Agentic AI in Travel and Hospitality: A Sectoral Lens on Strategy, Power, and Institutional Change

Author: L. Verma

Affiliation: Independent Researcher

Abstract

“Agentic AI” (systems that can plan, decide, and execute multi-step tasks with limited human prompting) is quickly moving from technology demos into real sector operations—especially in travel and hospitality, where booking, pricing, staffing, disruption management, and guest service are already data-rich and workflow-driven. This article explains why agentic AI is a sectoral phenomenon: its value and risks depend on the specific institutional environment of hotels, airlines, airports, and travel intermediaries. Using a simple but Scopus-style structure, the article integrates three classic theoretical lenses—Bourdieu’s theory of capital and field, world-systems theory, and institutional isomorphism—to interpret how agentic AI is adopted, who benefits first, and why organizations may converge on similar “AI strategies” even when their contexts differ. Methodologically, the paper uses a structured narrative review approach and a sectoral framework that distinguishes (1) customer-facing agents, (2) operations and workforce agents, and (3) revenue and risk agents, while highlighting governance and measurement. The analysis shows that agentic AI may (a) shift power toward platforms that control demand, data, and standards; (b) accelerate imitation and “AI-washing” through coercive, normative, and mimetic pressures; and (c) deepen a core–periphery pattern in which large, globally connected firms adopt safely and profitably while smaller operators face capability constraints. Findings propose practical, human-readable guidance: treat agentic AI as an organizational redesign program, not a software add-on; invest in data rights, model governance, and “human-in-the-loop” operating rules; and measure outcomes in guest trust, workforce load, and operational stability—not only cost savings.

1. Introduction

Travel and hospitality have always been shaped by intermediaries. In earlier eras, the gatekeepers were travel agencies and tour operators; later, online travel agencies, search engines, and review platforms became the dominant “front doors.” The next front door is increasingly automated decision-making: intelligent agents that can compare options, apply preferences, negotiate constraints, and complete transactions.

This shift is trending now because three conditions are aligning. First, many travel journeys are modular: search → compare → book → pay → check-in → stay/fly → handle exceptions → review. Second, travel is full of exceptions—delays, no-shows, overbooking, last-minute changes—where automation has clear value. Third, modern AI systems can now produce plans and actions, not just answers. That makes travel one of the clearest sector candidates for agentic AI.

However, sectoral adoption is not only a question of “can it work?” It is also a question of power, legitimacy, and organizational identity. If a hotel adopts an AI agent to handle late-night guest issues, what happens to service culture? If an airline uses agents to rebook passengers during disruptions, how does it maintain accountability and fairness? If a destination relies on AI agents to direct visitors away from overcrowded sites, whose interests does the system protect?

This article treats agentic AI as a sectoral and specialized topic: the same technology behaves differently across industries because industries have different regulations, labor structures, reputational pressures, and market intermediaries. The paper aims to answer three practical questions:

1. Where will agentic AI create the largest operational changes in travel and hospitality?

2. Who gains advantage (and who faces new constraints) as agentic AI spreads?

3. Why do organizations often converge on similar AI strategies, even when the “best” strategy might differ?

To answer these, we combine three theoretical perspectives that help explain patterns beyond the hype.

2. Background and Theory

2.1 Bourdieu: Fields, Capital, and Habitus in Service Industries

Bourdieu describes society as made of “fields” where actors compete for different forms of capital—economic, cultural, social, and symbolic. Travel and hospitality can be understood as a field where organizations compete not only on price and location, but also on symbolic capital: reputation, brand prestige, service promise, star ratings, and trust.

Agentic AI enters this field as a new kind of resource that can be converted into capital:

• Economic capital: improved margins via staffing optimization, reduced leakage, higher conversion, better revenue management.

• Cultural capital: “knowing how” to operate AI systems; AI literacy among managers; data governance competence.

• Social capital: relationships with platforms, technology partners, and distribution channels; access to shared data standards.

• Symbolic capital: the legitimacy of being “innovative,” “seamless,” “personalized,” and “safe.”

Bourdieu also highlights habitus—internalized dispositions shaped by past experience. In hospitality, habitus includes service etiquette, the meaning of “warmth,” the role of discretion, and the idea that a guest’s complaint is resolved through empathy and authority. Agentic AI can clash with this habitus if service becomes too scripted or if accountability becomes unclear. In short: adoption is not just technical; it is cultural.

2.2 World-Systems Theory: Core–Periphery Patterns in Tech Diffusion

World-systems theory views the global economy as structured into core, semi-periphery, and periphery positions. Technology often spreads first in the core: firms with capital, stable infrastructure, and strong institutional environments. In travel and hospitality, “core” positions are not only countries; they are also platform positions and network centrality. A global hotel chain with integrated systems behaves like a “core” actor compared to an independent property relying on multiple disconnected vendors.

Agentic AI intensifies core–periphery patterns because it depends on:

• high-quality data flows across systems (PMS, CRS, CRM, payments, housekeeping, revenue tools),

• standardized processes (so agents can act reliably),

• risk and compliance capacity (privacy, consumer protection, security),

• access to skilled labor (AI governance, prompt engineering, process engineering),

• bargaining power with vendors and platforms.

