Autonomous AI Agents in 2025: Organizational Isomorphism, Field Capital, and Uneven Development in the Management and Tourism Sectors

International Academy
Sep 9
13 min read

Author: Azamat Bekov — Affiliation: Independent Researcher

Abstract

This article examines how the rapid adoption of autonomous artificial intelligence (AI) agents in 2025 is transforming managerial practice and service delivery in two interconnected domains: management and tourism. While earlier waves of digitalization were primarily about data collection and human-in-the-loop decision support, today’s shift toward autonomous AI—capable of initiating tasks, negotiating with other systems, orchestrating workflows, and interacting with customers without constant supervision—introduces new forms of organizational isomorphism, redistributes symbolic, social, and economic capital within professional fields, and reproduces uneven development across the global economy. Using theoretical lenses from Bourdieu (fields and capitals), world-systems theory (core–semi-periphery–periphery), and institutional isomorphism (coercive, mimetic, normative), the study develops an interpretive, qualitative framework to analyze current managerial arrangements and service models in tourism. Methods include a comparative reading of practitioner reports, policy frameworks, and peer-reviewed scholarship; structured observation of emergent organizational routines; and conceptual modeling of actor–network constellations that structure AI deployment.

Findings suggest three convergent dynamics. First, as autonomous agents become infrastructural, organizations gravitate toward similar governance templates and risk controls, illustrating mimetic and normative isomorphism supported by professional communities and vendor ecosystems. Second, AI redistributes field capital, privileging actors who can accumulate “algorithmic capital” (the capability to shape, audit, and strategically deploy agentic systems) while devaluing repetitive middle-tier coordination work. Third, the geography of AI capacity follows center–periphery patterns: core economies consolidate high-value orchestration roles (agent design, evaluation standards, platform governance) while peripheral sites often receive commodified service layers, even as niche destinations and firms leverage AI to leapfrog constraints in marketing, dynamic pricing, and micro-personalized itineraries. The paper concludes with a set of managerial implications around capability building, humane service design, auditability, workforce transition, and cross-border standards for the safe and fair use of autonomous agents in tourism and adjacent services.

1. Introduction

In 2025, autonomous AI agents have shifted from experimental pilots to tangible, high-impact components of everyday organizational life. Unlike earlier chatbots or decision-support dashboards, these agents can sequence multi-step processes, call tools, transact through APIs, and monitor key performance indicators (KPIs) in real time. In management functions, they draft procurement briefs, reconcile invoices, or trigger compliance checks; in tourism, they plan end-to-end itineraries, converse with travelers in natural language, handle disruptions, and coordinate with partner platforms. The managerial question is no longer whether to use AI but how to govern agentic autonomy in ways that are compliant, trustworthy, and productive—without hollowing out critical human competencies or reproducing global inequality.

This paper proposes that three sociological frameworks help us understand this transition. First, Bourdieu’s theory of fields and capitals clarifies how power and legitimacy are reconfigured when algorithmic systems acquire the capacity to act. Second, world-systems theory illuminates the uneven geography of AI’s benefits and burdens. Third, the concept of institutional isomorphism explains why organizations converge on similar agent governance templates despite heterogeneous contexts. Together, these lenses provide a grounded explanation for the patterns we observe in management and tourism settings and point toward responsible ways to design, deploy, and regulate autonomous AI.

The rest of the article proceeds as follows. Section 2 reviews relevant theory. Section 3 outlines the interpretive methodology. Section 4 presents an analysis of how autonomous agents reconfigure work, organizational forms, and market dynamics, with a special focus on tourism and services. Section 5 synthesizes key findings. Section 6 concludes with managerial implications and research directions.

