Artificial Intelligence in IoT Market by Industry Vertical (Agriculture, Automotive, Energy & Utilities), Component Type (Connectivity Modules, Edge Devices, Platform), Connectivity Technology, Deployment Model, Application - Global Forecast 2025-2032
Description
The Artificial Intelligence in IoT Market was valued at USD 76.57 billion in 2024 and is projected to grow to USD 87.22 billion in 2025, with a CAGR of 14.21%, reaching USD 221.77 billion by 2032.
Establishing the strategic framework for integrating artificial intelligence into Internet of Things ecosystems to guide investment, operations, and partner decisions
Artificial intelligence is rapidly evolving from a specialized capability into an operational imperative across the Internet of Things ecosystem. Leaders must understand that AI in IoT does not merely add intelligence to devices; it reshapes data flows, decision-making hierarchies, and product lifecycles. This introduction frames the strategic lens through which executives should evaluate technological investments, organizational design, and partner selection, emphasizing that effective AI adoption requires alignment among sensors, connectivity, edge and cloud compute, and the analytic models that extract value.
In practical terms, the intersection of AI and IoT demands that stakeholders reconcile the technical nuances of device-level compute and sensor fidelity with higher-order considerations such as latency tolerance, privacy constraints, and lifecycle management. The narrative that follows highlights how AI-driven inference at the edge complements centralized analytics and how orchestration between these layers unlocks new operational efficiencies. As you read further, consider the implications for talent composition, procurement criteria, and vendor engagement strategies, since decisions made now will determine the pace at which organizations can commercialize use cases and scale secure, resilient IoT architectures.
How shifts in edge intelligence, sensor specialization, and connectivity diversification are redefining architectures, business models, and governance for AI-enabled IoT solutions
The AI-in-IoT landscape is undergoing transformative shifts that alter competitive dynamics, systems architecture, and value capture. One of the most consequential changes is the movement of intelligent processing from centralized cloud platforms toward distributed edge compute, which reduces latency, limits data egress, and enables real-time autonomy in devices. This trend coexists with advances in sensor miniaturization and specialization, enabling richer contextual signals and more robust model inputs. Consequently, organizations are rethinking where inference should occur and how models are updated, introducing new patterns for continuous integration and continuous delivery of model updates in constrained environments.
Concurrently, connectivity technologies are diversifying to meet differentiated needs for throughput, power consumption, and geographic reach. Low-power wide-area networks and optimized cellular standards are expanding the scope of feasible deployments beyond urban centers, while high-bandwidth wireless and wired connectivity serve latency-sensitive industrial applications. Business models are shifting as well; companies are packaging intelligence as subscription services and outcome-based contracts rather than one-off equipment sales. These shifts require governance frameworks that balance innovation velocity with risk controls, and they underscore the need for cross-functional collaboration between engineering, operations, legal, and product teams to realize the full potential of AI-enabled IoT solutions.
Assessing the strategic consequences of 2025 United States tariff measures on supply chains, procurement strategies, and resilient design approaches for AI-in-IoT deployments
The policy environment in 2025 has introduced a layer of strategic complexity for organizations deploying AI-enabled IoT solutions that integrate hardware, connectivity, and software across borders. Tariffs and trade measures implemented by the United States have prompted supply chain reconfigurations that extend lead times for certain components and create incentives for geographic diversification of sourcing. Organizations have responded by accelerating supplier qualification efforts, increasing inventory buffers for critical modules, and evaluating alternative manufacturing footprints closer to key end markets.
Strategically, these measures have highlighted the importance of modular design and firmware portability so that device lifecycles can be preserved even as component suppliers change. Procurement teams now place greater emphasis on long-term supplier relationships, multi-source qualification, and contractual protections against sudden cost escalation. From an operational perspective, companies are placing renewed emphasis on localization strategies for software and services to insulate revenue streams from tariff volatility. In planning terms, scenario-based risk assessments are now routine, and organizations that invest early in flexible architectures and diversified supplier ecosystems are positioned to mitigate the most disruptive effects of tariff-driven market shifts.
