How AI Calling Is Transforming Hospitality Support Centers

In this blog, we will go through:

What AI Calling can Automate in Hospitality Support Services
Total Cost of Ownership in AI vs Human Support
The Biggest adoption Blockers of AI Calling in Hospitality Support
Path to Fully AI-driven Call Centers in Hospitality Industry
FAQs about How AI Calling Is Transforming Hospitality Industry

AI can automate almost each and every task of the hospitality support from booking a new flight to rescheduling, cancellation or answering simple FAQ queries.

BUT;

When there are things like a customer wants to complain or where there is emotions involved, maybe it could be an angry customer, or in cases where it’s your highest loyalty tier member, or if it’s a corporate booking where you need to do some negotiations, this is where humans still shine because even though all of these can be looked up by an LLM and could be understood to some extent, there are specific customer sets who would still prefer the white glove treatment of talking to an actual human and getting their queries resolved.

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What AI Calling can Automate in Hospitality Support Services

The hospitality industry is all about creating meaningful, memorable experiences. This is how AI calling can satisfy hospitality businesses customers better and increase retention rates.

AI Replaces Manual Call Center Processes of Hospitality Support Services

Before AI, this is how hospitality support systems operated:

The customer canters have a PBX machine which basically translates the analog signals into digital signals that go into servers and then all the agents have their SIP connected through their laptops and they have a bunch of systems which tell the loyalty programs, different policies based on cancellations, editings, etc. An agent goes through all the data for each customer that comes in and addresses the query.

This is where AI changes the flow. Instead of agents going through all the data and navigating multiple systems, an LLM can instantly access the same information, identify intent, and resolve the query or route it correctly without delays.

Real-Time Personalization Using Customer Data (loyalty + occupancy signals)

Let’s take a typical example where a customer is enrolled in a loyalty program in gold tier. So for an LLM when a customer calls in it knows this information at the time. So all it has to do is go through their property management system, look at their loyalty tier and past transactions, and it could instantly come up with an offer.

Let’s say the property is 40% booked for the day and the property still has its luxurious or higher tier rooms available and because of customer loyalty status an LLM could pull in this data and probably offer them an upgrade for a lower cost.

So when authorized to do such transactions it could do it at a much faster pace than a typical agent would because for human agents to come up with such an offer is an actual decision tree. But for an LLM, it’s just equating all these things and proposing an offer to a customer, which in turn leads to customer satisfaction because their query was resolved quickly. They were offered what they would ideally like a lot quicker.

Upsell Opportunities with AI Agents

In terms of upselling, a human would have to go through previous logs and identify if there is a repetitive pattern. All of this while the customer is still on call. But the agent at the same time is going to do it much faster and try to upsell. So these are the things where the agents will start generating more revenue, especially from upselling the existing products.

But at the same time, you’re also seeing the opposite of that happen. Let’s say you have a promotional event going on and you have these five add-ons that we can put. Now the AI will try to sell all five of them. So it’s still at an evolving phase where people are trying to see the data properly and understand what exactly you need to upsell. That is right now done much better by a human because they understand the emotional aspect of it. That is where AI still lags, primarily from not being able to feed in the right data and give it a decision tree on how it needs to be done.

If it is a purely statistically driven decision, like a data-driven decision, an LLM could easily do an upsell, but if it involves a bunch of promos or any offers, humans could try to trick the system, they could try to bundle in combos and make it effectively free for them. So that is still something we would need to solve before we make it fully fledged and take the promotional aspect and upsell aspect out of the loop from humans into agents.

Intent detection and Smart Routing

If a customer has a query and it involves some complex logic, for example a customer needs connected rooms with specific amenities, something that the hotel has to go out of their way to do, this is where an LLM could also figure out that this is something beyond its reach and hand it off to the right human agent.

One thing that it can certainly help in is taking customers to the right agents. So in an IVR you have to wait for the whole decision tree to play out, but with an LLM it could identify the intent quickly and take you to the right person you need to talk to pretty much instantly.

In terms of intent, it also recognizes where its shortcomings are and it simplifies the routing quite a bit because even in a support department, there’s multiple different teams that you would want to reach out to. It identifies the intent of what team you would ideally want to talk to and route you to the right team.