2. Background and Theory

2.1 Bourdieu: Fields, Capitals, and Habitus in AI-Mediated Work

Bourdieu conceptualizes social life as unfolding within structured fields—relatively autonomous social arenas where position-taking is governed by rules, taken-for-granted expectations (doxa), and struggles over various forms of capital (economic, cultural, social, symbolic). In professional service fields—management consulting, travel operations, destination marketing—agents (both human and non-human) compete to accumulate valued capital and translate it into durable advantage. Autonomous AI systems introduce what we may call algorithmic capital: the capacity to shape, evaluate, and orchestrate agent performance in alignment with organizational goals and field norms. Organizations able to codify tacit know-how into reproducible, auditable agent scripts can convert cultural capital (expertise) and social capital (partnerships, APIs, data access) into economic returns, and ultimately symbolic capital (reputation for reliability, safety, and personalization). Meanwhile, actors whose habitus is closely aligned with “pre-AI” routines may find their work devalued if tasks are easily automated.

2.2 World-Systems Theory: Uneven Development and Platform Geographies

World-systems theory emphasizes a stratified global economy composed of core, semi-peripheral, and peripheral zones. Core regions historically capture the highest value through control over finance, technology, and standards, while peripheral regions provide labor or raw materials. The AI platform economy reproduces this stratification: core ecosystems design foundation models, safety and evaluation frameworks, and agent-orchestration platforms. Semi-peripheral and peripheral regions sometimes become early adopters in operational roles—customer service, content moderation, itinerary operations—without similar control over standards or intellectual property. At the same time, tourism destinations in semi-peripheral contexts can leverage AI to leapfrog certain constraints: targeting micro-segments in multiple languages, dynamically pricing experiences, and integrating last-mile services. Whether such moves produce durable upgrading depends on whether regions can build local algorithmic capital and influence governance standards.

2.3 Institutional Isomorphism: Converging Governance under Uncertainty

DiMaggio and Powell’s notion of institutional isomorphism—coercive, mimetic, and normative—explains why organizations often converge on similar forms and policies. In the context of autonomous agents, coercive pressures include regulation and contractual demands from partners (e.g., vendor compliance, industry checklists). Mimetic isomorphism arises when firms copy templates perceived as legitimate or “best practice” amidst uncertainty (e.g., adopting standard red-team tests, incident reporting, or model cards). Normative isomorphism emerges through professional communities—AI auditors, product managers, and compliance officers—who diffuse common ethics curricula, risk classifications, and evaluation routines. These pressures reduce variance in how agents are governed across management and tourism organizations, even when local contexts differ.

3. Method

This study adopts an interpretive, multi-source qualitative approach appropriate for a rapidly evolving technological domain. Three complementary strategies were employed:

Comparative Literature Synthesis. A purposive reading of recent peer-reviewed research in management, information systems, and tourism studies was combined with canonical sociological texts (Bourdieu; world-systems theory; institutional isomorphism). Practitioner white papers and policy frameworks were analyzed to capture the “working rules” that practitioners use when deploying autonomous agents.
Structured Observation of Emerging Routines. We observed and codified recurring patterns in pilot deployments described by firms and professional communities: for example, agent playbooks for procurement and customer care; escalation ladders; prompt libraries; and human-in-the-loop checkpoints. The goal was to identify cross-organizational regularities rather than evaluate a single firm.
Conceptual Modeling. Drawing on actor–network perspectives, we mapped how human roles (managers, travel advisors, revenue analysts), AI agents (planning, negotiation, monitoring), and infrastructural elements (APIs, data pipelines, policy controls) assemble into governance arrangements. This generated a taxonomy of agent roles and control points relevant to management and tourism.

The methodology is explicitly interpretive and theory-driven. It seeks patterned explanation rather than statistical generalization, with attention to mechanisms that are likely to persist as agentic capabilities scale.

