Discussion: Module 5 – AIML-April-2026

[James]

The AI technique I decided with was AI-Driven Sentiment Micro-Mapping. Instead of the complex “Digital Twin” model, this technique focuses solely on the language guests use to help you make various improvements to a hotel property and its marketing.

This involves using a simple Natural Language Processing (NLP) tool—many of which are now built directly into reputation management software like TrustYou or MARA—to break-down guest reviews into “Impact Scores.”

How it works: The AI scans thousands of words from reviews and tags them into specific sub-categories (e.g., “Shower Water Pressure,” “Wait Time for Coffee,” “Pillow Firmness”). The AI determines which specific sub-category has the strongest mathematical link to your Overall Rating.

Tutorial: Step 1: The Collection (The “Input”)

Instead of reading reviews one by one, you are going to gather them in bulk.

Open your property’s page on Google Maps or TripAdvisor.

Highlight and copy the text of the 20 most recent reviews.

Paste them into a simple Word doc or Notepad file just to hold them.

Step 2: The Analysis (The “AI Prompt”)

Open a chat with a standard AI and use the following prompt. Copy and paste this exactly:

*”I am going to provide 20 guest reviews. Please act as a Hospitality Consultant. Perform a ‘Sentiment Micro-Map’ by identifying:

The top 3 physical issues (amenities, furniture, maintenance).

The top 3 service issues (staff, timing, check-in).

One ‘quick win’ that would immediately improve our star rating.

Here are the reviews: [PASTE YOUR REVIEWS HERE]”*

Step 3: The Action (The “Daily Three”)

The AI will give you a summarized list. Your job is to pick three specific actions to take before your shift ends:

One Maintenance Ticket: (e.g., “AI noted 3 mentions of a loose bathroom tile in Room 204. Alert Engineering.”)

One Staff Briefing: (e.g., “AI noted guests feel the lobby coffee is cold at 9 AM. Move the refill time to 8:45 AM.”)

One Marketing Update: (e.g., “AI noted everyone loves the new pillows. Mention ‘Premium Bedding’ in our next social media post.”)

Why this works for a new hire:

Zero Cost: You don’t need a budget approval.

Speed: It turns an hour of reading reviews into 5 minutes of analysis.

Objective: It removes personal bias. If the AI says the towels are scratchy, it’s not “just one guest complaining”—it’s a data-backed trend.

• This reply was modified 4 months, 2 weeks ago by  James.
• This reply was modified 4 months, 1 week ago by  Dale.

[Jack]

Thank you for this informative learning plan, James. I appreciate the description and linear process you unfold for those of us reading your material!

I was moved to explore the digital twin definition as a result of your submission, as I was only familiar with the term in relation to disaster planning and crisis management (i.e., to have a purposeful redundancy of data/information in the event of a disaster to be able to keep operations reasonably on course!)

I learned that, in the context of AI, a digital twin uses 3-D modeling for simulation support, whereas AI sentiment micro-mapping targets human emotions — an essential focus when using baseline data to improve an existing system further.

I appreciate the perspective as I continue to learn new terminology in an evolving and dynamic state of new terms. I think that foundational glossaries are essential to level participant understandings and the playing field when employing/ engaging in AI-related tools to assist with decision-making.

I am a huge fan of the word “augmented intelligence” for the majority of where we find ourselves with the current state and implementation of AI uses.

Grateful to you for the opportunity to expand my horizon on this.

Jack

[James]

Thanks for your feedback Jack. I was also intrigued how digital twin uses 3-D modeling for simulation support.

[Miranda]

Hi James,

Your approach is practical and realistic for everyday hotel operations (instead of overly technical). The “Daily Three” idea was especially smart since it turns guest feedback into clear action steps that staff can actually implement right away; I could definitely see this helping management to respond faster to recurring issues while also improving the guest experience.

[James]

Thanks Miranda. I feel that with hotel operations, it’s good to be realistic instead of being overly technical. It makes for a smoother process and employees tend to enjoy that more.

[Craig]

Great and very clear to follow! The entire process of analyzing guest comments and trying not to get to emotionally exhausted by reading them one by one is now essentially obsolete when you use tools the way you explained.