Secure payment approaches and customer trust

Right now multiple AI labs are also proposing agent to agent payment systems. To keep it safe, it could work out of a system where customers have some balance in their wallet and that wallet is then used to perform the transaction. Instead of entering card or bank details every time, the customer already has money stored in a wallet, and the payment is made using that.

There is also one thing where you do all the transactions over the phone, like what kind of hotel you want, from what time to what time, and then you get an email with a payment link with a certain expiry time. That’s also a great approach because these kinds of transactions can happen very easily and have lower friction than just trusting an AI to handle your wallet. This leads to trust from the customer’s end as well.

Handling surge scenarios: storms, cancellations, peak demand

In case of hotel bookings or flight bookings where there are chances of mass cancellations due to flight disruptions, weather, or any geopolitical issue, everybody needs to reschedule, cancel, or find the nearest alternative flight. At times like these, the call center gets overwhelmed.

It’s not the first call that affects the human agent, but it’s like the 10th call. By the time the agent has continuously told customers that we’re sorry for the delay, we’re sorry for this, they’re already frustrated, and on the other end of it the customer is also frustrated.

First off, AI agents don’t have emotions nor do they get tired. Meanwhile human agents when they have to go through the same call like 10 times over on the same day in a tough situation it can affect their morale. But an AI agent is available 24/7 and they can answer your queries any time.

An LLM could be trained for it using past peak demand data and it could handle it how it was done in the past, and in complex cases it could be handed off to a human.

Context-Based Handling of Customer Interactions

Empathy depends on the context of the conversation. If it’s a simple query where someone just wants to know a simple FAQ or wants to do a booking, I don’t think there’s a lot of emotion or empathy that the customer is expecting.

Some situations do demand human intervention, especially when there are emotions involved. But in transactional cases, as if someone wants a new flight, an LLM could immediately suggest their options because it knows in advance that this customer is probably calling for their cancelled flight. So it can check on them immediately: are you calling for your cancelled flight? It takes off the frustration from their head because they are immediately addressed with the problem and they’re immediately offered a solution..

Reducing Lost Revenue from Missed Calls

One of the major issues that the hospitality industry has is unavailability of agents during peak time when the call center is busy, there are not enough agents, nobody wants to listen and you’re the fifth caller in the line and you’re going to drop off even before it reaches you’re the next caller.

The problem with a fully humanized call center is the infrastructure scalability aspect because you still have humans and physical infrastructure that are connected to the entire process. This makes it unscalable.

These things kind of overwhelm the support that we can actually deliver and that’s where the scalability aspect of these AI agents come into picture. With AI assistance, everybody gets the same and best kind of treatment. But at the same time if we are able to contact all the customers that are calling us without having dropping rates, that’s going to help us give better customer satisfaction and also this is a major part where we get the revenue from as well, so it also affects how much revenue we’re making out of these calls as well.

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Total Cost of Ownership in AI vs Human Support

When you take the total Total cost of ownership (TCO) of it, AI agents are comparatively cheaper. Because when you hire agents, human agents, it’s not just about their actual work timings. It’s also about their availability. It’s also about how they’re getting trained. It’s also about which team they’re getting trained in.

Everybody tries their best to give the same kind of training to all the employees, but it does not work out. Everybody has their own unique perspective on things, so it is going to change.

At the same time, when you deploy these AI agents, it’s going to scale much faster and it also makes sure the agents are going to the same knowledge base.

Overall, when you take the TCO, AI agents tend to be cheaper, but that is about 5 years from now. Right now, it’s still a bit expensive because traditionally when you ask an AI a question, it takes a few seconds, and gives the answer. That is how AI started, and eventually we have reached a point where bidirectional speech-to-speech is available, so right now it’s still in an evolving phase where it’s kind of expensive to run these.

So it works out when you have predictable patterns. The AI is able to get all the information up front and get the job done. But when you have more emotional situations, those are the places where you would still need human intervention.

Right now, the industry would be split between, or be a combination of, using agents and humans, but eventually it will reach a point where most of it can be automated and humans will take the most critical calls or any kind of extra promotions or approvals that need to be done.