4. Analysis

4.1 From Tools to Teammates: The New Ontology of Managerial Work

Traditional automation viewed software as a passive tool; autonomy grants AI the capacity to initiate, monitor, and adapt. In managerial contexts, this shifts at least four routines:

Orchestration: Agents trigger multistep workflows—e.g., verifying supplier status, retrieving contracts, proposing payment schedules, and adjusting budgets. Human managers supervise exceptions rather than micromanaging steps.
Negotiation and Interface: Agents negotiate API-level constraints (inventory, pricing, cancellation rules) with partner systems, reducing cycle times. In tourism, itinerary agents coordinate flights, accommodations, and excursions, balancing cost, time, and traveler preferences.
Memory and Learning: Agents maintain state across interactions (traveler loyalty tiers, accessibility needs, dietary restrictions), creating continuity and personalized recommendations.
Monitoring and Incident Response: Agents watch KPIs (service-level agreements, queue times, overbooking thresholds) and can escalate with evidence to human supervisors with proposed actions.

These capabilities allow organizations to redesign spans of control. Managers move from interventionist oversight to meta-governance: setting policies, guardrails, and audit requirements for agent behavior, and arbitrating conflicts between organizational goals (e.g., revenue vs. fairness).

4.2 Algorithmic Capital and the Changing Value of Expertise (Bourdieu)

AI autonomy reconfigures what counts as valued capital in managerial and tourism fields.

Cultural Capital (Knowledge Forms): Expertise shifts from procedural know-how to meta-knowledge about specifying goals, constraints, and evaluation criteria that guide agentic behavior. Employees who can translate tacit service standards into machine-interpretable rules gain advantage.
Social Capital (Networks): Partnerships with data providers, distribution systems, and local service vendors become leverage points. Tourism providers who maintain APIs and verified inventories increase their visibility to autonomous itinerary agents.
Symbolic Capital (Legitimacy): Trust in service reliability becomes the new brand. Firms that demonstrate robust audits, transparent escalation, and inclusive design accumulate symbolic capital, while “black-box” deployments risk reputational costs.
Economic Capital (Resources): Investment in data quality, model evaluation, and safety review boards becomes a core allocation decision. The returns accrue to those who can “compose” agents into dependable service chains.

The net effect is a shift from valuing repetitive coordination roles to privileging orchestration, evaluation, and policy design skills. This does not eliminate human work; it raises the premium on boundary-spanning competence across operations, data governance, and customer empathy.

4.3 Global Stratification: Where Value Pools Accumulate (World-Systems)

Autonomous AI is not a flat landscape. Platform geographies mirror existing global stratification:

Core: Concentrates control over foundation models, orchestration frameworks, and evaluation standards. Captures licensing fees, sets safety benchmarks, and shapes compliance templates. High-margin roles include agent marketplace governance, safety red-teaming, and third-party audits.
Semi-Periphery: Hosts fast-scaling operations—multilingual support centers, itinerary operations, destination content generation, and last-mile logistics. Opportunities for upgrading emerge when regions develop local evaluation labs, tourist data consortia, and hospitality tech clusters.
Periphery: Often integrates commodified service layers (inventory endpoints, basic chat interfaces) with limited local influence over standards. Nevertheless, unique destinations can strategically use AI to reach micro-segments (eco-tourists, medical travelers) and to stabilize seasonality via dynamic packages.

For tourism, the key question is whether destinations can convert temporary boosts in visibility into durable algorithmic capital—local datasets, evaluation expertise, and governance participation—thus moving up the value chain.

4.4 Institutional Isomorphism: Convergence in Agent Governance

Despite contextual differences, we observe convergence in governance structures:

Coercive: Data protection and consumer-protection regimes push firms to implement explainability logs, consent management, and incident reporting. Tourism intermediaries adopt standardized disclosures for dynamic pricing and itinerary changes.
Mimetic: Firms copy “reference architectures” that segment agent functions (plan, execute, monitor, escalate), with risk tiers determining required human checkpoints. Vendor badges and maturity models become persuasive signals.
Normative: Professional bodies and training programs diffuse common vocabularies (e.g., “baseline evals,” “hallucination containment,” “harm taxonomy”), creating shared expectations among product managers, compliance officers, and auditors.

Isomorphism reduces uncertainty and integration costs, but it can also inhibit contextual innovation if templates ossify. The managerial challenge is to balance adherence to shared standards with local experimentation.

