How to Navigate AI and Intellectual Property Issues

AI in Packaging Design: Creativity, Ownership & IP – What You Need to Know As AI becomes a core part of the FMCG design process, there’s a big conversation we have to have: how do we use it responsibly, protect IP, & keep the work original, ownable & on-brand? Here’s a topline practical guide for brands & creatives navigating AI in packaging: 1. Choose your tools wisely Not all AI platforms are equal. If you’re using tools like Midjourney, DALL·E or Firefly, read the terms. Some offer IP indemnity, others don’t. Avoid free AI tools for client work unless you’re 100% clear on usage rights. 2. Add human creativity AI is a brilliant assistant, but not an autonomous designer. Use it for ideas & rough drafts. Always refine, curate or build on top. That’s where you make it yours – and legally, where ownership starts. 3. Do your due diligence Check AI outputs like any creative work. Run reverse image searches. Check trademarks. With AI, the source is murky – double-check to avoid copying someone else’s IP. 4. Keep records Save prompts, iterations, edits. This is your creative paper trail. It protects your originality claim & builds a library of “safe” prompts over time. 5. Clarify ownership in contracts Agencies/freelancers: assign all IP clearly to the client, and confirm you used properly licensed AI tools. Clients: ask for that clarity upfront – especially for key brand assets. 6. Use alternative protections If copyright’s uncertain, register trademarks, designs or treat strong prompts as trade secrets. AI can create distinctive brand assets – just be sure they’re legally protected. 7. Set internal AI rules Decide how, when & where to use AI. For example, “Use AI for concepting, not final logos,” or “Avoid copying named artists’ styles.” Build consistency & reduce risk. 8. Stay informed The legal landscape is shifting fast. Follow changes in the UK, US & EU. What’s allowed today might not be tomorrow. Keep your teams updated. 9. Don’t use AI as a crutch Let it do the heavy lifting, but keep the finesse. AI brings speed, but humans bring taste, emotion & context. Clients will still pay for that. 10. Plan for the “what if” If a problem arises, be ready. Have reprint budgets, backup visuals & know your IP insurance options. Prevention is best – but being prepared is smarter. The takeaway? You don’t need to choose between innovation & protection – you can (and should) have both. AI can elevate creativity, but only if you own the process, the rights & the risks. In packaging, there’s no undo button once it hits shelves. So before your next campaign goes live, ask: Can we own this? Can we defend it? And does it still feel human? #AI #Design #PackagingDesign #FMCG #IntellectualProperty #BrandProtection #FutureOfDesign

Who Owns What AI Creates? Last year, a global retailer rolled out an AI-powered tool to help its marketing team generate product descriptions and social media content. The results were impressive: faster turnaround, consistent tone, and a noticeable lift in engagement. But when the legal team reviewed the program, red flags appeared. ❌ Ownership of content: Because the text was generated entirely by AI, much of it was not eligible for copyright protection under U.S. law. Without human creative input, the retailer couldn’t enforce IP rights if competitors copied their campaigns. ❌ Licensing of the tool: The AI platform’s terms of service granted the provider certain rights to reuse outputs. That meant some of the “unique” marketing language might not remain exclusive to the retailer. ❌ Training data risks: There was no guarantee that the AI hadn’t been trained on copyrighted material. If a rights holder challenged the use of their works, the retailer could be exposed to litigation. This isn’t a hypothetical anymore... cases are already moving through the courts. Perplexity recently lost a bid to dismiss a lawsuit brought by News Corp over alleged misuse of proprietary content. Meanwhile, OpenAI and other AI companies are leaning heavily on “fair use” defenses, with mixed results. Businesses relying on AI outputs without reviewing their contracts and compliance posture could be walking straight into the same risks. What businesses should do now: ✅ Document human input – Ensure employees edit, arrange, or meaningfully shape AI outputs so they qualify for copyright protection. ✅ Audit contracts – Review licensing terms of every AI tool in use. Know who owns the outputs, and what rights the provider retains. ✅ Protect your innovations – Use trade secrets, patents, and airtight NDAs to safeguard proprietary data and models. ✅ Monitor litigation trends – Laws around AI, copyright, and fair use are evolving rapidly across jurisdictions. What’s unprotectable in the U.S. may be protectable elsewhere, and vice versa. ✅ Lead with ethics and transparency – Beyond the legal risks, businesses face reputational harm if creators, regulators, or consumers believe their use of AI is exploitative or opaque. AI is no longer a futuristic add-on; it’s woven into daily business. But the line between innovation and infringement has never been thinner. Companies that treat AI as a business tool with legal guardrails, not as a magic shortcut, will be the ones that unlock its full potential without sacrificing their intellectual property. 👉 How is your organization navigating the IP risks and rewards of AI?