Thanks for sharing, James.

[Jack]

• This reply was modified 4 months, 1 week ago by  Jack.

doi.org

Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review | Tourism & Management Studies

Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review | Tourism & Management Studies

[Miranda]

One AI tool that is becoming really useful in business decision-making is predictive analytics. Predictive analytics uses past and current data to help businesses predict future trends and make smarter decisions. This can help with pricing, staffing, marketing, and understanding guest behavior.

Quick Tutorial for New Employees

Step 1: Identify the Goal
Decide what you are trying to predict, such as busy seasons, guest demand, or marketing performance.

Step 2: Gather Data
Collect important information like booking history, guest reviews, pricing trends, and occupancy rates.

Step 3: Use the AI Tool
Upload the data into a platform like Power BI or Tableau. The system will analyze patterns and generate forecasts.

Step 4: Review the Results
The AI may predict things like high-demand weekends or which guests are likely to return.

Step 5: Make Decisions
Use the insights to adjust staffing, pricing, or promotions before demand changes happen.

For example, if a hotel sees that occupancy is expected to increase during a holiday weekend, managers can schedule extra staff and raise room rates ahead of time.

Predictive analytics can help businesses make faster and more informed decisions while improving both operations and customer experiences.

[Jack]

Hi Miranda:

I think predictive analytics is an incredibly useful tool as well! I like the simplicity of your tutorial that would make learning about this less daunting and more favorable to prospective learners. Training is a crucial part of gaining acceptance and broadening understanding. Thanks for this concept and approach!

Best,

Jack

[Craig]

Miranda, this is so clear and concise its easy to follow and i think really great. Thanks for sharing

[Jack]

AI-Based Demand Forecasting Tutorial for Hospitality Administrative Interns

Industry Context

The hospitality industry increasingly relies on artificial intelligence (AI) and machine learning (ML) to improve market analysis, occupancy forecasting, and strategic pricing decisions. Hotels must continuously evaluate reservation demand, cancellation patterns, and pricing strategies to maximize occupancy while maintaining profitability. This challenge becomes even more important within the proposed MedHotel model, where discounted lodging may support recovering patients and caregivers during periods of projected vacancy.

AI-supported forecasting systems assist hotel leadership by analyzing historical reservation data and identifying patterns that improve occupancy prediction and pricing decisions. Henriques and Pereira (2024) explain that forecasting models using occupancy, reservations, pricing, and cancellation variables substantially improve revenue management and operational planning. Similarly, Chen (2024) found that machine learning techniques can successfully identify reservation trends and improve hotel booking prediction accuracy through the analysis of historical reservation behaviors and booking variables.

Recently, I have been trying to assist students with practical interns and came up with the idea of constructing a tutorial for someone starting a summer experiential learning opportunity. One way to propose a tutorial, using a process borrowed from Lean Six Sigma, a performance improvement methodology, is to outline the original process flow in a flow chart that depicts a Value Stream Analysis. Another is a basic Lean Six Sigma Breakthrough Equation that breaks down the outcome with the independent variables (e.g., available rooms, reservation trends, cancellation rates, etc. ) plus an allowance for human error or variation: y= f(x1,x2,x3…) + ε

This tutorial serves as an instructional guide for prospective hospitality administrative interns learning how to use predictive modeling for strategic decision-making and market analysis within hotel operations.

If I were trying to train an intern in helping with a forecasting task, I might engage in the following:

Technology Description

The AI technique selected for this tutorial is machine learning-based demand forecasting. The forecasting model uses historical reservation and occupancy data to estimate future room demand, projected vacancies, and pricing opportunities. I chose this as I plan to use it , in part, to justify my final project—a Med Hotel. I borrowed a number of variables from a host of sources

Implementation Tutorial for Prospective Interns

To assist new administrative interns, this summarizes a step-by-step process for implementing ML based predictive analytics within hospitality operations.

