Legal Perspectives AI

Navigating AI for Business – A Legal Perspective

West Hill recently returned from the 2025 WBENC National Conference in New Orleans, where we attended a notable panel: “Technology and Creativity for the Future.”  

Moderated by WBENC President and CEO Pamela Prince Eason, the panel featured insights from Brett Howroyd (ActOne), Diana Gonzalez (Amazon), and Kathy Golding (EY), each of whom shared how their organizations are using AI to fuel both business success and personal growth. 

A central theme of the session? Responsible innovation and ethical leadership are key to harnessing the opportunities presented by an AI-driven world.  

One panelist put it simply:

AI is like a car – you don’t need to know how the engine works in order to drive it.

We loved this analogy. But just like with driving, there’s a lot more to using AI in your business than simply knowing how to “turn it on.” You also need to understand the rules, responsibilities, and potential risks. Let’s break it down:

🚦What You Need to Know:

1. How the Car Works = AI Basics

How do I turn it on? Where is the gas cap located? What is the red warning light on the dashboard? Even if you don’t build cars, you still need to know the basics to drive one safely. The same goes for using AI.

For all AI tools — think ChatGPT, Otter.ai, Claude, and beyond–ask: 

• 💡What data was used to train the AI? Is it biased, proprietary, or outdated? 
• 💡What does the Acceptable Use Policy require? Are there limitations on how you can use the tool? 
• 💡Are your inputs confidential? Does the AI provider store, reuse, or train on your data?

2. Rules of the Road = AI Terms and Conditions and Applicable Laws

You can’t drive without a license or ignore the speed limit. Who has the right of way at an intersection? Likewise, when deploying AI in business processes, are there legal and contractual rules to follow? 

For AI technology used in your business – automation, customer chatbots, resume screening, etc:

• 💡Are your inputs or outputs being used to further train the AI models? 
• 💡Will the vendor agree to keep your business data confidential?  
• 💡Does this use of AI comply with applicable law (NYC, the EU Ai Act, state privacy regulations, etc.)?  

⚠️ A Crash Waiting to Happen . . .  

Just like driving, using AI comes with inherent risks. Here’s how to recognize and reduce them: 

3. Dangerous Road Conditions 

Sometimes AI models can hallucinate – or provide inaccurate outputs. Avoid deploying AI unmonitored in high-risk areas, including: 

• Recruitment and employment decisions 
• Safety-critical or regulatory compliance tasks 
• Monitoring or controlling critical infrastructure 
   
  ❗Risk Mitigation: Use human oversight and verification. Don’t let AI operate autonomously in high-stakes environments. 

4. Driver Error

Business teams can – and do – make mistakes when using AI. Common issues include:  

• Entering confidential information into public AI platforms 
• Failing to QA AI-generated outputs 
• Violating data privacy or employment laws when using AI tools (like meeting note transcription software) 
  
  ❗Risk Mitigation: Create a comprehensive AI Use Policy for your business. Tailor additional policies for specialized teams like HR, Legal, or Software Development, as needed. r a winter trip.

5. Distracted Driving

AI can be a helpful tool, but overreliance can dull innovation and open you to IP risks:  

• AI-generated content may infringe third-party copyrights  
• Materials generated heavily (e.g. marketing content or code) may not be eligible for copyright protection 
• The USPTO and other agencies are issuing new guidance on AI and patentability 
  
  ❗Risk Mitigation: Train employees on the intersection of AI and IP and maintain oversight when using AI in creative or strategic work. 

To stretch the analogy a bit further . . . 
6. 🚗 Going All-in on Cars = AI Development 

Some businesses aren’t just driving the car – they’re building or customizing them. If your company is integrating or developing AI products (e.g. LLMs, chatbots, APIs), you’re playing a different game. 

For AI product development: 

• 💡Do AI vendors terms limit your ability to build AI offerings? (e.g. Geographic or age-based restrictions) 
• 💡What AI-related disclaimers or notices must you provide to end users? 
• 💡Are your AI vendors meeting data privacy and security standards? 
  
  ❗Risk Mitigation: If your company is “manufacturing” with AI, you must understand the equivalent of emissions rules, safety standards and warranty obligations. Building AI into your offering requires a higher level of due diligence – and a robust compliance framework. 

🧠 Final Thoughts

AI holds tremendous promise – but businesses must navigate this landscape with legal insight and operational care. Whether you are a casual driver or full-blown automaker in the AI economy, staying informed and compliant is the only way to drive growth safely and stay in control.