Content uploaded by Jacques Bulchand-Gidumal
Author content
All content in this area was uploaded by Jacques Bulchand-Gidumal on Oct 01, 2020
Content may be subject to copyright.
Impact of Artificial Intelligence in Travel,
Tourism, and Hospitality
Jacques Bulchand-Gidumal
Contents
Introduction.................................................................. 2
Defining Artificial Intelligence................................................... 3
IT Foundations for AI.......................................................... 4
Big Data................................................................... 4
AI Fields of Interest for Travel and Tourism...................................... 5
AI Systems and Their Use in Travel and Tourism.................................... 8
AI Systems................................................................. 10
Devices and Integrated Systems................................................ 11
Smart Tourism and Smart Destinations.......................................... 12
Impacts of AI on Hospitality..................................................... 12
AI-Related Challenges in Travel and Tourism ....................................... 14
Issues Related to Tourists’ Adoption and Use of AI................................ 14
The Substitution of Humans................................................... 15
Ethics and Biases in AI....................................................... 16
Future Research............................................................. 17
Expected Future Developments.................................................. 17
Cross-References.............................................................. 18
References................................................................... 18
Abstract
Artificial intelligence (AI) is currently present in almost every area of travel
and tourism, appearing in different types of applications such as personalization
and recommender systems, robots, conversational systems, smart travel agents,
prediction and forecasting systems, language translation applications, and voice
J. Bulchand-Gidumal ()
Institute for Sustainable Tourism and Economic Development (TIDES), University of Las Palmas
de Gran Canaria, Las Palmas de Gran Canaria, Spain
e-mail: jacques.bulchand@ulpgc.es
© Springer Nature Switzerland AG 2020
Z. Xiang et al. (eds.), Handbook of e-Tourism,
https://doi.org/10.1007/978-3-030-05324-6_110-1
1
2 J. Bulchand-Gidumal
recognition and natural language processing systems. Recent improvements in
big data, algorithms, and computing power have enabled significant enhance-
ments in AI. In this chapter, we review how AI has changed and is changing
the main processes in the tourism industry. We start with the IT foundations of
AI that are relevant for travel and tourism and then address the AI systems and
applications available in the sector. We then examine hospitality in detail, as a
sector in which most of these systems are being implemented. We conclude with
the challenges that AI faces in the tourism sector, a research agenda, and draw a
scenario of the future of AI in tourism.
Keywords
Big data · Deep learning · Machine learning · Personalization · Forecasting ·
Robots
Introduction
Artificial intelligence (AI) relies on big data, processing capacities, and algorithms.
Each of these three elements has experienced significant improvements lately, as
several trends have coincided: first, the refinement of and advance in AI algorithms;
second, significant improvements in processing capacities; and third, in the context
of big data, the development of new and more powerful information sources and
architectures that allow for the storing and processing of massive amounts of data.
These improvements have, in turn, fueled significant enhancements in AI systems
and robotics, in a process known as the Fourth Industrial Revolution (Li et al. 2019).
Currently, AI applications are being developed and tested in all areas of the travel
and tourism industry, including personalization and recommender systems, personal
travel assistants, robots, prediction and forecasting systems, language translation
applications, and voice recognition, and natural language processing systems.
Artificial intelligence is particularly relevant to travel and tourism for several
reasons. Tourists need to make a series of decisions about future trips, for example,
choosing a destination, transport, accommodation, and activities, among other
things. These decisions will have a significant impact on tourists’ satisfaction with
their trip. However, the range of destinations, transport, accommodation, and activ-
ities currently available presents an almost infinite array of options necessitating
assistance. Tourism organizations and agents face a similar challenge when trying
to find the best match between customers and travel packages tailored to their needs.
Organizations have an almost infinite supply of potential customers. Thus, matching
demand with a product is an extremely complex process that seems well suited to
the capabilities of AI. Once at their destination, tourists must navigate the realm
of the unknown, characterized by differing habits, languages, cultural norms, and
cuisine, among many other features that may be unfamiliar to them. Again, AI
can help tourists in such “strange” environments, for example, recommending a
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 3
travel itinerary or helping with language and cultural barriers. Also, AI can help
organizations to personalize the experiences to tailor them to the desires of tourists.
While the tourism sector has been found to be an early adopter of most
innovations, actual cases of AI use remain scarce. Most existing literature relates to
laboratory scenarios and development cases. AI can currently be found embedded in
data processing systems in real environments and at the production stage of various
setups, for example, forecasting systems, robots, conversational systems, and voice
recognition systems. However, AI is likely to become involved in all realms of the
travel and tourism industry in the near future.
In this chapter, we review how AI has changed and is changing the main
processes in the travel and tourism industry. We conceive of a future scenario where
the current and future AI systems have been fully developed, deployed, integrated,
and interconnected. We also examine the industry’s challenges, especially privacy
issues, workplace issues, and the deployment of the necessary connectivity.
Defining Artificial Intelligence
Before defining AI, we believe that it would be interesting to first clarify what
intelligence means. Intelligence can be defined as a series of capabilities: the ability
to understand the environment and the phenomena that occur, the ability to take
advantage of past experiences, and the ability to combine the knowledge available
to respond appropriately to a new challenge (Rudas and Fodor 2008). Gretzel (2011)
summarizes these capabilities and says that intelligent systems are able to sense the
environment, learn, and use what has been learned in future situations.
Artificial intelligence is usually defined as a set of technologies that can imitate
human intelligence in the process of problem solving (Lai and Hung 2018). In the
same vein that airplanes obtain the same result (flying) as birds but use entirely
different mechanisms, AI tries to obtain similar results (thinking) as humans but
through different mechanisms.
