Artificial Intelligence (AI). Generative AI. Machine Learning. Deep Learning. Natural Language Processing (NLP). These technologies have been transformational for businesses across a full host of industries. For emerging businesses, these could be a whole new world. When focusing on getting a company off the ground and ensuring success for those involved—founders, employees, investors, etc.—there are some concerns regarding AI that would be wise to keep in mind.

Intellectual Property. Companies need to be strategic when trying to protect their intellectual property, including copyright, patents, and trade secrets. Before launching an AI product or service, conduct a freedom to operate analysis to ensure that the commercialization of your AI technology does not infringe on existing IP rights. If your AI incorporates open-source components, ensure compliance with their licenses, which may have specific distribution, modification, and disclosure requirements. Develop a comprehensive IP strategy that aligns with your business objectives and considers the lifecycle of AI technologies, from development and deployment to commercialization.

Regularly consult with IP attorneys to navigate the complex landscape of AI-related IP, including patentability issues, licensing strategies, and enforcement of IP rights – including:

• AI Copyright Infringement – this may arise when using copyrighted works to train AI systems. For example, including copyrighted works, such as photographs or written materials, as part of an AI data training set may raise infringement issues. Use in this context, however, may fall within the fair use exception to copyright infringement. Litigation and commercial negotiations in this area are ongoing.
• Reducing Infringement Risk by training employees on the basics of copyright law – ensure that the company and employees understand the attribution and acknowledgment requirements of the AI tools in use; beopen about the use of AI tools; and regularly provide training on AI policies.
• Protecting Ownership of IP – some key factors should be in place.
• Establish clear IP ownership terms in employment contracts and in contractor and partnership agreements.
• In collaborative AI projects, joint ownership of IP can arise. It is important to have agreements that specify each party’s ownership, use rights, and obligations to avoid disputes.
• AI often uses third-party components, such as open-source software or proprietary algorithms. Ensure that licensing agreements are clear about the scope of use, redistribution rights, and ownership implications.

Data Privacy and Security. We are a data driven world and accordingly all businesses should be concerned with implementing strong data privacy strategies.

• Privacy by Design – Integrate privacy into the design and development.
• Data Anonymization – Use techniques like anonymization.
• Data Protection Impact Assessments (DPIAs) – Conduct DPIAs before deployment.
• Robust Data Governance – Implement strong data governance frameworks.
• Data Minimization – Collect, process, and store the minimum amount of data necessary.
• Access Controls – Robust access controls and authentication measures.
• Audit & Monitor – Regularly audit AI systems for compliance, breaches, and misuse.
• Employee Training – Ensure that employees are trained on best data privacy practices.
• Transparency and Consent – Users should be informed about how their data is used and processed.
• Breach Plan – Have a data breach incident response plan.

Employment/Human Resources. AI is being used in recruiting and screening candidates, interviewing tools, and employee onboarding, among other ways. It is important for employers to be transparent about their use of AI.

• Ensure human oversight in decision making.
• Regularly validate and audit AI tools for fairness and to eliminate bias.
• Consider informing job candidates and employees about how AI is used.
• Prepare to accommodate job candidates for might be unable to use an AI tool to ensure no discrimination in present.
• Have a diverse applicant pool before applying AI and conduct a validation analysis.
• Seek indemnification or representations from third party tool providers.
• Stay current on existing or potential laws, regulations, and guidance.
• Adopt company policies that address the use of AI.

Contracting Evaluating Vendors. When setting up agreements with vendors, companies should be aware of the:

• Type of Technology Used – Is the AI internally developed from a proprietary base, built internally from an open-source base, or licensed from a third party?
• Experience and Expertise – Research the vendor’s reputation in the market.
• Training Data – Understand the training data, including reliability, quantity, and updates. Address use of company data for training.
• Privacy – Assess the vendor’s data security measures and privacy policies.
• Model Transparency and Bias – Assess the vendor’s model transparency, bias levels, and verification.
• Scalability and Integration – The AI solution should be scalable to grow with your business and capable of integration.
• Support and Maintenance – Include training for your team, technical support, and updates to the AI system.
• Compliance with laws, regulations, and guidelines – Pay attention to your vendor’s history and processes.

M&A and Investments. In addition to conducting due diligence in key areas such as data rights, employment, insurance, IP, and regulatory, among others, companies who are acquiring a business or those being sold should consider key risk mitigation strategies:

• AI Ethics and Governance – Ensure that the target has established AI ethics guidelines and governance frameworks.
• Continuity of Key Talent – Secure agreements for the continuity of crucial AI talent.
• Representations and Warranties – Robust and accurate description of the AI technologies, products, and services.
• Indemnification – Consider special indemnities.
• Insurance – Insurers are aware of and sensitive to areas of risk in transactions involving AI companies.
• Regulatory Approval – Consider scrutiny and approval processes.
• Post Merger Integration Plan – Develop plans for integrating AI technologies and teams, technology compatibility, and the alignment of AI strategies.

By staying informed about these emerging trends and incorporating AI technologies strategically into their business operations, emerging businesses can gain a competitive edge and capitalize on the opportunities presented by AI-driven innovation.

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