Hospitality employers are incorporating AI into nearly every aspect of their business, from the front of the house to the back of the house. You’re likely seeing the impact in recruiting, workforce management, operations, guest services, and more. While these AI tools have many benefits, they also come with significant risks employers should be prepared to address. Here’s what you need to know about popular AI uses in the hospitality industry, the legal risks of using them, and key practical steps you can take to stay compliant.
Key AI Compliance Areas to Track
- ADMT and ADS Laws: A growing number of states are regulating automated decision-making technology (ADMT) or automated-decision systems in the employment context.
- FCRA Compliance: A pending class action alleges that an applicant tracking tool collected personal information about job applicants without consent and effectively created a consumer report subject to Fair Credit Reporting Act (FCRA) requirements. While the case is pending, track similar developments.
- Consumer Privacy Protections: The California Consumer Privacy Act (CCPA) already governs what data you collect about employees, job applicants, and independent contractors, how you use it, and how you share it, and those same questions apply to your AI tools in California and in an increasing number of other states.
- Biometric Data Privacy Laws: Some states address biometrics through their broader consumer privacy laws, while others have separate biometric statutes to follow.
Top AI Tools for Hospitality Employers
1. Resume Screeners and Applicant Tracking Systems (ATS)
These tools have been around for a long time, but they’re getting more sophisticated. They use machine learning and natural language processing to screen, rank, and filter resumes based on selected criteria.
✅ Benefits: For HR teams in the hospitality industry that need to sort through a high volume of applications, resume screeners and applicant tracking systems can save significant time and help identify qualified candidates more efficiently.
⚠️ Risks: These tools use algorithms to make judgments based on how they were trained, which can create discrimination risks if, for example, the results have a disparate impact based on certain protected characteristics or the system fails to allow for reasonable accommodations. And don’t assume you’ve eliminated the risk just because a human makes the final hiring decision. Did the system screen out applicants that a human reviewer never saw? Is the AI tool making the final hiring decision for those applicants? Be sure you know how your system works and compliance requirements under applicable law.
Practical Point: Review whether your resume screening process triggers any notice requirements. You should review rejected applicants, not just those the system advances, and consider conducting your own independent bias audit rather than relying solely on the vendor’s testing.
2. Performance Evaluation Tools
AI can be used to analyze performance data, track high performers, and measure results against key performance indicators.
✅ Benefits: If you have employees across multiple locations, these tools can bring more consistency to the review process and help your managers identify top performers, as well as those who need additional coaching or training.
⚠️ Risks: The same concerns that arise when AI influences other employment decisions apply here. Bias and fairness remain concerns, and the output is only as reliable as the data going into the system.
Practical Point: Treat the AI-generated data as just one factor for the manager to consider and determine whether state laws on AI or ADMT apply. If needed, work with your attorney to develop a multi-state compliance plan.
3. Retention and Attrition Prediction Tools
These systems help you predict which employees are most likely to quit and help you build a proactive retention plan.
✅ Benefits: Retention and attrition prediction tools can help you identify workforce issues earlier and focus your efforts where they may have the greatest impact. They weigh factors such as pay increases, training opportunities, and opportunities for growth to identify employees who may be at risk of leaving.
⚠️ Risks: The algorithm won’t know what’s happening in an employee’s personal life that could affect retention. A spouse may relocate. An employee may experience an illness or another major life change. Those outside factors can drive turnover but never appear in the data, so the tools are limited in certain ways.
Practical Point: As with performance evaluation tools, use attrition predictions as just one of several indicators. They can help you develop a retention strategy, but they shouldn’t be the sole basis for significant workforce management decisions.
4. AI Training Tools
Training is one of the more straightforward ways employers are using AI. You can upload existing policies and procedures and use AI to quickly generate related training materials.
✅ Benefits: For hospitality employers, this can be a fast and relatively inexpensive way to turn materials you already have into effective training programs. It’s also generally a low-risk use of AI, so long as you’re verifying the information has been accurately transformed into training material.