The concept of AI has evolved over time (Buhalis et al. 2019), from initial
conceptualizations in which AI was defined as having some form of intelligence,
to more recent definitions and conceptualizations in which AI is defined as being
able to act autonomously on large amounts of data (Sterne 2017), to a future
where AI could exceed human intelligence, an event that has been called the
technological singularity (Kurzweil 2005). In this regard, the AI effect (McCorduck
2004) describes the phenomenon where as an AI application becomes mainstream,
it stops being considered AI. This is because of the tendency to imagine that the
application does not really contain AI (does not really think) but is just part of
normal computing. Thus, the contents and limits of AI are dynamic over time.
Buhalis et al. (2019) refer to the four types of AI mentioned by Hintze (2016).
The first type is reactive AI, which has no memory or use of the past. Deep Blue
is the best example of reactive AI. The second type is limited memory AI, which
has selective/limited recall. An example is how a self-driving car treats the objects
around it. The third type is the theory of mind. These are machines that can represent
4 J. Bulchand-Gidumal
other types of objects and their emotions, which would allow the machines to
interact socially. The fourth type is AI with self-awareness or consciousness. Both
Buhalis et al. (2019) and Hintze (2016) agree that the current challenges with AI
are in the areas of extending artificial intelligence’s memory, improving the ability
to use past memories and experiences to make better decisions, and developing the
capacity to process emotions and intuitions (Gretzel 2011). Transcending these four
types of AI is the concept of superintelligence (Bostrom 2016). Superintelligence is
defined as machine intelligence that surpasses general human intelligence.
Most current AI systems are domain specific. That is, they are systems capable
of solving problems related to specific areas and specific tasks, such as spam
filtering, understanding questions posed by humans, visually navigating a known
environment, or even driving an autonomous car. This type of AI has been defined
as “Weak AI” or “Narrow AI” (Russell and Norvig 2016). Future developments
will create general purpose AI or “Strong AI” (Russell and Norvig 2016), that is,
AI of the third and fourth types mentioned in the previous paragraph (systems that
have intelligence in more than one area, with consciousness and the capacity to
think). Recently, the concept of “Hybrid AI” (Wirth 2018) has emerged. Hybrid AI
is situated between Strong AI and Weak AI, to include AI that exceeds Weak AI
but without all the capacities of Strong AI. The travel and tourism sector requires
Hybrid AI and Strong AI due to the vast array of tasks and elements that need to be
integrated in order to develop the best possible experience for tourists.
IT Foundations for AI
Artificial intelligence systems require four basic elements to work: data, programs,
hardware, and interconnectivity between the different systems. We will not analyze
hardware in detail because the motto is that the more powerful hardware is, the
better. Artificial intelligence applications usually require large hardware capacities
(processing and storage) to run adequately, although there are certain hardware
architectures that are more well suited for AI. Regarding interoperability, one of the
key features of AI is its ability to have machines interoperate (Bowen and Morosan
2018; Gretzel et al. 2015), thus automating the aggregation and consolidation of
data from multiple sources (Buhalis and Leung 2018).
We will now analyze in detail the cases of data and algorithms for AI.
Big Data
Big data form one of the key IT foundations of AI since big data provide the
necessary input for AI systems to improve by learning, to find and understand
patterns of behavior, and to generate insights. Big data can be defined as a series
of datasets with a very large volume, generated at high speeds from a variety of
sources and with multiple types of data (De Mauro et al. 2016). Big data is usually
defined by four Vs: volume, velocity, variety, and veracity. Some definitions include
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 5
two more Vs: value and volatility. These features are relevant and must be carefully
considered, since the term big data is frequently used wrongly. Sometimes a process
is tagged as big data simply because it uses a database with a large number of
records. However, if there is only one source for all these records, or if they all
represent the same type of transaction (e.g. hotel reviews in a social media website,
transactions with an online travel agent), it would not be appropriate to call this
process big data.
Big data in tourism usually comes from two sources: the environment and the
tourist. The environment is the source of meteorological data, events occurring
at the destination, and information obtained in real time from sensors, Internet of
Things (IoT), and transactions. Tourists provide data before, during, and after their
trip in five ways: online activities, offline activities, biometric and emotional data,
wearables, and user-generated content (UGC). Before and during the trip, users
search and book services online, and their digital footprint can be traced (Gunter
and Önder 2016). During the trip, users also leave offline traces (e.g., movements,
booking, consumption), which are captured by several devices: GPS data, mobile
roaming, data from Bluetooth devices like beacons, IoT, and PoS. Also during
the trip, biometric and emotional data from users can be automatically gathered
(e.g., thermal images, face recognition). Likewise, and depending on user’s granting
access, data from wearables (e.g., smart watches, activity trackers, clothing) can be
collected during the trip. Lastly, UGC is generated during and after the trip. Such
content includes online reviews, comments in social networks, and pictures and
videos posted online (Li et al. 2018). User-generated content requires AI processing
before it can be properly used by researchers and analysts. Artificial Intelligence
can help in processing sentiment analysis in textual information (Schuckert et al.
2015) and analyzing and tagging characteristics of pictures, audios, or videos
that are shared by users (e.g., place, participants, sentiments). These techniques
greatly enhance UGC as a data source, by providing much richer information to
the processes that use these data.
A user profile can be created by joining these data sources together. This profile
can then be used to recommend products and services that are tailored to the user’s
needs. A summary of all the mentioned data sources and process of data generation
for the specific case of hospitality can be found in Fig. 1.