START PROCESS

1. Define the Revenue Goal

Determine occupancy and revenue targets necessary to cover operational expenses and contribute toward fixed costs by identifying available rooms, forecasted occupancy, average daily rate (ADR), variable cost per occupied room, fixed operating expenses, and desired contribution margin.

2. Collect Historical Reservation Data

Gather at least three to five years of historical hotel data, including occupancy percentages, reservation trends, cancellation rates, ADR, RevPAR, seasonal demand fluctuations, competitor pricing, and local event activity.

3. Identify Independent Variables

Identify reservation variables that influence room demand, such as booking lead time, day of week, seasonal demand, room type reserved, market segment, cancellation history, special requests, and repeated guest status.

4. Build the Forecasting Model

Use Excel regression tools, AI forecasting software, or revenue management systems to analyze historical reservation patterns and estimate future occupancy projections.

5. Estimate Projected Vacancies

Calculate expected unsold room inventory by comparing total room availability against forecasted occupancy levels to identify potential pricing opportunities.

6. Set the Discount Occupancy Goal

Establish a strategic objective for filling a percentage of projected vacancies during low-demand periods to improve occupancy and generate incremental revenue.

7. Calculate the Break-Even Discount

Determine the minimum acceptable discounted room rate that remains above variable operating costs while continuing to contribute toward fixed expenses and profitability.

8. Compare Pricing Scenarios

Evaluate multiple discount and pricing strategies to determine which approach generates the strongest contribution margin while improving occupancy levels.

9. Make a Strategic Recommendation

Present a data-driven recommendation summarizing forecasted occupancy, projected vacancies, recommended discount rates, expected contribution margins, and occupancy improvement opportunities.

10. Monitor and Adjust the Forecast

Continuously evaluate reservation pickup, cancellation behavior, competitor pricing, occupancy performance, and RevPAR results to refine forecasting accuracy and pricing decisions over time.

END PROCESS

Expected Outcomes

The implementation of AI-based predictive forecasting is expected to improve occupancy forecasting accuracy, pricing precision, and strategic decision-making within hospitality operations. Hotels can use forecasting systems to improve staffing efficiency, inventory planning, housekeeping schedules, and operational resource allocation.

Additionally, predictive analytics supports customer-centered decision-making by helping MedHotels provide affordable accommodations for recovering patients and caregivers during low-demand periods. Interns trained in predictive modeling gain valuable analytical and strategic management skills that support future leadership development within hospitality and healthcare operations.

Overall, this tutorial demonstrates how AI-supported ML forecasting can improve both financial sustainability and operational decision-making within the hospitality industry.

References

Chen, H. (2024). Machine learning for hotel reservation prediction. In Proceedings of the

2024 SIAM Undergraduate Research Online. Society for Industrial and Applied

Mathematics.

Henriques, H., & Pereira, L. N. (2024). Hotel demand forecasting models and methods

using artificial intelligence: A systematic literature review. Tourism & Management

Studies, 20(3), 39–51. https://doi.org/10.18089/tms.20240304

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language

model]. https://chat.openai.com/chat

Disclaimer: I used Generative AI to help format this submission. The input and outcome reflect my personal work and research.

doi.org

Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review | Tourism & Management Studies

Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review | Tourism & Management Studies

[Rachel]

Hi Jack! This tutorial clearly connects AI based demand forecasting to real hospitality operations in a way that would be so easy for interns to understand. I especially like how you incorporated Lean Six Sigma thinking and tied forecasting outcomes to the MedHotel concept, showing how data driven decisions can support both revenue goals and social impact. The step-by-step process makes predictive analytics feel practical rather than overly technical.

[Rachel]

Predictive analytics uses artificial intelligence to analyze historical guest data, identify patterns, and predict future customer behaviors and preferences. Hotels can use these insights to personalize guest experiences, improve operational efficiency, and make data-driven business decisions. As a new employee, understanding how to use predictive analytics will help you contribute to improving guest satisfaction while supporting the hotel’s long-term strategic goals. By using AI-generated insights, hotels can make proactive decisions rather than simply reacting to guest needs.

