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Agentic commerce and complex customer journeys: lessons from high-demand ticketing
High-demand ticketing offers an early view of how agentic commerce will reshape complex customer journeys, from AI-led discovery to brand-controlled payment, service and trust moments.
For customers, purchasing tickets to high-demand events, be it the World Cup, a major marathon or a sold-out concert residency; often means stress, uncertainty and hours spent in digital queues, with no guarantee of success.
These journeys provide a clear illustration of where agentic commerce could create value. In this model, AI agents act on behalf of customers to search, compare, recommend and sometimes initiate a transaction, reducing the need to manually navigate multiple platforms and offers.
In the context of high-demand ticketing, a customer could ask a large language model or an AI agent: “Find me two tickets for Céline Dion’s show in Paris, ideally in the pit, under €300 each, and only from trusted sellers.”
The agent could then scan available inventory, compare prices, assess seller reliability, check constraints and recommend the most relevant option. In some cases, this may resemble an app-within-an-app journey: the agent supports discovery and decision-making upstream, before handing the customer off to the brand or authorized seller environment for final payment, confirmation and service.
More than a new interface layer, agentic commerce redistributes roles across the journey. AI platforms may increasingly mediate discovery, comparison and recommendation upstream, while brands retain control where trust matters most: payment, confirmation, fulfilment, service and exception handling.
For brands, the challenge is no longer only to be discovered, but also to decide where they create value, how they remain relevant in compressed discovery and where they take back control.
01 Being visible to AI-driven discovery
The first challenge is discoverability. In an agentic model, brands are no longer competing only for search rankings or app traffic. They are competing to be surfaced, selected and recommended by AI systems.
This raises several questions for brands: whether their offer is visible in AI-assisted discovery, whether product, inventory and pricing data are structured clearly enough to be interpreted by machines, whether trust signals are explicit, reliable and machine-readable, and whether AI agents can effectively distinguish official channels from resellers or lower-quality intermediaries.
For ticketing platforms, event organizers and distributors, this is critical. If AI agents become a primary gateway for discovery, digital visibility will increasingly depend on structured data, authoritative content, verified sources and platform interoperability. SEO will remain relevant, but it will need to be complemented by approaches designed for AI-driven discovery and recommendation.
02 Designing journeys for both humans and machines
Agentic commerce also introduces a dual-interface model. Brands must design journeys that work for both human customers and machine agents. Human customers still need reassurance, transparency and control. AI agents, meanwhile, need direct access to reliable information, real-time availability and clear transactional pathways.
To support this interface, brands may need to strengthen several core capabilities:
- Inventory and pricing data must be accessible in real time,
- Key product and service information must be structured consistently,
- APIs and transactional pathways must support system-to-system interaction,
- Trust and brand positioning must remain intact when offers are summarized by third-party AI tools.
In ticketing, and in distribution in general, these requirements are sensitive. Availability changes quickly, legitimacy is essential and customer confidence can be affected if users are redirected to unclear sellers, outdated offers or inconsistent information.
03 Managing shorter and more decisive journeys
Agentic commerce also compresses the journey.
In a traditional model, customers move through multiple stages: awareness, consideration, purchase, care and advocacy. In an agentic model, many of those steps happen almost instantly. Once a need is expressed, the agent can shortlist options, evaluate trade-offs and present a recommendation within seconds.
Consequently, the key decision become fewer, but more important for the moments that remain: recommendation, approval, payment, confirmation and exception handling. Much of the exploratory work may happen out of sight, inside the agent’s logic, rather than through a long series of visible brand interactions.
For brands, this means they must focus on a few decisive moments when the agent determines whether:
- the brand is trustworthy,
- the offer is relevant,
- the transaction conditions are acceptable,
- the recommendation should be approved.
Therefore, trust design becomes central. Brands should structure journeys around three moments:
- Explain: clarify why an option is relevant,
- Approve: allow the customer to validate the choice, the seller, and the transition into the brand environment
- Escalate: provide access to human support when needed.
At the same time, some brands will try to remain present earlier in these compressed journeys through app-in-app patterns embedded in AI environments. These models may help brands reintroduce guidance, reassurance or differentiation before the handoff to payment and fulfilment. In that sense, app-in-app is not a contradiction of journey compression, but one possible response to it.
In high-demand ticketing, these moments are essential. Customers may accept automation, but not at the expense of control, transparency and legitimacy.
04 Defining the role of brand-owned agents
Brands should also consider where their own agents may add value. External AI agents may support discovery and comparison, but brands also need to decide where they want to retake control of the journey. Brand-owned agents matter most where brands still own trust-intensive moments. They can step in once intent is formed and help customers complete a purchase, understand terms and conditions, manage preferences, receive confirmations and resolve post-purchase issues.
In ticketing, this could include supporting payment and seat selection, confirming access rules, handling transfers or refunds, managing disruptions and protecting trust when stakes are high. Brand-owned agents can also support employees behind the scenes, helping human teams retrieve knowledge, prepare case summaries and resolve exceptions faster and more consistently. However, these use cases require clear governance.
Several questions should be addressed:
- Where should handoff happen between an external agent and the brand?
- Which moments should be handled by the brand’s own agent, and which should remain human-led?
- What context, data and intent need to be passed across interfaces to make the transition seamless?
- How can brands design agent experiences that create real value instead of duplicating what external agents already do?
- What level of autonomy is appropriate, when should human approval be required, and when should escalation to a human be mandatory?
The most effective approaches are likely to be those that define a clear operating model between external agents, brand-owned agents and human teams.
Preparing for a redistributed commerce journey
The opportunity for brands is not to control every step of the journey, but to create value where it matters most. As AI platforms begin to mediate more of discovery and comparison, brands can still differentiate through trusted checkout, reliable execution, proactive service and stronger exception handling. For customers, it means less effort without losing confidence or control. For employees, it means fewer repetitive tasks and better support in high-stakes moments.
High-demand ticketing offers a useful preview of this shift. Customers want speed and simplicity, but they also need trusted sellers, clear confirmation and reliable support when something goes wrong. Ticketing illustrates the next phase of commerce, where exploration may be delegated to AI, but trust is still won in the moments that matter most.
Curious to know more? Contact our experts:
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Charles Denis
Managing Partner, Sia Experience | Paris
Charles is Managing Partner of Sia Experience. He has been working for 15 years on digital transformation projects, particularly for energy companies.