This lens predicts that agentic AI adoption will be uneven: not because smaller actors do not want it, but because their structural position limits safe, effective adoption.

2.3 Institutional Isomorphism: Why “Everyone Ends Up Doing the Same Thing”

Institutional isomorphism explains why organizations become similar over time. Three pressures matter:

• Coercive isomorphism: regulatory demands, contractual requirements, platform rules, and insurance constraints that force standard practices.

• Normative isomorphism: professional norms—what hotel revenue managers, airline operations leaders, and auditors consider “best practice.”

• Mimetic isomorphism: imitation under uncertainty—copying competitors’ AI moves because nobody wants to look outdated.

Agentic AI is fertile ground for isomorphism. When the environment feels uncertain, firms adopt “AI strategies” that are more about legitimacy than value—pilot programs, AI dashboards, chatbots labeled as agents, or vendor-led implementations that look modern but do not change core processes.

The combined implication of these theories is clear: agentic AI is not merely a tool; it is a field-changing force that can reshape legitimacy, deepen inequalities, and produce convergence toward fashionable practices.

3. Method

This study uses a structured narrative review and a sectoral framework to translate theory into actionable insights for travel and hospitality leaders.

3.1 Data and Materials (Conceptual Evidence Base)

The article synthesizes recent industry and academic discussions on AI in service operations, hospitality technology, and organizational change, alongside foundational theory in sociology and institutional analysis. Because the purpose is sectoral interpretation rather than causal estimation, the “evidence” is conceptual and comparative.

3.2 Analytical Framework: Three Agent Types in the Sector

To reduce confusion, we categorize agentic AI into three functional types:

1. Customer-facing agents

   • search, recommendations, booking, itinerary building, concierge, complaint triage

2. Operations and workforce agents

   • staffing, task routing, housekeeping coordination, disruption handling, maintenance prioritization

3. Revenue and risk agents

   • dynamic pricing support, fraud signals, cancellation management, policy enforcement, compliance monitoring

3.3 Evaluation Criteria

We assess each category using five criteria that matter in travel/hospitality:

• Value leverage: does it improve conversion, margin, reliability, or satisfaction?

• Accountability: who is responsible when an agent makes a mistake?

• Data rights and interoperability: can the organization access and use the needed data legally and technically?

• Workforce impact: does it reduce load, deskill roles, or create new skills?

• Legitimacy and trust: will guests, regulators, and staff accept the system?

4. Analysis

4.1 Customer-Facing Agents: The New “Front Desk” and the New “Travel Agent”

Customer-facing agents promise frictionless service: instant answers, tailored offers, proactive reminders, and 24/7 support. In practice, three tensions emerge.

Tension 1: Personalization vs. creepiness

Hospitality has always used personalization (room preferences, loyalty status). Agentic AI can extend this into inference (predicting needs). But the more “mind-reading” it feels, the more it risks trust. Bourdieu helps here: personalization can become symbolic capital (a premium service cue) or symbolic violence (an unwanted intrusion), depending on how it is presented and controlled.

Tension 2: Brand voice vs. platform voice

If guests increasingly interact through AI agents embedded in platforms (apps, operating systems, super-app travel assistants), the brand risks becoming a commodity. World-systems theory predicts that actors controlling demand aggregation become “core” nodes. Hotels and airlines may respond by investing in direct channels, loyalty ecosystems, and data partnerships—attempting to remain central rather than peripheral.

Tension 3: Resolution vs. responsibility

A human front desk can bend rules, interpret tone, and absorb responsibility. An agent can escalate, but escalation logic must be designed. If an agent makes a promise (“late checkout approved”) and operations cannot deliver, the perceived failure is worse than if no promise was made. The solution is not “better prompts,” but a clear authorization model: what the agent can commit to, under what conditions, and how exceptions are handled.

Sector-specific insight:

Customer-facing agents can be most effective when they are tightly bound to operational reality: live inventory, staff capacity, and policy constraints. Otherwise, they create service debt.

4.2 Operations and Workforce Agents: The Hidden Engine Room

Many of the highest-impact use cases are not glamorous. They are about routing tasks, predicting bottlenecks, and handling irregular operations.

Hotels:

• housekeeping task sequencing based on arrivals, VIP status, and maintenance flags

• maintenance triage and part ordering

• staff scheduling based on demand forecasts and skill mix

• proactive detection of service failures (recurring complaints, room issues)

Airlines and airports:

• disruption management: rebooking, crew and gate reassignment support

• queue management in terminals

• baggage exception handling

• customer re-accommodation workflows

Why this matters theoretically:

Institutional isomorphism often makes companies chase visible AI (chatbots) rather than operational AI (workflow agents). Yet operational agents convert more directly into economic capital and reliability. Bourdieu would predict internal field struggles: operations leaders argue for stability, marketing leaders argue for visible innovation, IT leaders argue for control, and finance argues for measurable ROI.

Workforce consequences:

Agentic AI can reduce cognitive load (less triage) but also create new demands: monitoring, exception management, and system training. A common failure is “silent workload transfer,” where agents generate more alerts and tasks than humans can manage. The fix is work design: fewer handoffs, clear escalation thresholds, and metrics that track human load.