Why Predictive Analytics Matters: Hotels collect valuable guest information during every stay. AI can analyze this data to uncover trends and predict what guests are likely to want before they arrive. Predictive analytics can help hotels:

• Identify common guest preferences and behaviors

• Improve customer satisfaction through personalization

• Reduce service requests and complaints

• Increase guest loyalty and repeat bookings

• Support management decisions regarding services, staffing, and amenities

Step 1: Collect Guest Data

Begin by gathering guest information from the Property Management System

Look for:

• Preferred room temperature

• Television viewing habits or streaming preferences

• Lighting preferences

• Special requests

• Check-in and check-out patterns

• Previous stay history

Example Guest Profile:

Guest: John Smith

Preferred Temperature: 68°F

TV Preference: ESPN

Special Requests: Extra pillows, late check-in

Step 2: Analyze the Data Using AI

After collecting the guest information, use the AI tool to identify patterns and generate recommendations.

Example Prompt:

“Based on the guest profile provided, predict the ideal room setup before arrival. Recommend room temperature, lighting settings, entertainment options, and two additional personalization suggestions that could improve the guest experience.”

Step 3: Implement the AI Recommendations

Use the AI-generated insights to prepare the room before the guest arrives.

Temperature Adjustment

Set the room thermostat to the predicted preference.

Example:

• Set room temperature to 68°F.

Entertainment Setup

Prepare the television with preferred channels or a personalized welcome screen.

Example:

• ESPN loaded on the television.

• Personalized greeting displayed.

Room Environment

Adjust lighting and provide requested amenities.

Example:

• Dim lighting.

• Extra pillows placed on the bed.

These actions allow the hotel to proactively meet guest expectations.

Step 4: Add Personalized Enhancements

AI can also suggest additional touches that create a memorable experience.

Examples include:

• Personalized welcome message on the television

• Preferred music or streaming services ready upon arrival

• Room setup based on arrival time

• Recommended amenities based on previous stays

These enhancements strengthen guest relationships and increase the likelihood of repeat visits.

This benefits the hotel in many ways. Using predictive analysis supports strategic decision-making and improved customer satisfaction. Guests feel valued when their preferences are anticipated and accommodated. Staff spend less time responding to adjustment requests and service complaints and personalized experiences encourage repeat business and positive reviews.

Without predictive analytics the guest arrives to a room that is too warm, requests additional pillows, and spends time adjusting the environment. With predictive analytics the room is already set to the guest’s preferred temperature, extra pillows are available, and preferred entertainment options are prepared before arrival. The guest experiences a seamless and personalized stay from the moment they enter the room. By using AI to anticipate guest needs, hotels can improve customer satisfaction, increase loyalty, and operate more efficiently. Understanding how to apply predictive analytics will help employees contribute to both exceptional guest experiences and the overall success of the hotel!

[Craig]

Awesome i just want this to become the now! Tides are changing and to have companies with this level of foresight and direction will set them apart. Thanks Rachel

[Craig]

I recently took on some additional responsibilities around the Sustainability and waste reduction within the Airport lounge segment of my company globally. This brings up many challenges but the largest being food waste from buffets. Therefore i have been researching tools and identified Leanpath which due to its new advancement utilizing AI it really well aligned for ease of use and effectiveness.

Employee Tutorial

One AI technique that is becoming increasingly valuable in hospitality is computer vision. Computer vision uses artificial intelligence to analyze images and identify objects. In foodservice operations, tools such as Leanpath use computer vision to identify wasted food, estimate quantities, and help managers make better production decisions.

Step 1: Photograph leftover food

At the end of each meal period, use the Leanpath Snap AI tablet to take a photo of any leftover food in serving pans or buffet containers.

Step 2: Allow the Leanpath AI to Analyze the image

The system uses computer vision to identify the food item and estimate the quantity remaining. No manual calculations are required.

Step 3: Review the waste report

The AI automatically records the item and provides waste tracking data for managers to review.

Step 4 Identify Trends

Review reports to determine which items are consistently overproduced.

Example:

Scrambled eggs discarded daily

Fruit cupe left over frequently

Oatmeal always selling out.

Step 5: Adjust future production

Use the insights to modify production levels for future meal periods.