AI Fields of Interest for Travel and Tourism
Artificial intelligence has a vast array of subfields, depending on the specific goals,
tools, or methodologies that are used. We briefly explore the subfields that are of
greater interest for travel and tourism.
Ambient Intelligence
Ambient intelligence (AmI) is “[...] about sensitive, adaptive electronic envi-
ronments that respond to the actions of persons and objects and cater for their
needs.” (Aarts and Wichert 2009, 244). For example, a hotel room may adapt the
6 J. Bulchand-Gidumal
Fig. 1 Data generation and use in hospitality
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 7
temperature, music, and light to the user’s desire. It could even autonomously sense
the need for these adaptations, without specific requirements from the user. The
system can adapt the light in the room during the day, according to the user’s activity.
Ambient intelligence is not just suitable for private and small spaces such as hotel
rooms but can also be used in large public spaces, such as an airport or a concert
venue. Ambient intelligence can also be used to guide tourists (Basiri et al. 2018),
based on crowdsourced data in which patterns are recognized.
Natural Language Processing and Facial Recognition
Natural language processing (NLP) allows computers to process natural language
properly. The input can be through text or voice. In the latter case, there would
first be a voice recognition process, before the language processing occurs. Natural
language generation is usually part of NLP, as they allow IT systems to maintain a
conversation with the user. Natural language processing is one of the requirements
of automated translation. The importance of NLP in tourism is high, since it enables
virtual travel assistants, conversational systems, and robots (Tussyadiah and Miller
2019).
Face recognition is usually used to identify a person in a digital image or video.
For example, it could be used in the check-in process to automatically recognize a
guest. However, face recognition is not only useful for recognizing a specific person.
It can also be used to count the number of people in a certain area and even to detect
emotions in the people who pass by a certain point (e.g., happiness of those leaving
the breakfast buffet).
Machine Learning, Deep Learning, and Neural Networks
Machine learning and deep learning are both part of AI, deep learning being a
specific type of machine learning. Machine learning is a set of algorithms through
which the machines learn, as they repeat certain processes and obtain feedback on
how they performed in those processes. This feedback can be provided by humans
or developed by the machine after observing the results of previous processes (e.g.,
losing or winning a chess game). The training is usually conducted with very large
data sets, thus allowing for the algorithms to improve quickly. For example, a
machine may be taught to choose the best picture from a set of similar pictures
of a travel memory. After observing whether the customer engages with that chosen
picture or album, the machine can improve the selection process for future instances.
Deep learning is a technique of machine learning based on neural networks.
Unlike machine learning, where the algorithm is provided with a large set of rules,
in deep learning, the computer is given a model than can evaluate examples and a
small set of instructions on how to modify the model to make it stronger and more
accurate. Thus, the analysis starts at a superficial level but moves onto more complex
and deep layers in successive approaches (Bulchand-Gidumal 2016).
There are many uses of machine and deep learning in tourism, which are usually
integrated in other set of algorithms or applications: forecasting, translation, weather
8 J. Bulchand-Gidumal
predictions, sentiment analysis, fraud prevention, and image and video recognition
(Ma et al. 2018). However, deep learning is also a foundation for many of the fields
of interest that we have mentioned, such as speech recognition and object detection
in AmI (LeCun et al. 2015).
Neural networks are a group of techniques that can be used for machine and deep
learning. Thus, neural networks are a form of deep and machine learning. While we
previously stated that technology-based (artificial) systems do not have to mimic
the way that nature performs certain activities, such as flying or reasoning, one line
of work in AI has been to try to imitate human neurons and their connections,
through artificial neural networks (ANN), or simply neural networks. Artificial
neural networks are networks of a large amount of simple artificial neurons, each of
which imitate a human neuron. They are connected similarly to the way that human
neurons are connected. The theory of neural networks is that as the magnitude of
connected neurons approaches that of humans (approximately 1011), artificial and
natural systems can perform similarly. Currently, the main use of neural networks
in tourism has been related to forecasting (Claveria et al. 2015).
AI Systems and Their Use in Travel and Tourism
Artificial intelligence systems have several applications in tourism. From the con-
sumer perspective, AI helps users to find better and more relevant information, gives
them greater mobility, improves their decision-making, and, ultimately, provides
a better tourism experience (Gretzel 2011; Tussyadiah and Miller 2019). From
the business perspective, AI can be used in almost every aspect of management
(Buhalis et al. 2019), especially in promotion and productivity (Tussyadiah and
Miller 2019). Artificial intelligence is also expected to encourage more sustainable
travel (Tussyadiah and Miller 2019), by influencing customers to have a more social
perspective.
Artificial intelligence systems in the tourism industry can be stand-alone systems
or embedded in existing applications and systems. These systems include recom-
mender systems, personalization systems and techniques, conversational systems
(chatbots and voice assistants), forecasting tools, autonomous agents, language
translation applications, and smart tourism destinations. Although we analyze each
system separately, it must be stated that tourists will usually interact with technolo-
gies that integrate several of these systems. For example, a guest may interact with
a robot that integrates a conversational system, and, depending on the requirements,
a recommender system, a personalization technique, or an autonomous agent. The
dialogue with the user may be based on a chatbot or voice assistant.
Smart destinations are the ecosystems created in destinations in which these
advanced technologies are deployed together with other social and organizational
features, as will be described later in this section.
Figure 2illustrates the relationship between the IT foundations of AI analyzed in
the previous section and the AI systems and applications that are examined here.
