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October 10, 2023

Point A to Point AI: How gen AI can revolutionize travel and transport

The travel and transport industry is in need of an upgrade. Does generative AI hold the key?

When New York International Airport opened in 1948, it had six runways with a seventh under construction. For the first few years of operation, the airport managed just 73 takeoffs and landings per day and was expected to accommodate about 3.5 million passengers annually.

Fast-forward to the present day and this facility now goes by the name of John F. Kennedy International Airport. The airport has grown to six terminals, serving an average of 2.7 million passengers per month with an estimated 2,400 takeoffs and landings each day. Congested and prone to delays, many industry experts say the airport was never designed to handle the volume of flights and passengers it does currently – especially when the flight volume for surrounding airports has also increased dramatically.

Unfortunately, New York City-area airports aren’t the only facilities struggling to meet increases in traffic and changes in demand. In fact, much of the infrastructure used by the travel and transportation industry, including rail, roads, and ports, was built decades ago and never intended to serve at the current scale. Lack of investment to modernize, upgrade and expand these facilities and assets has contributed to inefficiency, and prompted ongoing questions about reliability and safety. At the same time, even as these industries are experiencing significant growth, they are facing mounting pressure to reduce emissions and shrink their carbon footprint.

As the travel and transportation industries seek solutions to their current challenges, generative AI has emerged as a potential answer, enabling new levels of multi-modality, sustainability, inclusivity, and self-healing capabilities. But with the possibilities of this technology virtually limitless, where and how should organizations focus their efforts to achieve the most value?

In this article, we explore the potential of gen AI across a range of industry-specific use cases. We will also offer practical considerations for organizations looking to advance their generative AI journey while staying true to their ethics and their principles.

Source: Cognizant Research
Figure 1

How gen AI can modernize travel and transport

Rapid advancement of generative AI in just the past year has underscored the implications of this new technology—implications so vast that many companies may find them overwhelming.

To help, our team has highlighted seven features of generative AI that are of particular use in the travel and transportation industries. In the table below we summarize these functions and their possible applications. In the following section, we’ll go into more detail, showing gen AI’s transformative power in seven distinct industry use cases.

Figure 2

Seven gen AI use cases for travel and transport

1.    Hyper-personalizing the passenger experience

Personalizing customer experiences is important in almost every sector, but in the travel industry, where experience is the product, personalization is everything.

The content-producing capabilities of generative AI enables travel organizations to create personalized content at scale. For example, travel operators can create personalized itineraries based on customer prompts, incorporating not only a traveler’s preferences, but external factors such as weather forecasts, passenger location, nearby special events and more.

Development of these applications is well underway. For instance, released a ChatGPT-powered plugin to deliver customized product recommendations and assist with itinerary planning. Users can enter their destination, trip dates and other preferences to reveal a suggested itinerary based on their prompts.

In many cases, offering personalized services also opens up opportunities for cross-selling and up-selling. For example, a company that offers personalized itinerary planning can integrate third-party sites, such as local restaurants and attractions, and offer their users the added convenience of one-click booking.

A hyper-personalized customer experience goes beyond booking and planning capabilities, though. Other applications include:

  • Immersive previews. The generation feature, in concert with enabling digital technologies such as AR/VR, can give customers a first-person preview of destinations, attractions or hotels, letting them make better purchase decisions.

  • Software agents. Gen AI-powered virtual assistants can converse with passengers in natural language and help them book tickets, reserve connecting transportation, or even navigate stations and airports.
2.    Inclusivity

Essential to personalizing the travel experience is creating a more inclusive experience for travelers. This can take any number of forms: from personalized recommendations for accessible accommodations and routes, to providing customer support in a variety of local languages.

For example, MakeMyTrip, India’s leading travel company, introduced voice-assisted booking in Indian languages to make the platform more inclusive and accessible. MakeMyTrip’s CTO, Sanjay Mohan, estimates that this feature will open the travel industry to 100-200 million new users who prefer to communicate in their native language and use a voice system, rather than a mobile app. Mohan also says the tool will help improve accessibility for users with disabilities who sometimes cannot use traditional digital tools.

