Over the next couple years, advancements in artificial intelligence (AI) will transform nearly all areas of hospitality operations, helping us run more efficient hotels and providing prescriptive guidance to drive improved strategies across the board.
Early adopters are already turning to machine learning today for automation and hoteliers who aren’t taking steps to prepare for the next generation of AI-driven hospitality risk being left behind.
Over the past few years, we’ve seen early adoption of AI and machine learning, primarily in guest communication. Web chatbots, trip planners, automated call centers, for example, are already transforming the way hotels interact with guests.
Much of this technology is machine-learning based and will get exponentially better as more advanced AI features are adopted. As we look ahead, teams managing back-office tasks and revenue management strategies will see opportunities in automated rapid decision-making open, but only if steps are taken to centralize their data and focus on building the right framework for innovation.
At its foundation, AI relies on the centralization of data used to train a model to make predictions and responses. In hospitality, there is an abundance of data that we can use to train AI models, ingesting information from our property management system, accounting system, point-of-sale system, central reservations system, booking engine and more.
While we look toward a future where AI is informing all our business decisions, taking steps to centralize this data today is critical to ensure we are building the right framework for innovation.
AI in BI
The ability of today’s business-intelligence tools to centralize data from all areas of the business and serve it up in a user-friendly format to inform decisions is incredibly powerful. Moving our data and software to the cloud has paved the way for easier and increased data centralization through advancements in areas like Integration Platform as a Service (iPaaS) and central data warehouses, such as Snowflake.
Now, rapid adoption of generative AI models, such as Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs), and continued adoption of analytical AI models, are enhancing the business intelligence discipline by providing a next level of analysis. Instead of simply serving up descriptive analytics, next-generation tools will decipher and analyze the relevant information for you and either serve up a recommended action or automatically take the next steps.
What kind of actions could we see AI taking for us?
- Anomaly detection. Was your food cost or labor cost abnormally high this week? Did your booking engine experience a spike in conversions? AI systems can detect those anomalies, alert the hotelier, highlight the necessary information and then recommend a suggested course of action.
- Turning data to prescriptive insights. OK, you’ve got a pretty dashboard—what specifically should you be looking at? AI will analyze reports and dashboards and generate actual insights, pinpointing areas to adjust and improve strategy.
- Conversational data. Can you ask your data questions? AI assistants provide the opportunity to send messages with questions about the data in reports and automatically receive insights and next-step suggestions in text format.
The evolution of AI has driven a shift in the type of data we consume today, from descriptive analytics to predictive analytics to prescriptive analytics. Descriptive analysis primarily focuses on summarizing historical data to understand past trends and events. Predictive analysis takes this a step further by using AI models to make predictions based on patterns found in historical data. Finally, prescriptive analysis harnesses AI to not only predict future scenarios but also recommend the best actions to achieve desired outcomes.
Soon, AI will enable a last step—let’s call it delegative—where after one approval click, software is automatically taking the recommended action for you. Once AI knows enough about our business and what decisions we would typically make, anomalies in data will automatically trigger optimal actions. For example, when a system detects that occupancy is spiking, AI might automatically schedule an extra housekeeping shift.
With time and repetition, hoteliers will be able to trust that their system is taking the right actions. When AI turns recommendations into actions, hoteliers can increase productivity and reallocate their time back to providing exceptional hospitality.
Who Owns the Data?
Over the past few decades, as data about the hotel business became more widely available and accessible, a growing concern was data ownership. On one hand, both brands and management companies need access to property-level and guest data to implement new strategies and tools, such as AI and personalization, that will improve efficiencies and guest experiences.
On the other hand, management companies are justified in protecting some financial data, such as profitability. For management companies that operate multiple brands, sharing data among competing brands also requires sensitivity and security measurements.
Guests also have rights to their data, and hotel leaders as well as solutions providers must become increasingly cognizant of those rights and put the right protections in place to keep guest data secure.
With protections in place, however, data sharing should be less of an issue today, and many solutions providers are moving toward more open systems that allow hoteliers to centralize data in a unified data warehouse accessible by different stakeholders in real time. Working with a solutions provider who understands the unique dynamics of the hospitality industry, and where the roadblocks exist today, is critical to ensuring necessary data is shared while certain data is protected.
Tensions will always exist over ownership of data, but, in the end, without increased data sharing, the industry will continue to lag other industries in innovation.
AI as a Catalyst
AI will be the catalyst to finally understanding the depths of performance and financial data to help run more profitable businesses. To get here, though, we must start with bringing all the available data together into what is commonly referred to as a “single source of truth.” From there, we can begin to surface the data into digestible formats, such as dashboards and reports. Only then can we begin generating actionable insights.
Helpdesk chatbots are a great start to making teams more efficient and helping hotels tackle their biggest challenge: labor. But the prospects of AI being applied to business metrics across an entire property or portfolio will have a lasting impact on the evolution of hospitality operations.
Niki Johnson is chief product officer at Otelier, a hospitality software data company. She oversees the overall strategy, development and success of the company’s product suite, focused on ensuring the product aligns with the company’s goals and meets hotelier needs.