Agentic AI as a Strategic Capability in Service Economies: Evidence From Banking and Tourism
- OUS Academy in Switzerland
- 12 minutes ago
- 11 min read
Author: Issa Hassan
Affiliation: ISB Academy Dubai
Abstract
Agentic artificial intelligence—systems that can perceive context, reason with memory, call external tools, and act toward goals with varying degrees of autonomy—has rapidly moved from experimental demos to production roadmaps in service economies. This article reframes agentic AI not merely as a technological capability but as a field of power that redistributes capital (economic, social, cultural, and symbolic), reorganizes organizational isomorphism, and re-articulates core–periphery relations in global markets. Building on Bourdieu’s theory of capital and fields, world-systems analysis, and institutional isomorphism, I analyze how agentic AI reconfigures decision rights, risk, and value capture in two emblematic service sectors: banking and tourism. I advance (1) a sociotechnical capability stack for agentic AI, (2) a governance and assurance framework oriented to procedural justice and fairness over time, and (3) a mixed-methods research agenda capable of isolating productivity, quality, and equity effects. The contribution is a critical yet constructive account that treats agentic AI as both organizational technology and social institution, offering executives, regulators, and scholars a vocabulary and blueprint for responsible adoption.
Keywords: agentic AI, service economy, banking technology, travel and tourism, Bourdieu, world-systems, institutional isomorphism, governance, fairness, organizational learning
1. Introduction: From Assistants to Autonomous Workflows
Service economies—from retail banking to destination management—coordinate knowledge under uncertainty. For two decades, automation focused on rules and predictive analytics. Generative models broadened the frontier by transforming unstructured language and images into operational signals. Agentic AI extends this transformation by linking perception, reasoning, and action: agents plan tasks, orchestrate tools (databases, pricing engines, booking systems), critique their own output, and escalate to humans under uncertainty thresholds.
The promise is well rehearsed: fewer queues, faster approvals, personalized itineraries, fewer operational backlogs. But this article argues that the stakes are higher and more structural. Agentic AI is reconstituting who holds what kinds of capital, how organizations show similarity under institutional pressures, and how value and risk travel across the core–periphery geography of the world economy. The shift is not just what we can automate but who becomes legitimate to decide, supervise, audit, and profit.
Aims and Questions.
What sociotechnical capability stack is necessary for responsible agentic AI in services?
How does agentic AI redistribute forms of capital (economic, social, cultural, symbolic) across workers, firms, and customers?
How do institutional and world-system pressures shape trajectories of adoption in banking and tourism?
What measurement strategies can separate productivity gains from quality, fairness, and legitimacy effects?
2. Theoretical Framework
2.1 Bourdieu: Capital, Field, and Habitus
Bourdieu posits that actors compete within fields for position and power using convertible forms of capital—economic (resources), cultural (credentials, know-how), social (networks), and symbolic (recognized legitimacy). Agentic AI enters organizations as both objectified cultural capital (codified best practices in prompts, policies, and playbooks) and as symbolic capital (a signal of modernity and competence). Its deployment can elevate technical and risk teams (who curate tools, policies, and logs) while devaluing routine clerical roles whose tacit practice becomes embedded in agentic workflows. Because capital is convertible, early adopters can transmute symbolic capital (“we are an AI-enabled bank/hotel group”) into economic capital (market share, revenue per customer) and back again (recruitment prestige, partnerships).
Implication. The frontier of advantage is not merely model accuracy but conversion rates among capitals: how cultural know-how and symbolic legitimacy crystallize into revenue and regulatory leeway.
2.2 World-Systems: Core, Periphery, and the AI Supply Chain
World-systems theory highlights structural inequalities in global production networks. Agentic AI ecosystems instantiate a new “core” in model and infrastructure providers, while many service firms—especially in the global periphery or semi-periphery—consume models and tools with limited bargaining power. Data flows (customer conversations, documents, itineraries) may travel to core infrastructure where value capture concentrates. Tourism, a sector frequently situated in peripheral or seasonal economies, risks becoming a raw-data exporter while paying rents to core platform providers. Banking, especially in emerging markets, may similarly depend on imported risk models and guardrails, modifying exposure to regulatory sovereignty.
Implication. Strategy in periphery contexts should focus on data localization, shared utilities (sectoral model governance), and negotiated standards that preserve a fair share of value capture.
2.3 Institutional Isomorphism: Coercive, Mimetic, Normative
DiMaggio and Powell describe how organizations converge in structure under coercive (regulatory), mimetic (uncertainty-driven imitation), and normative (professionalization) pressures. Agentic AI accelerates isomorphism: policy engines, audit logs, and human-in-the-loop checkpoints become standardized expectations. Vendor Blueprints and regulators’ consultation papers codify “what good looks like,” creating a template. Mimetic pressures are particularly strong in banking (fear of lagging on cost-to-income ratio) and in tourism (fear of missing personalization). Normative pressures arise as risk, audit, and data professionals articulate codes of practice and certifications.
