For decades, the travel industry has suffered from a collective hyper-fixation on the point of sale. Millions of dollars are poured daily into optimizing search algorithms, refining discovery interfaces, and smoothing out the booking funnel. The logic seems intuitive: the transaction is where the money changes hands, so the transaction is where the value lies.
But Steve Singh, Executive Chairman and CEO of the groundbreaking travel technology platform Spotnana, challenges this industry consensus. He argues that the true battleground for customer loyalty, operating margin, and brand trust begins only after the credit card is swiped.
Speaking ahead of his highly anticipated appearance at the Skift Global Forum in New York City on September 22–24, 2026, Singh outlined a vision for a radical restructuring of travel economics. By leveraging artificial intelligence to automate the notoriously friction-filled, labor-intensive world of post-booking servicing, Singh believes travel providers can slash their servicing costs by 50% or more.
At the core of this shift is a transition from manual, human-led triage to proactive, AI-driven resolution. It is a change that promises to not only rescue corporate travel budgets but also fundamentally elevate the traveler experience.
The Invisible Cost of the Post-Booking Void
To understand the magnitude of Singh’s proposition, one must look at the immense operational drag of traditional travel servicing.
When a flight is canceled, a meeting is rescheduled, or a traveler requires a refund, the seamless digital experience of modern booking often grinds to a halt. The traveler is thrust into a labyrinth of call centers, long hold times, and manual processing queues. On the backend, travel management companies (TMCs) and corporate travel departments must deploy legions of human agents to manually untangle ticketing codes, negotiate GDS (Global Distribution System) rules, and process manual voids.
"We talk endlessly about search, distribution, and booking," Singh says. "What really matters is what happens after the trip is purchased."
Historically, this "after-purchase" phase has been treated as a cost center to be minimized rather than an opportunity to be optimized. The industry has long accepted high servicing costs as an inevitable cost of doing business, driven by the sheer complexity of legacy distribution networks and fragmented supplier databases.
Singh argues that this defeatist view is obsolete. By utilizing modern, cloud-native infrastructure combined with conversational and agentic AI, the industry can dismantle these legacy bottlenecks. The goal is not merely incremental improvement, but a complete halving of the operational overhead associated with managing a trip from booking to arrival.
Dismantling the Queue: How AI Agents Take Over the Routine
The transition to automated servicing is no longer a theoretical projection; it is actively unfolding within modern travel ecosystems. Spotnana’s internal metrics reveal a striking shift in how disruption is managed.
Historically, routine tasks—such as processing a canceled flight segment, handling an unticketed reservation, or executing a standard refund—represented the bulk of a travel agent’s daily queue. These repetitive tasks clogged support lines and delayed assistance for travelers experiencing complex crises.
Today, Spotnana utilizes specialized AI agents designed to navigate these specific, routine workflows autonomously.
THE SERVICING PARADIGM SHIFT
[Legacy Model]
Routine Inquiries (Cancellations, Refunds, Unticketed Flights)
└── Sent to Human Agents ──> High Labor Costs & Long Hold Times
[Modern AI-Driven Model]
Routine Inquiries (Cancellations, Refunds, Unticketed Flights)
└── Handled by AI Agents ──> Instant Resolution & Zero Labor Cost
Complex & High-Value Scenarios (Disruptions, Bespoke Requests)
└── Sent to Human Agents ──> Personalized Care & Enhanced TrustBy delegating these routine processes to AI, the need for human intervention in basic transactional adjustments has plummeted. This does not mean human agents are becoming obsolete; rather, their role is being elevated.
"Human agents can now focus more of their time on higher-value services, where a human touch makes all the difference," Singh explains.
When an AI agent handles the bureaucratic paperwork of a cancellation, the human agent is freed to act as a concierge—helping a stranded traveler find an alternative route during a major weather event or managing complex, multi-destination itinerary changes that require nuanced human judgment.
The Integration Imperative: Why Real-Time Synchronization Matters
For AI agents to function effectively, they cannot operate in an information vacuum. One of the primary reasons travel servicing has historically been so difficult to automate is the fragmentation of data across different systems. A change made on an airline’s direct portal might not immediately reflect on a TMC’s platform, leading to confusion, double-bookings, and broken itineraries.