We’re having loud conversations right now about copyright in the age of AI. Big models - Anthropic, OpenAI, and the rest - were built by absorbing the internet wholesale. Authors, artists, journalists, experts… their work was ingested without permission, often scraped from torrent and piracy sites. That’s now being tested in court, with judges questioning not just the legality but the economics of how much creators are owed. Billions are at stake. But what we aren’t talking about is the other frontier of intellectual property: the workplace. Every company is sprinting to bolt AI into their organisation. Enterprise deals with Microsoft, OpenAI, or whoever the flavour of the month is. Then comes the hard part - getting employees to adopt the tools. And when they do, employees feed those systems every single day with their workflows, ideas, and creative processes. In other words: their IP. Here’s the dilemma. On one hand, if you’re in a white-collar job, you have to adopt AI. It’s the augmentation that makes you sharper, faster, more competitive in a shrinking talent market. On the other, by leaning in, you’re handing your intellectual property to the machine - owned by your employer, not by you. That raises two uncomfortable questions: 1. What happens when the model compounds your knowledge to the point you’re redundant? 2. What happens when you leave - if your “brain” is locked inside the company machine you helped train? We’ve always had an implicit contract between employee and employer: a fair exchange of time, skills, and value. But the parameters are shifting. If your creativity, workflows, and sparring practices are now raw fuel for corporate AI models, shouldn’t you have rights to what you leave behind? Organisations that want to recruit star talent need to think this through too. Why would top-tier employees, consultants, or domain experts join you if all they’re really doing is donating their best ideas to a machine that makes them less unique - and ultimately less valuable? If the deal looks like: “come here, feed our AI, make yourself obsolete,” the brightest minds will simply go elsewhere. So this is about more than compliance or efficiency. It’s about competitive advantage in the talent market. The companies that win will be those that rewrite the contract of value: drawing a line between what belongs to the organisation, what belongs to the machine, and what employees (or consultants) get to keep. And the employees who thrive will be the ones who understand this new contract and know how to safeguard their own IP, even as they leverage AI to augment their skills. Because in the AI era, “knowledge work” no longer lives in your head alone, it’s imprinted in the systems you use. And that means the future of work isn’t just about who gets augmented. It’s about who gets owned.

🚨AI, IP Infringement & the Legal Gaps ............. Who Owns the Blame? 🔎 Rise of AI-Driven IP Infringement AI is now capable of generating art, music, literature, and even inventions. This has blurred the lines between creator, owner, and violator. The big question: When AI infringes, who is accountable : the developer, the user, or the AI itself? ⚖️ International Perspectives US & EU frameworks lean on traditional copyright and patent systems. AI-generated works are generally not recognized as authorship. Liability often falls back on the human programmer or deploying entity. Global legal systems are struggling to move beyond human-centric laws. 🇮🇳 The Indian Context: Laws & Loopholes Indian Copyright Act (1957) defines “author” as a human, leaving no clarity for AI outputs. Courts have not yet adjudicated directly on AI authorship or liability. Patent law loopholes: AI-generated inventions face rejection as inventors must be human. Lack of clear policy guidance puts businesses and creators at risk. 🚧 Key Challenges Identified Attribution Gap: No legal clarity on who gets credit. Accountability Gap: Ambiguity over liability in AI-driven infringements. Regulatory Lag: AI evolves faster than lawmaking. Cross-Border Conflicts: Different jurisdictions interpret AI rights differently. ✅ Recommendations proposed by Experts and legal luminaries Update Copyright & Patent definitions to account for AI. Consider a “shared liability model” between developer, deployer, and user. Introduce AI-specific IP guidelines for both ownership and infringement. Strengthen international cooperation for harmonized AI-IP laws. 📚 Sources Referenced SCC Online Blog: Legal Accountability for AI-Driven Intellectual Property Infringements – An Analysis of International and Indian Laws (2025) Comparative insights from US, EU, and Indian IP law frameworks. #Corporategovernance #Independentdirectors #IPR #AI #Copyrights #Infringement

AI: The New Watchdog for IP Infringement Most companies don’t discover IP infringement the moment it happens. In fact, many teams only notice a copied design or product months after it’s already being sold. Modern IP-monitoring platforms now scan global product announcements, regulatory filings, technical specs, and even manufacturing data. Instead of relying on manual searches, AI compares new releases against your existing patents and alerts you when something looks too similar. A U.S. medical device company used an AI monitoring system that flagged a competitor’s upcoming device during its early regulatory review. The system detected a near-identical mechanism covered by the company’s patent well before the product reached the market. Because they identified the issue early, the company was able to approach the manufacturer, clarify their IP rights, and resolve the conflict without litigation. AI is shifting IP protection from reactive (“catch it after the damage is done”) to proactive (“spot it before it becomes a problem”). For R&D, legal, and product teams, this means earlier visibility, better decision-making, and significantly lower risk.