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 9
Fig. 2 Source: Own elaboration
10 J. Bulchand-Gidumal
AI Systems
Personalization and Recommender Systems
For many years, tourists had to decide on the destinations to visit, places to
visit in the destination, and the activities at the destination by using pictures in
catalogues. The Internet has increased the quantity of information available, and
UGC has also helped tourists to make better-informed decisions. However, even
with this additional information, price has been the most important component
in decision-making. Artificial intelligence changes this behavior, since it allows
tourists to find the alternatives that best suits them and allows businesses to tailor
their experiences to their customers’ specific requirements. It does so through
personalization techniques and recommender systems.
Recommender systems are tools and techniques oriented toward giving travelers
options that best fit their interests (Ricci et al. 2015). The use of recommender
systems in tourism has become increasingly important as the number of options
available to users has grown exponentially with online environments (Gavalas et al.
2014). Usually, recommender systems match the characteristics of available options
with user profiles in order to make suggestions about the most suitable options.
Personalization techniques try to provide users with customized information
based on their preferences and restrictions (Gao et al. 2010). Thus, personalization
techniques mean that companies change from marketing to many to marketing to
one. Personalization techniques require large amounts of information about user
behavior, so that an accurate profile can be defined. Gao et al. (2010) analyze in
detail the theories, techniques, and applications for personalization.
Conversational Systems: Chatbots and Voice Assistants
Conversational systems allow customers to engage in a conversation which is
usually related to information search. These conversations can span a long period
of time and involve several processes (Gretzel 2011). Conversational systems are
sometimes referred as chatbots or virtual agents (Buhalis et al. 2019). They involve
technologies such as NLP and speech recognition and are currently ubiquitous. For
example, they exist as personal assistants in smartphones and home speakers (with
commercial systems such as Apple Siri, Google Assistant, Microsoft Cortana, and
Amazon Alexa) and as textual chatbots in websites and kiosks. These systems are
becoming the reference point, as less effort is required for users to communicate
with them, and they present an experience closer to how humans naturally commu-
nicate. Melián-González et al. (2019) explain the determinants of chatbot usage by
tourists.
Forecasting
Forecasting is a technique in which historical and contextual data is used to make
estimates about the future, based on current trends. It is used in all types of sectors
and business, in order to make decisions that require a prediction of what will
happen. Forecasting is particularly well suited for AI algorithms (Claveria et al.
2015), especially with the presence of big data (Gunter and Önder 2016). Artificial
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 11
intelligence methods in forecasting can be divided into five categories: grey theory,
fuzzy time series, rough sets approach, support vector machines (SVMs), and ANNs
(Claveria et al. 2015).
In the tourism sector, forecasting can be used to understand tourist demand
(Buhalis and Leung 2018), to develop marketing strategies, for financial man-
agement and human resource allocation (Claveria et al. 2015; Huang 2014), to
detect scams in restaurants (Stalidis et al. 2015), and to support the management
of facilities and maintenance needs (Buhalis and Leung 2018).
However, the use of AI must be handled carefully, since the results of AI methods
have been mixed. On the one hand, Yu and Schwartz (2006) found that complex
models are not more accurate than simple, traditional models. Claveria and Torra
(2014) had more promising results, although the quality of the forecasting results
with neural networks was severely moderated by the degree of preprocessing. On
the other hand, several studies have found that AI methods had better prediction
accuracy. For example, Sun et al. (2019) used machine learning to forecast tourist
arrivals, Law and Au (1999) used neural networks for a similar purpose, and Huang
(2014) also used neural networks to forecast resort demand.
Language Translation Applications
Travel and tourism usually involve coming into contact with different languages.
However, language has been found to be one of the main barriers that tourists
face when travelling, as well as one of the sources of discomfort and anxiety
(Cohen 2004). In many cases, language barriers also prevent tourists from exploring
the local culture, as they adhere to franchises and known brands while abroad.
As personalization can help tourists find new places (Benckendorff et al. 2019),
automatic translation can facilitate the tourists’ navigation of the destination,
allowing them to explore and engage in all types of activities. Artificial intelligence
that is empowered by machine learning and NLP is helping the development of
automatic translation applications and simultaneous translation systems.
Devices and Integrated Systems
Robots
A robot is an autonomous machine (a physical object) that includes AI and senses
the environment, both of which allow the robot to make decisions and perform
actions. Physicality differentiates robots from other AI programs, and autonomy
differentiates robots from ATMs, check-in kiosks, and other similar devices. Robots
can be embodied in several forms (Tung and Law 2017): human-like, animal-like,
object, or functional.
Traditionally, robots were found in industrial settings. However, AI has allowed
robots to appear in service environments (Ivanov and Webster 2017), to the point
that these service robots are able to overcome many shortcomings of humans in
tourism, such as language barriers and labor shortages (Bowen and Morosan 2018).
Ivanov and Webster (2017) mention two types of service robots: professional service
12 J. Bulchand-Gidumal
robots and personal service robots. These AI-enabled professional service robots are
being used to streamline processes and enhance tasks that have been traditionally
performed by front office staff (Li et al. 2019).
Smart Travel Assistants
As artificial intelligence, mobile devices, natural language processing, and speech
recognition have improved, the concept of smart travel assistants has gained traction
and feasibility. These assistants are applications that are familiar with the user (i.e.,
his/her preferences, interests, availability) and are thus able to provide suggestions
on-demand or autonomously, anticipating the user’s needs. These systems have also
been called autonomous agents, intelligent travel agents, and smart concierges. An
assistant should be able to combine several services at a destination, taking into
account time and space restrictions, and find suitable ways to take the user from one
place to the other within a desired budget.