Sector-specific insight:

In service sectors, productivity is not only speed; it is also emotional labor. If agents remove routine tasks, staff may do more complex guest interactions—but only if staffing models and training reflect that shift.

4.3 Revenue and Risk Agents: Pricing Power, Fairness, and Compliance

Revenue management in hospitality and airlines is already algorithmic. Agentic AI adds the ability to coordinate across functions: adjust pricing and trigger targeted offers and adapt staffing plans. That sounds efficient, but it raises sensitive issues.

Fairness and transparency

Dynamic pricing can feel unfair if guests perceive arbitrary fluctuations. When an agent negotiates or “auto-applies” offers, the system must define boundaries: what counts as a fair price, what is allowed under consumer rules, and how to explain decisions in human terms.

Fraud and chargebacks

Payments in travel are high-risk: card-not-present transactions, refunds, cancellations. Agents can assist by detecting patterns and applying policy logic, but false positives harm genuine guests. Here, legitimacy matters: a firm that is too strict may protect revenue but lose symbolic capital.

Regulatory complexity

Travel and hospitality operate across jurisdictions. Data protection, consumer rights, accessibility, and anti-discrimination expectations are not uniform. Coercive isomorphism can occur when large platforms impose standards that become de facto rules for the sector.

Sector-specific insight:

Treat revenue and risk agents as “controlled automation.” Their actions should be auditable and reversible, with clear human oversight. The goal is not autonomy; it is dependable decision support with bounded authority.

4.4 The Sectoral Power Shift: Data, Standards, and Intermediation

Across all three categories, the largest strategic risk is losing control of the relationship with the guest. If agents become the main interface, then whoever controls the agent controls:

• what options are visible,

• how prices are compared,

• which policies are considered acceptable,

• what counts as “best value.”

World-systems theory suggests that the most powerful actors are those who set standards and capture network effects. In travel, that often means platforms and large system integrators. Smaller operators may become more dependent unless they coordinate through associations, shared standards, or cooperative data infrastructure.

Bourdieu adds that legitimacy is part of power. If “AI-enabled service” becomes a status marker, then firms may invest in AI to signal modernity, even when the implementation is shallow. This is classic mimetic isomorphism: the appearance of innovation becomes a competitive necessity.

5. Findings

The analysis yields seven sector-relevant findings.

Finding 1: Agentic AI adoption is an organizational redesign project.

The highest value comes when agents are embedded into workflows (authorization, escalation, and exception handling). Without redesign, agents create promises that operations cannot keep.

Finding 2: The biggest early wins are often operational, not conversational.

Customer-facing agents are visible, but operations agents tend to produce more reliable ROI through fewer bottlenecks, improved room readiness, smoother disruption handling, and better coordination.

Finding 3: Data rights and interoperability are strategic assets, not IT details.

Organizations that control clean, consistent data and can connect systems safely will adopt faster and with fewer failures. Those without interoperability remain stuck in pilots.

Finding 4: Agentic AI reshapes symbolic capital and brand meaning.

In premium hospitality, “human warmth” is part of the product. Over-automation can destroy brand value even if it cuts costs. Successful adoption protects the service habitus: humans handle empathy and discretion; agents handle routine coordination.

Finding 5: Institutional isomorphism will drive convergence and superficial adoption.

Many organizations will copy “agentic AI” language and buy similar tools to maintain legitimacy. This may increase vendor dependence and produce a market of look-alike solutions.

Finding 6: Core–periphery gaps may widen inside the sector.

Large chains and carriers can build governance, negotiate vendor terms, and spread costs across portfolios. Independent properties and small operators risk becoming dependent on packaged agents with limited customization and unclear data control.

Finding 7: Trust is the hidden KPI.

The sector will learn that the most important metric is not “automation rate” but trust outcomes: complaint resolution quality, clarity of responsibility, staff confidence, and guest willingness to share preferences.

6. Conclusion

Agentic AI is trending in travel and hospitality because the sector is structured around repeatable workflows with frequent exceptions, and because AI systems are now capable of taking action rather than only generating responses. But sectoral reality complicates the story. Agentic AI is not neutral: it changes who owns the guest relationship, how legitimacy is signaled, and which organizations can adopt safely.

Bourdieu reminds us that adoption is a struggle over capital and identity inside a field: firms will seek economic gains while protecting symbolic value. World-systems theory warns that adoption will be uneven: actors with central network positions and capability advantages will accelerate ahead. Institutional isomorphism explains why “AI strategies” will spread quickly—sometimes as real operational progress, sometimes as imitation for legitimacy.

For leaders, the practical takeaway is simple: treat agentic AI as a governed service system. Define what agents can do, how they escalate, how decisions are audited, and how staff are supported. Measure success in operational stability, guest trust, and workforce sustainability. In the travel and hospitality sector, the future will not belong to the most automated firms, but to the firms that combine automation with accountability and human-centered service design.

Hashtags

#AgenticAI #HospitalityTech #TravelInnovation #ServiceManagement #DigitalTransformation #RevenueManagement #AITrust

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