From Generative AI to Agentic AI: What the New Wave of Intelligent Systems Means for Management, Tourism, and Global Competition

One of the most discussed technology developments in April 2026 is the rapid move from general generative AI tools toward “agentic AI,” meaning systems that do not only produce text or images but can also plan tasks, coordinate actions, use tools, and support decision processes across workflows. Recent business and travel reporting suggests that organizations are no longer asking only whether AI can help with content creation; they are asking whether AI agents can reshape operations, customer service, planning, procurement, pricing, and knowledge work itself (Reuters, 2026a; Reuters, 2026b; McKinsey, 2026; Skift, 2026a). This article examines the rise of agentic AI as a management, tourism, and technology phenomenon. It argues that the importance of agentic AI is not only technical. It is social, organizational, and geopolitical. To understand this shift, the article uses three theoretical lenses: Bourdieu’s concepts of field, capital, and habitus; world-systems theory; and institutional isomorphism. The article adopts a qualitative interpretive review method, combining recent industry developments with established academic theory and prior research on digital transformation, platform power, automation, and organizational change. The analysis shows that agentic AI is becoming a new source of symbolic, technical, and organizational capital. It is also deepening uneven global dependence on core digital infrastructures while pushing firms and universities toward similar models of governance, compliance, and adoption. In management, agentic AI may move firms from dashboard-driven observation to action-oriented coordination. In tourism, it may change how travel is searched, bundled, sold, and serviced. In technology strategy, it may strengthen platform concentration while also creating room for smaller specialized innovators. The article concludes that agentic AI should not be understood simply as a software upgrade. It represents a new stage in organizational competition where legitimacy, trust, integration, and control over data ecosystems matter as much as model quality. For managers, educators, and policy observers, the central challenge is not whether agentic AI will spread, but how institutions will govern it without losing human judgment, accountability, and strategic autonomy.

Introduction

Every few months, a new technology phrase becomes popular. Many disappear quickly. Some remain and begin to shape strategy, investment, organizational design, and public debate. In April 2026, one of the clearest examples of such a phrase is agentic AI. Recent coverage across enterprise technology and travel points to a visible shift: organizations are moving from experiments with generative AI toward more integrated systems that can perform multi-step tasks, coordinate across software environments, and support operational execution rather than only generate outputs (McKinsey, 2026; Reuters, 2026a; Skift, 2026a; Skift, 2026b). In simple terms, the conversation is moving from “AI that writes” to “AI that works.”

This matters for several reasons. First, management practice is changing. For decades, managers have used dashboards, reports, and enterprise systems to observe the business. Agentic AI introduces the possibility of systems that not only inform decisions but help enact them: preparing procurement options, coordinating documents, supporting financial workflows, optimizing schedules, and responding to customers with more autonomy (Reuters, 2026b). Second, tourism and travel are becoming a major field of experimentation. The travel industry has always been shaped by information systems because it depends on search, trust, bundling, timing, inventory, and coordination across many actors. Recent reporting shows a growing belief that AI agents may affect trip planning, service recovery, personalization, and even the distribution structure of travel markets (Skift, 2026a; Skift, 2026b; PhocusWire, 2026). Third, this shift is not evenly distributed. Access to infrastructure, data, talent, and digital ecosystems remains concentrated. As a result, the spread of agentic AI may reinforce older global hierarchies even while it appears to democratize capability.

This article asks a broad but important question: What does the rise of agentic AI mean for management, tourism, and global competition when viewed through major sociological and organizational theories? To answer this question, the article takes a theoretical and interpretive approach. It does not test a single hypothesis with a large dataset. Instead, it uses current developments as an entry point to build a structured argument. Three theoretical perspectives are especially useful here.

The first is Bourdieu’s theory of field, capital, and habitus. Bourdieu helps explain why new technologies matter not only because of performance but because they become forms of distinction, legitimacy, and power. AI adoption is not simply technical. It is connected to status, symbolic value, and the ability of actors to position themselves advantageously in a competitive field.

The second is world-systems theory. This lens helps explain why technology waves are rarely neutral. Digital infrastructures, cloud capacity, compute resources, and model ecosystems are unevenly distributed. Core actors often capture more value, while semi-peripheral and peripheral actors depend on external platforms, standards, and technical architectures.