Leveraging multi-dimensional segmentation across application, industry vertical, component type, connectivity, and deployment models to prioritize AI-in-IoT opportunities
A nuanced understanding of market segmentation is essential to prioritize use cases and to tailor product development roadmaps. When viewed through the lens of application, opportunities span agriculture, connected car, healthcare, retail, smart grid, smart home, smart manufacturing, and transportation. Smart manufacturing itself demands attention across asset tracking, predictive maintenance, process optimization, and quality management, each of which imposes distinct requirements on sensors, uptime, and analytics. Regulatory constraints and operational cycles differ markedly between an agricultural telemetry installation and a high-throughput manufacturing line, so solution design must reflect domain-specific realities.
From an industry vertical perspective, differentiation arises across agriculture, automotive, energy and utilities, healthcare, manufacturing, retail, smart cities, and transportation and logistics. Manufacturing further bifurcates into automotive manufacturing, discrete manufacturing, and process manufacturing, and each of these sub-verticals prioritizes different reliability standards, safety certifications, and integration protocols. Component type segmentation clarifies the technological building blocks: connectivity modules, edge devices, platforms, sensors, services, and software. Connectivity modules encompass Bluetooth, cellular, LPWAN, satellite, and Wi-Fi, and LPWAN itself branches into LoRaWAN, NB-IoT, and Sigfox. Sensor types include motion sensors, optical sensors, pressure sensors, and temperature sensors, which directly influence model inputs and maintenance regimes.
Connectivity technology segmentation isolates Bluetooth, cellular, Ethernet, LPWAN, satellite, and Wi-Fi as distinct vectors for data transport, with LPWAN options such as LoRaWAN, NB-IoT, and Sigfox enabling low-power remote applications. Deployment model choices-cloud, hybrid, and on-premises-affect control, compliance, and operational responsibilities, where cloud variants can be community, private, or public, and hybrid options include edge-hybrid and multi-cloud hybrid approaches. Integrating these segmentation perspectives reveals where technical trade-offs and commercial opportunities align, enabling product teams to design offerings that meet both functional requirements and go-to-market realities.
Understanding how regional regulatory frameworks, infrastructure maturity, and procurement preferences in the Americas, EMEA, and Asia-Pacific shape AI-in-IoT deployment strategies
Regional dynamics materially influence adoption patterns, regulatory considerations, and partner ecosystems. In the Americas, demand is often shaped by a mix of advanced enterprise deployments and broad-based commercial use cases; the region emphasizes interoperability, cloud integration, and vendor partnerships that accelerate time to value. Enterprises in this geography typically prioritize scalability and integration with established enterprise IT stacks, and they frequently lead in outcomes-based contracting and cross-industry pilots that validate business cases.
Europe, Middle East & Africa presents a complex regulatory and infrastructural landscape where data protection, standards harmonization, and public sector investments in smart city initiatives create differentiated opportunities. Organizations operating across this region must navigate varying national regulations and infrastructure maturity levels, which makes modular, standards-based solutions and strong local partnerships particularly valuable. Meanwhile, Asia-Pacific exhibits a heterogeneous set of dynamics driven by rapid urbanization, robust manufacturing ecosystems, and aggressive national strategies for digitalization. Adoption here is often accelerated by government-led programs, vertically integrated suppliers, and widespread implementation of low-power connectivity in both urban and remote deployments. Across all regions, successful strategies integrate regional compliance considerations with supply chain planning, local partnerships, and deployment models that respect sovereignty and data residency requirements.
How integrated technology stacks, domain specialization, and partnership ecosystems determine competitive advantage and long-term value capture in AI-enabled IoT
Competitive positioning in the AI-in-IoT space is increasingly defined by the ability to offer integrated stacks, cross-domain expertise, and reliable lifecycle support. Leading companies combine sensor and connectivity expertise with robust edge compute, scalable platforms, and a services layer that includes deployment, integration, and managed analytics. Differentiation often emerges through domain-specific solutions that address the unique needs of smart manufacturing, healthcare, or transportation and logistics, where regulatory compliance and operational continuity are non-negotiable.
