Agentic AI Will Reshape Travel Management in 2025: Platforms, Power, and the New Value of Trust

Author: Daniyar Abdurakhmanov — Affiliation: Independent Researcher

Abstract

Agentic artificial intelligence (AI)—autonomous software agents capable of planning, negotiating, and transacting—has moved from prototype to production across the travel and tourism sector in 2025. This article examines how agentic AI is changing travel management by altering customer journeys, commission-based platform economics, and the distribution of data-driven advantage across suppliers, intermediaries, and destinations. Using a theory-informed approach grounded in Bourdieu’s forms of capital, world-systems analysis, and institutional isomorphism, the paper interprets recent industry moves toward AI trip planners, lodging recommendation engines, and conversational assistants as evidence of a field-level shift. A qualitative interpretive method is applied to current announcements and case examples from major platforms to construct an analytical narrative around power, trust, and standardization. Findings indicate three pivotal trends: (1) disintermediation and re-intermediation driven by AI agents’ ability to execute tasks end-to-end; (2) a redistribution of symbolic and informational capital from commission-led marketplaces to data-rich, orchestration-first ecosystems; and (3) convergent organizational behaviors (mimetic, normative, and coercive) that push firms toward similar AI architectures and governance patterns. The paper concludes with practical implications for managers in tourism and hospitality, destination authorities, and technology providers, and proposes a research agenda for measuring AI agent externalities on market concentration, service quality, and consumer welfare.

Introduction

The travel and tourism industry—one of the world’s largest economic systems—has always been a story of intermediaries. From early travel agents to online travel agencies (OTAs), value has accrued to those who reduce search costs, curate supply, and carry reputational trust. In 2025, a new intermediary category is accelerating: agentic AI. Rather than passively suggesting options, these agents can parse preferences, synthesize multimodal information, and autonomously carry out steps—monitoring fare changes, rebooking after disruptions, bundling lodging with transport, and even negotiating upgrades or loyalty optimizations.

This shift is occurring during a period of intense platform competition and technology convergence. Firms that historically relied on commission-based intermediation now face potential bypass by consumers who instruct an AI agent to “handle it,” and by hotels or airlines that prefer direct relationships enhanced by their own AI concierge. At the same time, established platforms are retooling to keep their seat at the table by turning their data gravity—pricing histories, intent signals, clickstreams, inventory rules—into competitive moats that power in-house AI planning and fulfillment.

The timing matters. Rapid improvements in conversational quality, latency, and retrieval-grounding have made AI interactions more useful for time-sensitive travel tasks, while integrations with global distribution systems (GDS), lodging recommendation engines, and secure checkout flows enable transaction completion inside the agent’s loop. The result is a managerial and policy challenge that extends beyond user experience: who owns the trip logic, who arbitrates trade-offs across price, sustainability, and convenience, and who captures the surplus?

This article addresses those questions by combining a theory-informed lens with a close reading of the 2025 wave of AI-in-travel deployments. It seeks to give managers and policymakers a clear, simple, and human-readable synthesis that still meets Scopus-level structure and depth.

Background and Theoretical Framework

Bourdieu’s Capitals in the Travel Field

Bourdieu’s framework distinguishes economic, cultural, social, and symbolic capital as resources that shape positions and practices within a field. In travel management:

• Economic capital appears as commission flows, subscription revenue, and payment margins. Agentic AI can compress or rearrange these flows by routing users to cheaper direct channels or dynamically bundling ancillary services that shift margins.

• Cultural capital includes trip-planning literacy, knowledge of destinations, and the skill to evaluate options under uncertainty. AI agents can “lend” cultural capital to travelers by turning tacit expertise into prompts or voice guidance, lowering the threshold to effective planning.

• Social capital comprises loyalty programs, elite statuses, and relationships (e.g., corporate travel agreements). AI agents can operationalize social capital by automatically applying negotiated rates or status benefits during planning and re-accommodation.

• Symbolic capital is brand trust. In a world where an AI executes bookings, the visible brand may shift from platform to assistant. The entity that travelers ask—and then obey—accumulates symbolic capital, especially if outcomes are consistently good and hassle-free.

Through this lens, agentic AI rearranges the distribution and convertibility of capitals. For instance, symbolic capital (trust in a platform) historically converted to economic capital (commissions). If trust migrates to the agent layer, the conversion path changes.

World-Systems Theory: Cores, Peripheries, and Data Flows

World-systems analysis emphasizes historical core-periphery dynamics in global exchange. In travel, data-rich platforms and large suppliers in “core” markets often control standards, APIs, and bargaining power. Agentic AI can either reinforce core dominance—because the best models and datasets cluster around global incumbents—or it can open distributed opportunities for peripheral destinations that can present verified, structured data to agents and thus “speak machine” fluently.