The third is institutional isomorphism, especially as developed by DiMaggio and Powell. This perspective helps explain why organizations often become more alike when facing uncertainty, professional norms, regulatory pressures, and imitation. When firms, universities, tourism operators, and public bodies all begin adopting similar AI governance frameworks and workflows, isomorphism offers a powerful explanation.

The article is timely because the current discussion is no longer limited to future possibilities. Reporting this month indicates that enterprises are actively building agentic infrastructure, major software providers are redesigning products around agents, and the travel sector is moving from AI experimentation toward orchestration, integration, and control over customer access (McKinsey, 2026; Reuters, 2026a; Reuters, 2026b; Skift, 2026a; PhocusWire, 2026). Yet much of the current conversation remains either promotional or narrowly technical. What is often missing is a deeper academic interpretation that connects technology with institutions, markets, and social power.

This article aims to fill that gap in simple but scholarly English. It is written for readers interested in management, tourism, higher education, and digital strategy. It argues that agentic AI is best understood as a new organizing logic. It changes how work is coordinated, how legitimacy is produced, how firms imitate each other, and how global asymmetries are reproduced. The central claim is not that agentic AI will replace human management. Rather, it will change what management means. It will also change who holds advantage in tourism and technology ecosystems. In that sense, the question is larger than software. It concerns the future of organizational authority, operational trust, and strategic autonomy in a world increasingly structured by intelligent systems.

Background and Theoretical Framework

Agentic AI as a New Stage of Digital Transformation

Generative AI attracted global attention because it made machine output visible, accessible, and surprisingly fluent. It could write, summarize, translate, and respond in natural language. Yet many organizations soon discovered that useful business value depends on more than fluent outputs. It depends on integration with internal data, process logic, permissions, workflows, and governance. Recent industry writing suggests that this is exactly why the discussion has shifted toward agentic AI: systems capable of planning, reasoning across steps, using enterprise tools, and helping turn intention into action (McKinsey, 2026; Reuters, 2026a; Reuters, 2026b).

This shift follows a broader pattern in digital transformation research. Earlier studies of enterprise systems, platforms, and automation showed that value does not come from the technology alone but from its fit with routines, structures, and capabilities (Bharadwaj et al., 2013; Vial, 2019). In tourism, digital transformation has long involved booking systems, online intermediaries, review platforms, data personalization, and service automation (Buhalis & Law, 2008; Gretzel et al., 2015). Agentic AI extends this trajectory. It may not remove existing structures; instead, it may intensify them by making coordination faster, more predictive, and more centralized.

Bourdieu: Field, Capital, and Habitus

Pierre Bourdieu’s work is useful because it shows that social life is organized through fields in which actors compete for different forms of capital: economic, cultural, social, and symbolic (Bourdieu, 1984; Bourdieu, 1986). A field is a structured space of positions, such as higher education, tourism, management consulting, or enterprise technology. Actors in a field compete according to rules that are partly explicit and partly taken for granted. Their dispositions, or habitus, shape how they perceive opportunities and respond to change.

Applied to agentic AI, Bourdieu helps answer several questions. Why are organizations eager to present themselves as AI-ready? Why do some firms receive prestige simply by appearing early in adoption? Why do consultants, technology firms, universities, and tourism brands all seek to signal competence in AI? The answer is that AI capability has become a new form of capital. Technical capacity is one part of it, but not the only part. There is also symbolic capital: the power to be seen as innovative, future-ready, efficient, or globally relevant.

This matters in management because the adoption of agentic AI is already shaping field positions. Firms with strong data systems, recognized brands, strategic partnerships, and digital talent can convert those advantages into new AI-related capital. Meanwhile, firms with weaker internal structures may attempt symbolic compensation, using public language about AI transformation even before they achieve real operational integration. In tourism, where reputation and trust are central, the symbolic use of AI may become especially important. A travel company may use AI not only to improve service but to project modernity, convenience, and responsiveness.

Bourdieu also helps explain resistance. Habitus is durable. Managers formed in earlier eras may see AI as a support tool rather than an organizing layer. Professionals whose identity depends on expert judgment may resist systems that seem to reduce autonomy. Thus, agentic AI enters fields already shaped by unequal resources and established dispositions. It does not arrive in a vacuum.

World-Systems Theory

World-systems theory, especially associated with Wallerstein, emphasizes the unequal structure of the global economy. The world is divided into core, semi-peripheral, and peripheral zones, with different levels of control over capital, production, and value capture (Wallerstein, 1974). Although the theory emerged in relation to historical capitalism, it remains useful for thinking about digital systems. Cloud infrastructure, chips, foundational models, data platforms, and enterprise software ecosystems are not evenly distributed. They are concentrated in relatively few countries, firms, and technical networks.