⚠️ Risks: AI-generated training can feel generic and may not reflect your workplace culture. As with everything else discussed, the human element is important. Traditional training gives employees someone inside the organization to approach with questions. You may lose that connection if your training program is completely automated.
Practical Point: AI can help you build the training, but don’t remove people from the process. Make sure the training reflects your workplace culture, and employees know who they can approach with questions.
5. Predictive Scheduling Tools
Scheduling is particularly important in the hospitality industry, since staffing needs can change based on demand, peak seasons, and other business patterns. Predictive scheduling tools analyze those patterns and help employers forecast demand and adjust staffing in real time.
✅ Benefits: Better forecasting can help you manage staffing and workflow more efficiently while giving employees greater predictability, which can make it easier for them to plan their budgets and personal time.
⚠️ Risks: Employers may be subject to a patchwork of rules, such as Oregon’s statewide predictive scheduling law and city ordinances in places like New York City, San Francisco, and Philadelphia. State and local laws may require advance notice, good-faith estimates, and premium pay for certain schedule changes. You’ll also need to monitor for bias and ensure consistency in the scheduling process.
Practical Point: Make sure the platform you use applies the correct rules for your jurisdiction, train managers on when and how they can override the system and include scheduling tools in your bias-testing program.
6. Guest-Facing Chatbots, Reservation Systems, and AI Concierges
In addition to employment-related tasks, hospitality businesses are increasingly using AI to improve the guest experience. AI tools include chatbots and call-handling technology, as well as apps and kiosks for contactless check-in and concierge features to manage guest interactions and customer service.
✅ Benefits: These tools can interact with large numbers of guests to resolve common and basic issues without taking up employees’ time. They can also speed up the arrival and departure process and use data you already collect to provide a more personalized experience.
⚠️ Risks: There are some issues that technology can’t resolve, so you should ensure an employee is available to handle more complex questions or issues. Additionally, these systems may collect a significant amount of consumer data along the way, raising notice, disclosure, and data-security issues. And AI call-recording technology can create additional privacy concerns.
Practical Point: Give guests clear disclosures about AI use and data collection at the point of interaction. Be sure you understand what happens to the information collected, including what your vendor does with the data the technology captures. Review with your attorney to ensure compliance with applicable privacy laws.
Conclusion
We will continue to monitor developments related to AI in the hospitality industry and provide updates as warranted, so make sure you are subscribed to Fisher Phillips’ Insight System to get the most up-to-date information. If you have questions, please contact your Fisher Phillips attorney, the authors of this Insight, or any attorney on our Hospitality Industry Team or our AI, Data, and Analytics Team.
About the authors:
Kate Dedenbach is a seasoned data privacy attorney who advises consumer-facing and heavily regulated businesses on compliance with global privacy and data governance laws, including the Gramm-Leach-Bliley Act (GLBA), the General Data Protection Regulation (GDPR), the UK Data Protection Act, the Personal Information Protection and Electronic Documents Act (PIPEDA), the California Consumer Privacy Act (CCPA), CAN-SPAM, the Telephone Consumer Protection Act (TCPA), and state mini TCPA laws. She has particular experience counseling consumer-facing businesses on privacy issues involving adtech, digital marketing, consumer profiling, tracking technologies, and consumer loyalty programs.
Nan Sato is head of Fisher Phillips’ Tokyo office and the co-chair of the firm’s International Practice Group. She advises international companies on legal matters around the globe. She is a Certified Information Privacy Professional/Europe and Canada (CIPP/E & CIPP/C), and also has extensive experience in the labor and employment law aspect of sports.
Nan regularly represents U.S. companies operating abroad as well as foreign companies who are looking to expand into the U.S. market. Clients see Nan as a partner in strategically setting up borderless workforces and navigating legal issues arising out of their global operations. She has extensive experience in analyzing and minimizing the risks of international remote working arrangements.
