| Field | Details |
|---|---|
| Market Study Period | 2020 - 2035 |
| Market Size (2025) | USD 18.70 Billion |
| Market Size (2026) | USD 22.75 Billion |
| Market Size (2035) | USD 155.40 Billion |
| Segment Share (by Segment) | Natural Language Processing (38.5%), Machine Learning (26%), Robotic Process Automation (9.5%), Image Recognition (14.5%), Predictive Analytics (11.5%) |
| Largest Market | North America (41.2%) |
| Fastest Growing Market | Asia Pacific (CAGR: 34.2%) |
| List of Major Players |
| Year | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 | 2031 | 2032 | 2033 | 2034 | 2035 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Market Size (USD Billion) | 18.70 | 22.75 | 27.87 | 34.40 | 42.66 | 52.99 | 65.80 | 81.59 | 101.07 | 125.10 | 155.40 |
Global Accelerating Enterprise GenAI Workflow with IaaS Network Service Market is projected to grow from USD 18.7 Billion in 2025 to USD 155.4 Billion by 2035, reflecting a compound annual growth rate of 18.7% from 2026 through 2035. This market encompasses the provision of Infrastructure as a Service IaaS network capabilities specifically designed to support, accelerate, and optimize generative Artificial Intelligence GenAI workflows within enterprise environments. It involves leveraging robust, scalable, and high-performance network services to facilitate the immense data transfer and computational demands of GenAI models, enabling seamless integration and efficient operation across various business functions. Key market drivers include the explosive adoption of GenAI technologies for tasks like content creation, code generation, and personalized customer experiences, coupled with the increasing need for agile and scalable IT infrastructure. Enterprises are increasingly recognizing the strategic advantage of GenAI in enhancing productivity, fostering innovation, and gaining a competitive edge, thereby fueling the demand for specialized IaaS network services that can handle these complex workloads. The desire for reduced operational costs and improved resource utilization through cloud based infrastructure also significantly contributes to market expansion.
Important trends shaping this market include the rise of hybrid and multi cloud strategies for GenAI deployment, allowing enterprises to leverage the strengths of different cloud providers while maintaining data sovereignty and compliance. There is also a growing emphasis on low latency and high bandwidth network solutions to support real time GenAI applications, alongside the integration of advanced security measures to protect sensitive data processed by these models. The convergence of AI ML operations MLOps with network automation is another critical trend, streamlining the deployment, monitoring, and management of GenAI workflows. Market restraints primarily revolve around data privacy concerns, regulatory complexities across different jurisdictions, and the significant initial investment required for sophisticated IaaS network infrastructure capable of supporting advanced GenAI. Furthermore, the shortage of skilled professionals with expertise in both GenAI and cloud network management poses a challenge. Opportunities abound in the development of industry specific GenAI solutions tailored for sectors like healthcare, finance, and manufacturing, as well as in the provision of specialized consulting and integration services. The market also presents opportunities for innovative pricing models and pay per use services that cater to varying enterprise needs.
North America leads the market due to its advanced technological infrastructure, high concentration of major cloud service providers, and a strong culture of early adoption of cutting edge technologies like GenAI. The region benefits from significant investments in research and development, a robust venture capital ecosystem, and a large number of enterprises actively experimenting with and deploying GenAI solutions across various industries. Asia Pacific is poised to be the fastest growing region, driven by rapid digital transformation initiatives, increasing cloud adoption rates, and a burgeoning start up ecosystem focused on AI innovation, particularly in countries like China, India, and Japan. Governments in these nations are actively promoting AI development and providing incentives for enterprises to integrate advanced technologies. The public cloud segment holds the largest market share, owing to its scalability, flexibility, and cost effectiveness in supporting dynamic GenAI workloads. Key players such as IBM, Amazon Web Services, ServiceNow, Oracle, NVIDIA, SAP, Microsoft, Adobe, Cisco, and Atlassian are focusing on expanding their cloud infrastructure, enhancing their GenAI platforms, forging strategic partnerships, and investing in research and development to offer comprehensive solutions that integrate GenAI capabilities with robust IaaS network services. Their strategies involve developing vertical specific offerings, improving network performance, and embedding AI driven automation to simplify complex GenAI deployments for enterprise clients.
This topic describes enhancing enterprise Generative AI workflows by leveraging Infrastructure as a Service network services. It involves optimizing data transfer, model training, and inference processes critical for GenAI applications. IaaS network services like high bandwidth, low latency connections, and secure private links accelerate the movement of massive datasets and model parameters. This minimizes bottlenecks during data ingestion, parallel computing for model training, and real time response for deployed AI models. The significance lies in achieving faster iteration cycles, improving model accuracy through quicker data access, and enabling more responsive AI driven business solutions within the enterprise by ensuring robust and scalable network foundations for demanding GenAI workloads.