One challenge regarding travel assistants is the question of the system’s final
owner. Currently, mobile apps and systems are usually used for free. In many cases,
large corporations (e.g., Google, Facebook) pay for the system’s costs. Therefore,
the question is whether the travel assistant will be serving the tourist or the system’s
developer. In this regard, a new type of marketing is expected to be developed in the
next few years, which could be called travel assistant marketing, that is, marketing
oriented to travel assistants instead of to tourists.
Smart Tourism and Smart Destinations
Smart tourism can be defined as “[...] tourism supported by integrated efforts at
a destination to collect and aggregate/harness data derived from physical infras-
tructure, social connections, government/organizational sources and human bod-
ies/minds in combination with the use of advanced technologies to transform that
data into on-site experiences and business value-propositions with a clear focus on
efficiency, sustainability and experience enrichment.” (Gretzel et al. 2015, 181).
Artificial intelligence thus has a critical role in the development and deployment
of smart tourism, since the transformation of data into experiences and value
propositions will be empowered by AI.
That is, smart tourism and smart destinations are digital ecosystems in which AI
play a key role. Nevertheless, in order to develop these ecosystems, there are several
other social and organizational components that have to work and interact together.
Impacts of AI on Hospitality
Since hospitality is one of the main industries in tourism, we decided to analyze
the impact of AI on hospitality in a more detailed manner, by examining the
technologies and applications mentioned previously in the context of hotels. In order
to review the AI applications that are currently being used, or under development, or
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 13
will be implemented in a foreseeable future, we have divided the hospitality industry
into two main areas: operations and marketing.
In the field of operations, AI allocates rooms and other resources according to
guest value, helps in the preventive management of the facilities, bases the animation
offer on the past behaviors and predictions of tourist arrivals, adapts the cuisine
available to the tastes of the existing customers, enables room cleaning robots,
helps select the appropriate employee for the facilities and the products offered,
facilitates intelligent systems by enabling natural conversations with guests (e.g.,
at check-in, in-service demands), allows for the integration of dynamic information
into business processes, facilitates the use of robots in the front desk, as concierges,
and for delivery, improves stock management, improves energy management of the
facilities and tourist consumption, enables the creation of an environment in which
the guest can feel at home, provides guests with access to their own digital services,
and supports finance management by taking into account expected revenues and
arrivals.
In the field of marketing and commercialization, AI improves forecasting, adjusts
prices and offers made to existing and potential customers, enhances customer
relationship management (CRM) systems, helps develop personalized services
and experiences through mass customization, allows the deployment of intelligent
marketing, helps with the development of customized predictions, support agents
and smart sales assistants, creates offers in real time that are sent to the user through
a context-based and content-based approach, and allows for marketing to be used as
a queue management tool.
One specific challenge that hotels must face is that big data is one of the
foundations of the deployment of AI. However, it is difficult to describe the data
sets available to hotels as big data. While the volume of data available to hotels is
usually high, and there is some variety to the data, the data is usually limited to the
guest’s interaction with the hotel website before the trip, and his/her behavior in the
hotel. Hotels have little data on their guests’ profile, their interests and preferences,
their preferred destinations and other characteristics, and their behavior outside the
hotel. This means that hotels can only have big data about their guests by pairing
with other businesses that can complement their data.
In the final stage, when AI is fully developed and implemented in the hospitality
industry and the systems are all integrated and can interoperate, almost all the tasks
that are currently performed by humans will be able to be performed by robots,
AI, and natural language systems. However, this does not mean that the hospitality
industry will be run without the presence of humans. Humans will continue to have
two main functions. First, humans will develop a small set of tasks that are extremely
difficult to automate, even with the development of the capabilities of robotics and
AI. Second, the presence of humans will be used as a distinction and luxury; it will
be a differential factor. In other words, if humans are inefficient from an economic
perspective, their presence will be justified from a differentiation perspective or
through an increase in quality, as is currently the case in gas stations.
Figure 3illustrates the continuum that different hotels will be located on. On
one end of the continuum is the efficient hotel that fully takes advantage of the
14 J. Bulchand-Gidumal
Fig. 3 AI technologies and application in tourism
capabilities of technology, automation, robots, and AI. It requires a limited amount
of humans in its operation. These efficient hotels will be attractive to value conscious
guests (Bowen and Morosan 2018), because they can save the main costs of
the hospitality industry (Gursoy 2018). At the other end of the spectrum is the
distinguished hotel, in which humans will be used in different touch points as a way
of differentiation. However, even in these distinguished hotels, guests will always
have the option of using self-service technologies based on AI, if they prefer to do
so. In between, there will be several types of hotels in which different combinations
of humans and technology will be found.
AI-Related Challenges in Travel and Tourism
Artificial intelligence currently has and will have many positive effects on the travel
and tourism sector. However, there are some challenges and risks that have to be
addressed. We analyze in detail three main issues: the tourists’ perspective of AI,
the substitution of humans by machines, and the ethics and biases in AI. We finish
this section with some suggestions for future research.
Issues Related to Tourists’ Adoption and Use of AI
The first challenge with AI is the tourists’ thoughts, attitudes, and perceptions of
these technologies. As with any other technology or innovation, tourists can be
grouped into the categories mentioned by Rogers (2010): innovators, early adopters,
early majority, late majority, and laggards. Taking into account the risks and benefits
of AI, Tussyadiah and Miller (2019) found three types of users: laggards (who
perceive high levels of risks and low levels of benefits for AI), aficionados (who
perceive high levels of benefits and low levels of risks for AI), and realists (who
are aware of both the likely benefits and risks of AI). These authors found that
that the people with negative sentiments toward AI are those who have not used
such technologies before (Tussyadiah and Miller 2019). Regarding user adoption
of robots, as is the case with other technologies, the main drivers are perceived
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 15
usefulness and performance (Bowen and Morosan 2018). However, Gretzel (2011)
warns that there is a need to move from research on intentions of use, to research on
actual use, use patterns, and nonuse.
