AI Litigation Trends 2025: How to Protect Your Business from Emerging Legal Risks
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Artificial intelligence has revolutionized business operations, but it has also opened a Pandora’s box of legal liability. As AI adoption accelerates across industries, so does AI-related litigation. From copyright infringement claims against generative AI companies to employment discrimination lawsuits targeting automated hiring systems, 2025 has emerged as a watershed year for AI legal disputes. Companies deploying AI technologies face unprecedented risks that demand proactive legal strategies and experienced counsel.
The AI Litigation Explosion: Understanding the Landscape
AI litigation is no longer theoretical—it’s happening now, and the stakes are enormous. Courts across the country are grappling with novel legal questions about AI liability, and early decisions are shaping the regulatory landscape for years to come. Businesses that fail to understand these emerging risks face potential class action lawsuits, regulatory enforcement actions, intellectual property disputes, and reputational damage that can fundamentally threaten their operations.
The complexity of AI litigation stems from its intersection with multiple areas of law: intellectual property, privacy, employment, consumer protection, and product liability. A single AI deployment can trigger exposure across all these domains simultaneously. This convergence requires sophisticated legal analysis and comprehensive risk management strategies that traditional compliance approaches cannot adequately address.
Critical AI Litigation Trends Companies Must Understand
- Generative AI Call Center Tools: The New Litigation Frontier
Following the February 2025 Ambriz v. Google decision, a flood of lawsuits has targeted companies using generative AI for customer service interactions. These cases allege that AI tools transcribe, analyze, and store customer communications without proper consent, violating state wiretapping laws and privacy statutes.
The legal theory is straightforward but devastating: when customers call a company, they consent to speak with a human representative or leave a voicemail, but they don’t necessarily consent to having their conversation fed into an AI system that transcribes, analyzes, and potentially stores their communications indefinitely. Plaintiffs argue this constitutes unlawful interception under laws like California’s Invasion of Privacy Act, which allows statutory damages of up to five thousand dollars per violation.
For companies operating call centers that handle thousands or millions of customer interactions, the potential liability is astronomical. A class action involving millions of customer calls could result in damages reaching into the billions—not including attorney fees, litigation costs, and the business disruption caused by discovery and trial preparation.
- Copyright Litigation: The Battle Over Training Data
Copyright litigation against AI companies has become one of the most significant legal battlefields of 2025. Authors, visual artists, musicians, and other copyright owners have filed numerous lawsuits against firms like OpenAI, Meta, Anthropic, and Stability AI, alleging these companies unlawfully used copyrighted material to train their generative AI models without permission or compensation.
The fundamental question at stake: does training AI models on copyrighted content constitute fair use, or does it require licensing agreements and royalty payments? Early rulings have been mixed, creating uncertainty for both AI developers and businesses deploying AI tools.
In February 2025, Thomson Reuters won partial summary judgment against AI startup Ross for copying Westlaw’s case headnotes to train a competing legal research tool. This decision suggests courts may take a narrow view of fair use when AI companies directly copy proprietary databases and copyrighted compilations, even if the copying occurs during the training process rather than in the final output.
These copyright battles have profound implications beyond AI developers. Companies purchasing or licensing AI tools may face secondary liability if those tools were trained on infringing content. Businesses must carefully evaluate the provenance of training data used in any AI system they deploy and ensure their vendor contracts include appropriate indemnification provisions.
- Employment Discrimination: AI Hiring Tools Under Fire
AI-powered hiring and employment tools face intense scrutiny over discrimination concerns. The Mobley v. Workday case exemplifies the risks companies face when automated decision systems allegedly lead to unlawful discrimination against protected classes based on age, race, gender, disability, or other characteristics.
On August 28, 2025, the court ordered Workday to produce a list of customers that have used its AI features since September 2020, demonstrating that AI discrimination cases can expose not just the vendor but potentially hundreds of client companies to discovery, investigation, and liability. This creates a multiplication effect where a single lawsuit against an AI vendor can metastasize into widespread exposure for its entire customer base.
California’s Employment Regulations Regarding Automated-Decision Systems, which became effective October 1, 2025, clarifies that employers are responsible for discriminatory employment decisions made by automated systems—even when those systems are provided by third-party vendors. This regulation eliminates any illusion that companies can outsource liability along with their hiring processes.
The employment discrimination risk extends beyond hiring. AI tools used for performance evaluation, promotion decisions, compensation analysis, and termination recommendations all create potential liability if they produce discriminatory outcomes, even if the discrimination is unintentional or stems from biased training data rather than explicit programming.