𝗧𝗵𝗮𝘁 𝗡𝗗𝗔 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝘀 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗱𝗲 𝘀𝗲𝗰𝗿𝗲𝘁𝘀 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗹𝘆. 𝗜𝘁 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗽𝗮𝘁𝗲𝗻𝘁𝘀. Here's the agreement trap that catches even sophisticated companies: assuming one IP protection clause protects all IP. Company A engages an AI consultant under a standard NDA with a "Confidential Information" clause. Six months later, the consultant files a patent application on "their" invention—the optimization method they developed while working on Company A's system. Company A waves the NDA. "You can't do that!" The consultant's lawyer smiles. "Show me where it says we can't." Here's what Company A didn't know: 𝗶𝗻 𝘁𝗵𝗲 𝗨.𝗦., 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘀 𝗼𝘄𝗻 𝘁𝗵𝗲𝗶𝗿 𝗽𝗮𝘁𝗲𝗻𝘁 𝗿𝗶𝗴𝗵𝘁𝘀 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁—even when inventing on your dime. Unlike copyrights (where "work for hire" may apply), patent rights require explicit assignment. Company B uses the same consultant but adds specific language: "Consultant hereby assigns all inventions and patent rights arising from the engagement." They own everything invented. The devil is in the details. Each IP type needs its own magic words: • 𝗧𝗿𝗮𝗱𝗲 𝗦𝗲𝗰𝗿𝗲𝘁𝘀: "Shall maintain in confidence" • 𝗖𝗼𝗽𝘆𝗿𝗶𝗴𝗵𝘁𝘀: "Work for hire" or "assigns all copyrights" • 𝗣𝗮𝘁𝗲𝗻𝘁𝘀: "Assigns all inventions and patent applications/patents" • 𝗧𝗿𝗮𝗱𝗲𝗺𝗮𝗿𝗸𝘀: Specify ownership if created by contractor The dangerous assumptions: • "Our NDA covers everything" (Confidentiality ≠ ownership) • "Work for hire includes patents" (Copyright concept—irrelevant to patents) • "Standard employment agreements transfer all IP" (Check the actual language) • "What works in California works in Germany" (Every jurisdiction has quirks) Even sophisticated companies get this wrong. I see bulletproof NDAs that protect secrets perfectly but let contractors walk with patent rights. Or employment agreements that capture copyrights but miss inventions entirely. The solution? Review your agreements for explicit patent assignment language. Look for "inventions," "patents," and "patent applications"—not just "intellectual property" or "work product." The difference between broad language and specific terms can cost you your core technology. Time to audit your agreements? Let's ensure they actually protect what matters. (Disclaimer: As this post should make clear, specific wording really matters—any language examples here are merely informational and do not constitute legal advice, which should be obtained from an attorney before acting on anything in this post.) #patents #intellectualproperty #contracts

Employees using AI at work will be the workplace issue of 2026. Not remote work. Not noncompetes. Not DEI. AI. Because employees are already using it — to draft emails, summarize documents, create work product, prepare presentations, and even help with performance reviews — whether employers have approved it or not. And most companies are completely unprepared. If your organization doesn't have a workplace AI policy, you don't have an AI strategy — you have an unmanaged risk. Every business, regardless of size or industry, should have a clear, practical AI "Responsible and Approved Use" policy that covers at least these 10 essentials: 1.) Approved vs. prohibited AI tools Identify which AI tools employees may and may not use for company business, and establish a process for reviewing and approving new AI technologies as they emerge. 2.) Confidentiality and data protection Prohibit employees from inputting confidential, proprietary, personal, or client information into AI systems and from training AI models on company data without express authorization. 3.) Accuracy and human responsibility Require human review of all AI-generated content and confirm that employees—not AI tools—remain fully responsible for the accuracy, quality, and compliance of their work. 4.) Bias and discrimination safeguards Prohibit the use of AI in ways that create or perpetuate bias, particularly in hiring, promotion, performance evaluation, discipline, or termination decisions. 5.) Intellectual property ownership and protection Clarify that AI-generated work created in the scope of employment is company property and must not infringe third-party intellectual property rights. 6.) Legal and regulatory compliance Require all AI use to comply with applicable laws and regulations, including those governing discrimination, wage-and-hour, privacy, data protection, and intellectual property. 7.) Transparency and disclosure expectations Define when employees must disclose AI use internally and when disclosure is required in communications with customers, clients, regulators, or the public. 8.) Limits on employment-related decisions Prohibit fully automated employment decisions and require meaningful human involvement in any AI-assisted hiring or other employment-related decisions. 9.) Security, IT, and cybersecurity alignment Require AI use to comply with IT and cybersecurity standards and prohibit the use of unapproved or personal AI tools for company business. 10.) Training, enforcement, and accountability Require periodic training on appropriate AI use and provide that violations of the AI policy may result in discipline, consistent with existing company policies and procedures. None of this about being anti-AI. It's about being intentional, lawful, and smart. Like it or not, AI is here to stay. Now is the time to get ahead of it. Does your business have an AI policy? If not, what are you waiting for?