Partnership models and ecosystems play a central role in how companies capture value. Hardware vendors that form deep alliances with platform providers and managed service operators can deliver turnkey solutions that shorten implementation cycles. Software companies that prioritize model governance, explainability, and seamless over-the-air updates create stickiness through operational predictability. Service providers that excel at systems integration, change management, and post-deployment optimization enable customers to transition from pilots to scaled production. Talent and R&D investments in embedded AI, low-power inference, and robust security practices further separate market leaders from fast followers. Ultimately, the most successful companies will be those that translate cross-layer technological competence into domain-aligned commercial propositions and dependable long-term support.
Practical and prioritized actions for industry leaders to build resilient architectures, governance, and commercial models that scale AI-in-IoT initiatives effectively
Industry leaders must adopt practical, prioritized actions to capture value from AI-enabled IoT while managing risk. First, invest in modular architectures that allow interchangeable components and firmware portability so that supply chain disruptions and tariff-driven adjustments do not force costly redesigns. Second, prioritize edge-capable models where latency, privacy, or bandwidth constraints demand local inference, and establish robust CI/CD processes for safe model updates in production. Third, adopt interoperability and standards-based approaches to ensure solutions can integrate with existing enterprise systems and third-party services, reducing vendor lock-in and accelerating deployment timelines.
Additionally, develop multi-source procurement strategies and strengthen supplier qualification processes to reduce single-vendor dependencies. Build governance frameworks that explicitly address data provenance, model explainability, and security postures, and align these frameworks with regional compliance needs and customer expectations. Invest in cross-functional teams that combine domain experts, data scientists, and reliability engineers to bridge the gap between proof-of-concept achievements and operational reliability. Finally, consider commercial innovations such as outcome-based pricing and managed services to create recurring revenue streams and deeper customer relationships, while using phased pilots to demonstrate ROI and derisk larger rollouts.
A transparent and reproducible research methodology combining expert interviews, secondary validation, and scenario analysis to support strategic decision-making in AI-in-IoT
The research methodology underpinning this analysis synthesizes qualitative and quantitative inputs to create an evidence-based perspective on AI-in-IoT adoption. Primary interviews with domain experts, product leaders, and supply chain managers provided context on technical trade-offs, procurement dynamics, and commercial models. Secondary research complemented these interviews by reviewing standards, regulatory guidance, and public product documentation to validate technology attributes and interoperability considerations. Triangulation across sources ensured that insights reflect consistent patterns rather than isolated anecdotes.
Analytical rigor included mapping use cases to technical requirements and overlaying segmentation schemas to reveal where certain component types and deployment models align with industry needs. Scenario analysis was used to explore supply chain and policy contingencies, and sensitivity checks were applied to test assumptions about technology adoption timelines and integration complexity. Throughout, emphasis was placed on reproducibility of findings and transparency of assumptions, so that decision-makers can adapt the framework to their organizational context and update it as new data or regulatory developments emerge.
Synthesis of strategic priorities and practical enablers that convert AI-enabled IoT experimentation into dependable, scalable operational capabilities
In conclusion, artificial intelligence is maturing as an enabler of operational transformation within Internet of Things ecosystems, and leaders who align technical architecture, commercial models, and governance frameworks will secure the most durable advantages. The convergence of edge intelligence, diversified connectivity, and domain-specific sensors creates powerful new capabilities, but realizing them requires deliberate choices about deployment models, supplier ecosystems, and regulatory compliance. Organizations that invest early in modular design, robust model management, and multi-source procurement will be better positioned to weather policy shifts and supply chain variability.
Moreover, regional nuances and vertical-specific requirements necessitate targeted strategies rather than one-size-fits-all playbooks. Companies that blend technical excellence with pragmatic commercial constructs-such as managed services and outcome-linked pricing-stand to accelerate adoption and deepen customer relationships. As the landscape continues to evolve, continuous learning, iterative piloting, and disciplined governance will be the hallmarks of organizations that turn AI-enabled IoT from a competitive experiment into a reliable, scalable business capability.
