(Short Version) AI and Automation in Unconventional Sectors: The Impact of Artificial Intelligence on Travel and Hospitality

10 min readSep 6



It is crucial to distinguish between artificial intelligence (AI) and automation, as they are often conflated in discourse. While both concepts entail utilizing technology to streamline processes, AI can discern patterns autonomously, accumulate knowledge through experience, and make decisions independently, devoid of explicit human programming. Automation, conversely, is confined to predefined, mechanistic tasks. Despite this nuanced differentiation, the demarcation between AI and automation is becoming increasingly blurred in practical applications. In contemporary business landscapes, particularly within the travel and hospitality industry, both AI and automation play pivotal roles, and they are no longer the exclusive domain of major industry players or online travel agencies, thanks to the growing accessibility of advanced technologies and Infrastructure. Nevertheless, the adoption of automation in the hospitality sector, especially in Europe, faces impediments in the form of perceived initial costs and a need for comprehensive understanding. Hoteliers may perceive automation tools as superfluous or even fraught with risks. These apprehensions often stem from prior adverse encounters with technology implementation despite statistically low probabilities of encountering issues (a cognitive bias known as the “availability heuristic”). Responsibility also lies with technology vendors, who should educate hoteliers regarding the advantages of automation without appearing overtly sales-oriented. Regardless of the causative factors, the challenge persists: independent hotels contend with inherent challenges, while chain hotels, despite their technological acumen, grapple with protracted decision-making processes dictated by parent corporations. The journey toward automation adoption within the hospitality industry is intricate, yet it can potentially ameliorate the ubiquitous issue of time scarcity in hotel management.


The persistent skepticism concerning automation, and by extension, AI, underscores the ongoing debate regarding the human touch versus technological advancement. Recognizing that AI and human-driven customer service can be symbiotic rather than mutually exclusive is critical. Automation can supplant numerous behind-the-scenes processes, yet its integration remains confined mainly to established branded and upscale hotels or innovative independent properties. This limited adoption engenders a paradoxical economic scenario, as the costs incurred through abstaining from technological investment may ultimately eclipse the initial expenses. The challenge encompasses multifaceted dimensions, encompassing technological considerations, cultural shifts, managerial decisions, and organizational dynamics. This inertia within the hotel industry starkly contrasts the innovation-centric approach of OTAs, which exhibit a greater willingness to experiment, learn from failures, and adapt. Regrettably, technological inertia pervades the realm of hotels, implicating technology suppliers, consultants, and hoteliers alike. Privacy concerns further compound the complexities of automation adoption, particularly within the context of data-intensive AI systems. Europe, especially, faces distinctive challenges associated with acquiring guest data, exacerbated by nebulous regulatory frameworks. Notably, in March 2023, OpenAI’s ChatGPT faced a temporary ban in Italy due to concerns related to age verification and data collection practices. Additionally, reservations concerning the erosion of the “human touch” persist, notwithstanding research suggesting that addressing employee apprehensions can significantly enhance productivity, potentially mitigating an annual loss of a billion dollars.