The Biggest adoption Blockers of AI Calling in Hospitality Support

Firstly because the technology is still new. It’s a bit complex. It’s a bit complex to get rid of or say modify the current workflow to get things done, and like all AI integration problems the data is not ready.

We still have all the customer data spread out across different kinds of data sets, some of it is still on excel sheets as well. So getting all that into an AI ready system and then integrating AI is going to be the biggest challenge. The problem more than the cost is going to be about the data.

I think how accessible this data is probably still an issue because there might be services which still run on legacy software which do not expose any API. So the adoption might be slower on that front. Meanwhile, large conglomerates can probably fast-track this whole process because they have dedicated software delivery partners who help them integrate these things at a much rapid pace.

Path to Fully AI-driven Call Centers in Hospitality Industry

For systems to go completely or for the call center systems to be completely AI-driven, even for the hospitality and travel services industry, it’s pretty easy because the patterns are known and pretty much all major cloud providers have an offering where it’s easier for them to deploy regardless of scale. Whether you’re a large hospitality conglomerate or if you’re a small hotel just starting out, it’s pretty easy to get started and there are integration service partners who would help you out with it.

As we get deep into how AI progresses, the cost of running these AI models are coming down quite a bit. Over the years, we have seen pretty much like 50 to 60% reduction in the cost, like token cost to run LLMs. Across modalities, especially for voice, it’s coming down quite a bit. So we can see the ease of implementation and the cost of implementation coming down quite a bit.

AWS has a pretty comprehensive suite of tools to get started. Anyone can sign up to Amazon Connect, which is their telephone and agent services, and they can hook it up to Lex and Bedrock.

With Bedrock they can identify certain functions because intent is easy to find and it’s also easier to define what action it needs to perform, and with not a lot of code you can set up your functions and you’re good to go.

If it is a simple customer transaction, simple lookups, you have to write like two to three functions and you’re pretty much good to start.

Because it lives in the Bedrock ecosystem as well, and when your model starts solving complex problems, you can put everything into the knowledge base and give it that memory of complex tasks that it has solved before. Or if you have a corpus of recordings from the past of support tickets or textual conversations, you can also put it into the knowledge base and help out the LLM to identify the situations a lot more easily.

Conclusion

Currently we are at a point where we’re experimenting with AI and seeing how it goes. But it’s not just experimentation

It’s just growing way more rapidly than what we expected. Just like how AI is reshaping how we do coding, it’s also reshaping the entire support industry irrespective of what industry we are targeting. Especially in a sensitive sector like hospitality and travel, we’re going to see a lot of improvements that are coming through where you just call a call center number and you’re able to book full-fledged packages to go on tours.

That is where we’re heading to. The major blocker is going to be how you’re going to process and clean your data up. Once that is done, everything that comes on top of it is just putting the layers on top of it.

FAQS about How AI Calling Is Transforming Hospitality Support

1. What can AI calling automate in hospitality support services?

AI can automate almost each and every task of the hospitality support from booking a new flight to rescheduling, cancellation or answering simple FAQ queries.

2. Where do humans still shine in hospitality support?

When there are things like a customer wanting to complain or where there are emotions involved, maybe it could be an angry customer, or in cases where it’s your highest loyalty tier member, or if it’s a corporate booking where you need to do some negotiations, this is where humans still shine..

3. How does AI calling use customer data for personalization?

When a customer calls in, LLM takes their information from knowledge bases and CRM instantly. It goes through their property management system, looks at their loyalty tier and past transactions, and it could instantly come up with an offer.

4. Can AI agents handle upsells in hospitality support?

A general notion that people carry right now is an AI agent cannot do upsells. But because of how context aware it is in each call, it can do upsells a lot faster and a lot more efficiently than humans do. If it is a purely statistically driven decision, like a data-driven decision, an LLM could easily do an upsell.

5. How does AI help with smart routing?

If a customer has a query and it involves some complex logic, an LLM could figure out that this is something beyond its reach and hand it off to the right human agent. In an IVR you have to wait for the whole decision tree to play out, but with an LLM it could identify the intent quickly and take you to the right person you need to talk to pretty much instantly.