Example

A lounge prepares 200 portions of scrambled eggs each morning. After several weeks of Leanpath analysis, the AI identifies that approximately 40 portions remain unused each day. The manager reduces production to 165 portions, reducing waste while still meeting guest demand.

Computer vision allows hospitality leaders to make data-driven decisions that reduce food waste, lower operating costs, and support sustainability goals while maintaining a positive guest experience.

Prior to AI Leanpath it seems used weighing scales which becomes cumbersome so this new advancement in the technology supported by AI is game changing.

AI Food Waste Solutions – Leanpath | Food Waste Prevention

• This reply was modified 3 months, 3 weeks ago by  Craig.
• This reply was modified 3 months, 3 weeks ago by  Craig.

leanpath.com

Just a moment...

Just a moment...

[Rachel]

Hi Craig! I’ve never thought about this as something that would be manually calculated, and I think it could be so useful in many different areas. Cruises could especially benefit from something like this because I imagine their buffets generate a lot of waste as well. I’m sure factors like airport demand, the number of sea days versus port days, and overall passenger activity all affect food consumption. Having a system like this could help better predict demand and significantly reduce waste.

[Craig]

100% I know from my Cruise ship days it was a huge concern and something we were very conscious of.

A big improvement was removing the tray so people could only pick up 1 plate at a time and then controlling the food prep closer to when mealtimes change and towards the end of the day however it was never an exact science.

I did have a crazy idea that we would charge people for what they put on their plates and did not consume (eyes bigger than their belly) it never landed of course but maybe would make people think twice.

[Kimberley]

I will be using Sentiment Analysis for market analysis and strategic decision-making.

Sentiment analysis is a natural language processing (NLP) technique that automatically identifies and classifies the emotional tone behind text, such as guest reviews, social media mentions, and client feedback. Sentiment analysis uses machine learning models to scan text and classify it as positive, negative, or neutral, and in more advanced applications, to detect specific emotions or themes for example, frustration with wait times or praise for staff attentiveness. it will convert hundreds or thousands of unstructured reviews into structured, trackable data. It will detect patterns that a person skimming reviews one at a time would likely miss. Ultimately, it will support faster, more confident strategic decisions, from service adjustments to marketing messages.

Tutorial

1. Define your business question first. This is because sentiment analysis produces the most useful results when it is aimed at a specific, answerable question rather than run as a vague exercise.

An example is “Has guest sentiments about our new booking system improved since launch?”

2. Identify where your relevant data is: such as review platforms (Google, Tripadvisor, Yelp), post-visit surveys, social media mentions and comments, internal client/guest communication logs. Ensure that you are taking data from a consistent time period such as last 60 days so that your analysis reflects a comparable period.

3. Choose your sentiment analysis tool. You may use a built-in sentiment feature within review management platforms like Google Business Profile Insights, or a dedicated sentiment analysis software like MonkeyLearn.

4. Run the analysis:

Copy the latest 30 reviews from google reviews.

Paste the text data into your chosen tool.

Run the sentiment classification eg. positive, negative or neutral.

5. Interpret the output.

Look for trends over time, and not just a single snapshot percentage. Use this to determine if the sentiments are improving, worsening, or stable.

Cross-reference negative sentiment spikes with specific dates or events eg. a system outage, a staffing change, a price increase.

6. Translate your findings into a recommendation.

Document what the data showed, what does that mean for the business, and what action should follow.

7. Present your findings with your supervisor.

Use plain language, visuals such as charts or trend lines. lead with the recommendation and then support with the evidence, so as to no overwhelm stakeholders.

[Samir]

Tutorial Using AI to Guest Check-In for a new hire

Purpose:

This simple tutorial will help new hotel hire understand how to use AI-powered front desk assistant to streamline check-ins, personalize guest interactions, and handle requests efficiently — all while maintaining the warm, human touch that defines great hospitality.

1. Understanding the AI Assistant

What It Is:

The AI Assistant is a smart tool integrated into our front desk system. It can:

[Samir]

OpenAI. (2026). ChatGPT (Feb 13 Version) (Large language model).

https://chatgpt.com