The delineation of artificial intelligence can be a convoluted endeavor, given the varying perspectives among experts in the field. For the purpose of this article, AI can be expressed as the replication of cognitive functions exhibited by humans, animals, and even botanical entities. AI is fundamentally an interdisciplinary domain arising from the synergistic collaboration of diverse fields. The question of whether machines possess the capacity for thought presents a philosophical conundrum. Some posit that thought is an exclusively human phenomenon, while others tether it to logical processes or interactions with the environment. Recent dialogues surrounding AI sentience, epitomized by Google’s LaMDA, emphasize the importance of focusing on the manifestation of intelligence within a system rather than attributing it to innate sentience. In the public sphere, Alan Turing is often synonymous with the advent of AI. However, AI as a conceptual framework was formally introduced in the late 1950s, two years after Turing’s passing, and the seminal moment transpired in 1956 when a professor at Dartmouth College convened a group of scientists to embark upon the exploration of “thinking machines,” marking the semantic inception of AI. The historical underpinnings of AI can be traced to earlier thinkers, including Aristotle’s syllogisms. However, notable progress occurred during the Middle Ages, with luminaries such as Al-Khwarizmi, who introduced fundamental algorithmic concepts. Eminent contributors to AI’s development include George Boole, Augustus De Morgan, Charles Babbage, Ada Lovelace, John Von Neumann, Norbert Wiener, Joseph Weizenbaum, William Ross Ashby, Claude Shannon, and many others. Esteemed institutions such as MIT, IBM, DARPA, Stanford University, and the CYC project played pivotal roles in shaping the AI landscape. The CYC project, spearheaded by Douglas Lenat, harbored the ambitious objective of imbuing machines with “common sense.” The 1990s, however, witnessed the most significant advancements in AI, propelled by augmented computational power and heightened accessibility to technology. Notably, IBM’s Deep Blue’s triumph over chess grandmaster Garry Kasparov in 1997 ignited fervor around AI capabilities. Kasparov subsequently authored a treatise on AI and raised questions concerning the integrity of Deep Blue. This historical moment evoked memories of the past, notably the revelation that the “Turk” chess machine was a deceitful contrivance in 1769. Over the years, AI has conquered diverse domains, including games and practical applications in healthcare, autonomous vehicles, and stock market trading.


AI and robotics have transcended the boundaries of traditional human domains, permeating areas once deemed the exclusive purview of human creativity, including religion. A notable example is the Kannon Mindar, a Buddhist monk android I encountered during a visit to Kodaiji Temple in Japan in 2019. This android’s recital of the Heart Sutra ignited controversy, with some perceiving it as sacrilege. Monastic figures such as Tensho Goto, on the contrary, contend that Buddhism revolves around the spiritual journey rather than the embodiment of its principles. Other intelligent androids include Honda’s ASIMO, Hector, Topio, Robear, Kismet, Jibo, and Sony’s Aibo. These technological advancements in Japan are, at least partially, a response to demographic challenges stemming from an aging population and declining birth rates, reconfiguring a demographic quandary into an engineering puzzle.


The precipitous advancement of AI and robotics is underpinned by the transformative potential of cloud computing, a technology that has ushered in a paradigm shift. Unlike conventional local server-based systems, cloud architectures dispense with the need for localized infrastructure. Leading providers in the realm of cloud computing include Amazon Web Services (AWS), Microsoft Azure (which powers ChatGPT), Google Cloud, Salesforce, IBM, Oracle, SAP, Workday, ServiceNow, and VMWare. The advantages conferred by cloud architectures encompass the absence of upfront hardware expenditures, adaptable pay-as-you-go models (exemplified by infrastructure as a Service or Software as a Service), and facile scalability. Furthermore, quantum computing, though still in the experimental stage, harnesses the unique properties of qubits to process information within superimposed states, engendering unprecedented speeds in problem-solving. For instance, Google’s Sycamore quantum computer executed tasks within seconds that would have taken classical computers millennia to accomplish. Quantum computing has the potential to surmount the constraints posed by Moore’s Law, holding profound implications for the trajectory of AI and computing at large. The ramifications of AI’s ubiquity are profound, heralding what could be one of the most momentous shifts in human history.


AI’s versatility extends across diverse domains, encompassing healthcare, finance, transportation, and gaming, propelled by factors such as escalating computational power, the proliferation of big data, and advancements in machine learning. However, pervasive concerns regarding the risks associated with AI endure. Visionaries like Stephen Hawking have articulated apprehensions regarding the potential perils of unchecked AI advancement. In March 2023, luminaries within the tech industry called for temporarily suspending the development of super-powerful AI systems due to safety concerns and the exigency of formulating shared safety protocols. While this appeal does not advocate for an outright cessation of AI development, it reflects a mounting trepidation regarding the unrestrained expansion of AI capabilities. Elon Musk’s establishment of xAI, an AI-focused startup, after endorsing the AI safety pause, raises inquiries concerning the evolution of his stance on AI ethics and safety. Nevertheless, it is imperative to recognize that assessments of AI risks often commence with a flawed premise. Deft of human biases, machines possess the latent potential to render more equitable decisions. In scenarios where impartiality assumes paramount importance, such as the realm of justice, automation promises beneficial outcomes. For instance, Estonia’s Ministry of Justice seeks to optimize court procedures by utilizing machines, which excel in probabilistic reasoning and detect subtle patterns devoid of human biases.