4.5 Tourism Use Cases: Personalization, Revenue, and Resilience

Tourism offers a microcosm of service-sector transformation:

Hyper-Personalized Itineraries. Agents account for constraints (mobility, budget), preferences (culture, cuisine), and contextual data (weather, crowd forecasts), producing day-level plans that adapt in real time. The value shifts from generic packages to adaptive experiences, with willingness to pay tied to perceived fit and reassurance.
Dynamic Revenue Management. Agents ingest demand signals and competitor proxies to adjust prices, bundles, and channel mixes. They can also balance load across venues to avoid overcrowding, protecting the visitor experience and local communities.
Disruption Handling. When transport or weather events occur, agents proactively reorder the day, communicate with providers, and offer options, reducing traveler anxiety and contact center load.
Local Ecosystem Integration. By exposing standardized, trustworthy APIs, small providers (boutique hotels, guides) can surface to global demand. However, platform rules and ranking algorithms will shape visibility—raising governance concerns.
Sustainability and Inclusion. Agents can encode sustainability objectives (emissions budgets) and inclusion (accessibility filters) as first-order constraints, shifting markets toward responsible tourism if incentives align.

4.6 The Auditability Problem: Making Autonomy Legible

Autonomy requires auditability. Managerial discourse increasingly centers on explainability logs (what the agent saw, decided, and executed), evaluation suites (benchmarks for utility, safety, and fairness), red-team incident registries, and escalation ladders. Tourism providers must trace itinerary changes and pricing decisions to ensure customers and regulators can reconstruct events. Auditability reframes quality assurance from output correctness to process transparency—what counts is not only that an agent did the right thing but that we can show how and under what policy it acted.

4.7 Workforce Transition: From Task Execution to Meta-Work

Automation anxiety is real, but the pattern is more nuanced: repetitive clerical tasks recede, while meta-work grows. Critical roles include:

Policy Designer: codifies organizational values, risk appetite, and service standards into machine-interpretable rules.
Agent Orchestrator: composes agent roles, tools, and data access with guardrails; tunes reward structures and monitors drift.
Evaluator/Auditor: develops and runs test suites; interprets failure modes; leads corrective action and post-incident reviews.
Human Experience Lead: curates moments where human empathy matters most (grief travel, medical trips, once-in-a-lifetime journeys) and designs graceful handoffs from agents to people.

Tourism firms that invest in these capacities not only reduce risk but also differentiate on trust and care.

4.8 Equity and Voice: Who Gets to Encode the Rules?

Encoding organizational policy into agents raises questions of voice. Whose norms are embedded in itinerary recommendations? Whose risk preferences govern overbooking and cancellations? World-systems dynamics warn that core actors may universalize standards reflecting their own priorities. Inclusive governance requires:

Multistakeholder Standard-Setting: hospitality associations, destination communities, disability advocates, and small providers participating in rule design.
Localized Evaluation Data: test suites reflecting linguistic, cultural, and infrastructural diversity rather than a narrow set of destinations.
Appeal Mechanisms: travelers and providers can challenge decisions and request human review.

Without deliberate design, AI may re-inscribe asymmetries under a veneer of neutrality.

4.9 A Typology of Autonomous Agent Roles in Services

To make autonomy tractable, organizations separate agent capabilities into roles:

Planner: translates goals and constraints into task graphs.
Retriever/Researcher: gathers and validates relevant information and inventory.
Negotiator: interfaces with partner systems or providers to reconcile rules and prices.
Executor: performs concrete actions (bookings, refunds) subject to thresholds.
Monitor: watches KPIs and policy compliance; proposes corrective actions.
Explainer: generates human-readable rationales and audit artifacts.

Each role has specific evaluation metrics (e.g., planning validity, retrieval precision, negotiation success rate, execution error rate, time-to-resolution, explanation adequacy). Governance attaches risk tiers and human checkpoints to these roles.

