ABy focusing on the traveler, their interests, their needs, and presenting information or options in a way that flows as a natural extension of the actions they intended to take. Using that same traveler-persona centric design, provide seamless and frictionless access to other portions of the journey integrating to services such as airline reservations, rental car, ride share, dinner reservations, hotel, baggage tracking, digital IDs, and more. Today more than ever, all travelers and consumers in general, want absolute certainty their digital identity, personal data, and financial information is secure. Combining these elements will entice travelers to consistently return for a predictable travel experience. As example, Kyndryl helped a major US passenger and cargo airline to rework their passenger experience from the ground up by upgrading their infrastructure and providing on-demand capabilities. This enabled the airline to deliver real-time, actionable insights to their customers, improving their travel experience and increasing their trust and loyalty. Particularly important as travel providers extend their data sources to include more input to include more legs of the traveler journey.

AAcross the board, timing predictability is emerging as a key differentiator. From time to process through TSA for air travel or origination clearance for freight, through predictable arrival time of passenger carrier or freight logistics, predictable arrival time consistency is a differentiator. How? Within the passenger travel industry, travel companies aim to strengthen loyalty and generate new sources of revenue by capturing a greater share of the traveler’s entire journey. Likewise, I see freight transportation and logistics companies differentiating their service by improving operational transparency through creating and making available new customer insights to drive new sources of revenue, and improve route and scheduling optimization.

ATransportation companies encounter a major challenge in coordinating and optimizing complex and dynamic operations. Generative AI and machine learning can assist in addressing this challenge by providing data-driven insights, predictions, and recommendations that can enhance operational efficiency and lower costs. · Generate optimal routes and schedules for vehicles and drivers based on real-time traffic, weather, demand, and other factors. · Forecast the demand for transportation services and adjust the pricing accordingly. This can help balance supply and demand, increase revenue, and improve customer satisfaction. · Optimize inventory levels and allocation of goods and resources across warehouses, distribution centers, and vehicles. This can reduce waste, spoilage, stock-outs, and overstocking. Provide natural language interfaces for workers and customers to interact with the system. This can improve data input and output, reduce errors and misunderstandings, and enhance user experience and engagement. The maturing of AI and Generative AI means we’re at a point where they can help automate these processes, creating operational efficiency and downstream accuracy. The focus is on both operational efficiency and optimizing asset utilization. Both of these two dimensions can be improved with the application of AI and Gen AI. Many of the major cost drivers within these industries are found in labor-intensive processes. The maturing of AI and Generative AI means we’re at a point where they can help automate these processes, creating operational efficiency Those cost drivers are eliminated through automation -- autonomous vehicles, robots, drones, buses, air taxis, baggage handling, and security. These are major drivers of cost. Combining advanced AI with hi-speed, secure, and ubiquitous connectivity across airports, hotels, warehouses, and highways enables these innovations for broader enablement and reduced payback period. Integration of disparate data sources across the partner ecosystem is emerging, equipping industry participants is a key Kyndryl tenant. What’s different looking forward is the application of artificial intelligence, machine learning, and generative AI. These technologies can be deployed to reduce supply chain congestion, lower environmental emissions, reduce queue time, lower costs, and ultimately synchronize the movement of goods, real-time