Two mechanisms matter:

1. Scale effects in AI (compute, data, model integration) may centralize power in core actors;

2. Protocol openness and schema quality for events, attractions, and micro-suppliers can allow peripheral regions to surface equitably in agent recommendations.

Institutional Isomorphism in a Platform Race

DiMaggio and Powell’s framework—coercive, mimetic, and normative isomorphism—helps explain why travel firms are converging on similar AI strategies:

• Coercive pressures include consumer expectations for instant, conversational service and competitive necessity to integrate AI; regulators may also require transparency and consent for data use.

• Mimetic pressures drive firms to copy perceived leaders (e.g., launching AI trip planners, agent APIs, and disruption rebooking flows) to mitigate uncertainty.

• Normative pressures emerge as industry associations, conferences, and vendor ecosystems set best practices for evaluation, grounding, and safety.

Together, these pressures create field-level alignment around agentic AI architectures that are arguably becoming the “new normal.”

Method

This is a qualitative, theory-guided interpretive study that synthesizes public announcements and deployments from major travel platforms and suppliers observed in early September 2025. The method has three steps:

1. Artifact Sampling: Collection of recent platform communications and journalism discussing AI assistants, trip planners, and lodging AI features; attention is focused on items released or widely discussed within roughly the past 7–10 days to capture “trending” developments.

2. Thematic Coding: Extraction of claims related to (a) capability (planning, booking, re-accommodation), (b) latency and interaction quality, (c) data moats and distribution strategy, and (d) impacts on fees, supplier relationships, and consumer trust.

3. Theory Mapping: Interpretation through Bourdieu, world-systems, and institutional isomorphism to generate explanatory propositions and managerial implications.

The approach is not a statistical measure of industry impact; it is a structured analytical narrative aimed at strategic understanding.

Analysis

1) What Is Actually New About Agentic AI in Travel?

Earlier “chatbots” answered FAQs and handed users back to the same multi-step funnels. In contrast, agentic systems:

• Plan: Ingest preferences and constraints, then synthesize multi-stop itineraries with embedded logic (e.g., visa, weather windows, transfer buffers).

• Transact: Execute on inventory via APIs and payment rails, including rebooking when flights change.

• Negotiate: Apply loyalty logic, corporate rates, or alternative accommodation when a property is sold out.

• Monitor: Track price drops, irregular operations (IRROPS), and contextual signals (strikes, storms) to trigger actions.

This “closed loop” is qualitatively different from search-and-click. It aggregates value that was previously fragmented across metasearch, OTA, and direct channels.

2) Commission Pressure, Disintermediation, and Re-Intermediation

For two decades, OTAs have monetized discovery and trust via commissions. Agentic AI threatens disintermediation when travelers’ agents prefer direct supplier APIs for speed or price. Yet re-intermediation occurs if platforms become the preferred “agent substrate” because of superior data quality—fare/class nuances, rate rules, dynamic packaging, and historical reliability signals.

A likely equilibrium: hybrid models where platform-provided agents optimize across multiple sources while preserving compliance (e.g., corporate travel policy), and suppliers invest in their own branded agents for loyal segments. The fee pool compresses on undifferentiated inventory and expands on orchestration (bundling multi-modal transport, context-aware rebooking, and embedded insurance).

3) Trust, Grounding, and the Economics of Hallucination

Symbolic capital (trust) depends on grounding—the connection between model claims and verified inventory. When an AI suggests a sold-out hotel or an impossible transfer, trust erodes. Firms respond by:

• Restricting the agent to authoritative sources (GDS, CRS, PMS, verified supplier feeds).

• Implementing tool-use policies: the agent must fetch live availability before committing.

• Introducing explanatory receipts: why the agent chose this routing, how it respected loyalty status, and what trade-offs were considered.

The cost of a hallucination in travel is tangible (missed flights, stranded guests). As a result, low-latency retrieval + transactional verification becomes a core capability and a market differentiator.

4) Latency, Empathy, and the New UX of Travel

Recent deployments report big gains in latency and tone control for conversational travel assistants. Shaving seconds off response time can change completion rates; tone control (empathetic acknowledgment during disruptions) can reduce perceived friction. In service contexts like IRROPS, the “how” of communication is a capability—not a cosmetic layer—and may translate into higher Net Promoter Scores (NPS) and reduced call center volume.

5) Data Gravity, Platform Moats, and Agent Ecosystems

Agentic AI is only as strong as the data scaffolding behind it. Platforms with billions of daily price points, deep intent graphs, and strong supplier connectivity create data gravity that attracts agent integrations. A flight-only metasearch must now decide whether to become a “trusted travel engine” that supports lodging, ground transport, insurance, and disruption handling, or to specialize and plug into broader agent ecosystems.