Additional applications:

  • Summarization. Gen AI applications can condense lengthy travel guides or itineraries into concise and accessible formats, making it easier for individuals with cognitive or reading impairments to understand and plan their trips.

  • Software agents. Gen AI-enabled tools can act as personal travel assistants, catering to individual needs and providing personalized recommendations based on user preferences, mobility requirements, and accessibility standards.

  • Sentiment analysis. Transport operators can also leverage sentiment analysis and classification to suggest service offerings tailored to passengers with special needs.

  • Inclusivity. The generation and translation features can be used in combination to develop innovative solutions, such as using AR/VR-enabled avatars to convert travel announcements into sign language in real time.
3.    Sustainability

Generative AI can be used to influence passenger behavior and enable a modal shift towards more sustainable options. For example, a city council may leverage generative AI to power a mobile app that produces customized walking, cycling or public transportation routes that will not only take the user to their final destination, but also pass a number of popular landmarks, cafés and restaurants along the way. The app could also calculate the amount of carbon offset by these more sustainable travel methods and suggest opportunities for people to reduce their carbon footprint when traveling.

Additional applications:

  • Emissions reduction. AI systems can analyze the vast amounts of data related to transportation patterns, energy consumption, and environmental impact to identify areas for improvement and suggest sustainable strategies for the sector.

  • Customer engagement. Sentiment analysis can help monitor public opinion and feedback related to sustainable travel and transport initiatives, while gauging the effectiveness and acceptance of sustainability efforts.
4.    Streamlining transport operations

Data has long been used in the transport industry to help improve efficiency, avoid delays and cut costs. With the advanced features of generative AI, transportation operators can now gather, summarize and analyze data from a vast number of sources in real-time, enabling operators to respond with greater accuracy and precision to disruptions, helping them unlock new levels of operational efficiency.

For example, generative AI can streamline operations and improve decision-making by automating sensor data collection and extracting data from video feed and incident logs to present insights in a structured format. An airline can use data to optimize flight paths, based on an aircraft’s maintenance history, weather patterns, flight conditions or other factors.

Additional applications:

  • Experience enhancement. Generative AI tools can categorize passenger feedback based on sentiment analysis and suggest improvements.

  • Network planning. This technology can be used to process historic network design documents to identify route constraints that can feed into network planning.

  • Data extraction. Gen AI-enabled tools can be used to extract unstructured data from sources like legal papers, policy documents, and engineering standards to automate operational controls, reporting, and performance monitoring.
5.    Empowering the workforce

Employees in the travel and transport sectors make decisions based on a large volume of structures, as well as unstructured data, including telemetry, remote sensing devices, operations logs, and video feeds. Managing this unstructured data, including the large volume of historic records, is extremely challenging and often contributes to sub-optimal decision-making.

Gen AI’s extraction features can process this high volume of unstructured real-time data, as well as historic data, into a single accessible format, helping workers make better decisions—on everything from scheduling and rostering to compliance and safety.

Gen AI can also be used to power next generation chatbots, which can automate some aspects of the customer service function, and help agents determine how to respond to more complex situations. Indeed, Air India recently invested $200M in ChatGPT to improve the capabilities of its digital workforce. The airline will use a generative AI-enabled chatbot to automate real-time customer support, improve existing FAQ content, enhance pilot briefings and more.

Additional applications:

  • Training and development. Generative AI’s generation feature, in conjunction with AR/VR capabilities, can help develop immersive training content, including a variety of scenario-based modules.
6.    Modernizing maintenance

An efficient transportation system relies on the health of its underlying infrastructure. In many parts of the world, unfortunately, the roads, railways, airports and ports we use today were built decades ago and often suffer from service-affecting failures which result in travel disruptions.

Understanding these assets and their conditions lets operators take a proactive approach to maintenance. Gen AI’s extraction feature can help analyze data from historic records and maintenance logs (as well as unstructured data sets like engineering schematics and models) to conduct risk assessments and detect potential anomalies.

At the same time, using gen AI technology to manage data collection and analysis can significantly reduce the workload of asset engineers. Having access to accurate, timely data lets them make better decisions about where to focus efforts and prioritize resources.

