Loyalty Intelligence Brief
Latest Brief: Loyalty and Agentic Commerce
Members-only strategic intelligence for travel and loyalty leaders. Written by Roger Williams and co-authored with Reclaim Protocol
A comprehensive guide on AI shifting into the key buyer for travel. Learn about the protocol landscape, the two-channel strategy, and contingent offers as a new acquisition tool.
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Agentic commerce threatens travel margin
This Brief will teach loyalty managers how to position loyalty as a competitive tool in the agentic travel marketplace.
Protocol landscape, decoded: MCP, A2A, AP2, UCP
Supporting protocols are rapidly developing. This Brief delivers an independent evaluation of every protocol that matters.
Contingent offers: a new loyalty member acquisition play
Introducing the "Contingent Offer" - an original framework for converting a competitor's elite flyers into your own enrolled members, verified instantly and privately at the moment of purchase.
18-month agentic commerce roadmap for loyalty leaders
This isn't a trend report that leaves you wondering what to do next. This Brief closes with a quarter-by-quarter execution plan and the metrics to track now, and who should own the agent channel.
Members-only intelligence
The Loyalty Intelligence Brief (LIB) is Loyalty Click's members-only research series for executives who set loyalty and commercial strategy in travel. Each Brief takes a single force reshaping the industry and turns it into decision-grade intelligence: what's actually happening, why it matters to your program, and what to do in the next 18 months. LIB is a living body of work, updated as the landscape moves, so what you read stays current with the market you're navigating. The first brief launched on this platform is "Loyalty and Agentic Commerce".
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Loyalty and Agentic Commerce
Loyalty Intelligence Brief for Airline, Hotel, Rental Car, and Cruise Executives
Loyalty Intelligence Brief
The new buyer for travel is agentic
Travel buying is transitioning from a traditional search-driven user experience to a conversation between a human and artificial intelligence. AI agents now act as buyers, executing travelers' natural-language requests.
Contingent offers powered by instant status match.
Roger Williams
Co-authored with Reclaim Protocol
AI agents now act as buyers, executing travelers' natural-language requests such as "book me a long weekend in Cabo under $1,800, aisle seat, ocean view, and use my points if I can get a better deal."
The latter portion of that prompt, instructing the agent to use points, is technically only available within the direct channel; however, airlines and hotels are not fully supporting loyalty integration for agentic commerce or offering a full agentic experience on their websites. The time when consumers will expect to see a prompt conversational interface rather than the traditional search bar and calendars on their favorite airline or hotel website is fast approaching.
Agents can search, compare, negotiate, book, pay, and rebook travel with minimal human input. However, the industry has largely overlooked a key strategic question: how will loyalty member data be shared across third-party channels? This question is critical to delivering a differentiated experience, particularly for elite members of loyalty programs who may choose to use third-party travel-booking tools that are currently ahead of direct-channel platforms in delivering an agentic experience.
This brief will also examine the driving forces behind the adoption of agentic booking experiences and why consumers now expect them. Adoption is also being fueled by trust, and as the average person uses AI more in their everyday lives, trust is less of a concern than it was 18 months ago. Executives should consider clearly defining data ownership policies, evaluating the trade-offs between reach and control, and ensuring that strategic investments in AI-driven channels align with their desired position in the value chain.
Protocols are the
foundation of trust
There are four main protocols that allow the agentic commerce process to work. These include: MCP (Model Context Protocol), A2A (Agent2Agent), AP2 (Agent Payments Protocol), and Settlement Rails. You can see these protocols at work, supporting a consumer-agent conversation, in our free Agentic Travel Simulator.
Google and 20+ partners launched the Universal Commerce Protocol (UCP) in January 2026, with loyalty on its capability roadmap and hotel booking as its first new vertical.
How the agent queries live inventory and tools from each merchant.
How agents discover and delegate to one another, using published "Agent Cards" in the way a business card describes to another party what you do.
This is Google's payment authorization framework, built on three mandates: Intent (what the customer is searching for), a Cart Mandate (prices locked in by each merchant), and a Payment Mandate (proof that a human approved the charge).
Tokenized payment credentials via programs such as Visa's Trusted Agent Protocol and Mastercard's Agent Pay. This allows the agent to process the payment without holding the customer's actual card number.
The myopic focus on
third-party distribution
channels
The industry conversation has heavily centered on the third-party agent channel. The technological framework required to handle transactions without direct human presence advanced from initial concept to live production in about nine months.
Key developments began in September 2025 when OpenAI and Stripe introduced the Agentic Commerce Protocol (ACP), and Google simultaneously rolled out its Agent Payments Protocol (AP2) alongside more than 60 payment partners, highlighting a flight-plus-hotel booking as its primary demonstration.
Subsequently, in January 2026, Google and over twenty collaborators introduced the Universal Commerce Protocol (UCP), which incorporates loyalty milestones into its development pipeline and debuted with hotel bookings as its initial vertical market.
