GenAI Copiloting Use Cases for Hoteliers Applicable Today

5 min read HITEC Booth #2154
GenAI Copiloting Use Cases for Hoteliers Applicable Today — Photo by Created by HN with DALL·E

One of the foremost ways that generative artificial intelligence (GenAI) will help hoteliers is through tools that function much like how individual people are using ChatGPT today: to help with the creation of text. Before looking at specific use cases that senior hotel leaders and managers can try out right now, some theory is in order.

First, we throw around this term ‘generative’ nowadays like it’s table stakes, but beyond simply implying the generation of new text or computational results, what it actually indicates are rules not limited to the specific examples provided by the one’s previous experiences. That is, the black box of an immense volume of training data pushed through a ‘deep’ set of thousands or millions of layers of constantly reweighted processing nodes can now produce text that approximates human intelligence and human imagination, in what we often label as a large language model (LLM).

So, with machines able to imitate many of the text-based tasks that would occupy a hotel manager’s time on a day-to-day basis, it’s then a matter of leveraging GenAI to work more efficiently and free up time for more complex projects. To quote an industry icon, Matthew Upchurch, the longstanding CEO of Virtuoso, who has cited this mantra for over a decade now, Automate the predictable so you can humanize the exceptional.

This leads us to the buzzy term for 2024: copiloting. In other words, use AI tools to handle an increasing amount of the repetitive (and boring!) tasks while you focus on the real work. As we’ve seen from the latest Oracle article on this particular subject, there are several key areas where hoteliers can start to use AI as that copilot to help with text-based work.

First and easiest to test out is summarization. The underlying idea here is that we are all too pressed for time to read through lengthy documents when their nuanced details or technical specifications may not be important to helping us make a decision at that particular moment. A copilot could thus be prompted to generate key insights and takeaways from the document so that you can have an overview that is actionable for your purposes.

Summaries like this need not be limited to text documents either; you can ask for insights from numerically driven reports, charts, tables, images or a pool of guest feedback, all with the intent of guiding future actions. An area where many hospitality companies are eagerly investigating summarization tools is in job application evaluation in order to quickly identify the candidates from the initial batch who are most likely to succeed in a role before proceeding to a formal review process.

After summarization comes suggestions where instead of only generating a concise synopsis, now the AI is offering recommendations. Perhaps the foremost example of this that we now all encounter is Microsoft Word’s predictive text tool – along with the word suggestions for Apple of Android SMS – where the most next string appears in soft grey then you need only hit tab rather than type out all those letters individually, and also risk mistyping in the process. Each tab saves only microseconds, but done often enough as part of a newly ingrained habit and this alone is a tremendous timesaver.

Thinking about what’s possible with suggestions and hitting the tab key more frequently is but only the start, with numerous tools being developed to further advance this AI niche. A copilot can be prompted to suggest additional resources for further instruction on a subject matter, while others can go even further by creating a succinct how-to guide or best practices document with increasing degrees of personalization as more integrations are brought into the feedback data. Think suggested survey questions for guests, suggested tips for resolving errors, suggested actions based on new report findings, suggested marketing messages that are bespoke to certain guest segments or even suggested market segmentations not strictly based on traditional human assumptions.

Third and related to the generative capabilities of summarization and suggestions is assisted authoring. To continue with the mention of suggested marketing messages, this would be one step further wherein instead of simply recommending a new text string for a room description or Google search ad, the AI would be prompted to write the first draft with the human then stepping in as editor and approver. Besides those two specific examples where GenAI tools can be recruited to do the heavy load involved with the first draft, think about food menus, spa treatment descriptions, activities, text for the website, job postings, internal bulletins or performance reviews.

Wherever there is text, GenAI can or will soon be able to help. Ultimately, it’s about liberating your managers and service agents through the quick creation of content so that they improve the hotel’s guest service. Like the predictive text in Microsoft Word, think less about earthshattering shifts in how you work and more about how you can use save seconds at a time, which will add up to something far greater in the long run.

Chatbots, Robotics & AI

Adam Mogelonsky

Partner at Hotel Mogel Consulting Ltd.
Adam Mogelonsky

As one of two principals at Hotel Mogel Consulting Ltd., Adam Mogelonsky is a strategic advisor primarily for independent properties, small hotel groups and technology vendors for the industry, specializing in helping brands determine the best path to increased profitability whatever that direction requires. As a thought leader, he has coauthored seven books on hotel management and over 1,200 articles over the past decade across a variety of trade publications, in addition to regular podcast and conference panel appearances. Lately, the focus for Hotel Mogel has been on automation, tech stack auditing, labor efficiencies, employee retention programs, heightening managerial productivity and upselling practices to maximize total revenue per guest.

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