From this perspective, agentic AI can be read as part of a new digital division of labor. Core actors build the infrastructures, define standards, control interfaces, and capture large shares of value. Semi-peripheral actors may adapt, localize, and integrate. Peripheral actors often consume, depend, and pay. This does not mean that innovation cannot emerge outside the core. It can, especially in domain-specific or region-specific applications. But dependence on external compute, cloud services, or platform access often limits autonomy.

This is highly relevant to tourism and management. Tourism is a global sector, but much of its digital architecture is controlled by large intermediaries, platforms, and infrastructure providers. If AI agents become a new gateway between travelers and suppliers, then control over these agents may become a strategic bottleneck. Recent travel reporting already suggests concern that the future of travel may depend on who controls trust, data, and customer access rather than technology in the abstract (PhocusWire, 2026). World-systems theory helps us see why this concern is structural. Agentic AI may deepen centralization under the language of convenience.

Institutional Isomorphism

Institutional isomorphism explains why organizations facing uncertainty often become more similar over time (DiMaggio & Powell, 1983). They do so through three main mechanisms: coercive, mimetic, and normative pressures. Coercive pressures come from regulation, funders, or powerful partners. Mimetic pressures arise when organizations imitate others in uncertain environments. Normative pressures come from professional norms, expert communities, and shared training.

All three mechanisms are visible in the current AI wave. Coercive pressures include data governance requirements, procurement rules, safety frameworks, and sector-specific regulation. Mimetic pressures are strong because many organizations are unsure what successful AI adoption looks like. They imitate visible leaders, industry templates, or vendor playbooks. Normative pressures appear through consultants, professional bodies, academic discourse, and management education, which define what “responsible AI” or “AI maturity” should mean.

Isomorphism is especially powerful in higher education, hospitality, and corporate management. Universities increasingly develop similar AI policies. Hospitality groups explore similar automation and personalization tools. Enterprises publish similar transformation language. This does not mean organizations are identical in practice, but it does mean that similar forms, committees, and governance rituals spread quickly. Agentic AI may therefore become institutionalized not only because it is efficient, but because it becomes expected.

Method

This article uses a qualitative interpretive review method. It is not a laboratory study and does not rely on primary interview data. Instead, it synthesizes theory, prior academic scholarship, and current developments in order to produce an analytically grounded argument about a rapidly evolving topic. This method is appropriate when a phenomenon is new, conceptually significant, and not yet fully stabilized in empirical literature.

The material used in the analysis comes from three layers.

First, the article draws on established academic literature in sociology, organizational studies, digital transformation, platform studies, and tourism technology. This includes classical theoretical texts by Bourdieu, Wallerstein, and DiMaggio and Powell, as well as more recent scholarship on digital transformation, algorithmic management, platforms, and tourism innovation.

Second, it uses recent industry and business reporting from April 2026 to identify the current shape of the debate around agentic AI. Recent sources indicate several patterns: firms are building infrastructures for agentic systems; software providers are redesigning enterprise tools around AI-assisted workflows; travel companies are shifting from AI pilots toward operational integration; and strategic concern is growing around data control, trust, and fragmentation (McKinsey, 2026; Reuters, 2026a; Reuters, 2026b; Skift, 2026a; Skift, 2026b; PhocusWire, 2026). These sources are not treated as final truth. Rather, they are treated as indicators of discourse, market movement, and field-level framing.

Third, the article develops an analytical interpretation by reading these current developments through the selected theories. In this sense, the method is abductive. It moves back and forth between theory and contemporary evidence. Instead of asking whether one theory can fully explain the phenomenon, it asks what each theory reveals and where their insights overlap.

There are limitations. Because agentic AI is evolving quickly, some current examples may change. Promotional industry language may overstate maturity. Also, the article does not claim that all sectors or regions experience AI adoption in the same way. However, these limitations do not remove the value of interpretive analysis. On the contrary, they make such analysis useful, because periods of uncertainty are exactly when organizations need deeper conceptual understanding.

Analysis

1. From Assistance to Coordination

A key analytical point is that agentic AI changes the organizational meaning of AI. Early generative AI adoption was largely assistive. It helped users draft text, summarize information, or generate ideas. Agentic AI promises something more coordinated: systems that can manage sequences, interact with tools, and support process completion. This difference matters because it shifts AI from the edge of work toward the center of workflow design.

