Agentic AI in Travel and Enterprise: How Autonomous Systems Are Reshaping Tourism Management and Organizational Workflows

Author: Azamat Karimov — Affiliation: Independent Researcher

Abstract

Agentic Artificial Intelligence (AI)—autonomous systems capable of acting, reasoning, and learning with minimal human intervention—is emerging as a transformative force across tourism management and enterprise workflows. This paper explores how these systems are reshaping travel intermediation, destination governance, and internal organizational processes. Using Bourdieu’s concept of capital, world-systems theory, and institutional isomorphism as analytical lenses, we examine who gains and who loses as agents reconfigure value chains and labor divisions. A qualitative synthesis of current industry practices and conceptual theories reveals three key findings: first, agentic AI compresses intermediation layers in tourism, threatening traditional commission-based models while empowering suppliers with open data ecosystems; second, enterprises deploying AI agents see significant efficiency gains but face labor displacement concerns; and third, destination management increasingly requires dynamic, data-driven coordination rather than static regulations. The study concludes with governance recommendations on transparency, accountability, and labor transition policies to ensure equitable adoption.

1. Introduction

Travel and tourism have historically relied on intermediaries—travel agencies, tour operators, and booking platforms—to reduce information complexity and manage customer experience. Similarly, organizations across industries have long used human labor for tasks such as customer service, knowledge management, and administrative processing.

The arrival of agentic AI—AI systems with decision-making autonomy—signals a structural shift. In tourism, autonomous systems now manage itinerary planning, booking, and real-time problem resolution. In enterprises, AI agents handle information retrieval, automate repetitive workflows, and collaborate with human workers on complex tasks.

Despite enthusiasm, the rise of agentic AI raises theoretical and practical questions:

• How does automation redistribute economic and symbolic capital?

• Will power concentrate in digital “cores,” leaving smaller actors dependent?

• Why are organizations converging so quickly on “AI agent strategies”?

This paper addresses these questions using three sociological frameworks:

1. Bourdieu’s capital theory (economic, social, cultural, symbolic capital)

2. World-systems theory (core–periphery dynamics)

3. Institutional isomorphism (mimetic, coercive, normative pressures)

2. Theoretical Background

2.1 Bourdieu: Capital in Digital Transformation

Bourdieu conceptualizes social life as competition over various forms of capital:

• Economic capital: financial resources and assets

• Social capital: networks and relationships

• Cultural capital: knowledge, skills, credentials

• Symbolic capital: legitimacy and prestige

Agentic AI reorganizes these capitals. Economic capital concentrates around firms controlling infrastructure (cloud computing, AI models), while cultural and symbolic capital accrue to actors demonstrating transparency, safety, and ethical use of AI.

2.2 World-Systems Theory: Core and Periphery

World-systems theory distinguishes between “core” actors with technological dominance and “peripheral” actors dependent on them. In tourism, global booking platforms represent the core; small hotels, local attractions, and emerging destinations form the periphery. Agentic AI could either:

• Centralize power if proprietary ecosystems dominate, or

• Decentralize power if open standards allow direct supplier-to-consumer interactions.

2.3 Institutional Isomorphism

DiMaggio and Powell describe three forces driving organizational similarity:

• Mimetic: copying successful peers under uncertainty

• Coercive: complying with regulations or client demands

• Normative: following professional norms and standards

The rapid diffusion of AI agents illustrates all three: firms imitate competitors, respond to customer expectations, and align with emerging professional standards on AI ethics and safety.

3. Method

This research adopts a qualitative conceptual approach combining:

1. Literature synthesis on AI in tourism, enterprise automation, and digital governance

2. Theoretical integration using Bourdieu, world-systems, and isomorphism frameworks

3. Case observations from early AI deployments in travel management and enterprise workflows

The goal is not statistical generalization but conceptual clarity and future research hypotheses.

4. Analysis

4.1 Tourism Intermediation: From Human Agents to AI Agents

Traditional tourism relies on layered intermediaries: supplier → aggregator → online travel agency → consumer. Agentic AI compresses this chain by automating:

• Search: scanning flights, hotels, experiences

• Negotiation: optimizing prices, loyalty points, refund terms

• Execution: booking, rebooking, customer support

Implication: suppliers with open APIs and transparent policies gain visibility, while commission-based intermediaries risk margin erosion.

4.2 Enterprise Workflows: AI as Operational Colleague

Inside organizations, AI agents now:

• Retrieve documents

• Summarize regulations

• Automate form processing

• Handle first-line customer queries

This reduces response times and operational costs but creates labor displacement risks in administrative and customer-service roles. Firms face ethical and strategic questions about workforce transitions and skill upgrading.

4.3 Destination Management: Dynamic vs. Static Controls

Tourism congestion traditionally relies on blunt instruments like daily visitor caps or flat fees. Agentic AI enables dynamic management:

• Real-time pricing based on congestion levels

• Adaptive ticketing for attractions

• Routing travelers across time slots and locations

Such systems balance sustainability, visitor experience, and economic goals more effectively than static policies.

4.4 Capital Reallocation through AI

• Economic capital: moves toward AI platform providers and data-rich suppliers

• Social capital: shifts to actors forming strategic data-sharing partnerships

• Symbolic capital: accrues to firms demonstrating ethical, transparent AI adoption

4.5 Core–Periphery Outcomes

Two possible futures emerge:

1. Centralization: global platforms dominate AI ecosystems, locking smaller players into dependency

2. Decentralization: open protocols enable small destinations and local suppliers to interact directly with consumer-facing agents

4.6 Isomorphic Pressures Driving Adoption

Organizations adopt AI agents because:

• Competitors are adopting (mimetic)

• Regulations demand efficiency/transparency (coercive)

• Industry standards encourage best practices (normative)

5. Findings

1. AI compresses intermediation: reducing layers in tourism booking and enterprise workflows

2. Labor structures shift: routine cognitive tasks decline; demand for AI governance and integration skills rises

3. Destination governance evolves: from static fees to dynamic, real-time optimization

4. Capital redistributes: economic and symbolic capital concentrate around AI infrastructure owners and ethical adopters

5. Adoption accelerates via isomorphism: competitive imitation and regulatory compliance drive rapid diffusion

6. Conclusion

Agentic AI is transforming tourism management and enterprise workflows by automating intermediation, restructuring labor, and enabling adaptive governance. Using Bourdieu, world-systems, and institutional isomorphism theories, this paper shows how AI adoption redistributes power, capital, and organizational practices.

For equitable outcomes, policymakers and managers must:

• Ensure open standards to prevent monopolization

• Implement labor transition programs for displaced workers

• Mandate transparency and auditability in AI decision-making

Future research should track long-term impacts on employment, sustainability, and power concentration across tourism and enterprise ecosystems.

Hashtags

#AgenticAI #TourismManagement #EnterpriseAutomation #DigitalTransformation #AIandSociety #FutureOfWork #SustainableTourism

References

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