Please Note: PDF & Excel + Online Access - 1 Year
Establishing the strategic framework for integrating artificial intelligence into Internet of Things ecosystems to guide investment, operations, and partner decisions
Artificial intelligence is rapidly evolving from a specialized capability into an operational imperative across the Internet of Things ecosystem. Leaders must understand that AI in IoT does not merely add intelligence to devices; it reshapes data flows, decision-making hierarchies, and product lifecycles. This introduction frames the strategic lens through which executives should evaluate technological investments, organizational design, and partner selection, emphasizing that effective AI adoption requires alignment among sensors, connectivity, edge and cloud compute, and the analytic models that extract value.
In practical terms, the intersection of AI and IoT demands that stakeholders reconcile the technical nuances of device-level compute and sensor fidelity with higher-order considerations such as latency tolerance, privacy constraints, and lifecycle management. The narrative that follows highlights how AI-driven inference at the edge complements centralized analytics and how orchestration between these layers unlocks new operational efficiencies. As you read further, consider the implications for talent composition, procurement criteria, and vendor engagement strategies, since decisions made now will determine the pace at which organizations can commercialize use cases and scale secure, resilient IoT architectures.
How shifts in edge intelligence, sensor specialization, and connectivity diversification are redefining architectures, business models, and governance for AI-enabled IoT solutions
The AI-in-IoT landscape is undergoing transformative shifts that alter competitive dynamics, systems architecture, and value capture. One of the most consequential changes is the movement of intelligent processing from centralized cloud platforms toward distributed edge compute, which reduces latency, limits data egress, and enables real-time autonomy in devices. This trend coexists with advances in sensor miniaturization and specialization, enabling richer contextual signals and more robust model inputs. Consequently, organizations are rethinking where inference should occur and how models are updated, introducing new patterns for continuous integration and continuous delivery of model updates in constrained environments.
Concurrently, connectivity technologies are diversifying to meet differentiated needs for throughput, power consumption, and geographic reach. Low-power wide-area networks and optimized cellular standards are expanding the scope of feasible deployments beyond urban centers, while high-bandwidth wireless and wired connectivity serve latency-sensitive industrial applications. Business models are shifting as well; companies are packaging intelligence as subscription services and outcome-based contracts rather than one-off equipment sales. These shifts require governance frameworks that balance innovation velocity with risk controls, and they underscore the need for cross-functional collaboration between engineering, operations, legal, and product teams to realize the full potential of AI-enabled IoT solutions.
Assessing the strategic consequences of 2025 United States tariff measures on supply chains, procurement strategies, and resilient design approaches for AI-in-IoT deployments
The policy environment in 2025 has introduced a layer of strategic complexity for organizations deploying AI-enabled IoT solutions that integrate hardware, connectivity, and software across borders. Tariffs and trade measures implemented by the United States have prompted supply chain reconfigurations that extend lead times for certain components and create incentives for geographic diversification of sourcing. Organizations have responded by accelerating supplier qualification efforts, increasing inventory buffers for critical modules, and evaluating alternative manufacturing footprints closer to key end markets.
Strategically, these measures have highlighted the importance of modular design and firmware portability so that device lifecycles can be preserved even as component suppliers change. Procurement teams now place greater emphasis on long-term supplier relationships, multi-source qualification, and contractual protections against sudden cost escalation. From an operational perspective, companies are placing renewed emphasis on localization strategies for software and services to insulate revenue streams from tariff volatility. In planning terms, scenario-based risk assessments are now routine, and organizations that invest early in flexible architectures and diversified supplier ecosystems are positioned to mitigate the most disruptive effects of tariff-driven market shifts.
Leveraging multi-dimensional segmentation across application, industry vertical, component type, connectivity, and deployment models to prioritize AI-in-IoT opportunities
A nuanced understanding of market segmentation is essential to prioritize use cases and to tailor product development roadmaps. When viewed through the lens of application, opportunities span agriculture, connected car, healthcare, retail, smart grid, smart home, smart manufacturing, and transportation. Smart manufacturing itself demands attention across asset tracking, predictive maintenance, process optimization, and quality management, each of which imposes distinct requirements on sensors, uptime, and analytics. Regulatory constraints and operational cycles differ markedly between an agricultural telemetry installation and a high-throughput manufacturing line, so solution design must reflect domain-specific realities.