In management terms, this means AI may increasingly affect middle layers of coordination rather than only front-end productivity. Scheduling, procurement support, knowledge retrieval, customer handling, meeting preparation, and internal compliance tasks are all examples where multi-step systems can generate value. Recent enterprise reporting reflects exactly this movement, describing a shift from isolated AI features toward integrated “agentic” applications and data architectures (McKinsey, 2026; Reuters, 2026b).

From a Bourdieuian angle, this changes the forms of capital that matter. The winners are not simply those with access to language models, since such access is becoming more widespread. The winners are those who can integrate models with data quality, internal systems, process authority, and organizational trust. In other words, the real capital lies in orchestration capacity. Symbolic capital still matters, but durable advantage increasingly depends on field-specific operational capital.

2. Agentic AI and the Reorganization of Managerial Authority

Management has often been described as the art of planning, organizing, leading, and controlling. Digital tools already transformed these functions, but agentic AI may reshape them further. When intelligent systems can suggest actions, prepare alternatives, trigger workflows, and interact with multiple systems, managerial authority becomes more hybrid.

This does not necessarily reduce the importance of managers. Rather, it may change their role. Managers may spend less time collecting information and more time setting rules, validating judgments, handling exceptions, and governing system behavior. This resembles the logic of algorithmic management seen in logistics and platform labor, where human oversight remains but is reorganized around targets, systems, and exceptions (Möhlmann & Zalmanson, 2017; Kellogg et al., 2020).

Institutional isomorphism helps explain why many firms now present AI governance as a strategic necessity. Once competitors begin adopting agentic systems, and consultants define maturity models, non-adoption starts to look like weakness. Managers then face not only a technical choice but a legitimacy challenge. They must show boards, customers, regulators, and employees that the organization is modern, responsible, and capable. Similar governance structures spread: AI committees, policy documents, review processes, sandbox experiments, and vendor frameworks. These structures may be useful, but they are also ceremonial in the institutional sense. They signal seriousness.

3. Tourism as a Strategic Test Case

Tourism is a particularly revealing field for agentic AI because it combines high information intensity with strong emotional, logistical, and reputational dimensions. Travelers compare options, manage budgets, react to uncertainty, and seek convenience. Providers coordinate flights, hotels, local services, pricing, customer communication, and problem resolution. The sector has long been transformed by digital intermediaries, review systems, and mobile access (Buhalis & Law, 2008; Gretzel et al., 2015).

Recent travel industry reporting suggests that AI is now moving from simple content generation toward orchestration and market control. Discussions this month highlight integration, planning, and the possibility that AI systems may become a new interface between traveler and supplier (Skift, 2026a; Skift, 2026b; PhocusWire, 2026). If so, agentic AI may influence not only operations but distribution power.

World-systems theory is especially useful here. Tourism often appears local because it concerns destinations, hotels, attractions, and experiences. Yet its digital infrastructure is global. Search engines, booking systems, cloud platforms, payment networks, and software layers are mostly controlled by large actors. If agentic AI becomes the new point of entry for trip planning, recommendation, and booking, local tourism firms may become even more dependent on external platforms that mediate demand. A hotel in a peripheral destination may have excellent service, but if access to customers is filtered through core digital infrastructures, value capture remains unequal.

At the same time, agentic AI can also create opportunities. Smaller operators may use AI tools to improve multilingual communication, automate routine support, optimize pricing, and enhance itinerary design. This is especially important for tourism SMEs, which often struggle with limited staff and fluctuating demand. The key question is whether AI will decentralize capability or recentralize market power. The likely answer is both, but unevenly.

4. Symbolic Power and the Performance of Innovation

One reason agentic AI spreads quickly is that organizations perform innovation publicly. Announcements, partnerships, pilot projects, and strategy documents all contribute to symbolic positioning. Bourdieu’s framework helps us see this clearly. In competitive fields, actors seek distinction. Being seen as advanced matters. This is why AI language now appears not only in technology firms but in consulting, hospitality, education, finance, and public administration.

The symbolic dimension is not superficial. It affects investment, hiring, customer trust, and elite attention. However, symbolic capital can also hide weak foundations. Many organizations may use the language of agentic transformation before they possess the data governance, process maturity, or staff training needed for safe implementation. This creates a gap between performance and capability.