The idealized vision of AI swiftly transitions into a dystopian scenario when human involvement is reintroduced into the equation. AI systems, crafted by human hands and informed by inherently human-centric data, are inevitably imbued with our imperfections. The predicament lies not within artificial intelligence itself but rather within human fallibility. An illustrative example is Microsoft’s Twitterbot @TayandYou, conceived for engaging and playful conversations but spiraled into misogynistic tirades due to negative user interactions. Microsoft promptly deactivated the bot after a mere 16 hours and 96,000 tweets. Subsequently, the company issued apologies and pledged to enhance AI safeguards. Today, Microsoft is actively engaged in AI ethics and co-founded the Partnership on AI (PAI) to address ethical considerations pertaining to non-biological intelligence.


Certain futurists, including myself, advocate for the institution of an equivalent of the Hippocratic oath for AI engineers, akin to the ethical framework adhered to by medical practitioners. While Isaac Asimov’s Three Laws of Robotics have become somewhat clichéd, they encapsulate the fundamental principle of “do no harm” among AI developers, a concept we may aptly denominate the “Asimovian oath.” For instance, instances where facial recognition technology, such as Amazon Rekognition, or predictive policing systems like PredPol, have manifested biases underscore the imperative of ethical considerations.


OpenAI, a preeminent player within the AI landscape, has played an instrumental role in reshaping perceptions of AI beyond the confines of academic discourse. OpenAI comprises both OpenAI, Inc. and OpenAI, L.P., founded in 2015 by visionary luminaries such as Ilya Sutskever, Greg Brockman, and others, with initial board members including Elon Musk and Sam Altman. By 2023, OpenAI had secured investments amounting to $11 billion, bolstering its research endeavors. OpenAI made significant inroads with “OpenAI Gym” in 2016 and “Universe” in 2016. In 2018, Elon Musk relinquished his role within the organization, and OpenAI subsequently pivoted toward a for-profit model in 2019, entering into a strategic partnership with Microsoft to leverage computational resources. The unveiling of GPT-3 and an associated API for commercial deployment transpired in 2020, followed by the introduction of DALL-E in 2021, and a milestone moment occurred in 2022 with the release of ChatGPT’s free preview, attracting over a million sign-ups within a mere five days. Microsoft further solidified its commitment by investing $10 billion, integrating ChatGPT into Bing and various other products. Competing offerings, exemplified by Google’s Bard and Meta’s LLaMA, entered the scene.


Much like the World Wide Web became synonymous with the Internet, ChatGPT has assumed a central role in the contemporary discourse on AI. While discussions regarding the Internet commenced as early as the 1970s, widespread accessibility was not achieved until 1993 with the advent of Mosaic. ChatGPT, an innovation conceived by OpenAI, debuted in November 2022, catapulting OpenAI’s valuation to approximately $29 billion. Within the era of AI, the mastery of prompt engineering assumes paramount importance for effectively utilizing AI systems such as ChatGPT. Prompt engineering entails crafting specific prompts to elicit desired responses from AI language models. A comprehensive understanding of AI behavior and the implementation of best practices empower users to unlock the full potential of ChatGPT across diverse domains, ranging from code generation to the composition of marketing content. Large Language Models (LLMs) like ChatGPT represent AI models that generate text resembling human language based on input data. These models have undergone extensive training on extensive textual datasets to discern language patterns and generate coherent responses. However, their responses can be generic. The art of prompt engineering necessitates practice and refinement. Despite challenges such as initial investment costs and data privacy concerns, the AI market is poised for rapid expansion. Strategic planning and foresight are vital in surmounting these obstacles, as the integration of AI promises long-term benefits. AI can augment guest satisfaction within the hospitality and travel industry by analyzing behaviors and offering personalized recommendations. While AI excels at analytical tasks, human empathy remains indispensable.