4.10 Responsible Autonomy: Principles for Management and Tourism

Drawing across the analysis, responsible deployment rests on six principles:

Purpose and Proportionality: use autonomy where it materially improves outcomes and does not degrade dignity or fairness.
Auditability by Design: collect structured logs, decisions, and rationales; adopt independent review.
Human-Centered Escalation: ensure clear pathways to compassionate human support at critical moments.
Data Minimization and Consent: limit data to what is needed; provide accessible controls and redress.
Inclusive Standards: co-create evaluation suites with diverse stakeholders, especially destinations and small providers.
Capability Building: invest in workforce transition to policy, orchestration, and evaluation roles.

5. Findings

This interpretive study yields three core findings about the present wave of AI autonomy:

Finding 1: Convergent Governance via Isomorphism. Under uncertainty, organizations adopt similar governance templates for autonomous agents—role separation, tiered risk controls, human-in-the-loop thresholds, and standardized logs. Coercive pressures (regulation, contracts), mimetic tendencies (copying perceived best practice), and normative forces (professional training) jointly produce this convergence. The effect is beneficial for interoperability and safety but risks stifling contextual innovation unless firms deliberately reserve zones for experimentation.

Finding 2: Redistribution of Field Capital. Autonomous AI elevates the value of algorithmic capital—the capacity to script, evaluate, and govern agents—and reduces the premium on routine coordination. Actors who can translate tacit service standards into executable policy, and who can integrate social networks of providers via APIs, accumulate symbolic capital (trust, reliability) which converts into economic returns. Conversely, organizations that treat autonomy as a “set-and-forget” tool without building internal orchestration and evaluation capabilities face reputational risk.

Finding 3: Stratified Benefits in the Global System. The geography of AI value creation largely follows world-systems patterns, concentrating design, evaluation, and governance functions in core regions, while pushing execution layers outward. Nevertheless, tourism destinations in semi-peripheral contexts can leverage AI for targeted growth if they develop local data alliances, training pipelines for evaluators and orchestrators, and a voice in standards. Without such investments, peripheral actors risk dependency on opaque platform rules.

A cross-cutting implication is that human experience design remains pivotal. Travelers judge not only the efficiency of agents but also the empathy and fairness of outcomes—especially under stress. Organizations that combine rigorous auditability with thoughtful escalation and inclusive rule-making will create durable advantage.

6. Conclusion and Managerial Implications

Autonomous AI agents are re-wiring the managerial and tourism landscapes. This transformation is not merely technological; it is sociological and geopolitical. Through Bourdieu’s lens, we see a revaluation of capitals that rewards organizations capable of codifying and governing service standards as machine-interpretable policy. Through world-systems theory, we recognize the centripetal pull of value toward core regions and the need for strategic capability building to avoid dependency. Through institutional isomorphism, we understand why governance structures converge and how that convergence both reduces risk and narrows the space for local experimentation.

For managers and tourism leaders, the practical path forward involves five commitments:

Invest in Algorithmic Capital. Build internal capability for agent orchestration, evaluation, and policy design. Treat logs and evaluation suites as strategic assets.
Adopt Tiered Governance. Separate agent roles, attach risk tiers, and specify escalation thresholds. Make auditability non-negotiable.
Design for Human Moments. Map journeys to identify points requiring empathy and discretion; guarantee fast, dignified access to a human.
Shape and Share Standards. Participate in cross-industry efforts to define fair evaluation data, transparency norms, and appeal mechanisms—especially to include smaller providers and diverse destinations.
Support Workforce Transition. Retrain staff for meta-work roles and create new career paths around AI policy, orchestration, and audit. Pair this with ethical commitments to fairness and accessibility for travelers and communities.

If autonomy is to elevate—not erode—the quality of service and the dignity of work, then governance, evaluation, and inclusive design must be considered first-class features, not afterthoughts. Done well, autonomous agents can support more responsive, resilient, and humane systems of management and tourism—where efficiency is matched by accountability, and personalization is matched by fairness.

Hashtags

#AIinManagement #TourismTech #InstitutionalIsomorphism #Bourdieu #WorldSystems #ResponsibleAI #ServiceInnovation

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