In tourism, this gap may be risky because service quality is directly experienced. If an AI-driven travel system promises frictionless planning but fails during disruption, trust may decline. In management more broadly, weak implementation can lead to errors, over-automation, data leakage, or reputational damage. Recent reporting on agentic AI already emphasizes risk, permissions, security, and the need for human oversight (Reuters, 2026a; Reuters, 2026b). Thus, symbolic capital must eventually be converted into operational capital. Otherwise, legitimacy erodes.

5. The New Importance of Data, Trust, and Governance

A repeated theme in recent 2026 reporting is that AI value depends on infrastructure, governance, and orchestration, not only model power (McKinsey, 2026; Skift, 2026a). This fits strongly with earlier digital transformation research, which has long shown that successful technology adoption requires complementary assets and organizational change (Bharadwaj et al., 2013; Vial, 2019).

Agentic AI makes governance even more important because it acts closer to execution. A summarization model can be wrong and still create limited harm. An agent that books, approves, routes, or communicates at scale can create much larger consequences. This raises key issues: Who authorizes the agent? What data can it access? When must a human intervene? How is performance monitored? What happens when objectives conflict?

Institutional isomorphism predicts that once these concerns become visible, organizations will converge around governance templates. We should expect more standardization in risk classification, model access levels, approval flows, audit trails, and staff training. This process is already beginning across sectors. Yet governance itself may become a source of inequality. Large firms can build sophisticated controls. Smaller organizations may depend on vendor-provided defaults and therefore surrender autonomy.

6. Higher Education, Knowledge Work, and the Formation of New Habitus

Although this article focuses on management, tourism, and technology, the implications for higher education are also significant. Universities and research-oriented institutions are not outside the AI shift. They train future managers, shape professional norms, and compete for relevance. As agentic AI spreads, educational institutions may feel pressure to revise curricula, assessment models, research training, and digital governance.

Bourdieu’s notion of habitus is helpful here. Future professionals are being socialized into an environment where delegating parts of cognition to systems may become normal. Students may increasingly treat AI tools as research assistants, writing partners, planning aids, and analytical supports. This does not necessarily reduce learning, but it does change what counts as competence. Memory, synthesis, prompt design, source evaluation, and ethical judgment may take new forms.

At the same time, institutional isomorphism suggests that universities may adopt similar policy language under pressure from accreditation, peer competition, and public expectation. This may create a surface of alignment without deep pedagogical agreement. Some institutions will integrate AI meaningfully into research and professional preparation. Others will mainly produce policy statements. The field of education thus mirrors the broader management field: legitimacy and capability do not always move at the same speed.

7. Global Stratification and Strategic Dependency

World-systems theory directs attention to the geography of AI power. Agentic systems require more than models. They require cloud capacity, developer ecosystems, enterprise integration, capital, and trusted interfaces. These are concentrated. As a result, many organizations outside the digital core may become consumers of agentic infrastructures designed elsewhere.

This is not a new pattern. Similar dependencies appeared with operating systems, cloud services, search engines, and digital advertising. But agentic AI may deepen the issue because it sits closer to operational decision-making. If a tourism chain, university, or logistics firm relies on external AI agents for mission-critical workflows, it may lose bargaining power and strategic visibility. Dependence becomes not just technical but organizational.

Still, semi-peripheral actors are not passive. They may build specialized applications in local languages, region-specific tourism contexts, regulatory niches, or sector-focused services. The most likely path for many organizations is not full independence but selective capability. They will depend on core infrastructures while differentiating through domain expertise, trust, and local adaptation. This is an important finding because it suggests that not every actor must become a frontier model builder. Strategic intelligence may lie in choosing where dependence is acceptable and where autonomy is essential.

Findings

The analysis generates several main findings.

First, agentic AI is becoming a new organizational logic rather than just a new software feature.

The current shift is from isolated generation toward coordinated execution. This makes AI relevant to workflow design, management structure, and service delivery rather than only personal productivity (McKinsey, 2026; Reuters, 2026b).

Second, the rise of agentic AI changes what counts as valuable capital in competitive fields.

Using Bourdieu’s framework, the most important capital is increasingly not access to AI alone but the ability to combine technical systems with trustworthy data, process authority, organizational legitimacy, and field-specific knowledge. Symbolic capital remains powerful, but operational capital becomes decisive.

Third, tourism is likely to be one of the sectors most visibly transformed by agentic AI.

