understand is that AI regulation is happening right now at multiple levels simultaneously – federal agencies like the FTC and SEC are issuing guidance, states are passing their own laws and international frameworks like the EU AI Act are creating compliance obligations even for US companies – and waiting for clarity is not a strategy.
The regulatory landscape is fragmented and evolving rapidly, which means you need to build adaptable compliance frameworks rather than checking boxes on static requirements. What we tell clients is this – document everything – your training data sources, your testing methodologies, your decision-making processes, your bias mitigation efforts – because when regulations do crystallize, you'll need to demonstrate that you've been acting responsibly all along.
The companies that will thrive are those treating compliance as a design principle, not a retrofit after regulations are finalized. And frankly, many of the emerging requirements around transparency, fairness and accountability aren't just legal obligations – they're good engineering practice that we were implementing in AI systems back in the 1980s. Get ahead of this now, because the regulatory environment will only get more complex and retrofitting compliance into deployed AI systems is exponentially harder and more expensive than building it in from the start.
LD: How has your prior practice and experience positioned you to be a go-to lawyer for AI issues?
WM: Most lawyers advising on AI are learning the technology as they go – I actually built AI systems before I ever went to law school, designing and coding self-modifying algorithms that integrated early machine learning into laboratory management systems. My master's thesis in systems architecture was focused on neural networks and gave me a deep technical foundation that most attorneys simply don't have. When I combine my hands-on software engineering experience with nearly three decades of patenting AI inventions for clients across industries – from medical devices to defense systems – I can do something very few lawyers can: I actually understand what's happening under the hood.
At Munck Wilson, our AI team can read our clients' code, discuss training architectures and algorithmic approaches in their language, spot the patentable innovations they might not even recognize and identify the legal risks embedded in technical design choices before they become problems.
We’re not translating between engineers and lawyers, we’re fluent in both worlds. That means when a client comes to me or our AI team with an AI issue, whether it's IP protection, regulatory compliance, or liability exposure, we’re not just applying legal frameworks to technology we barely understand; we’re bringing significant technical and legal expertise to bear on problems we’ve been working on since before most people knew what neural networks were.