From an industry vertical perspective, differentiation arises across agriculture, automotive, energy and utilities, healthcare, manufacturing, retail, smart cities, and transportation and logistics. Manufacturing further bifurcates into automotive manufacturing, discrete manufacturing, and process manufacturing, and each of these sub-verticals prioritizes different reliability standards, safety certifications, and integration protocols. Component type segmentation clarifies the technological building blocks: connectivity modules, edge devices, platforms, sensors, services, and software. Connectivity modules encompass Bluetooth, cellular, LPWAN, satellite, and Wi-Fi, and LPWAN itself branches into LoRaWAN, NB-IoT, and Sigfox. Sensor types include motion sensors, optical sensors, pressure sensors, and temperature sensors, which directly influence model inputs and maintenance regimes.
Connectivity technology segmentation isolates Bluetooth, cellular, Ethernet, LPWAN, satellite, and Wi-Fi as distinct vectors for data transport, with LPWAN options such as LoRaWAN, NB-IoT, and Sigfox enabling low-power remote applications. Deployment model choices-cloud, hybrid, and on-premises-affect control, compliance, and operational responsibilities, where cloud variants can be community, private, or public, and hybrid options include edge-hybrid and multi-cloud hybrid approaches. Integrating these segmentation perspectives reveals where technical trade-offs and commercial opportunities align, enabling product teams to design offerings that meet both functional requirements and go-to-market realities.
Understanding how regional regulatory frameworks, infrastructure maturity, and procurement preferences in the Americas, EMEA, and Asia-Pacific shape AI-in-IoT deployment strategies
Regional dynamics materially influence adoption patterns, regulatory considerations, and partner ecosystems. In the Americas, demand is often shaped by a mix of advanced enterprise deployments and broad-based commercial use cases; the region emphasizes interoperability, cloud integration, and vendor partnerships that accelerate time to value. Enterprises in this geography typically prioritize scalability and integration with established enterprise IT stacks, and they frequently lead in outcomes-based contracting and cross-industry pilots that validate business cases.
Europe, Middle East & Africa presents a complex regulatory and infrastructural landscape where data protection, standards harmonization, and public sector investments in smart city initiatives create differentiated opportunities. Organizations operating across this region must navigate varying national regulations and infrastructure maturity levels, which makes modular, standards-based solutions and strong local partnerships particularly valuable. Meanwhile, Asia-Pacific exhibits a heterogeneous set of dynamics driven by rapid urbanization, robust manufacturing ecosystems, and aggressive national strategies for digitalization. Adoption here is often accelerated by government-led programs, vertically integrated suppliers, and widespread implementation of low-power connectivity in both urban and remote deployments. Across all regions, successful strategies integrate regional compliance considerations with supply chain planning, local partnerships, and deployment models that respect sovereignty and data residency requirements.
How integrated technology stacks, domain specialization, and partnership ecosystems determine competitive advantage and long-term value capture in AI-enabled IoT
Competitive positioning in the AI-in-IoT space is increasingly defined by the ability to offer integrated stacks, cross-domain expertise, and reliable lifecycle support. Leading companies combine sensor and connectivity expertise with robust edge compute, scalable platforms, and a services layer that includes deployment, integration, and managed analytics. Differentiation often emerges through domain-specific solutions that address the unique needs of smart manufacturing, healthcare, or transportation and logistics, where regulatory compliance and operational continuity are non-negotiable.
Partnership models and ecosystems play a central role in how companies capture value. Hardware vendors that form deep alliances with platform providers and managed service operators can deliver turnkey solutions that shorten implementation cycles. Software companies that prioritize model governance, explainability, and seamless over-the-air updates create stickiness through operational predictability. Service providers that excel at systems integration, change management, and post-deployment optimization enable customers to transition from pilots to scaled production. Talent and R&D investments in embedded AI, low-power inference, and robust security practices further separate market leaders from fast followers. Ultimately, the most successful companies will be those that translate cross-layer technological competence into domain-aligned commercial propositions and dependable long-term support.