Because tourism relies on complex coordination, personalization, timing, and trust, AI agents may affect trip planning, customer support, disruption management, and market access. However, benefits for local operators may coexist with stronger dependence on large digital intermediaries (Skift, 2026a; PhocusWire, 2026).

Fourth, institutional isomorphism helps explain the speed of adoption discourse.

Organizations are not adopting AI only because it is efficient. They are also reacting to uncertainty, professional norms, competitive imitation, and governance expectations. This leads to convergence in language, policy, and structures even when practical capability differs.

Fifth, world-systems theory reveals that agentic AI may deepen global asymmetries.

The infrastructure behind AI agents remains concentrated. This means that many organizations in semi-peripheral and peripheral contexts may improve capability while becoming more dependent on external platforms and standards. The benefits of AI may spread, but control over value capture may remain uneven.

Sixth, governance is becoming the central strategic question.

As AI moves closer to execution, questions of permissions, accountability, transparency, and human oversight become more important. This applies in management, tourism, education, and public administration alike.

Seventh, the future role of managers is being redefined.

Managers are less likely to disappear than to become governors of hybrid systems. Their work may shift from direct coordination of every task toward supervision of decision environments, exception handling, ethical judgment, and institutional trust.

Conclusion

Agentic AI is one of the most important technology discussions of April 2026 because it marks a deeper transition in the meaning of digital intelligence. The central issue is no longer whether machines can generate useful content. It is whether organizations can embed intelligent systems into real workflows, decision structures, and customer relationships. That is why the topic matters not only for software developers but for managers, tourism professionals, educators, and policymakers.

This article has argued that agentic AI should be understood through multiple layers. Technically, it expands what AI systems can do. Organizationally, it shifts value from isolated tools to integrated processes. Sociologically, it creates new forms of capital, distinction, and legitimacy. Institutionally, it spreads through imitation, norms, and pressure. Globally, it risks reinforcing unequal dependence on concentrated digital infrastructures.

The three theoretical lenses used here each add something essential. Bourdieu shows that AI adoption is a struggle within fields, where symbolic and operational capital matter. World-systems theory shows that digital transformation unfolds in an unequal global order, where some actors define the rules and others adapt to them. Institutional isomorphism shows that organizations become similar under uncertainty, producing rapid convergence in AI governance language and strategic signaling.

For management, the lesson is clear: the value of agentic AI depends less on excitement and more on data quality, process design, authority structures, and trust. For tourism, the lesson is equally important: AI can improve service and efficiency, but it may also reshape who controls the customer relationship. For higher education and knowledge institutions, the challenge is to prepare professionals who can work with intelligent systems without surrendering judgment, ethics, and critical thinking.

A simple conclusion follows. Agentic AI is not just about doing old tasks faster. It is about changing how institutions coordinate action, claim legitimacy, and compete. The organizations that benefit most will not be those that merely announce AI strategies. They will be those that build responsible capability, understand their dependence, protect their autonomy where needed, and keep human accountability at the center.

In the coming years, many organizations will adopt the language of agentic transformation. Some will do so deeply and intelligently. Others will imitate without preparation. The difference between these paths will shape not only competitiveness, but also fairness, trust, and institutional resilience. That is why agentic AI deserves careful academic attention now. It is already becoming part of the structure of contemporary management and global service economies.

Hashtags

#AgenticAI #ManagementInnovation #TourismTechnology #DigitalTransformation #InstitutionalChange #AIandSociety #StrategicManagement

References

• Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482.

• Bourdieu, P. (1984). Distinction: A Social Critique of the Judgement of Taste. Harvard University Press.

• Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education (pp. 241–258). Greenwood.

• 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.

• DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.

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

• Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.

• McKinsey. (2026). Building the foundations for agentic AI at scale. Article.

• Möhlmann, M., & Zalmanson, L. (2017). Hands on the wheel: Navigating algorithmic management and Uber drivers’ autonomy. In Proceedings of the International Conference on Information Systems.

• PhocusWire. (2026). Envisioning travel in 2046: Industry leaders map the future. Article.

• Reuters. (2026a). Agentic AI: Greater capabilities and enhanced risks. Article.

• Reuters. (2026b). Oracle reworks its suite of cloud software as “agentic apps”. Article.

• Skift. (2026a). AI infrastructure will define travel’s next competitive era. Article.

• Skift. (2026b). Travel is facing a new test: AI fragmentation. Article.

• Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144.

• Wallerstein, I. (1974). The Modern World-System. Academic Press.