Artificial intelligence has several benefits and risks for users. For tourists, one
primary benefit of AI is that it can help them navigate unknown environments, thus
reducing the anxiety and fear that tourists often feel (Buhalis et al. 2019). It could
also help them develop new and memorable experiences (Li et al. 2019).
As for the risks, the three main concerns of tourists relate to the fear of
surveillance, an AI divide and of a society entirely guided by technology. Regarding
the fear of surveillance, several authors (Gretzel 2011; Tussyadiah and Miller 2019)
have mentioned the threat to privacy that AI systems can pose, because they gather
massive amounts of data, and, most importantly, have the ability to derive patterns
and information from the data.
As happened with the digital divide at the beginning of the century caused by
lack of access, there is a risk that there will be an AI divide. This AI divide could
be caused by the reluctance of some users to participate in AI environments, due to
their perceptions of risks in AI systems.
As for the concern regarding a society fully guided by technology, tourists
will probably have to choose between more automated, efficient, and cost-efficient
services and less automated and human-based luxury services. For example, hotels
will probably be located on a continuum, as described above.
The Substitution of Humans
The substitution of the human workforce by machines has been taking place since
the First Industrial Revolution. However, for a long time, machines were only able to
replace humans in simple, routine tasks. With the growth of AI and AI-empowered
technologies, a new generation of machines has appeared (such as service robots)
that can now compete with and replace humans in almost every possible task
(Brynjolfsson and McAfee 2011). This means that the tourism sector, which for
a long time had been immune to this situation, is now at risk. Bowen and Morosan
(2018) estimate that 25% of the workforce in hospitality could be replaced by robots
in the next decade, thus categorizing the adoption of robots as a paradigm shift.
Some traditional functions in tourism (such as the front desk of hotels) could even
disappear (Bowen and Morosan 2018). Some authors have named AI as the largest
threat to mankind (Musk 2014).
In fact, worker displacement has been one of the main concerns regarding the
impact of AI in tourism (Tussyadiah and Miller 2019), not only because of the loss
of jobs but also because the worker’s loss of a sense of belonging (Li et al. 2019).
There has been an intense debate in the academic literature over which jobs
are more susceptible of being performed by machines. Sigala (2018) states that
machines are good at complex reasoning and at algorithm-based and repetitive tasks,
while humans are best at generalization, perception, creativity, and interaction with
16 J. Bulchand-Gidumal
the real world. However, as Sigala (2018) herself mentions, it is not the case that
machines and technologies are unable to perform the tasks that humans are currently
best at. It is simply that it is currently more expensive for computers to perform such
tasks.
Therefore, although the initial consensus (based on industrial and office settings)
was that low-skilled jobs were more threatened, the current data and research has
a slightly different view (Brynjolfsson and McAfee 2011). The phenomenon of job
polarization seems to point to a situation in which low-skilled and high-skilled jobs
are the safest from automation, although the reasons are different for each cases.
High-skilled jobs are safe because of the complexity of tasks and the existence
of nonroutine tasks, while low-skilled jobs are safe because of the low costs of
posts, the diversity of tasks, and the existence of nonroutine tasks. Instead, medium-
skilled jobs, which are mostly associated with routine work, are more prone to being
performed by technology (Melián-González et al. 2019).
Several authors (e.g. Brynjolfsson and McAfee 2011; Ivanov and Webster 2017)
propose that in service contexts, technologies will not take over jobs but will instead
be used to enhance employees and liberate them from routine tasks, thus allowing
employees to have more time for better service. Artificial intelligence can thus
augment their capacities, in a process that has been called AI augmentation and
hybridization (Benckendorff et al. 2019).
In this regard, one of the main challenges of the tourism industry is that the
tourism business could lose the sense of hospitality (Bowen and Morosan 2018),
which is one of the main features of the tourism business.
Ethics and Biases in AI
The expected impact of AI on all aspects of life and society is massive. As part of
the Fourth Industrial Revolution, its impact is comparable to that of machines and
computers. This creates certain ethical challenges that need to be discussed. Two of
these have already been mentioned, i.e., loss of privacy and fear of a society entirely
guided by technology.
Another important and relevant risk associated with the widespread use of AI
concerns bias. All humans are biased (including, obviously, those who create AI
algorithms), and bias is natural in humans, e.g., bias relating to race, gender, age,
and economic status. The problem is that AI is much more powerful than humans
and might possibly facilitate amplification of the biases embedded in algorithms
(Smith 2019). This could potentially mean that as AI learns, it learns to implement
biased structures that are then replicated. In this sense, it has been recommended
that AI systems should be transparent, robust enough to withstand manipulation
and predictable (Bostrom and Yudkowsky 2014). Artificial intelligence systems
will constantly have to make trade-offs, and they should be able to make balanced
decisions that maximize the benefits for all participants. Lastly, if superintelligence
(Bostrom 2016) systems are developed, the AI systems built should include ethics
as a base feature (Bostrom and Yudkowsky 2014).
