The relatively short history of big data, spurred by the information explosion in the past decade, means that the data revolution has only just begun. Intelligence centricity and the pressing need to predict and respond in real time has driven businesses to transform their core and develop acuity by deploying AI-ML solutions. While the adoption trends vary across industries, for the travel, transport and hospitality (TTH) it has been a transition from traditional business intelligence (BI) to self-service BI. Or to put it simply, enabling automation and intelligence with data lakes, visualization tools, event driven decision support system and customer data platforms that uncover the untapped customer behavior and needs.
With AI-ML it is possible to have smart rooms with voice-driven apps control the curtains, lighting, room temperature. For example, the customer’s voice can be built in to the customer data platform (CDP) to drive the shopping cart strategy; AI-ML can drive hyper-personalization enabling micro segmentation and refinement of subscription services strategy. Key driving factors for the adoption of intelligent, decision-centric solutions in TTH include:
Personalized Customer Services: Across TTH, Al is being used to deliver in-person customer services. From chatbots and personal assistance like voice activated, and text based personal assistants to facial recognition programs that allow for remote check-ins, frictionless interactions, touchless card vending and human trafficking detection.
Loyalty Programs and Brand Monitoring: What kind of loyalty programs to offer and identifying the person to whom the program can be offered are part of offering. The value, attrition and potential AI-ML solutions: detecting anomalies in large and diverse datasets. The other features that come with this solution include, Al driven-Natural Language Processing (NLP), customer sentiment analysis, Brand Meter Dashboards with near real-time social analytics.
Operational Excellence: AI-ML applications can help with fraud detection, revenue management, energy management and predictive maintenance. For instance, fraud detection with programs like finance seamless inventory and receivables management; invoice extraction and validation. An AI-based revenue management application can aid strategy for optimizing distribution channels and pricing. It can also help with predicting demand patterns and customer behavior. Use of AI-ML is also important for smart energy management driven by internet of things (IoT) and to achieve sustainable operations. Used comprehensively, AI-ML can be deployed for predictive maintenance to detect anomalies, predict failure and ensure a higher safety standard.
We foresee airline retailing, hyper-personalization, connected ecosystems, hyper-automation and self-service to emerge as the cutting-edge industry trends that would lead the change in the future. A change that would involve the creation of AI-centric travel products and solutions leveraging an AI-first approach and augmented by AI to transform existing solutions and embed the intelligence on top of it to enable data driven solutions.
The two-pronged approach for near-term success as well as a continued DNA of exploration and experimentation will involve a focus on effective use cases such as, customer-initiated problem or opportunity statements; and exploratory and research focused use cases driven by future trends and industry relevance.
Based on our experience of working with the TTH industry, the adoption of AI-ML will be led by customers riding the wave of data driven solutions.(as in figure).
Anil Prakash Singh is an ex TCSer who was the domain consultant of Travel, Transport and Hospitality at TCS. He brings more than 16 years of experience working across multiple brands in TTH. He specializes in commercial, operations, technology strategy, innovation, and product implementations. He has delivered significant business value for customers through disruptive technologies such as AI, Data science, Microservices, Data Platforms etc.