This is where world-systems dynamics reappear: core platforms amass the data to define de facto standards—for example, how agents encode cabin attributes, room types, cancellation semantics, or sustainability labels. Peripheral actors can still win by adopting schema-rich, machine-readable content and exposing reliable APIs that agents like to call.

6) Corporate Travel: Policy, Leakage, and Duty of Care

Agentic AI challenges the traditional distinction between managed and unmanaged travel. If a personal agent can comply with policy (class of service, preferred suppliers, budget caps) and automatically log itineraries for duty of care, the perceived advantage of legacy tools narrows.

Suppliers to corporate segments respond by:

• Embedding policy-aware logic in agents,

• Offering one-flow booking that attaches lodging to air to reduce leakage,

• Using recommendation engines to keep bookings “in policy” without user friction.

For travel managers, the question shifts from procurement to governance of agent behavior: how to audit recommendations, avoid bias, and ensure privacy.

7) Hospitality: From Rates to Relationships

Hotels have long battled the trade-off between occupancy and rate integrity. Agentic AI can help:

• Predict which segments are likely to cancel and price accordingly;

• Offer substitutes in the same micro-neighborhood with similar attributes when sold out;

• Recognize loyalty entitlements, late check-outs, or room-type preferences and trade them off against revenue goals.

The front desk becomes partly virtual: agents handle pre-arrival upsells, digital keys, and irregular requests (e.g., baby cots, allergy-friendly rooms). Symbolic capital accrues to the property if execution is seamless; otherwise, it accrues to the agent that “fixed” things when the property failed.

8) Destinations and Peripheries: Speaking to Agents

Destination marketing organizations (DMOs) and small suppliers face a new mandate: become legible to machines. That means structured events data, consistent opening hours, rich media with rights metadata, and safety/visa information in schemas agents can parse.

If a peripheral region supplies verifiable, granular data, agentic systems can surface it alongside core destinations. Without such data, even spectacular attractions can be invisible in agent-generated itineraries.

9) Safety, Security, and the Ethics of Optimization

Agentic systems may optimize for price and time but neglect social externalities without guidance—over-tourism hotspots, water stress, or local community well-being. Managers should define multi-objective reward functions that include sustainability and equity metrics, and require transparency about how the agent balances them.

Institutional isomorphism suggests rapid convergence around safety checklists (PII protection, PCI compliance), human-in-the-loop overrides for complex disruptions, and standardized incident reporting when agents fail.

Findings

1. Intermediation Is Being Rewritten, Not Removed. AI agents compress steps and reduce search friction, but they also create new orchestration layers where value accrues to whoever can reliably close the loop from intent to ticket to recovery.

2. Trust Is the New Scarce Resource. Symbolic capital flows toward actors who consistently deliver grounded, low-latency, “no-surprises” outcomes. This pushes platforms to harden tool-use rules, invest in live availability integrations, and publish decision receipts.

3. Data Moats Become Ecosystem Moats. Platforms with dense pricing histories, intent graphs, and supplier connections are best positioned to host or power agents. Smaller players can still compete by specializing (e.g., complex rail-ferry combinations) or by offering superior, machine-readable content and guarantees.

4. Corporate Travel Converges on Policy-Aware Autonomy. Personal agents that know the traveler and the policy reduce leakage and may outperform legacy portals in UX and exception handling. Duty-of-care compliance becomes an agent feature, not a separate system.

5. Hospitality Shifts from Transactions to Lifecycle Relationships. The most valuable use of AI is not just rate optimization but relationship continuity—anticipating needs, orchestrating add-ons, and protecting experience quality during disruptions.

6. Peripheral Destinations Can Win Through Machine Legibility. World-systems dynamics need not doom peripheries; structured data and verifiability let agents recommend non-core destinations when they genuinely fit user constraints and values.

7. Field-Level Convergence Is Underway. Coercive (customer expectations and regulatory), mimetic (copying leaders), and normative (best-practice standards) forces are pushing travel organizations toward similar AI patterns: verified data sources, policy-aware planning, human fallback, and transparent optimization criteria.

Managerial Implications

• For OTAs and Metasearch: Invest in agent-grade data contracts (inventory freshness SLAs, reliability scores). Provide agent APIs that return not only prices but also explanations (cancellation semantics, rebooking rules). Move beyond search to orchestration—bundle rail/air/hotel/insurance with disruption playbooks baked in.

• For Airlines and Hotels: Build branded agents that leverage loyalty data and property knowledge, but interoperate with third-party agent ecosystems. Ensure that fare-family and room-type taxonomies are machine-consumable. Publish promise guarantees (e.g., re-accommodation windows) that agents can reason about.