Enterprises are increasingly leveraging GenAI for critical operations, demanding robust and scalable infrastructure. This trend signifies a move towards seamlessly integrating GenAI applications into existing enterprise ecosystems. Instead of isolated deployments, organizations are building comprehensive workflows where various GenAI models and traditional applications collaborate.
The "hybrid IaaS" component highlights a pragmatic approach. Enterprises are not solely relying on public cloud, but strategically combining their on premises private cloud infrastructure with public cloud services. This allows them to maintain control over sensitive data and workloads while leveraging the elasticity and specialized services offered by public cloud providers for compute intensive GenAI tasks. Network service orchestration becomes paramount here, ensuring efficient data flow and communication between these diverse environments, accelerating enterprise GenAI adoption and operationalization.
Enterprises are rapidly adopting Generative AI, demanding real time insights from vast datasets. This fuels the need for extremely low latency in GenAI workflows. Edge AI emerges as a critical solution, bringing processing power directly to data sources, minimizing round trip times to distant cloud servers. Concurrently, 5G networks provide the high bandwidth and minimal latency necessary to transport data swiftly and reliably between edge devices and centralized GenAI models, or even between edge devices collaboratively processing information. This synergy of Edge AI and 5G effectively eliminates network bottlenecks that would otherwise hinder immediate GenAI responses. It enables near instantaneous inference and content generation, unlocking new applications across diverse industries where speed and responsiveness are paramount for enterprise competitiveness and operational efficiency.
Enterprises are rapidly embracing generative AI to automate routine and complex workflows. This adoption is driven by GenAI's ability to significantly enhance operational efficiency, reduce human error, and free up employee time for higher value tasks. From automating customer service and content generation to streamlining software development and data analysis, GenAI powered solutions are proving transformative across various business functions. Organizations are realizing substantial productivity gains and cost savings by integrating GenAI into existing systems. The tangible benefits, coupled with increasing accessibility of powerful GenAI models and infrastructure as a service network services, fuel a self reinforcing cycle of exponential growth. Businesses seek competitive advantages, further propelling this widespread integration for workflow automation.
Enterprises are rapidly adopting Generative AI across their workflows, necessitating robust infrastructure. Traditional IaaS often struggles to meet the extreme computational demands of GenAI models, which require massive processing power for training and inference. This fuels a significant increase in demand for IaaS solutions specifically engineered for scalability and high performance. These specialized platforms offer elastic resources, accelerated computing capabilities like GPUs, and optimized network fabrics to handle the immense data throughput and parallel processing intrinsic to GenAI. Businesses seek to leverage GenAI for innovation without infrastructure bottlenecks, driving the imperative for agile, powerful, and reliable cloud infrastructure that can evolve with their escalating AI needs.
The relentless progress in artificial intelligence and machine learning models, especially generative AI, is a primary catalyst. Sophisticated new algorithms allow enterprises to automate complex workflows previously requiring human intervention. These advancements drive demand for specialized infrastructure tailored for GenAI. This includes high performance computing, optimized GPUs, and dedicated memory architectures capable of processing massive datasets and executing intricate model inferences efficiently. Enterprises are investing in these specialized platforms to harness the full potential of next generation AI, leading to increased adoption of IaaS network services that can deliver the necessary computational power and connectivity at scale. This interplay of advanced models and bespoke infrastructure accelerates the integration of GenAI across diverse business operations.
Realtime GenAI workflows demand swift data transfer and processing. A significant restraint lies in IaaS network latency and bandwidth limitations. Even with global acceleration efforts, the physical distance between users, GenAI models, and data storage within IaaS environments can introduce noticeable delays. Inadequate bandwidth further exacerbates this issue, creating bottlenecks that hinder the rapid exchange of information crucial for interactive GenAI applications. This constraint impacts the responsiveness and overall user experience, especially for tasks requiring immediate feedback or processing of large data streams. Enterprises adopting GenAI need reliable, low latency, high bandwidth network services to prevent performance degradation and ensure seamless operation of their accelerated GenAI initiatives. This remains a key challenge for widespread enterprise GenAI adoption.
Global GenAI IaaS deployments face significant restraints from data gravity and regulatory compliance. Data gravity dictates that large datasets, essential for GenAI model training and inference, are inherently difficult and costly to move across geographical boundaries. This immobility leads to latency issues and increased infrastructure expenses when organizations attempt to centralize or distribute GenAI workloads globally.
Simultaneously, a patchwork of diverse and evolving international data privacy regulations, such as GDPR and CCPA, imposes stringent requirements on data residency, sovereignty, and access. Organizations must ensure their GenAI deployments comply with each region's specific laws, often necessitating redundant data storage, localized compute, and complex access controls. This regulatory complexity adds substantial overhead, delays deployment timelines, and limits the seamless global scalability of GenAI services, hindering the overall market acceleration.
