Technology

 As AI technologies become embedded in business operations — from marketing automation and customer analytics to hiring, product design, and supply-chain management — companies must manage new legal, contractual, and ethical risks.

AI offers transformative opportunities, but it also introduces challenges in data privacy, intellectual property, employment law, and regulatory compliance. Thoughtful planning and legal guidance are essential to integrate AI responsibly and protect the company’s interests.

Small and mid-sized companies are increasingly adopting AI to enhance productivity and decision-making. Common applications include:

• Customer service automation through chatbots and virtual assistants

• Predictive analytics for sales, finance, and operations

• AI-assisted content generation and marketing personalization

• Fraud detection and cybersecurity monitoring

• Recruitment and HR decision tools to streamline hiring

While these tools can increase efficiency and profitability, businesses must ensure they are legally compliant and that AI systems are used transparently and ethically.

• Key Emerging Legal Issues in AI

  1. Data Privacy and Security

  AI systems depend on large data sets — often containing sensitive personal or customer information. Companies must comply with evolving privacy laws, such as the Texas Data Privacy and Security Act (TDPSA), the California Consumer Privacy Act (CCPA), and the EU’s General Data Protection Regulation (GDPR) if they serve international customers.

  Best practices:

  • Maintain clear privacy disclosures and obtain proper consent for data use.

  • Implement data-minimization and anonymization measures.

  • Use secure, encrypted data-storage and sharing protocols.

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  2. Intellectual Property (IP) Ownership

  AI systems can generate original content, designs, or inventions — raising questions about who owns the IP. Current U.S. law generally requires human authorship for copyright or patent protection. Businesses must ensure contracts and policies clarify ownership of AI-assisted creations and that employees or vendors cannot claim competing rights.

  Best practices:

  • Include AI-related IP provisions in employee, contractor, and vendor agreements.

  • Document human oversight in AI-generated works.

  • Review software licensing terms for restrictions on commercial use of AI tools.

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  3. Bias, Discrimination, and Employment Practices

  AI-based recruitment or performance-evaluation tools can inadvertently introduce bias or discrimination if algorithms are trained on unbalanced data. The Equal Employment Opportunity Commission (EEOC) and other regulators are increasingly scrutinizing the use of AI in employment decisions.

  Best practices:

  • Audit AI systems for bias before and after implementation.

  • Maintain human oversight in hiring and evaluation processes.

  • Train HR teams on compliance with anti-discrimination and labor laws.

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  4. Contractual and Liability Risks

  If an AI system makes an incorrect prediction or faulty recommendation, determining liability can be complex. Businesses should carefully review vendor contracts and insurance coverage to allocate responsibility for AI errors, cybersecurity breaches, or regulatory violations.

  Best practices:

  • Negotiate strong indemnification and limitation-of-liability clauses in AI vendor contracts.

  • Verify that insurance policies cover cyber and AI-related risks.

  • Maintain internal governance procedures for AI decision-making.

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  5. Regulatory Compliance and Ethical Use

  Governments worldwide are introducing new AI regulations emphasizing transparency, accountability, and risk management. U.S. agencies such as the Federal Trade Commission (FTC) have warned against deceptive or unfair AI practices, while the EU AI Act sets compliance standards for “high-risk” AI systems.

  Best practices:

  • Conduct AI risk assessments and maintain documentation of how models are trained and deployed.

  • Create internal AI use policies governing approval, oversight, and data retention.

  • Establish an ethics or compliance committee to review high-impact AI initiatives.

A practical AI governance program should address:

1. Policy development: Define how your business uses AI and what safeguards apply.

2. Transparency: Inform customers, employees, and partners when AI is used.

3. Data stewardship: Assign responsibility for data quality, accuracy, and compliance.

4. Accountability: Maintain audit trails for AI-assisted decisions.

5. Ongoing monitoring: Review systems periodically for performance, bias, and legal compliance.

By embedding governance from the outset, SMEs can mitigate risk while demonstrating responsible innovation to customers, investors, and regulators.