Practical and prioritized actions for industry leaders to build resilient architectures, governance, and commercial models that scale AI-in-IoT initiatives effectively
Industry leaders must adopt practical, prioritized actions to capture value from AI-enabled IoT while managing risk. First, invest in modular architectures that allow interchangeable components and firmware portability so that supply chain disruptions and tariff-driven adjustments do not force costly redesigns. Second, prioritize edge-capable models where latency, privacy, or bandwidth constraints demand local inference, and establish robust CI/CD processes for safe model updates in production. Third, adopt interoperability and standards-based approaches to ensure solutions can integrate with existing enterprise systems and third-party services, reducing vendor lock-in and accelerating deployment timelines.
Additionally, develop multi-source procurement strategies and strengthen supplier qualification processes to reduce single-vendor dependencies. Build governance frameworks that explicitly address data provenance, model explainability, and security postures, and align these frameworks with regional compliance needs and customer expectations. Invest in cross-functional teams that combine domain experts, data scientists, and reliability engineers to bridge the gap between proof-of-concept achievements and operational reliability. Finally, consider commercial innovations such as outcome-based pricing and managed services to create recurring revenue streams and deeper customer relationships, while using phased pilots to demonstrate ROI and derisk larger rollouts.
A transparent and reproducible research methodology combining expert interviews, secondary validation, and scenario analysis to support strategic decision-making in AI-in-IoT
The research methodology underpinning this analysis synthesizes qualitative and quantitative inputs to create an evidence-based perspective on AI-in-IoT adoption. Primary interviews with domain experts, product leaders, and supply chain managers provided context on technical trade-offs, procurement dynamics, and commercial models. Secondary research complemented these interviews by reviewing standards, regulatory guidance, and public product documentation to validate technology attributes and interoperability considerations. Triangulation across sources ensured that insights reflect consistent patterns rather than isolated anecdotes.
Analytical rigor included mapping use cases to technical requirements and overlaying segmentation schemas to reveal where certain component types and deployment models align with industry needs. Scenario analysis was used to explore supply chain and policy contingencies, and sensitivity checks were applied to test assumptions about technology adoption timelines and integration complexity. Throughout, emphasis was placed on reproducibility of findings and transparency of assumptions, so that decision-makers can adapt the framework to their organizational context and update it as new data or regulatory developments emerge.
Synthesis of strategic priorities and practical enablers that convert AI-enabled IoT experimentation into dependable, scalable operational capabilities
In conclusion, artificial intelligence is maturing as an enabler of operational transformation within Internet of Things ecosystems, and leaders who align technical architecture, commercial models, and governance frameworks will secure the most durable advantages. The convergence of edge intelligence, diversified connectivity, and domain-specific sensors creates powerful new capabilities, but realizing them requires deliberate choices about deployment models, supplier ecosystems, and regulatory compliance. Organizations that invest early in modular design, robust model management, and multi-source procurement will be better positioned to weather policy shifts and supply chain variability.