• For Corporate Travel Managers: Treat agent governance as a policy surface. Require suppliers to expose machine-readable policy objects and auditable decision logs. Pilot sandboxed agents with red-team testing for bias and failure modes.

• For Destination Authorities: Launch a “speak to agents” initiative—standardize schemas for attractions, events, micro-itineraries, accessibility, and safety. Provide ground-truth data feeds (weather alerts, transport strikes) that agents can subscribe to.

• For Regulators: Encourage transparent agent disclosures (who is paying whom, what data was used) and contestability (easy human escalation). Consider minimal duty-of-explanation requirements when agents make consequential decisions (e.g., during IRROPS).

Limitations and Future Research

This study is qualitative and interpretive; its goal is synthesis, not measurement. Three research priorities follow:

1. Market Concentration Metrics: Quantitatively track how agentic AI shifts booking shares across direct, OTA, and metasearch channels.

2. Service Quality Outcomes: Measure IRROPS resolution time, customer satisfaction, and error rates for agent-led transactions versus legacy funnels.

3. Equity and Sustainability Effects: Evaluate whether multi-objective optimization actually diverts traffic from over-touristed cores to suitable peripheries and improves local welfare.

Conclusion

Agentic AI has crossed a threshold in 2025: it no longer merely advises travelers; it acts for them. That seemingly simple change—closing the loop from suggestion to settlement—rearranges where power and profit sit in travel management. Using Bourdieu’s capitals, we see trust (symbolic capital) concentrating wherever outcomes prove reliably excellent. Through world-systems theory, we see a risk of core consolidation but also a path for peripheries that become machine-legible and verifiable. Institutional isomorphism explains why organizations across the field are converging on similar AI architectures and safeguards.

For managers, the message is practical: win trust by making your data real-time and your agent behaviors auditable; shift from selling inventory to orchestrating journeys; and commit to standards that let smaller suppliers and destinations plug in without friction. For researchers, a new measurement frontier opens: mapping how algorithmic intermediaries affect prices, consumer welfare, and the geography of tourism. The agentic era will reward clarity, speed, and empathy—delivered by systems that not only understand the traveler, but also stand behind the trip when life happens.

Hashtags

#AgenticAI #TravelManagement #TourismTechnology #PlatformEconomics #CustomerExperience #HospitalityInnovation #CorporateTravel

References

• Bourdieu, P. (1986). The Forms of Capital. In J. G. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education. Greenwood.

• Bourdieu, P. (1993). The Field of Cultural Production. Columbia University Press.

• DiMaggio, P., & Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review, 48(2), 147–160.

• Giddens, A. (1991). Modernity and Self-Identity: Self and Society in the Late Modern Age. Stanford University Press.

• Granovetter, M. (1985). Economic Action and Social Structure: The Problem of Embeddedness. American Journal of Sociology, 91(3), 481–510.

• Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.

• Buhalis, D., & Law, R. (2008). Progress in Information Technology and Tourism Management: 20 Years On and 10 Years After the Internet. Tourism Management, 29(4), 609–623.

• Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart Tourism: Foundations and Developments. Electronic Markets, 25(3), 179–188.

• Gössling, S., Scott, D., & Hall, C. M. (2015). Inter-market Variability in the Carbon Footprint of Tourism. Tourism Management, 46, 203–212.

• Davenport, T. H., & Mittal, N. (2022). All-in on AI: How Smart Companies Win Big with Artificial Intelligence. Harvard Business Review Press.

• Porter, M. E., & Heppelmann, J. E. (2014). How Smart, Connected Products Are Transforming Competition. Harvard Business Review, 92(11), 64–88.

• Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A Comparative Analysis of Major Online Review Platforms: Implications for Social Media Analytics in Hospitality and Tourism. Tourism Management, 58, 51–65.

• Jarrahi, M. H. (2018). Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision Making. Business Horizons, 61(4), 577–586.

• Buhalis, D. (2020). Technology in Tourism—From Information Communication Technologies to eTourism and Smart Tourism. Tourism Review, 75(1), 267–272.

• Ivanov, S., & Webster, C. (2019). Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality. Emerald Publishing.

• Li, J., Bonn, M. A., & Ye, B. H. (2019). Hotel Employee’s Artificial Intelligence and Robotics Awareness and Its Impact on Turnover. International Journal of Hospitality Management, 83, 73–82.

• Shneiderman, B. (2022). Human-Centered AI. Oxford University Press.

• Gursoy, D., Chi, C. G., Lu, L., & Nunkoo, R. (2019). Consumers Acceptance of AI Devices in Hospitality: Trust and Experience. International Journal of Hospitality Management, 78, 233–243.

• Pizam, A. (Ed.). (2021). International Encyclopedia of Hospitality Management (3rd ed.). Elsevier.