Meeting Corporate AI Challenges with Interim Legal Expertise
Artificial intelligence has moved from sci-fi concept to everyday business tool faster than anyone expected, and it’s creating headaches for corporate legal departments everywhere. AI technologies—from ChatGPT writing marketing copy to machine learning algorithms making hiring decisions—have become integral to how businesses operate, bringing with them a whole new set of legal questions that barely existed a few years ago.
The challenge for in-house legal teams is real. They’re already juggling heavy workloads across traditional practice areas, and now they’re being asked to figure out AI governance, evolving data privacy rules, intellectual property questions nobody’s dealt with before, and regulatory requirements that seem to change monthly. Most of these challenges require specialized knowledge that goes well beyond what conventional legal training covers.
Intellectual Property Gets Complicated
AI and intellectual property law make for a messy combination. When an AI model creates something, who actually owns it? What happens when that AI learned from copyrighted material? These aren’t hypothetical questions anymore—they’re showing up in real legal disputes with real consequences.
Current IP challenges include figuring out how new AI court cases affect existing patents, copyrights, and trademarks. The ownership of AI-generated content remains murky, with major implications for businesses using AI-created materials. Then there’s the complicated web of rights and licensing issues when you use copyrighted data to train AI models.
Companies building their own AI face additional strategic considerations. Developing patent portfolios around AI innovations, negotiating licensing deals, and protecting trade secrets through well-crafted confidentiality agreements all require expertise that builds on traditional IP law while tackling AI-specific challenges.
M&A Just Got More Complex
Buying or selling companies with AI components has become significantly more complicated. Due diligence now means evaluating AI systems, data sets, algorithmic bias risks, and compliance with emerging AI regulations. Understanding a company’s traditional assets isn’t enough anymore—you need to understand their AI capabilities, potential liabilities, and regulatory exposure.
Modern due diligence has to cover AI system architecture, where training data came from, how well models perform, and compliance with applicable AI rules. After the deal closes, integration requires careful attention to AI-related contracts, licensing agreements, and regulatory commitments that might not have been on anyone’s radar in traditional transactions.
Data Privacy in the AI World
Data privacy becomes significantly more complex when AI enters the picture. AI amplifies every existing data privacy challenge and creates entirely new ones. The patchwork of state privacy laws—California’s CCPA and CPRA, Colorado’s Privacy Act, Virginia’s Consumer Data Protection Act—each have different implications for how AI systems can collect, process, and use personal data.
International rules add another layer of complexity. GDPR compliance for AI systems operating in Europe, HIPAA considerations for healthcare AI, and data transfer restrictions under various international frameworks all require specialized knowledge that most in-house teams don’t have time to develop.
Good AI data governance goes beyond just checking compliance boxes—it’s about building frameworks that can adapt as regulations evolve. This means establishing proper data processing agreements, updating privacy policies to reflect AI use cases, and developing incident response procedures that account for AI-specific risks.
Regulatory Compliance in a Fast-Moving Environment
The need for specialized expertise is perhaps most apparent in regulatory compliance. The EU AI Act is already in effect, the US is considering various AI accountability measures, and states are beginning to pass their own AI-specific legislation. Brazil, Canada, and other countries are developing their own frameworks. The regulatory environment is changing so rapidly that even full-time specialists struggle to keep up.
The challenge for general counsel isn’t just understanding what the rules are today—it’s building compliance programs that can adapt as new rules emerge. Effective AI compliance means designing and implementing programs that are flexible enough to evolve with the regulatory landscape.
This includes identifying “high-risk” AI applications under the EU AI Act, developing registration and documentation procedures, implementing algorithmic bias testing protocols, and creating governance structures that balance innovation with compliance requirements.
Why Specialized Legal Support Makes Sense
The complexity and rapid evolution of AI legal issues create a strong case for specialized legal support. Most organizations need access to AI legal expertise without hiring full-time specialized personnel. The interim counsel model provides flexible access to specialized knowledge, allowing organizations to scale legal support according to specific project requirements and business needs.
AI legal issues often emerge in concentrated periods—during system deployments, regulatory audits, or dispute resolution processes. Flexible legal support models can provide intensive expertise during critical periods while maintaining cost-effective advisory relationships during routine operations.
Beyond the Big Four
While IP, M&A, data privacy, and regulatory compliance represent the major AI legal issues, there are plenty of other areas where specialized support makes sense. AI system portability and reusability issues, commercial contracts for AI procurement and collaboration, and employment law implications of algorithmic decision-making all present their own challenges.
The common thread is that these issues require lawyers who don’t just understand traditional legal principles, but who also understand how AI systems actually work, what the technical risks are, and how legal frameworks need to adapt to technological realities.
Looking Ahead
The AI legal landscape isn’t going to get simpler anytime soon. As AI becomes more sophisticated and more integrated into business operations, the legal challenges will likely become more complex. Organizations that recognize this reality and start building relationships with specialized AI legal support now will be better positioned to navigate whatever comes next.
The companies that will thrive in the AI era aren’t necessarily the ones with the most advanced technology—they’re the ones that can deploy that technology while managing the legal and regulatory risks effectively. For most companies, that means accessing specialized expertise that goes well beyond what traditional in-house legal teams were designed to handle.
The interim counsel model represents a practical approach to accessing specialized AI legal expertise when needed, without the overhead of maintaining such expertise on a full-time basis. As AI continues to reshape business operations, flexible access to specialized legal support will likely become an increasingly important component of corporate legal strategy.