The accelerating global adoption of Generative AI by enterprises creates an immense opportunity for high performance IaaS networking. Enterprise GenAI workflows, spanning model training, fine tuning, and real time inference, are inherently data intensive and computationally demanding. These advanced AI applications require unprecedented levels of network throughput, ultra low latency, and consistent bandwidth to operate efficiently. Traditional cloud networking often struggles to meet these stringent requirements, leading to performance bottlenecks, extended processing times, and increased operational costs. The significant opportunity lies in specialized IaaS networking solutions tailored precisely for GenAI. This involves offering dedicated high bandwidth connections, optimized data paths, hardware accelerated network interfaces, and intelligent traffic management designed to eliminate data movement constraints. Providers who can deliver such robust, scalable, and ultra fast networking infrastructure will empower enterprises to unlock the full potential of their GenAI investments. This allows for faster model iteration, rapid deployment of AI powered services, and seamless integration of GenAI across critical business functions, driving innovation and competitive advantage.
The opportunity is to deliver specialized, secure, and scalable IaaS network services indispensable for global enterprise GenAI adoption. As Generative AI workflows rapidly accelerate worldwide, businesses critically require underlying network infrastructure that not only ensures robust security for sensitive AI data but also offers immense scalability to manage the fluctuating, high demand computational loads of AI model training and inference. This involves providing advanced network architectures that support seamless, high throughput connectivity across hybrid and multi cloud environments. Enterprises seek reliable, low latency solutions facilitating efficient data movement and processing essential for GenAI model performance and rapid deployment. The increasing enterprise reliance on AI necessitates integrated security measures protecting intellectual property and ensuring regulatory compliance across the entire GenAI lifecycle. Providers offering adaptive, high performance IaaS network services are uniquely positioned to enable enterprises to confidently integrate Generative AI, driving innovation and maintaining competitive advantage across global operations, especially within dynamically expanding digital economies. This market demands resilient, future proof networking solutions.
Share, By Application, 2025 (%)
Why is Public Cloud dominating the Global Accelerating Enterprise GenAI Workflow with IaaS Network Service Market?
Public Cloud models hold a substantial majority share due to their inherent scalability, cost efficiency, and ease of access to advanced computing resources vital for training and deploying large GenAI models. Enterprises leverage public cloud providers for their robust infrastructure as a service offerings, global network services, and the ability to quickly provision and scale up or down based on fluctuating GenAI workload demands, without significant upfront capital investment. This flexibility and accessibility significantly accelerate GenAI adoption across various industries.
What application segments are driving the demand for GenAI workflows within this market?
Natural Language Processing NLP stands out as a primary driver, given its foundational role in GenAI capabilities like content generation, summarization, and intelligent chatbots. Machine Learning ML also remains crucial, underpinning the algorithms and training processes for all GenAI applications. Robotic Process Automation RPA and Predictive Analytics are increasingly integrated, leveraging GenAI for enhanced automation and more accurate forecasting. Image Recognition benefits from GenAI in tasks like synthetic data generation and advanced visual content analysis, broadening its utility across sectors like retail and healthcare.
How do different service types and industry verticals interact within this evolving market?
Infrastructure as a Service IaaS forms the backbone for enterprises deploying GenAI workflows, providing the fundamental compute, storage, and networking resources. Platform as a Service PaaS offerings simplify development by providing preconfigured environments, while Software as a Service SaaS solutions deliver ready-to-use GenAI applications. Industry verticals like Banking and Financial Services BFS and Healthcare are rapidly adopting GenAI for fraud detection and personalized patient care, respectively. Retail leverages GenAI for personalized shopping experiences and supply chain optimization, with Telecommunications enhancing network management and customer service through these advanced AI capabilities.
The global landscape for enterprise GenAI workflows utilizing IaaS network services is complex and rapidly evolving. Data privacy remains paramount with GDPR, CCPA, and similar national laws dictating strict data handling, anonymization, and cross border transfer rules for model training and inference. IaaS providers and enterprises must navigate these diverse requirements, ensuring robust security architectures and compliance.
Emerging AI ethics and governance frameworks, like the EU AI Act and NIST guidelines, impose increasing demands for transparency, explainability, fairness, and accountability in GenAI deployment. Enterprises bear responsibility for mitigating model bias and ensuring human oversight, impacting their choice and configuration of IaaS platforms. Intellectual property considerations are a significant challenge, particularly regarding copyrighted training data and the ownership of AI generated outputs, necessitating careful licensing and provenance tracking.