Additional applications:

  • Regulatory compliance. Remote condition data and data collected from asset inspection can be summarized for regulatory reporting. Generative AI’s summarization feature can also be used to improve the speed of condition analysis and automate compliance reporting.

  • Resource optimization. When a number of maintenance tasks need attention, gen AI-powered tools can prioritize and help in intervention decisions.

  • Training and development. AR/VR-based scenario simulations can offer enhanced training and development for engineers.
7.    Improving health and safety

Safety is any travel or transportation company’s top priority, and gen AI can help improve it.

For example, generative AI can extract data from incident logs, historic health and safety reports and video feeds of incidents or near-miss instances and use it to identify patterns and suggest actionable steps toward improvement. Gen AI can also analyze traffic sensor data, video feeds, telemetry and GPS data to identify potential violations of traffic laws, even drafting new safety protocols, emergency response plans, and standardized procedures.

Additional application:

  • Software agents. Generative AI can assist workers in real-time, providing safety reminders, delivering relevant information, and automating routine safety tasks.

A roadmap for your generative AI journey

While generative AI is evolving rapidly and the regulatory landscape remains unclear, we believe that companies need to begin laying the groundwork now. Let these five key steps guide your gen AI journey:

  1. Build a strategy that focuses on business value.

    Every generative AI strategy should focus on business value – not technology capabilities. While the use cases for generative AI are virtually limitless, companies should narrow their focus to those specific business challenges that gen AI and supporting technologies can help solve. As part of this process, companies should also closely monitor business metrics to ensure these programs are helping the business achieve strategic objectives.

  2. Mature underlying data capabilities.

    Generative AI models require large amounts of clean, accurate, timely data. Before embarking on a generative AI initiative, companies should confirm they have a robust data foundation, including cloud capabilities, in place and operational.

  3. Build a partner ecosystem.

    Expertise is hard to come by in the fast-growing world of gen AI. Companies should build an ecosystem of technology partnerships that will allow them to build and launch initial use cases, while developing the capability to scale those efforts over time. As well as partnering with hyperscalers, working with a business transformation partner can help companies overcome the current skills shortage and rapidly build maturity.

  4. Be responsible.

    Given that the regulatory landscape is evolving to catch up with the advent of generative AI, it is incumbent on companies to ensure they are using this technology in a safe, secure, responsible, and ethical way. That means developing models that promote inclusivity and reduce bias, as well as deploying programs that uphold data privacy standards and respect the user’s boundaries.

  5. Stay agile.

    Generative AI is a rapidly evolving and advancing field. Companies should ensure they can adapt their approach as the technology changes. Flexibility is always the key to success in a high potential, but uncertain environment.

Arriving at Point AI

After almost a year of gen AI headlines, travel and transportation companies are recognizing that it’s no longer a question of if but when this technology will play a role in the future of their business.

In this article, we explored several ways that generative AI will change the way these industries operate—but gen AI is a moving target. Its capabilities expand every day, which requires organizations to constantly evaluate new use cases, and revisit their guidelines for using this technology safely and securely.

The road ahead is complex, to be sure. But it’s a road worth taking given the technology’s potential to solve many of the industries’ perennial challenges. We hope you join us in this journey from point A to point AI, making our industry more efficient, inclusive, safe and sustainable.

To learn more, visit the Travel and Hospitality section of our website or contact us.

Pulin Baghela

Director, Consulting, Cognizant

Author Image of Pulin Baghela

Pulin is a leader in Travel & Transportation within Cognizant’s consulting practice. He has 17 years of experience in digital strategy, IT consulting, and IT transformation for the rail, road and aviation sectors. He holds an M.B.A. in IT and Strategy from the Indian Institute of Management, Kozhikode.

Debroop Sengupta

Senior Manager, Consulting, Cognizant

Author Image of Debroop Sengupta

Debroop is a senior manager consulting in Travel & Transportation within Cognizant’s consulting practice. He has 14 years of experience in digital strategy, transformation roadmap and product management. He holds an M.B.A. in Strategy and Operations from XLRI Jamshedpur.

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