To solve this, Spotnana has spent years building deep, direct integrations with global airlines, hotel chains, and ground transportation providers. These direct connections allow for real-time synchronization of data. If a traveler or an AI agent modifies a booking on one system, that change is instantly broadcasted and updated across every system involved in the journey.
"As technology evolves, travelers have higher expectations that they should be able to ask anyone for assistance throughout their journey," Singh notes. "This is a key reason we have worked with leading travel providers to build direct connections that synchronize booking changes across systems, so a change made anywhere is reflected everywhere."
This level of synchronization is critical for earning traveler trust. In an era where anyone can sell a ticket, the companies that succeed will be those that deliver flawless execution when things go wrong. Service is where trust is earned or lost, and travelers grade their providers on every single interaction.
The 95% Curation Standard in the Age of Conversational AI
As the front-end of travel search evolves from traditional, grid-based form-filling to conversational AI interfaces, the nature of product discovery is also changing.
In a traditional search interface, a traveler is presented with dozens, sometimes hundreds, of flight or hotel options. They must manually filter, sort, and compare prices. In a conversational AI paradigm, however, the interface is much tighter. A traveler might ask an AI assistant to "book a flight to Chicago next Tuesday morning," and the AI will return only two or three curated recommendations.
This shift presents a massive challenge for travel platforms: how do you ensure that the highly curated options presented by the AI are actually the best choices for the traveler?
CONVERSATIONAL AI CURATION GOAL:
Present the options a traveler would have selected at least 95% of the time if they had reviewed the full, uncurated list of available options.Singh views this curation challenge through the lens of traveler trust. Spotnana’s strategy is designed to solve for the traveler by aiming for a "95% accuracy standard." The goal is to ensure that the highly filtered options presented by their conversational AI match the exact choice the traveler would have made themselves 95% of the time, had they spent hours combing through the complete, uncurated inventory.
Achieving this level of precision requires deep learning models that understand not just historical booking patterns, but also the contextual nuances of the traveler’s current request.
Natural Language Search and the New Data Burden for Suppliers
The rise of conversational AI also shifts the burden of proof onto travel suppliers. When travelers search for travel options using natural language, they are no longer constrained by rigid database fields like "date," "destination," and "price."
Instead, a traveler might type or speak a highly specific request: "Find me a quiet hotel room on a high floor, with a direct ocean view, at a property that offers early check-in and has a workspace suitable for video calls."
To match a traveler with the perfect room under this search paradigm, the booking platform must have access to highly detailed, structured, and accurate product data from the supplier. Traditional, aggregated distribution channels often strip away these rich details, reducing a hotel room or an airline seat to a generic commodity.
This is why Singh advocates so strongly for direct API integrations with suppliers. Direct connections allow hotels and airlines to pass rich, multi-dimensional product information—including real-time ancillary availability, precise room attributes, and flexible check-in policies—directly into the Spotnana platform.
Suppliers who fail to provide this granular data risk becoming invisible to the conversational AI search engines of the future. If the AI cannot verify that a hotel room has a "direct ocean view" or "early check-in," it simply will not recommend it to the traveler.
A New Era for Travel Economics
As the travel industry gathers in New York City for the Skift Global Forum, the discussions will undoubtedly touch on the macroeconomic pressures facing global travel: rising operational costs, shifting corporate travel budgets, and the relentless demand for personalization.
While much of the media attention remains focused on consumer-facing booking apps and flashy generative AI search tools, Steve Singh’s insights point to a quieter, far more lucrative revolution happening behind the scenes.
By restructuring the economics of post-booking servicing, platforms like Spotnana are proving that AI’s greatest value to the travel industry is not just in helping travelers find their next destination, but in ensuring that their journey is seamlessly supported every step of the way.
Singh will expand on these strategies at the Skift Global Forum on September 22–24, 2026, sharing the stage with other prominent industry leaders, including Sierra’s Bret Taylor, Booking Holdings’ Glenn Fogel, and Expedia Group’s Ariane Gorin. For an industry searching for its next major margin expansion, the message is clear: look past the booking button.