Travel ecosystems are actively utilizing these frameworks today: Mindtrip deployed agentic flight bookings driven by Sabre that feature in-chat PayPal checkouts in May 2026, while Google continues to integrate automated flight and lodging arrangements into its AI Mode alongside major hospitality brands like Booking.com, Expedia, Marriott, IHG, and Choice Hotels.
The direct channel
remains unexplored
The industry has overlooked a critical layer: the direct-channel agent or the plug-and-play AI Concierge. While attention centered on third-party ecosystems, an equally vital advancement quietly emerged: proprietary AI concierge architectures driving agentic commerce on a provider's own branded channels, capturing data owned entirely by the supplier.
A prime example is Via.ai, which has operated in live production for roughly 18 months with Issta, a prominent Israeli travel group generating approximately $800M in yearly bookings. According to Via.ai's 2026 case study, their AI sales agent, "Daniel," is active across Issta's site and WhatsApp, handling over 10,000 monthly conversations and producing $1.7 million in monthly gross booking value - equivalent to a $20.4 million annualized run-rate.
Rather than a simple pilot, this serves as a proven sales stream. This Brief analyzes both frameworks to show why a comprehensive strategy demands an integrated two-channel approach rather than an exclusive choice.
The Issta deployment is strategically instructive due to its two-flow commercial model, with one AI layer serving two distinct sales engines:
Flow 1: the channel that closes itself. For routine demand (flights, point bookings, standard packages), Daniel handles the journey end-to-end: clarifying preferences through natural conversation, presenting options, answering follow-ups, and closing the booking online. While conventional travel websites expect customers to arrive knowing exactly what they want, only to lose them to rigid search filters and abandonment, Daniel lets customers start with uncertainty and converts it.
The reported results: 5× conversion versus other digital lead sources, +30% average booking size, and roughly $700K per month in self-service sales. As Issta senior executive Tali Noy puts it: "Daniel has become a real commercial channel for us, not just a service layer. Customers ask, clarify, compare, and then book - and that changes the economics of the digital channel."
Flow 2: the human team multiplier. For complex, higher-value demand multi-destination trips, cruises, and premium holidays, Daniel does not force customers into self-service. It qualifies the opportunity, captures preferences, budget signals, constraints, and purchase intent, and routes it to the right human specialist with the discovery work already done.
The economics of this flow are striking: specialists close 13% of Daniel-qualified leads versus a 10% baseline, at an average basket of $11,428 versus $3,125 / 4.75× the sales per lead of other sources generating roughly $1 million per month in specialist-closed sales. "The biggest impact is not that AI replaced people," notes Issta senior executive Nevo Gal. "It is that our specialists receive better-qualified opportunities with clearer customer intent that allows the team to spend more time closing the right deals."
The direct approach maintains the provider's brand and customer relationship. To advance adoption, executives should assess organizational readiness for AI-driven interactions, map integration points with existing systems, and identify priority use cases. The Issta model suggests the first decision is not whether to deploy, but which flow to implement first: closing simple demand, qualifying complex demand, or turning every conversation into structured commercial intelligence.
Airlines are delaying full autonomy in booking not due to technological uncertainty, but because agentic AI conflicts with their legacy infrastructure, risk aversion, and internal silos:
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The Legacy Infrastructure Trap: True agentic commerce requires flexible modifications and pricing. However, airlines remain tied to rigid, PSS/PNR-era systems. In these environments, a single AI error regarding fare rules or EU261/DOT liabilities could result in a costly revenue crisis.
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The Low-Stakes Pivot: To avoid transactional liability, carriers are limiting AI to low-risk customer service and discovery roles. This is evident in recent launches such as Malaysia Airlines' Mavis, Delta's Concierge, and Qatar Airways' Sama.
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Organizational Paralysis: Revenue management teams are reluctant to relinquish control of pricing and bundling to autonomous algorithms. Additionally, the "agentic channel" lacks a single corporate owner, resulting in fragmented pilot programs across IT, marketing, and customer care.
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The Strategic Risk: Executives are misinterpreting modest initial consumer demand as a reason to delay action. This is a critical error. Early user behavior is already being used to train third-party AI models. If airlines use AI only for customer service while third-party platforms perfect autonomous booking, they risk losing the customer relationship entirely.
The Issta case demonstrates that a travel retailer with simpler infrastructure has already built a $20 million run-rate agentic sales channel, proving the technology is commercially viable. Carriers should deploy branded AI concierges on existing APIs now to capture customer intent before customer habits shift elsewhere.
The consumer's time is
the new loyalty currency
Travel is well-suited for agentic commerce because trip planning is time-consuming and often frustrating for consumers. Travelport research shows travelers now spend over four hours and visit up to 277 webpages before booking, compared to only 38 in 2013, while browsing about 10,000 fare and ancillary options, up from 500 in 2010.
An agent that reduces this process to a five-minute supervised conversation delivers time savings unmatched by fare sales or app redesigns. This productivity will drive consumer adoption over the next 18 months, regardless of supplier readiness. The main remaining barrier is trust, which Juniper Research identifies as the top adoption challenge. New mandates, agent identity, and dispute-resolution protocols are now addressing this issue.