Moreover, regional nuances and vertical-specific requirements necessitate targeted strategies rather than one-size-fits-all playbooks. Companies that blend technical excellence with pragmatic commercial constructs-such as managed services and outcome-linked pricing-stand to accelerate adoption and deepen customer relationships. As the landscape continues to evolve, continuous learning, iterative piloting, and disciplined governance will be the hallmarks of organizations that turn AI-enabled IoT from a competitive experiment into a reliable, scalable business capability.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
196 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Deployment of federated learning frameworks for privacy-preserving AI analytics across distributed IoT healthcare sensors
- 5.2. Integration of energy-efficient edge AI accelerators to enable real-time analytics in battery-powered smart city sensors
- 5.3. Implementation of AI-driven digital twin platforms in industrial IoT systems for predictive maintenance and process optimization
- 5.4. Use of explainable AI frameworks in IoT security gateways to enhance transparent threat detection in critical infrastructure networks
- 5.5. Adoption of neuromorphic computing modules within edge IoT nodes to reduce latency and energy consumption in robotics control
- 5.6. Emergence of tactile internet ecosystems combining AI and 5G connectivity for ultra-reliable low-latency remote operation of autonomous devices
- 5.7. Scaling cloud-native AI orchestration services to unify cross-domain IoT data streams for comprehensive analytics and decision making
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Artificial Intelligence in IoT Market, by Industry Vertical
- 8.1. Agriculture
- 8.2. Automotive
- 8.3. Energy & Utilities
- 8.4. Healthcare
- 8.5. Manufacturing
- 8.5.1. Automotive Manufacturing
- 8.5.2. Discrete Manufacturing
- 8.5.3. Process Manufacturing
- 8.6. Retail
- 8.7. Smart Cities
- 8.8. Transportation & Logistics
- 9. Artificial Intelligence in IoT Market, by Component Type
- 9.1. Connectivity Modules
- 9.1.1. Bluetooth
- 9.1.2. Cellular
- 9.1.3. LPWAN
- 9.1.3.1. LoRaWAN
- 9.1.3.2. NB-IoT
- 9.1.3.3. Sigfox
- 9.1.4. Satellite
- 9.1.5. Wi-Fi
- 9.2. Edge Devices
- 9.3. Platform
- 9.4. Sensors
- 9.4.1. Motion Sensors
- 9.4.2. Optical Sensors
- 9.4.3. Pressure Sensors
- 9.4.4. Temperature Sensors
- 9.5. Services
- 9.6. Software
- 10. Artificial Intelligence in IoT Market, by Connectivity Technology
- 10.1. Bluetooth
- 10.2. Cellular
- 10.3. Ethernet
- 10.4. LPWAN
- 10.4.1. LoRaWAN
- 10.4.2. NB-IoT
- 10.4.3. Sigfox
- 10.5. Satellite
- 10.6. Wi-Fi
- 11. Artificial Intelligence in IoT Market, by Deployment Model
- 11.1. Cloud
- 11.1.1. Community Cloud
- 11.1.2. Private Cloud
- 11.1.3. Public Cloud
- 11.2. Hybrid
- 11.2.1. Edge Hybrid
- 11.2.2. Multi-Cloud Hybrid
- 11.3. On-Premises
- 12. Artificial Intelligence in IoT Market, by Application
- 12.1. Agriculture
- 12.2. Connected Car
- 12.3. Healthcare
- 12.4. Retail
- 12.5. Smart Grid
- 12.6. Smart Home
- 12.7. Smart Manufacturing
- 12.7.1. Asset Tracking
- 12.7.2. Predictive Maintenance
- 12.7.3. Process Optimization
- 12.7.4. Quality Management
- 12.8. Transportation
- 13. Artificial Intelligence in IoT Market, by Region
- 13.1. Americas
- 13.1.1. North America
- 13.1.2. Latin America
- 13.2. Europe, Middle East & Africa
- 13.2.1. Europe
- 13.2.2. Middle East
- 13.2.3. Africa
- 13.3. Asia-Pacific
- 14. Artificial Intelligence in IoT Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Artificial Intelligence in IoT Market, by Country
- 15.1. United States
- 15.2. Canada
- 15.3. Mexico
- 15.4. Brazil
- 15.5. United Kingdom
- 15.6. Germany
- 15.7. France
- 15.8. Russia
- 15.9. Italy
- 15.10. Spain
- 15.11. China
- 15.12. India
- 15.13. Japan
- 15.14. Australia
- 15.15. South Korea
- 16. Competitive Landscape
- 16.1. Market Share Analysis, 2024
- 16.2. FPNV Positioning Matrix, 2024
- 16.3. Competitive Analysis
- 16.3.1. Amazon.com, Inc.
- 16.3.2. Microsoft Corporation
- 16.3.3. Alphabet Inc.
- 16.3.4. International Business Machines Corporation
- 16.3.5. Cisco Systems, Inc.
- 16.3.6. Intel Corporation
- 16.3.7. Huawei Technologies Co., Ltd.
- 16.3.8. Siemens Aktiengesellschaft
- 16.3.9. NVIDIA Corporation
- 16.3.10. Hitachi, Ltd.
- 16.3.11. Rockwell Automation, Inc.
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