DO'S & DON'TS to Maximize Data and Drive Revenue

At Lighthouse, we see AI as a game-changer in terms of transforming commercial strategies for the hospitality industry. The shift has been remarkable—from a time not so long ago, when critical data was scarce - to an era where the sheer volume of information can be overwhelming.

This abundance of data presents a new challenge: how to manage and utilize it effectively when it far exceeds human analytical capacity.

In this landscape, AI is not just a tool but an essential ally. It cuts through complexity, allowing Revenue Managers and commercial teams to focus on the most critical information to make strategic decisions.

Previously, our industry grappled with siloed and fragmented data. AI helps manage data complexity, reduces time spent on low-value tasks, and frees teams to focus on high-value activities. We firmly believe that AI can and will continue to empower your commercial team to perform more efficiently and effectively.

We see three primary impacts of AI for travel & hospitality commercial teams:

1. Uncover Hidden Insights: AI reveals critical insights buried in data, supporting smarter decision-making.
2. Capture Missed Opportunities: AI helps spot overlooked opportunities, improving revenue and guest experiences.
3. Work Smarter, Move Faster: AI streamlines operations and decision-making, enabling teams to operate more efficiently and effectively to optimize commercial team performance.

As we move into the second half of 2024 and beyond, managing data complexity remains a central challenge. Lighthouse advocates for AI integration to overcome this hurdle, equipping clients with the tools to navigate, analyze, and act upon the right information.

While this list of ‘Do’s’ and ‘Don’ts’ is by no means exhaustive, it underscores essential best practices and priorities that can significantly improve the effectiveness of AI implementation for your commercial team.

DO’S

1. Prioritize data quality: Ensure your data is accurate, consistent and relevant. High-quality data is the foundation of effective AI solutions.
2. Rigorously evaluate vendors: Choose AI solutions and vendors with proven expertise and a strong track record. Look for those who understand the unique challenges hoteliers face.
3. Run pilot programs: Start with small scale programs to test AI solutions. This helps identify potential issues and assess efficacy before a full-scale rollout.
4. Focus on integration: Ensure that the AI solutions you’re considering can seamlessly integrate with your existing systems. AI can improve operational connectivity by integrating various aspects of operations, reducing bottlenecks, and improving workflow efficiency
5. Embrace continuous learning: Encourage ongoing education and adaptation as AI technologies evolve, ensuring your team stays ahead of the curve.
6. Use AI for predictive analytics: Utilize AI to forecast market trends and demand patterns, allowing you to optimize pricing strategies and improve occupancy rates with minimal human intervention.
7. Adopt AI for automated reporting: Implement AI tools to automate the generation of daily performance summaries, ensuring timely and accurate insights that are easily shareable with all relevant stakeholders.
8. Secure your data: Ensure that sensitive data is not used in public AI tools. Maintain strict data security protocols to protect proprietary data - as well as guest data.
9. Implement strong data management practices: Establish robust protocols to maintain data quality, accuracy, and consistency over time,
10. Collaborate with experts: Work closely with domain experts to ensure AI solutions are tailored to meet the needs of you and your team.
11. Improve operational efficiency: Use AI to streamline operations by automating routine tasks, allowing your teams to focus on high-value, strategic activities.
12. Make sure your data is comprehensive. Your data should cover all necessary aspects to explore problems thoroughly. Ensure it has breadth and depth.
13. Prepare thoroughly: Invest time and resources to prepare your data before implementing AI. Clean, structure and organize your data to maximize AI’s power.
14. Hold vendors accountable: Keep your vendors accountable for building AI best practices into their solutions rather than expecting your team to create them from scratch. Demand transparency and accountability in AI development.
15. Incorporate feedback loops: Implement processes to gather and act on feedback from AI users, ensuring continuous improvement and adoption the technology

DON’TS

1. Expect low quality data to create high quality outcomes. High-quality AI outputs depend on the quality of the data fed into the system. Using poor-quality/biased data will lead to poor results, so prioritize collecting and maintaining high-quality data.
2. Ignore data bias: Be aware of the risks of biased or poor-quality data affecting AI outcomes. Always ensure your data is accurate and unbiased.
3. Disregard staff training: Don't assume your team will automatically know how to use new AI tools. Proper training is essential for successful implementation.
4. Ignore the need for scalability. Ensure your AI solutions can grow and scale with your needs to avoid any future limitations.
5. Assume that AI will eliminate the need for human oversight. AI augments human decision-making, but doesn’t replace the need for human expertise.
6. Neglect data security: Never compromise on data security. Protecting data from breaches and unauthorized access is vital.
7. Assume AI will fix all problems: AI is a powerful tool, not a cure-all. It should be part of a broaders strategy that includes optimization paired with human expertise and oversight.
8. Overcomplicate AI implementations: Keep it simple. Overcomplicating AI solutions can lead to inefficiencies and increased costs.​​
9. Neglect AI maintenance: Regular upkeep is crucial for AI systems to perform well. Skipping maintenance can lead to a decline in their effectiveness over time.
10. Rush AI Deployment: Take the time to thoroughly research, evaluate, vet and test AI solutions before implementing them at scale.
11. Ignore feedback: Pay attention to feedback from both staff and guests regarding AI tools. Ignoring feedback can lead to missed opportunities for improvement.
12. Ignore the value of external expertise: consider consulting with AI experts to help drive your implementation strategy and address any complex challenges.
13. Forget to define clear objectives: implementing AI without clear goals can lead to wasted resources and subpar results. Define specific and measurable objectives from the start.
14. Overlook AI's limitations: Understand that AI can make mistakes. Regularly review and validate AI outputs, and be prepared to intervene when necessary to correct errors or biases.
15. Underestimate the importance of context: ensure that the AI solutions you leverage are fed with contextually relevant data to produce meaningful and actionable insights.

Conclusion

While we're just getting started on this journey, it's critical that you hold your tech vendors accountable for adopting emerging technology and ensuring their systems serve their users well.

The market will be divided between those who adopt AI and become more efficient and those who fail to adapt and are gradually displaced. By managing data complexity effectively, Lighthouse equips our customers with the capability to uncover insights, capture missed opportunities, and operate more efficiently.

In the rapidly evolving world of AI, the real value comes from the synergy between sophisticated tools and high-quality, differentiated datasets. Remember that the success of AI lies not just in its technological capabilities but in the quality of the data that powers it.

Focus on quality data, and you'll achieve quality outcomes. Embrace AI with a commitment to high-quality data, and your hotel will thrive in this evolving landscape.