Early adopters to laggards
The technology adoption lifecycle is a sociological model that describes adopting or accepting a new product or innovation according to defined adopter groups' demographic and psychological characteristics. The demographic and psychological (or "psychographic") profiles of each adoption group were initially specified by agricultural researchers in 1956. (This was the same year AI research was born at a workshop at Dartmouth College.)
According to research from McKinsey a few years back, hospitality was third from the bottom of all industries in digitalization. If you use the adoption lifecycle for all industries, people in the hospitality industry will be on the right side of the chart - more late adopters and laggards. However, people will be in all stages if you isolate the adoption lifecycle for the hospitality industry. Our industry has some very innovative and visionary companies and many people who will not change until forced to. Most people in the industry wait for someone else to test new ideas before they are prepared to be one of the early majority or wait a bit longer and become the late majority.
I have used Chat GPT and other AI tools for some time, and my productivity has at least doubled. For this blog post, I asked Chat GPT to analyze a few quotes from interviews with hoteliers and vendors in the hotel industry and place the people in the different stages of the adoption lifecycle. Chat GPT placed the hotel people in the early majority (the bias is that they would not have been interviewed if they had not been somewhat positive to AI) and vendors as visionaries or early adopters. Chat GPT explained that the words people used to express themselves placed them in different stages.
AI is the new buzzword
AI is the new buzzword, and every mega-chain hotel CEO is talking about AI. Here are a few anonymized random quotes from CEOs.
- "AI is going to transform every industry, and the hotel industry is no exception. AI can help hotels improve the guest experience, increase efficiency, and reduce costs."
- "AI is one of the most important technologies that will shape the future of our industry. We're already using AI to personalize the guest experience, improve operational efficiency, and drive revenue growth."
- "AI is a game-changer for the hotel industry. It has the potential to revolutionize the way we interact with guests, deliver services, and operate our businesses."
- "AI is the future of the hotel industry. It's already helping us improve the guest experience, make our operations more efficient, and grow our business."
Everyone is talking about AI. Yes, that is right, they are talking, talking, and talking. Make an experiment and replace the word AI with Digitalization. Could you reflect on how successfully the hotel industry has capitalized on the digitalization wave? It has taken a long time, and we are still not there.
Hotels have been laggards in digitalization for several reasons, including:
- The hotel industry is traditionally fragmented, with many small, independent hotels. This has made it difficult for the industry to adopt new technologies on a large scale. The fragmentation will maintain for many years even if the mega-chains are growing fast.
- The hotel industry is also very risk-averse. Hoteliers are often reluctant to invest in new technologies that they don't fully understand, which could disrupt their business models. There is no reason to believe that hotel owners and operators suddenly would become less risk-averse.
- The hotel industry is also very competitive. Hoteliers are constantly looking for ways to cut costs and improve efficiency, and they may be hesitant to invest in new technologies that could increase their costs. Hoteliers are obsessed with costs, and this is not likely to change even if an AI robot explains that revenue is crucial for success - not managing cost.
Hotels might adopt AI faster
Hotels have been slow to adopt new technologies in the past, and there is no guarantee that they will embrace AI any faster. However, I believe that some factors could lead to faster adoption of AI in the hotel industry, including:
- The increasing pressure from guests and competitors: As guests become more demanding and competitors start to use AI to gain an edge, hotels will feel more pressure to adopt AI to stay competitive. However, there is no hurry, the innovative hotels are few, and most competitors are lagging - like in digitalization.
- The falling cost of AI technology: AI technology cost is decreasing, making it more affordable for hotels to adopt. This will make it easier for hotels to experiment with AI and see how it can benefit their businesses. However, most hotels never experiment and wait for proven technology before investing.
- The growing availability of AI-powered solutions: Many AI-powered solutions are and will be available for the hotel industry soon. This makes it easier for hotels to find solutions tailored to their needs. However, hotels already have many systems to help them improve the guest experience, grow revenue and maximize profits. Most hotels manage without these systems and will also survive without AI.
Of course, adopting AI in the hotel industry will accelerate (from zero) in the coming years. However, it's likely to be a gradual process, as hotels need time to understand the benefits of AI and overcome the associated challenges. In the meantime, as plenty of hotels are underinvested in technology, preparing for using AI is necessary.
Data is fundamental for AI
The CEOs talked about improving the guest experience by using AI. The fundamentals of being able to use AI are accurate and enough data. Data quality and silos are two of the most prominent challenges hotels face when adopting AI.
- Data quality: The quality of data in hotels is often poor. This is because data is usually collected from various sources, such as booking systems, CRM systems, and transaction software, such as POS, M&E systems, RMS, Spa Systems, and many systems scrape data from websites. This data is often not standardized and can be challenging to integrate and analyze. In addition, hotels need to implement processes where the front desk staff manually collects data from guests and methods for mapping the reservations from external systems to the correct codes in the hotel PMS.
- Data silos: Hotel data is often siloed in different systems. Data is not shared between various departments, making it difficult to understand a guest's journey.
These challenges can make it difficult for hotels to use AI effectively. For example, AI models can only make accurate predictions if the training data is high-quality. If data is siloed, using AI to capture the information to personalize the guest experience can be challenging.
There are several things that hotels can do to address these challenges. They can:
- Invest in data quality: Hotels can invest in data quality initiatives to improve the quality of their data. This includes cleaning and standardizing data and ensuring solid processes are in place to verify that data is accurate and up-to-date.
- Break down data silos: Hotels can break down data silos by integrating their different systems. This will allow them to share data between various departments and understand a guest's journey.
By addressing the challenges of data quality and data silos, hotels can make it possible to use AI effectively. This will allow them to improve the guest experience, increase efficiency, and grow revenue.
Demand Calendar manages data
On top of the action list for all hotels is to manage their vast amounts of data. This is the pain that
Demand Calendar tackles and provides a solution to the challenge of siloed data in the hospitality industry. Integrating and unifying disparate data sources ensures a comprehensive view of the hotel business, including guest spending patterns. In the context of siloed systems, where data is kept separately across different departments or software systems, Demand Calendar is the connecting piece. Demand Calendar collects, stores, and harmonizes data from multiple sources in one easily accessible system, breaking down the information silos and promoting better data flow.
As for data quality, Demand Calendar offers mechanisms for offsetting bad data quality, such as consolidating duplicate profiles and making hotels aware of data quality problems for continuous improvement. Processes involve features for manual data verification, anomaly detection, and feedback mechanisms to allow users to flag and rectify data quality issues. Over time, these processes significantly improve the overall quality and reliability of the data.
Demand Calendar already has 100+ marketing, sales, revenue management, and top management functions to effectively and efficiently manage all commercial work and total revenue reporting in hotels. These functions will already today help hotel teams become more profit-oriented and grow revenue to maximize profits.
Based on our knowledge of managing hotel data from many internal and external sources, we are AI-ready and will use AI in functions where it makes sense to help hotel teams get their jobs done faster and more accurately. The job is still the same: Attracting guests and selling more to each guest. Our mission is to provide the best solution.