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 17
Future Research
Apart from the questions mentioned in previous sections, AI development in the
travel and tourism sector requires research in several areas. Three derive from
similar challenges to those identified by Murphy et al. (2017) involving the use
of robots in tourism and hospitality: firstly, customer acceptance of AI systems
in travel and tourism; secondly, the impact of AI on the economics of the travel
and tourism sector; and thirdly, the aforementioned effects of AI on the workplace
and the substitution of humans. We have also identified another four questions:
firstly, analysis of the ongoing impacts of AI on marketing and operations; secondly,
changes in the working dynamics of the travel and tourism sector in areas such
as product development and packet composition; thirdly, the impact that AI may
have on sustainability (Tussyadiah and Miller 2019); and lastly, research into how
information generated during travel and tourism activities can be integrated with
data from other sources to better understand customer profiles and behavior.
Expected Future Developments
The future of AI in tourism is open. On the one hand, there is an optimistic view. In
this view, society can address AI’s main challenges. Privacy issues will be solved,
connectivity will be implemented in order for AI systems to be deployed, and
workers and AI systems will be able to work hand in hand. Under this paradigm,
AI can be conceived of as a group of technologies that will enhance the tourism
experience and make it better for all the actors. Businesses will be able to understand
their customers better and thus design products, services, and experiences that are
better tailored to their needs. It will also be possible for businesses to dynamically
create personalized packages according to client interests. Technologies will replace
and complement certain jobs, thus reducing overall operational costs and leading
to savings that can then be partially passed on to customers. It also means that
businesses will be able to offer services at an affordable price which might
previously have been prohibitively expensive (Bowen and Morosan 2018). In other
cases, technology can enhance particular jobs or free workers from certain tasks,
improving service and customer support.
From the customer’s perspective, AI will allow them to prepare their trips more
quickly, with significantly lower transaction costs and a fully personalized package
that suits their needs and interests. They will receive predictive offers that fit their
requirements. During the trip, technologies will help tourists to navigate unknown
environments seamlessly, reducing the anxiety and fear of the unknown. Language
and cultural differences will not be barriers to tourism, but an additional attraction
instead. Technologies will allow customers to receive the best possible service while
guaranteeing privacy as much as possible (Bowen and Morosan 2018).
On the other hand, there is a less optimistic perspective. In this view, most of
the industry’s jobs will be substituted by machines, which will cause the loss of the
18 J. Bulchand-Gidumal
hospitality feeling. There will be settings in which using machines is compulsory
and not just an option, as is already occurring in many airports. Tourists will have
to deal with machines and robots that are not ready to be used in a production
environment and business will choose to use lower-cost products even if their
performance is suboptimal. In most cases, technology will substitute humans, but
the possible labor costs will not be translated to customers, who will pay the same
getting a worse overall experience. The privacy and safety of data will not be
guaranteed. Employees will find it difficult to work hand in hand with robots and AI
systems, and organizations will not be entirely ready to adopt AI systems.
As with the cases of most paradigm-shifting technologies, it is likely that none
of the two scenarios will be entirely accurate, and the future will be a combination
of both.
Cross-References
AI and the Travel Experience
Artificial Intelligence and Machine Learning
Big Data
Open and Commercial (Big) Data in Tourism
Post Smart Tourism
Recommender Systems
Robotics in Travel, Tourism and Hospitality
Smart Destinations
Smart Tourists and Intelligent Behavior
References
Aarts E, Wichert R (2009) Ambient intelligence. In: Bullinger HJ (ed) Technology guide. Springer,
Berlin/Heidelberg, pp 244–249
Basiri A, Amirian P, Winstanley A, Moore T (2018) Making tourist guidance systems more
intelligent, adaptive and personalised using crowd sourced movement data. J Ambient Intell
Humaniz Comput 9(2):413–427
Benckendorff PJ, Xiang Z, Sheldon PJ (2019) Tourism information technology. CABI, Boston
Bostrom N (2016) Superintelligence: paths, dangers, strategies. Oxford University Press, Oxford
Bostrom N, Yudkowsky E (2014) The ethics of artificial intelligence. In: The Cambridge handbook
of artificial intelligence. Cambridge University Press, Cambridge, pp 316–334
Bowen J, Morosan C (2018) Beware hospitality industry: the robots are coming. Worldwide Hosp
Tour Themes 10(6):726–733
Brynjolfsson E, McAfee A (2011) Race against the machine: how the digital revolution is
accelerating innovation, driving productivity, and irreversibly transforming employment and
the economy. Digital Frontier Press, Lexington
Buhalis D, Leung R (2018) Smart hospitality—interconnectivity and interoperability towards an
ecosystem. Int J Hosp Manag 71:41–50
Buhalis D, Harwood T, Bogicevic V, Viglia G, Beldona S, Hofacker C (2019) Technological
disruptions in services: lessons from tourism and hospitality. J Serv Manag 30:484–506
Impact of Artificial Intelligence in Travel, Tourism and Hospitality 19
Bulchand-Gidumal J (2016) Aprendizaje profundo y su impacto en turismo. In La actividad
turística española en 2015:(edición 2016): 419–422. Síntesis
Claveria O, Torra S (2014) Forecasting tourism demand to Catalonia: neural networks vs. time
series models. Econ Model 36:220–228
Claveria O, Monte E, Torra S (2015) A new forecasting approach for the hospitality industry. Int J
Contemp Hosp Manag 27(7):1520–1538. https://doi.org/10.1108/IJCHM-06-2014-0286
Cohen E (2004) Contemporary tourism: diversity and change. Elsevier, Boston
De Mauro A, Greco M, Grimaldi M (2016) A formal definition of big data based on its essential
features. Libr Rev 65(3):122–135
Gao M, Liu K, Wu Z (2010) Personalisation in web computing and informatics: theories,
techniques, applications, and future research. Inf Syst Front 12(5):607–629
Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in
tourism. J Netw Comput Appl 39:319–333
Gretzel U (2011) Intelligent systems in tourism: a social science perspective. Ann Tour Res
38(3):757–779
Gretzel U, Sigala M, Xiang Z, Koo C (2015) Smart tourism: foundations and developments.