- State AI Regulations: A Patchwork of Compliance Obligations
Colorado became the first state to adopt comprehensive AI regulation with its Anti-Discrimination in AI Law, effective February 2026. This statute imposes requirements on developers and deployers of high-risk AI systems, including impact assessments, risk management programs, disclosure obligations, and consumer rights to opt out of certain automated decisions.
Other states are moving quickly to regulate AI. California, New York, Texas, and Illinois have all introduced or passed AI-related legislation addressing various aspects of AI deployment, from algorithmic discrimination to deepfakes to automated decision systems. This regulatory fragmentation creates substantial compliance challenges for national businesses that must navigate conflicting requirements across multiple jurisdictions.
The compliance complexity is compounded by the fact that many AI regulations are new, untested, and ambiguous. Enforcement priorities and interpretations remain unclear. Companies face the uncomfortable reality of building compliance programs for regulations whose practical application won’t be understood until after the first wave of enforcement actions and litigation.
Protecting Your Business: Essential AI Risk Management Strategies
- Comprehensive AI Audits and Inventories
Most companies don’t have complete visibility into where and how AI technologies are being used across their organizations. Employees may be deploying AI tools without IT approval or legal review. Third-party vendors may be incorporating AI into their services without disclosure. Shadow AI adoption creates blind spots that can become litigation landmines.
Companies need comprehensive AI audits that identify all AI systems in use, document their purposes and functionalities, assess their potential legal risks, and ensure appropriate governance controls are in place. This inventory should cover not just enterprise AI deployments but also departmental tools, employee-initiated AI usage, and AI embedded in vendor products and services.
- AI Governance Frameworks and Policies
Effective AI governance requires clear policies establishing who can deploy AI technologies, what approval processes must be followed, what testing and validation is required before deployment, how AI systems will be monitored for discriminatory or problematic outputs, and what documentation must be maintained for audit and litigation purposes.
These policies must be practical and enforceable, not merely aspirational compliance documents. They should integrate with existing information security, privacy, and risk management programs while addressing AI-specific concerns like algorithmic bias, explainability, and automated decision-making.
- Vendor Due Diligence and Contract Protections
Companies purchasing AI tools and services must conduct thorough due diligence on vendors. This includes evaluating how AI models were trained and whether training data was lawfully obtained, assessing whether the AI system has been tested for discriminatory outcomes, reviewing the vendor’s AI governance practices and compliance programs, understanding what data the AI system will access and how it will be used, and confirming the vendor maintains adequate insurance and financial resources to honor indemnification obligations.
Vendor contracts must include robust indemnification provisions covering intellectual property infringement, privacy violations, discrimination claims, and other AI-related liabilities. Companies should negotiate contractual rights to audit vendors, receive regular reports on AI system performance and testing, and discontinue use if problematic issues arise.
- Employee Training and Responsible AI Use
Human oversight remains critical even when deploying sophisticated AI systems. Employees need training on responsible AI use, understanding AI system limitations, recognizing when AI outputs require additional verification, identifying potential bias or discrimination in AI-generated results, and complying with policies governing AI deployment and usage.
This training should be role-specific, with different content for executives making AI investment decisions, IT professionals implementing AI systems, managers using AI tools for employment decisions, and frontline employees interacting with AI in customer-facing contexts.
Contact Jimerson Birr
AI litigation is accelerating, and early court decisions are establishing precedents that will shape liability standards for years. Companies that take proactive steps now to assess AI risks, strengthen governance frameworks, and ensure regulatory compliance will be far better positioned than those who adopt a wait-and-see approach.
The cost of prevention is invariably lower than the cost of defense. A comprehensive AI legal audit costs a fraction of what companies spend defending a single class action lawsuit or responding to a regulatory investigation. Our firm provides comprehensive legal services designed to help businesses harness AI’s benefits while managing its risks, including: Strategic AI Counseling, Transactional Services (Agreements, Licensing, Compliance, etc.), Litigation Defense (Class Action, Data Privacy, Biometric Data Privacy, Product Liability, etc.), and Regulatory Compliance (Audits, Legislation Tracking, Regulatory Investigations, etc.).
Contact our firm today to schedule an AI risk assessment and learn how we can help you deploy AI technologies confidently while protecting your business from emerging legal threats. Our team combines deep technical understanding with sophisticated legal expertise to provide the strategic guidance you need in this rapidly evolving landscape.