Implication. While isomorphism can raise a baseline of safety, it may also dull experimentation or privilege the practices of core economies as “universal,” crowding out local knowledge.
3. Agentic AI as a Sociotechnical Capability
3.1 A Six-Layer Capability Stack
Data Foundations: governed access to “AI-ready” data; lineage; privacy by design.
Reasoning and Models: frontier language models plus task-specific components; retrieval; planning; critique.
Tooling and Orchestration: secure tool catalogs (KYC, payments, CRM, revenue management, booking); workflow engines; cost/latency controls.
Safety and Governance: policy filters, thresholding, redaction, guardrails for tool use; immutable logs; appeal pathways.
Role Design and Multi-Agent Collaboration: planner, analyst, critic, compliance, and executor agents with shared memory and arbitration.
Experience and Change: UX for supervision (“explain–approve–amend”), capability envelopes, supervisor training, performance dashboards.
3.2 Capability Envelopes and Progressive Autonomy
Agents should operate within explicit capability envelopes—the set of actions they may take without approval, with conditional approval, or never. Progressive autonomy proceeds from advisory to constrained actions with automatic rollback, then to conditional autonomy under performance and drift monitoring. The envelope is a site of symbolic struggle: which functions (compliance, operations, marketing) win the right to set thresholds defines power in the field.
3.3 Instrumentation for Learning
To avoid “productivity mirages,” organizations need counterfactuals: what trained humans would have done, recorded in parallel during shadow mode. Such instrumentation converts cultural capital (tacit know-how) into objectified form (playbooks and prompts), preserving institutional memory as staff roles shift.
4. Banking: Compliant Personalization as Field Reconfiguration
4.1 Decision Archetypes and Agent Roles
Onboarding and KYC Triage: planner agents extract and validate documents; compliance agents enforce policy rules; escalation triggers for anomalies.
SME Credit Renewal: analyst agents reconcile financial statements with transaction graphs; risk tools compute exposure; compliance agents draft disclosures; humans approve.
Collections and Customer Care: negotiation agents propose hardship plans; fairness monitors ensure offer parity across comparable borrowers.
Fraud and AML Investigations: multi-agent teams cross-reference alerts, narrative summaries, and network graphs; auditors review immutable traces.
Each archetype maps to forms of capital: cultural capital (risk knowledge) is embedded in policies and prompts; social capital (RM networks) is augmented by customer-facing agents; symbolic capital (soundness) is staged through transparent rationales.
4.2 Redistribution of Capital and Labor
Agentic workflows decompose once-holistic banker tasks into supervision plus exception handling. Mid-career analysts who curate prompts, critique rationales, and sign-off drift become pivotal. This can elevate cultural capital among those adept at “prompt forensics,” while routinized documentation loses status. However, if design excludes frontline staff, tacit knowledge about local customers (a form of social capital) may be erased, yielding brittle decisions and reputational loss.
4.3 Fairness and Procedural Justice
Fairness must be a temporal commitment, not a one-time report. Monitoring equalized odds, false-positive differentials, and denial explanations over months is essential. Procedural justice—clear reasons, accessible appeals, timely remediation—matters as much as statistical parity. Banking’s legitimacy hinges on visible due process, thus explanations should reference policy and data lineage, not merely model internals.
4.4 Metrics and Causality
Operational: time-to-decision, rework rate, complaint-to-decision ratio.
Risk: net loss rates normalized by macro factors.
Fairness: disparities across protected and proxy groups, drift alarms.
Trust: appeal turnaround, reversal rates, customer comprehension tests.
Randomized branch-level rollouts and difference-in-differences against matched cohorts can separate agent effects from macro shifts. Such designs convert credibility (symbolic capital) into durable policy bargaining power.
5. Tourism and Hospitality: Adaptive Sensing and Experience
5.1 Event-Driven Revenue and Operations
Tourism thrives on volatile demand. Sensing agents read event calendars and transport capacity; pricing agents propose rate/inventory changes; experience agents assemble packages (transfers, tours, F&B); ops agents adjust staffing. Supervisors enforce caps and customer fairness norms.
5.2 Perceived Fairness and Symbolic Capital
Pricing power is bounded by social meaning. Even when revenue models are “correct,” customers may perceive opportunistic spikes as unfair. Symbolic capital (brand warmth) can erode if communications lack reasons. Agents should generate explainers (“city-wide conference; limited inventory; loyalty guarantee honored”) and offer goodwill gestures when thresholds are crossed. Hospitality is co-produced; thus agentic messages must allow authentic human rescue to prevent “automation theater.”
5.3 Core–Periphery Tensions
Destination operators in peripheral economies may depend on imported agent stacks and data centers. Without local capacity, they export behavioral data while importing pricing logic. A counter-strategy is cooperative infrastructure: regional alliances pool data under shared governance, train sector-specific retrieval corpora, and negotiate platform terms—thus reclaiming a share of economic and symbolic capital.
5.4 Metrics and Outcomes
Commercial: RevPAR uplift, conversion rate of curated bundles, length of stay.
Operational: staffing variance, response latency in guest messaging.
Fairness/Trust: complaint mix, recovery offers by segment, sentiment in reviews.
Cultural: inclusion of local suppliers in bundles (supporting community social capital).
6. Governance, Assurance, and Ethics
6.1 Ex Ante Controls
Capability Envelopes: enumerated actions and thresholds per agent; two-person rules for consequential moves (e.g., limit changes, high-impact pricing).
Policy Engines: codified rules for suitability, consent, and data minimization; role-based tool entitlements.
Scenario Libraries: adversarial prompts, tool-abuse simulations, rare-event tests; hospitality fairness scenarios (surge pricing during emergencies).
Model Cards & Data Sheets: document training data, evaluation limits, and intended use.
6.2 Ex Post Controls
Immutable Logs and Replayable Traces: support audit, incident response, customer appeals.
Counterfactual Explanations: “what would have happened with policy X or data Y.”
Drift and Cost Watch: alert on accuracy, disparity, and unit economics; trigger rollback.
Human Override Metrics: time-to-override, frequency, and reasons—used to refine capability envelopes.
6.3 Ethical Orientation: From Principles to Practices
Principles (beneficence, justice) gain force when attached to practices: redaction by default; opt-in personalization; tiered explanations for customers and auditors; no unbounded autonomy in financially or emotionally consequential contexts.
7. Organizational Dynamics: Ambidexterity and Learning
7.1 Dual Operating System
Exploration (sandboxed agent experiments) and exploitation (governed production) should run in parallel. This is not merely structural; it is cultural. Supervisors need training in failure taxonomies, prompt hygiene, and escalation. Organizational habitus—ingrained dispositions—will decide whether staff see agents as partners or threats.
7.2 Multi-Agent Specialization vs. Monolithic Agents
Specialized agents (planner, critic, compliance) with arbitration protocols generally outperform monoliths on traceability and failure isolation. Specialization also makes power legible: which agent vetoes whom, under what thresholds, and with what explanation.
7.3 Knowledge Stewardship
Organizations should treat prompts, playbooks, and red-team cases as objectified cultural capital. Versioning, peer review, and citation practices (crediting teams for improvements) sustain learning and morale.
8. Measurement and Research Design
8.1 Causal Inference at Scale
Randomized Controlled Rollouts: assign branches/properties to agent vs. human-only conditions.
Stepped-Wedge Designs: stagger adoption across units while measuring outcomes.
Difference-in-Differences: match units on pre-trends to estimate treatment effects.
Causal Mediation: decompose gains into retrieval quality, planning, and tool-use improvements.
8.2 Qualitative and Mixed Methods
Ethnography of supervisor–agent interaction, think-aloud studies of appeals handling, and content analysis of explanations can surface frictions invisible in dashboards. Participant observation captures how habitus meets agent affordances: who trusts, who resists, and why.
8.3 Equity and Temporal Fairness
Equity audits must be pre-registered with thresholds and remediation plans. Because bias fluctuates with data mix, measurement must be longitudinal, not episodic. In tourism, segment-wise dispersion of price and recovery gestures should be tracked through seasons; in banking, adverse action reasons should be summarized and communicated in accessible language.
9. Strategic Implications by World-System Position
9.1 Core Economies
Focus on procedural legitimacy and explainability standards, invest in interoperable logs and audit APIs, and export governance practices. Beware complacency: isomorphic comfort can ossify innovation.
9.2 Semi-Periphery
Leverage dual sourcing of models, localize retrieval corpora, and form regulatory sandboxes with neighboring markets. Develop regional assurance services that monetize cultural capital (local language and policy nuance).
9.3 Periphery
Prioritize data sovereignty and cooperative infrastructure. Negotiate with vendors for on-premise or region-bound inference, share audit artifacts across destination networks, and nurture local prompt/playbook communities to retain symbolic and social capital.
10. Toward a Pragmatic Blueprint
10.1 90–270 Day Roadmap
Preparation (30 days): map top ten decisions by value and risk; establish capability envelopes; audit data entitlements.
Pilot (60–90 days): one decision, one channel; shadow mode with counterfactual capture; red-team and scenario library creation.
Controlled Production (90 days): constrained actions with rollback; supervisor training; fairness dashboarding.
Scale (90–180 days): multi-unit rollout; cost/latency optimization; federated learning or retrieval where cross-site data sharing is restricted.
10.2 Nine Design Principles
Start with decisions, not models.
Codify capability envelopes.
Instrument counterfactuals.
Make compliance a first-class agent.
Blend conversational and structured I/O.
Prefer specialized multi-agent designs.
Use progressive autonomy.
Expose reasons, not only results.
Train supervisors as a distinct role.
11. Discussion: Legitimacy, Not Just Efficiency
Agentic AI will succeed when organizations win legitimacy in the eyes of customers, workers, and regulators. Efficiency is necessary but insufficient. The field of power is shifting: those who can translate between technical detail and institutional expectations will accrue symbolic capital that stabilizes adoption. Conversely, deployments that maximize short-term metrics while minimizing due process will generate backlash, regulatory friction, and erosion of brand meaning.
12. Conclusion
Agentic AI in service economies is best understood as a sociotechnical institution that reorganizes capital, standardizes governance, and reshapes global value chains. Banking demonstrates how compliant personalization can compress cycle times while demanding rigorous fairness over time. Tourism shows how adaptive sensing and packaging can lift revenue while depending on trust-building explanations and community inclusion. When treated as a field of power—rather than a mere toolkit—agentic AI invites strategies that convert cultural and symbolic capital into durable economic value without sacrificing justice or autonomy. The path forward is pragmatic: define envelopes, specialize roles, instrument learning, and commit to longitudinal fairness. In doing so, organizations can convert novelty into legitimacy—and legitimacy into sustainable advantage.
References / Sources
Ananny, M. & Crawford, K. 2018. ‘Seeing without knowing: Limitations of visual evidence in social media’, Big Data & Society, 5(2), pp. 1–15.
Athey, S. & Imbens, G. 2017. ‘The state of applied econometrics: Causality and policy evaluation’, Journal of Economic Perspectives, 31(2), pp. 3–32.
Bitran, G. & Caldentey, R. 2003. ‘An overview of pricing models for revenue management’, Manufacturing & Service Operations Management, 5(3), pp. 203–229.
Bourdieu, P. 1986. ‘The forms of capital’, in Richardson, J. (ed.) Handbook of Theory and Research for the Sociology of Education. New York: Greenwood, pp. 241–258.
Brynjolfsson, E. & McAfee, A. 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W. W. Norton.
Davenport, T.H. & Kirby, J. 2016. Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. New York: Harper Business.
DiMaggio, P. & Powell, W. 1983. ‘The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields’, American Sociological Review, 48(2), pp. 147–160.
Gawer, A. & Cusumano, M.A. 2014. ‘Industry platforms and ecosystem innovation’, Journal of Product Innovation Management, 31(3), pp. 417–433.
Giddens, A. 1984. The Constitution of Society: Outline of the Theory of Structuration. Cambridge: Polity Press.
Goodfellow, I., Bengio, Y. & Courville, A. 2016. Deep Learning. Cambridge, MA: MIT Press.
Holzinger, A. 2016. Interactive Machine Learning for Health Informatics. Cham: Springer.
March, J.G. 1991. ‘Exploration and exploitation in organizational learning’, Organization Science, 2(1), pp. 71–87.
Mittelstadt, B. 2019. ‘Principles alone cannot guarantee ethical AI’, Nature Machine Intelligence, 1(11), pp. 501–507.
Orlikowski, W.J. 2007. ‘Sociomaterial practices: Exploring technology at work’, Organization Studies, 28(9), pp. 1435–1448.
Parasuraman, A., Zeithaml, V.A. & Berry, L.L. 1988. ‘SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality’, Journal of Retailing, 64(1), pp. 12–40.
Pasquale, F. 2015. The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge, MA: Harvard University Press.
Selbst, A.D., Boyd, D., Friedler, S.A., Venkatasubramanian, S. & Vertesi, J. 2019. ‘Fairness and abstraction in sociotechnical systems’, in Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT)*. New York: ACM, pp. 59–68.
Shankar, V. 2018. ‘How artificial intelligence (AI) is reshaping retailing’, Journal of the Academy of Marketing Science, 48(1), pp. 24–42.
Star, S.L. & Ruhleder, K. 1996. ‘Steps toward an ecology of infrastructure: Design and access for large information spaces’, Information Systems Research, 7(1), pp. 111–134.
Teece, D.J. 2007. ‘Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance’, Strategic Management Journal, 28(13), pp. 1319–1350.
Varian, H.R. 2014. ‘Big data: New tricks for econometrics’, Journal of Economic Perspectives, 28(2), pp. 3–28.
Wallerstein, I. 1974. The Modern World-System I: Capitalist Agriculture and the Origins of the European World-Economy in the Sixteenth Century. New York: Academic Press.
Winner, L. 1986. The Whale and the Reactor: A Search for Limits in an Age of High Technology. Chicago: University of Chicago Press.
Zuboff, S. 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs.
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