Electron Mark 25(3):179–188
Gunter U, Önder I (2016) Forecasting city arrivals with Google analytics. Ann Tour Res 61:
199–212
Gursoy D (2018) Future of hospitality marketing and management research. Tour Manag Perspect
25:185–188
Hintze A (2016) Understanding the four types of AI, from reactive robots to self-aware beings.
The conversation, Available at: http://theconversation.com/understanding-the-four-types-of-ai-
fromreactive-robots-to-self-aware-beings-67616. Last accessed 17 July 2019
Huang HC (2014) A study on artificial intelligence forecasting of resort demand. J Theor Appl Inf
Technol 70(2):265–272
Ivanov SH, Webster C (2017) Adoption of robots, artificial intelligence and service automation by
travel, tourism and hospitality companies–a cost-benefit analysis. In: Artificial intelligence and
service automation by travel, tourism and hospitality companies–a cost-benefit analysis. Inter-
national scientific conference “contemporary tourism – traditions and innovations”, Oct 2017,
Sofia University, pp 19–21
Ivanov SH, Webster C, Berezina K (2017) Adoption of robots and service automation by tourism
and hospitality companies. Revista Turismo Desenvolvimento 27(28):1501–1517
Kurzweil R (2005) The singularity is near: when humans transcend biology. Penguin, New York
Lai WC, Hung WH (2018) A framework of cloud and AI based intelligent hotel. In: Proceed-
ings of the 18th international conference on electronic business, ICEB, Guilin, 2–6 Dec,
pp 36–43
Law R, Au N (1999) A neural network model to forecast Japanese demand for travel to Hong
Kong. Tour Manag 20(1):89–97
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
Li J, Xu L, Tang L, Wang S, Li L (2018) Big data in tourism research: a literature review. Tour
Manag 68:301–323
Li JJ, Bonn MA, Ye BH (2019) Hotel employee’s artificial intelligence and robotics awareness and
its impact on turnover intention: the moderating roles of perceived organizational support and
competitive psychological climate. Tour Manag 73:172–181
Ma Y, Xiang Z, Du Q, Fan W (2018) Effects of user-provided photos on hotel review helpfulness:
an analytical approach with deep leaning. Int J Hosp Manag 71:120–131
McCorduck P (2004) Machines who think. A personal inquiry into the history and prospects of
artificial intelligence, 2nd edn. A K Peters/CRC Press, Boca Raton
Melián-González S (2019) The impact of digital technology on work. Available at SSRN: https://
ssrn.com/abstract=3353258
Melián-Gonzalez S, Gutiérrez-Taño D, Bulchand-Gidumal J (2019) Predicting the intentions to
use chatbots for travel and tourism. Curr Issues Tour. https://doi.org/10.1080/13683500.2019.
1706457
20 J. Bulchand-Gidumal
Murphy J, Hofacker C, Gretzel U (2017) Dawning of the age of robots in hospitality and tourism:
challenges for teaching and research. Eur J Tour Res 15:104–111
Musk E (2014) Available at https://twitter.com/elonmusk/status/495759307346952192.Last
accessed 27 July 2019
Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In:
Recommender systems handbook. Springer, Boston, pp 1–34
Rogers EM (2010) Diffusion of innovations. Simon and Schuster. New York
Rudas IJ, Fodor J (2008) Intelligent systems. Int J Comput Commun Control III(Suppl.):132–138
Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited,
Harlow
Schuckert M, Liu X, Law R (2015) Hospitality and tourism online reviews: recent trends and future
directions. J Travel Tour Market 32(5):608–621
Sigala M (2018) New technologies in tourism: from multi-disciplinary to anti-disciplinary
advances and trajectories. Tour Manag Perspect 25:151–155
Smith CS (2019) Dealing with bias in artificial intelligence. Available at https://www.nytimes.com/
2019/11/19/technology/artificial-intelligence-bias.html. Last accessed 17 Dec 2019
Stalidis G, Karapistolis D, Vafeiadis A (2015) Marketing decision support using artificial intelli-
gence and knowledge modeling: application to tourist destination management. In: Kavoura A,
Sakas DP, Tomaras P (eds) Procedia—social and behavioral sciences 175:106–113. Elsevier,
Madrid
Sterne J (2017) Artificial intelligence for marketing: practical applications. Wiley, Hoboken
Sun S, Wei Y, Tsui KL, Wang S (2019) Forecasting tourist arrivals with machine learning and
internet search index. Tour Manag 70:1–10
Tung VWS, Law R (2017) The potential for tourism and hospitality experience research in human-
robot interactions. Int J Contemp Hosp Manag 29(10):2498–2513
Tussyadiah I, Miller G (2019) Perceived impacts of artificial intelligence and responses to positive
behaviour change intervention. In: Information and communication technologies in tourism
2019. Springer, Cham, pp 359–370
Wirth N (2018) Hello marketing, what can artificial intelligence help you with? Int J Market Res
60(5):435–438
Yu G, Schwartz Z (2006) Forecasting short time-series tourism demand with artificial intelligence
models. J Travel Res 45(2):194–203
