Skift Global Forum Preview: What Happens After the Booking? Spotnana CEO Has a Number

The travel industry is currently at a crossroads, navigating a transition from legacy infrastructure to cloud-native, AI-driven platforms. For years, the "cost-to-serve" has been a lead weight on the balance sheets of Travel Management Companies (TMCs) and online travel agencies. Traditional servicing involves a labyrinth of manual processes, where human agents spend the bulk of their time navigating Global Distribution System (GDS) queues, interpreting cryptic airline fare rules, and manually processing cancellations or refunds. Singh believes this model is not only unsustainable but ripe for a total architectural overhaul. By leveraging artificial intelligence and direct content integrations, he posits that the industry can slash servicing costs by 50% or more, a figure that would represent a tectonic shift in the economics of travel distribution.

The Servicing Frontier: Beyond the Search Box

In the current landscape, search and booking are largely considered "solved" problems. Most platforms offer a functional, if not always inspired, way to find a flight or a hotel. The real friction begins when a traveler needs to change their plans. Whether it is a missed connection, a family emergency, or a simple change in itinerary, the process of rebooking or securing a refund remains one of the most significant pain points in the traveler journey.

Singh points out that the industry’s fixation on the "sale" ignores the reality that trust is built during the moments of crisis. "Anyone can sell a ticket," Singh asserts, highlighting that the true differentiator for modern travel companies is the ability to provide seamless, proactive support. The "half nobody talks about"—servicing—is where the traveler’s relationship with a brand is either solidified or shattered. In a world where content is increasingly fragmented across New Distribution Capability (NDC) channels, direct connects, and traditional GDS feeds, the ability to synchronize these changes across multiple systems in real-time is the new gold standard.

The Economics of Automation: Cutting Costs by Half

The 50% reduction in servicing costs that Singh envisions is not merely a theoretical target; it is a projection based on the current capabilities of AI agents. Traditionally, routine tasks such as handling canceled segments, managing unticketed flights, and processing refunds required a human touch. These tasks are repetitive, rule-bound, and high-volume—characteristics that make them perfect candidates for automation.

Spotnana’s internal data suggests that a significant portion of what used to land in a travel agent’s queue can now be handled autonomously by AI. By deploying AI agents that can read, interpret, and execute transactions based on complex airline policies, the need for human intervention in routine servicing is plummeting. This shift does not necessarily mean the end of the human travel agent; rather, it suggests a migration toward higher-value interactions. When the AI handles the mundane logistics of a refund, the human agent is freed to focus on "high-touch" scenarios—such as navigating a traveler through a multi-city disruption or providing personalized recommendations that require emotional intelligence and nuanced judgment.

This economic shift has profound implications for the business models of travel providers. If the cost of servicing a traveler drops by half, margins expand, and companies can reinvest those savings into better technology or more competitive pricing. Furthermore, a lower cost-to-serve allows companies to scale more efficiently, handling higher volumes of travelers without a linear increase in headcount.

The Fragmented Content Reality: Solving for NDC and Beyond

One of the greatest challenges to efficient servicing is the increasing fragmentation of travel content. The rise of NDC (New Distribution Capability) has allowed airlines to offer more personalized and dynamic content, but it has also created a "silo" problem. When a booking is made through a direct connection or an NDC pipe, it often exists outside the traditional ecosystem that human agents use for servicing.

Singh’s strategy at Spotnana has been to build a platform that is "content-agnostic." By creating direct connections to airlines and hotel chains, Spotnana ensures that booking changes are synchronized across all systems instantly. This means that if a traveler makes a change on a mobile app, that change is immediately visible to the airline, the TMC, and the corporate travel manager. This level of synchronization is essential for the "proactive" servicing Singh advocates. If the system knows a flight is delayed before the traveler does, the AI can begin searching for alternatives and presenting them to the traveler before they even reach the airport.

The New Era of Curation: Conversational AI as the Gateway

The shift toward conversational AI—driven by Large Language Models (LLMs)—is fundamentally changing how travelers interact with data. In a traditional search interface, a traveler is presented with a list of hundreds of flights, which they must then filter by price, duration, or airline. Singh argues that this model is being replaced by a "curation" model, where the traveler describes their needs in natural language, and the AI presents a handful of the best options.

This transition places a massive burden on the AI to be accurate. Singh notes that the goal is to solve for the traveler by ensuring the AI selects the option the traveler would have chosen themselves 95% of the time. "Curation is coming up more as a design consideration," Singh explains. As the interface moves from a grid of results to a conversational dialogue, the "recommendation engine" becomes the most critical piece of the tech stack.

To earn a spot in these AI-driven recommendations, travel providers—airlines, hotels, and car rental companies—must provide richer, higher-fidelity data. An AI cannot recommend a hotel room with an "ocean view and early check-in" if that information isn’t available in the data feed. Consequently, the bar for product information is being raised. Direct integrations are becoming the preferred method for sourcing this data because they offer the depth and accuracy that traditional "scraped" or aggregated data lacks.

Trust as the Ultimate Currency in Modern Travel

Ultimately, the drive toward AI-driven servicing and conversational booking is about one thing: trust. In an era where travelers have more choices than ever, they are increasingly gravitating toward platforms that "just work." A seamless servicing experience—where a cancellation is handled in seconds without a phone call—builds a level of loyalty that no marketing campaign can match.

As Steve Singh prepares to share these insights at the Skift Global Forum, the broader industry is watching closely. The promise of a 50% reduction in servicing costs is a powerful incentive, but the true value lies in the improved traveler experience. By automating the routine and focusing human talent on high-value interactions, the travel industry has the opportunity to move past the "transactional" nature of the past and into a new era of relationship-based commerce. The companies that succeed will be those that realize the journey doesn’t end when the ticket is bought—it’s only just beginning.