The integration of ChatGPT into websites or applications within the hospitality and travel industry has the potential to elevate customer experiences and streamline operational processes. ChatGPT serves as a virtual concierge, adept at responding to queries and automating responses, thereby reducing the burden on support teams and saving valuable time. Additionally, it facilitates multilingual communication and offers real-time translation capabilities, bridging language barriers and enhancing staff-customer interactions. ChatGPT can also serve as a valuable resource for staff training, augmenting workforce skill sets, and knowledge. Furthermore, the automation capabilities of ChatGPT extend to optimizing various processes, including check-in and check-out procedures. Its analytical prowess, coupled with access to comprehensive data, empowers the generation of hyper-personalized recommendations tailored to individual preferences, potentially boosting revenue. In the emergent landscape of the metaverse, AI models like GPTs democratize the creation of 3D assets, which holds significant implications for virtual property tours and enhanced booking experiences.


Numerous companies and entities within the travel and hospitality industry have embraced ChatGPT, harnessing its capabilities to drive innovation and enhance customer engagement. Some noteworthy implementations include:

  • Booking.com: Scheduled to unveil its AI Trip Planner on June 28, 2023, integrating Booking.com’s machine learning models with the ChatGPT API to enrich travel planning through conversational AI.
  • Expedia: Offers an AI-powered trip planning experience within its application, enabling users to engage in personalized conversations for recommendations and simplified hotel booking.
  • OpenTable: Collaborates with ChatGPT to deliver restaurant recommendations, while Kayak leverages its capabilities to offer tailored travel suggestions.
  • MyRealTrip (South Korea): Deploys ChatGPT within its AI Trip Planner for itinerary creation and recommendations.
  • KAYAK: Integrates ChatGPT to enable natural language queries for personalized travel suggestions.
  • Duve: Utilizes ChatGPT-4 to enhance communication between hotels and guests, augmenting the overall guest experience.
  • Plan.AI, Roam Around, and Vacay: Leverage ChatGPT to generate efficient travel itineraries, enhancing convenience for travelers.
  • Magpie: Harnesses ChatGPT’s API for optimizing marketing content and translations within the realm of tour and activity providers.
  • MyTrip.AI: Offers ChatGPT-powered tools for marketing, sales, and customer service within the travel industry.
  • Navan: Integrates generative AI into its product feature set, offering personalized recommendations and assistance to corporate travelers.
  • Trip.com: Introduces “TripGen,” a chatbot within its application to facilitate seamless travel planning and advisory services.
  • Wingie Enuygun Group: Launches “ENBot,” aiding users in swiftly finding flight tickets, initially in Turkey and subsequently on a global scale.
  • GuideGeek: Operates on WhatsApp, delivering trip planning suggestions and flight information through ChatGPT.


These developments and applications demonstrate the transformative power of ChatGPT in the hospitality and travel industry, promising improved customer experiences, streamlined operations, and enhanced business performance. Imagine a personalized travel experience where AI recommends the best route, accommodation, and travel details based on a user’s past experiences, continually learning to enhance the journey. Automated self-service can further improve this, allowing users to adjust their itineraries in real time. As travel agencies adapt, AI can assist in negotiating better deals while continuously enhancing its learning capabilities. Well, there’s no need to imagine it. It’s already happening.




Published Author | Organizer of the 1° Travel & Hospitality Event in the Metaverse | Renaissance Futurist | Founder @ Travel Singularity | Transhumanist