🔍 Using AI? Here’s Why You Must Understand Your Data Source AI is a game-changer, but with great power comes great responsibility—especially when it comes to data. Many AI tools deliver incredible results, but if you don’t know where your data is sourced from, you’re setting yourself up for potential trouble. Here’s why: 🛡️ Data Integrity Matters: AI is only as good as the data it’s trained on. If the source data is biased, outdated, or incorrect, the output could mislead your decision-making. 🔒 Protect Your Intellectual Property: Some AI tools use open-source models or datasets. If you’re feeding sensitive, proprietary information into these tools without understanding how it’s used, you might inadvertently expose your intellectual property. 🏛️ Compliance Is Critical: Industries like finance, healthcare, and law require strict adherence to data privacy regulations. Using AI without knowing the data lineage can lead to non-compliance, fines, or worse. How to Protect Yourself and Maximize AI’s Potential: 1️⃣ Ask Questions: Before using an AI tool, ask how it sources, stores, and processes data. Transparency is key. 2️⃣ Use Closed Systems for Proprietary Data: When dealing with sensitive information, consider using AI solutions that allow for closed-loop systems to keep your data secure. 3️⃣ Validate the Output: Don’t rely solely on AI-generated insights. Cross-check results with trusted sources to ensure accuracy. 4️⃣ Train Your Team: Ensure your team understands the risks and best practices for using AI tools responsibly. AI is a fantastic tool, but it’s not a “set it and forget it” solution. Success requires thoughtful implementation, informed decisions, and a clear understanding of the technology. 💬 What’s your approach to ensuring AI outputs are reliable and compliant? Let’s discuss! #AI #DataIntegrity #DigitalTransformation #ArtificialIntelligence #AICompliance #TechLeadership #BusinessInnovation #AIEthics

AI Governance is rarely about the right answer, but the right questions. That was one of the most insightful and uncomfortable takeaways last night at our session with the Private Directors Association® , Baker McKenzie, and WOMEN x AI (WxAI) The case study was my favorite to date - an exploration of the convergence of IP law, open science, and AI in the context of biotechnology. The discussion that ensued was an absolute masterclass in navigating the nuances of AI governance. 9 insights for boards navigating the nuance of AI value creation and risk management: 1. The Emotional Quotient: AI-related conversations can be the most emotionally-charged a board will have with their management team. Ensure you are seeing the motives behind the argument. 2. The IP Trap: No, you can't ask Claude to generate protectable IP. To ensure your intellectual property is defensible, there must be a specific human insight or process that makes the output protectable. 3. The Transparency Mandate: Moving away from the black box is non-negotiable. Boards must provide transparency into the datasets being leveraged. 4. Legacy vs. New Law: Regulation is shifting constantly, but often it’s the legacy regulation being interpreted in the context of AI that proves particularly challenging. 5. The Risk of Early Disclosure: Everyone gets excited about their newfound innovations. Ensure your management team understands the importance of process. Disclosing your AI innovation too early can inadvertently put your intellectual property at risk. 6. No One Size Fits All: Good governance is not a template; it is bespoke to your organization's values and context. It is also constantly evolving - what works today will require a different lens tomorrow. 7. Unregulated Risks: There tends to be a lot of scrutiny on regulated domain spaces, but historically unregulated domains and technologies often pose the greatest potential risk to individuals and society. 8. The Training Imperative: One of the most important things you can do is invest in education at both the Board and workforce levels. AI literacy is the foundation of oversight. 9. Agnostic Oversight & The Expert Trap: You don’t need to be a technical specialist to provide effective governance. The core strategic dilemmas of IP ownership, transparency, and risk are universal. Relying on a single "AI expert" can unfavorably skew board dynamics. True resilience requires collective literacy and a wide variety of perspectives. A huge thank you to our expert panelists for sharing such interesting interesting insights on the risks and realities of AI: Julia Kazaks of the Arc Institute, and Keo McKenzie, Baker McKenzie. And finally, massive thanks to my incredible team who brought both the compass and this experience to life: Tamara Gracon, Jenny Kay Pollock, and Joanna Ridgway. If you are interested in exploring an AI governance case study with your board and executive team, please reach out to learn more.