As exciting as AI and ML may seem, we shouldn’t get overly excited about them just yet. The many benefits come with a couple things you should watch out for.
Consider this – I travel with my kids a lot, and a hotel who pays attention to this may notice that I buy a lot of ice cream. Proactively offering this to me when I’m on-property with my family is probably a good idea. It may make me more likely to buy. It also enhances my experience because I feel like the hotel knows me.
However, offering ice cream when I am staying for a business trip confuses and possibly alienates me. Machines may lack the proper understanding to always offer the right recommendations at the right time. As another example, think about how well Siri can type what you say, but there can be a considerable gap between what you say and what you mean. Context matters.
Explainability (Asking ‘Why’)
Hoteliers like defining clear rules using “if then else” logic: if this happens, that happens. If a guests hits $1,000 in spending, for example, they receive a certain reward, reach a specific loyalty tier, etc. ML can also help you figure out which campaign a particular guest should receive. But the problem is, machines only present cold, hard facts, and to them, guests are just numbers and data.
Machines lack the ability to explain their reasoning. As a result, you may send campaigns or discount offers you as a hotelier don’t fully understand. Longer-term, this could even hinder the change management necessary for successful adoption of these technologies.
Human vs. Machine
Hoteliers care a lot about the guest experience, rightfully so, and giving it up to machines is tough – even if it drives revenue. However, machines do not spell the end of hotel staff. The key is to use AI and ML to augment and enhance the human element, rather than replace it.
For example, your guests may appreciate the convenience of asking Alexa simple questions, but they will still enjoy a friendly smile and conversation with your staff. The machine can also help recommend the specific surprise or delight a guest may want, which can then be delivered physically by your staff.
As another example outside hospitality cited by McKinsey & Company, the large-scale deployment of bar-code scanners and associated point-of-sale systems in the United States in the 1980s reduced labor costs per store by an estimated 4.5 percent, but cashiers’ employment grew at an average rate of more than 2 percent between 1980 and 2013.
AI and Machine Learning in Hospitality
There you have it. AI and ML are a lot to take in, but it’s worth educating yourself on the benefits and watch-outs of these rapidly evolving technologies, especially as a manager or operator. If you’re interested in learning more, reach out to us. We’re always here to chat.
For more on Artificial Intelligence and Machine Learning, check out the rest of our series: