Embodied AI forces firms to redesign how value is created: business models shift from one-off hardware sales toward continuous, data-driven orchestration across ecosystems. That shift creates tensions—openness versus control, scaling versus local fit, automation versus reliability, and monetization versus trust—that firms must manage.

Main Finding

Embodied AI—AI physically embedded in machines that sense, act, and learn in situ—fundamentally transforms business models. It creates a “double loop” of learning (an internal, closed operational loop inside the adopting firm and an external, open loop across an ecosystem of hardware/software providers, platforms, and users), shifting value creation from discrete asset sales toward ongoing, data-driven workflows, lifecycle orchestration, and recurring, trust-dependent monetization. The authors develop two complementary conceptual models (a transition model and an integrative embodied-AI business-model grid), derive nine theoretical propositions, and identify four systemic tensions shaping business-model outcomes. Agriculture is used as a revealing template to illustrate these dynamics.

Key Points

• Definition: Embodied AI is intelligence embedded in physical systems (robots, machinery) whose cognition emerges through continuous sensing–decision–action–learning cycles in the real world; distinct from decision‑support or merely connected IoT devices by its closed-loop adaptation and enacted actions.
• Double loop:
  • Internal closed loop: the adopting firm gains operational feedback, improving routines and workflow via machine-mediated learning.
  • External open loop: data produced in use flows to ecosystem actors (OEMs, software/cloud providers, platform orchestrators, component suppliers), enabling system updates, algorithmic improvement, and service innovation beyond the firm.
• Business-model shift:
  • From asset/episodic logics (one-time equipment sales) to adaptive, integrated, service- and platform-oriented models (subscriptions, outcome/performance-based contracts, lifecycle orchestration).
  • Value creation moves from predefined product performance to continuous workflow optimization and context-dependent performance.
  • Value delivery depends on tightly coupled activities across organizations (content, structure, governance); value capture hinges on control of data, access to learning, and monetization rights.
• Integrative framework: extends traditional business-model/activity-system lenses (value creation/delivery/capture × activity content/structure/governance) to capture continuous interaction and interorganizational learning central to embodied AI.
• Actors/market dynamics: incumbent OEMs can consolidate power by bundling hardware, proprietary software, analytics, and services; startups, agribusinesses, and platforms play complementary roles; business models become ecosystem-level rather than firm-contained.
• Four systemic tensions:
• Openness versus control (data sharing and interoperability vs. proprietary control and lock‑in).
• Scaling versus local fit (benefits of scale and shared learning vs. context-specific adaptation needs).
• Automation ambition versus reliability constraints (desire for higher autonomy vs. safety/accuracy limits in complex environments).
• Monetization versus trust (extracting recurring revenue from operational data vs. farmer/user concerns about privacy, fairness, and liability).
• Agriculture as template: embodied AI enables selective spraying, autonomous harvesting, predictive inputs—illustrating how traditional, asset-heavy industries adopt hybrid product–service–platform models, and how datafication raises questions of ownership, trust, and revenue sharing.

Data & Methods

• Type: Conceptual, theory-building study (no original empirical data).
• Approach: Iterative conceptual synthesis and theory adaptation following Jaakkola’s conceptual theorizing method. Domain literatures synthesized: embodied AI/robotics, AI-enabled business-model innovation, digital business models, and agricultural digitalization. Method-theory lenses: business-model components (Teece) and activity-system dimensions (content, structure, governance) from Zott & Amit.
• Outputs: Two complementary models (transition model of business-model shift; integrative embodied-AI business-model grid), nine propositions (logical/theoretical claims grounded in literature), and identification of four systemic tensions. Agriculture used as an illustrative/revealing context to surface mechanisms.
• Evidentiary basis: Logical argumentation grounded in extant literatures and illustrative industry examples (OEMs, agritech), not primary empirical testing; propositions intended for subsequent empirical validation.

Implications for AI Economics

• Property rights and data governance: Embodied AI makes operational data a primary asset. Who owns, controls, and monetizes in-use data determines rent allocation. Economists should study contractual forms (data-licensing, revenue-sharing, vertical integration) and the welfare implications of different property regimes.
• Market structure and market power: Vertical integration of hardware, software, analytics, and services by OEMs/platforms can create strong lock-in and multi-sided market power. Antitrust and competition analyses must consider data-driven switching costs and ecosystem control.
• Pricing and revenue models: Move from one-off sales to recurring/subscription, pay-per-use, outcome-based, or performance-linked pricing. Research should compare welfare and investment incentives across models, and analyze optimal pricing under asymmetric information about local performance.
• Investment incentives and coordination problems: Providers and adopters face complementarities and hold-up risks (asset specificity in physical machinery + data dependence). Modeling incentives for upfront hardware R&D versus ongoing software/learning investments is critical.
• Externalities and public goods: Shared learning across farms can generate positive externalities (faster model improvement) but also coordination failures (free-riding on data contributions). Policies or platforms that internalize externalities (data commons, co-ops, regulated sharing) merit evaluation.
• Risk, liability, and insurance economics: Embodied AI’s physical actions raise liability exposure and reliability concerns. Insurability, contract design for responsibility allocation, and regulation of safety standards will affect adoption and pricing.
• Productivity measurement and empirical strategy: Traditional productivity measures may understate embodied-AI benefits because gains accrue through workflow optimization, learning-by-use, and cross‑firm improvements. Empirical work should use panel data, diff-in-diff, instrumental variables, randomized trials, and network/ecoystem-level data to identify causal impacts of embodied AI on outcomes (yields, costs, labor displacement).
• Entry, innovation, and standards: Standards and interoperability affect rivalry and entry. Economists should study how standardization vs proprietary platforms shapes innovation rates, diffusion speed, and consumer surplus.
• Policy design: Data-governance rules, liability frameworks, and competition policy will shape market outcomes. Economists can inform policy choices by modeling trade-offs between innovation incentives (proprietary control) and broader social welfare from openness and shared learning.
• Suggested empirical agenda:
  • Measure how control over in-use data predicts firm market value, pricing, and entry barriers.
  • Compare performance and adoption across asset-sale vs service/outcome-based contracts.
  • Quantify spillovers from aggregated operational data (how much do models improve with added farm-level data?).
  • Evaluate welfare effects of different data property regimes and platform governance rules.

Overall, the paper reframes embodied AI as an interorganizational, learning-centered technology whose economics hinge on data flows, governance, and ecosystem coordination—pointing to a rich set of economic questions about ownership, pricing, market structure, incentives, and regulation that warrant empirical and theoretical follow‑up.