According to travel insider sources, about 40% of customers who interact with the AI concierge choose to complete their booking with a human agent. Ori Gal from Via.ai describes this not as a failure of automation, but as a "warm lead generator" model, and the Issta case study now quantifies exactly what that model is worth.
The AI gathers destination, budget, dates, travel party, and preferences before handing off a fully qualified lead, and those handed-off leads close at 13% versus a 10% baseline, at 3.7× the average basket - making the human-handoff flow, at roughly $1 million per month, the larger of Issta's two AI-driven revenue engines. The handoff is not the failure mode of agentic commerce; at current trust levels, it is the profit center.
Industry benchmarks for AI-powered chat or agent handoff in sectors such as banking and e-commerce show similar trust curves, with handoff rates typically ranging from 30% to 50% in the first year of deployment. As trust infrastructure improves through 2026 and 2027, autonomous completion rates are expected to rise. However, the Issta data demonstrates that the warm-lead approach is not just a transitional solution; it is a durable commercial engine.
The zero-party-data
opportunity in agentic
commerce
The industry has fixated on one moment: the booking. The bigger opportunity lies beyond it, in two areas that both lead back to loyalty.
The post-booking revenue opportunity: After the initial sale, an AI concierge continues to interact with the traveler, making additional sales. Activities, transfers, insurance, dining, upgrades - all offered through conversation rather than a checkout screen the customer ignores.
For airlines, it's the fix for low ancillary conversion. For hotels, it's the upgrade conversation that used to require a front-desk agent. For cruise lines, it's a shore-excursion and cabin-upgrade concierge running from booking to embarkation.
The concierge approach serves as a continuous ancillary revenue channel for airlines and hotels. Upgrades and experience bundling, without relying on pre-arrival promotional emails, are more likely to convert. For cruise lines, it manages shore excursions, dining reservations, and cabin upgrades through ongoing conversations from booking to embarkation.
The zero-party data opportunity is the true commercial advantage. The greatest value in agentic concierge commerce comes from zero-party data generated through natural customer conversations, not just bookings or ancillary sales.
Via.ai's case study explicitly names this the deployment's "third value": beyond the two sales flows, every conversation creates structured customer intelligence, destination interest, budget sensitivity, timing, family composition, trip purpose, objections, product gaps, and purchase intent.
This compounds into what the case study calls "a real-time demand layer" for the retailer. Notably, Via.ai acknowledges this is "the part of the deployment that the P&L does not yet fully capture": the sales channel delivers measurable impact today, while the data layer improves personalization, merchandising, supplier strategy, and campaign performance over time.
During these conversations, customers voluntarily convey insights unavailable through web analytics, surveys, or transactional data, such as destination motivations ("I need a beach quieter than Cancún"), budget flexibility ("I'd upgrade if it were under $400"), travel-party constraints, ancillary interests, timing sensitivity, loyalty preferences ("I'd rather use points if I can get above 1.4 cents per point"), and competitive signals ("I saw a cheaper package on a competitor — can you match it?").
AI concierges capture these signals in real time, including revenue leakage, to identify unmet customer demand, price objections, and areas where competitors gain ground. This level of market research, once available only through costly consumer panels, is now generated continuously from every customer interaction.
Zero-party data directly enhances loyalty programs by continuously enriching member profiles. This transforms loyalty from a transaction-tracking tool into a true customer intelligence asset. An AI agent instructed to "always match my preferences" requires a rich data profile, which only providers with sustained conversational engagement can build. As a result, zero-party data shifts loyalty programs from cost centers to valuable enterprise data assets.
Omnichannel loyalty
defense: Two channel
agent strategy
Without loyalty, an anonymous agent shopping 10,000 options simply ranks on price, and distribution collapses into a price war.
Loyalty is the one asset that stops commoditization, and travel owns it at scale: per The World's Largest Loyalty Programs 2026, airline and hotel programs hold roughly 2.4 billion member accounts, and the major US airline programs are each worth $25–32 billion.
Loyalty’s power in agentic commerce is that it gives an agent something to weigh besides price — status, points value, and member benefits the agent can factor into a total-value comparison. Therefore, a loyalty-data-supporting supplier is judged on its worth to this customer rather than on the lowest fare, which is the only way to win a booking without being the cheapest.
Putting loyalty to work takes two channels:
Third-party agents
Make loyalty machine-readable: status, pricing, points, and rules published as policies that the protocol stack can read (UCP, NDC, AP2) - so an outside agent can apply a member's benefits correctly on its own.
Your own agent
Run a branded AI concierge that handles bookings and post-booking conversations directly, maintains the customer relationship, captures zero-party data, and flags where you're losing revenue.
The two channels reinforce each other: the data your own concierge captures enriches the member profile that makes a customer's third-party agent prefer you. Deploying both isn't a choice between the loyalty moat and the agent channel — it's using one to build the other.
This Brief gives executives what they need to act within the 18-month window: the market vision, the protocol map with readiness verdicts, the AI concierge framework, the four-step preparation playbook, and the risk framework.