These days, all anyone talks about is artificial intelligence. Every sector seems permeated by it, and everything seems destined to become “smarter” than ever. I don’t doubt it’s truly the future—not only in revenue management—but, as with everything, timing is crucial.
While AI may already be ready for the world of revenue, I’m not sure all operators are prepared for it. There are processes that need to be fully understood; otherwise, there’s a risk of using technology merely as a set of automated routines.
Some predict that AI applied to RMS (Revenue Management Systems) will lead to the extinction of many revenue managers, just as sales managers were largely displaced by OTAs. Some are already concerned about their jobs. The complex relationship between machine and human dates back to the Industrial Revolution—an eternal conflict.
AI has become the trend of the moment for many software solutions, and after years of talking about BIG DATA, it now seems that nothing can move forward without AI. But, as always, it’s essential to distinguish between simple algorithms and complex intelligences.
We, too, are cultivating our own AI project. The market is ready, and we want to provide a solution that can grow alongside its usage.
MyForecast RMS is a software that has successfully combined complex data analysis with ease of daily use. While advanced systems exist, there’s the reality that strategies need to adapt quickly to stay “time-to-market.” Recent years have been a true test for operators, pushing them to be flexible and prepared for changes, transformations, and revenue types that no AI could have handled.
Whether AI represents complexity or excessive simplification is yet to be determined. What we’ve always aimed to avoid is over-simplification. Revenue management, for us, has never been just about setting a price; it’s about a set of strategies that consider not only objective data—numbers and figures—but everything that defines any product in the market: marketing, distribution, and communication.
Our AI project for MyForecast RMS is certainly not the first of its kind, but it is designed to streamline some of the processes involved in pricing strategies while keeping human intelligence central. Our initial goal will be to dynamically suggest a sales rate using AI algorithms, specifically machine learning and deep learning techniques.
This model aims to recommend the optimal selling price by considering the hotel’s unique characteristics, market pressures, and external variables.
So far, we’ve begun developing the core of this model, which will gradually expand to include all variables impacting the optimal sales rate—the rate that maximizes a property’s revenue at any given time.
This all-Italian project has also enabled us to fund a PhD scholarship in Computer Science, co-financed by MyForecast RMS through the PNRR (National Recovery and Resilience Plan). This scholarship focuses on the study and development of innovative machine learning algorithms for revenue management systems.
The goal is ambitious: PhD candidate Susanna Saitta will work alongside the MyForecast RMS project team, specifically Angelo Scilipoti, a Hotel Revenue Manager who has already made significant strides in developing the predictive algorithm.
These systems are dynamic, and only time will tell whether RMS solutions will replace some revenue managers or free them from certain burdens, allowing them to devote that time to broader strategic thinking.
Tina Ingaldi