AI Language Comprehension in Hotels
This blog highlights how advanced AI agents using NLU technology can transform hotel operations. By understanding context and intent, these AI agents streamline tasks, saving time and ensuring staff can focus on delivering exceptional guest experiences. InHotel.io is pioneering this future with their upcoming Staff Assistant.
Use algorithms to process the image and extract important features from it
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Use machine learning to classify the image into different categories
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Filter the images based on a variety of criteria, such as color, texture, and keywords
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Automatically group similar images together and apply a common label across them
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Convert the extracted features into a vector representation of the image
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Overview
Can your AI differentiate "How do I check room availability?" vs. "Check today’s room availability."?
This may seem like a small distinction, but it reflects two very different intents—one asking for guidance and the other requesting immediate action. We certainly expect an intelligent assistant (whether human or AI) to recognize this difference and respond accordingly, right?
The Power of Smart Language Comprehension
We are preparing hotels for a future where AI agents act as proactive companions to human staff, delivering >10X ROI by operating with limited supervision and executing potentially complex operational workflows, saving staff hours each day on routine tasks and allowing them to focus on creating exceptional guest experiences.
To trust the AI agents, it's critical that they understand the intent behind each instruction they receive.
For example:
- "When should I check occupancy?": The hotelier likely wants advice on best practices or SOPs and the AI should interpret this as a request for guidance, not for an immediate data lookup.
- "Where to check occupancy?": The staff member is probably seeking guidance on how or where to find the occupancy data, not the data itself. The AI should understand this and provide instructions rather than retrieving the figures.
- "What is our occupancy?": The hotelier likely means to request today's specific occupancy data for their hotel. The AI should interpret this as a command to retrieve the information from the PMS, avoiding any unrelated details.
These examples highlight the importance of accurately interpreting intent to ensure the AI takes the right action based on the staff member's true needs. To achieve this, we are developing Natural Language Understanding (NLU) technology, which enables AI agents to detect intent within a broader context and respond accordingly. Unlike simple chatbots, AI agents with advanced NLU can comprehend context, perform meaningful tasks, and assist staff with informed decision-making, transforming AI from handling basic queries to providing real-time operational support.
By correctly interpreting intent, NLU ensures the AI assistant's actions align with what the staff or other AI agents actually require.
Learning from Prototyping
During our initial GM Assistant prototype testing, we found that AI agents initially struggled to differentiate between general "how-to" inquiries, PMS-specific "how-to" inquiries, and direct requests for action in the PMS. This resulted in undesired outcomes, as well as delays in providing the correct information. For instance, the AI agents sometimes performed unnecessary actions, such as writing code to retrieve data, only to later realize that a simple lookup in the PMS documentation would have sufficed.
By introducing and refining our NLU model, our early tests show that the AI agents have significantly improved their accuracy in interpreting different intents. This allows them to select the most appropriate tools or workflows, delivering more timely and relevant responses. While there is still work to be done, the initial results have been highly encouraging.
The Future of AI in Hotel Operations
At InHotel.io, we envision a future where AI assistants are as intuitive and easy to interact with as a trusted colleague, allowing hotel staff to focus on delivering exceptional guest experiences without worrying about complex setups or extensive training. The key to achieving this is precise intent recognition. Without it, workflows can break down, leading to errors and operational inefficiencies instead of streamlining processes.
That’s why we are committed to investing in advanced Natural Language Understanding (NLU) technology. NLU enables AI agents to act as smart companions to hotel staff, efficiently managing routine tasks and supporting strategic decision-making, while minimizing human oversight. By accurately detecting and responding to staff needs, our agents can bridge skill and capacity gaps and enhance overall efficiency.
Our first NLU-powered assistant, the generic Staff Assistant, will soon be available. Stay tuned for its launch updates, and discover how AI can transform the way your team works.