Furthermore, IaaS network security falls under critical infrastructure regulations in many jurisdictions, requiring stringent cybersecurity measures, resilience planning, and incident reporting. Competition law authorities are also scrutinizing market dominance by major cloud providers, potentially influencing interoperability standards and data portability requirements for GenAI services. This regulatory patchwork demands proactive compliance and adaptable policy strategies from all stakeholders.
The global enterprise GenAI workflow market is profoundly shaped by emergent technologies and innovations in IaaS network services. High performance, ultra low latency networking fabrics are essential, optimizing data transfer for large language models and real time inference. Innovations include AI optimized network acceleration, serverless GenAI deployments, and advanced containerization techniques for scalable model execution. Enhanced network security paradigms, such as zero trust architectures and confidential computing at the network layer, are crucial for protecting sensitive GenAI data and intellectual property. The rise of edge AI integration and sophisticated hybrid cloud networking solutions enables distributed GenAI processing closer to data sources, reducing latency and bandwidth costs. Furthermore, programmable networks, offering dynamic resource allocation and bespoke network slicing, are customizing connectivity for specific GenAI application demands, ensuring unparalleled reliability and speed. These advancements are fueling rapid adoption across diverse enterprise use cases.
Trends, by Region
North America Market
Revenue Share, 2025
Asia Pacific · 34.2% CAGR
Asia Pacific is poised to be the fastest growing region in the Global Accelerating Enterprise GenAI Workflow with IaaS Network Service Market from 2026 to 2035, exhibiting a remarkable CAGR of 34.2%. This explosive growth is fueled by robust digital transformation initiatives across diverse industries. Enterprises in nations like India and Southeast Asian countries are rapidly adopting GenAI to automate complex workflows and enhance operational efficiencies. Increased investments in cloud infrastructure and the escalating demand for scalable IaaS network services are key drivers. The region’s large talent pool in AI and data science further accelerates GenAI adoption, creating a fertile ground for innovation and market expansion. This convergence of factors positions Asia Pacific as a critical hub for GenAI workflow acceleration.
The U.S. leads in global accelerating enterprise GenAI workflow, driven by robust IaaS network service market expansion. Significant investment in scalable infrastructure and advanced AI models fuels rapid innovation and adoption. This creates a highly competitive environment for cloud providers and AI developers, with a strong focus on secure, high-performance computing essential for GenAI model training and deployment across diverse industries.
China's IaaS network service market is critical for global enterprise GenAI workflows, accelerating development and deployment. Its vast infrastructure supports compute-intensive AI, addressing data sovereignty and regional latency. Chinese providers are expanding globally, offering competitive solutions for multinational corporations seeking to optimize GenAI model training, inference, and data management, driving enterprise digital transformation.
India emerges as a pivotal hub in the accelerating global enterprise GenAI workflow, driven by its burgeoning talent pool and robust digital infrastructure. The nation is witnessing a rapid adoption of GenAI, leveraging IaaS network services to power scalable, secure, and efficient AI applications. This confluence positions India as a key player in shaping the future of global AI innovation and deployment.
Geopolitical shifts like trade restrictions on advanced chips and AI development could fragment the IaaS network service market. Nations prioritizing digital sovereignty might foster local GenAI IaaS ecosystems, leading to varied data residency and compliance demands. Supply chain vulnerabilities for high performance computing hardware, exacerbated by international tensions, could drive up IaaS costs and introduce lead times for capacity expansion, impacting enterprise GenAI workflow scalability.
Macroeconomic factors such as sustained high interest rates would increase capital expenditure for IaaS providers, potentially raising service prices for enterprises. Inflationary pressures on energy and skilled labor would further impact operational costs. Conversely, a global economic slowdown might initially dampen enterprise AI investments, but could also accelerate GenAI adoption for efficiency gains, creating a countercyclical demand for scalable IaaS network services. Regulatory uncertainty surrounding AI ethics and data governance adds a layer of risk and compliance complexity.
Microsoft Azure announced the 'Enterprise GenAI Workflow Hub,' a new platform integrating their IaaS network services with advanced GenAI tools for seamless deployment and management. This initiative aims to provide businesses with a unified environment for accelerated AI model development, training, and operationalization within their secure cloud infrastructure.
AWS and NVIDIA unveiled a strategic partnership to enhance GenAI workflow acceleration on AWS's IaaS network. This collaboration focuses on integrating NVIDIA's next-generation GPU clusters and AI software stack directly into AWS services, offering unparalleled performance and scalability for enterprise-grade GenAI applications.
IBM completed the acquisition of 'FlowGenius AI,' a leading startup specializing in enterprise GenAI workflow orchestration and automation. This acquisition bolsters IBM's existing IaaS offerings by integrating advanced AI-driven workflow management capabilities, enabling customers to streamline complex GenAI development and deployment cycles.
ServiceNow launched 'AI Accelerate for IT Operations,' a new product designed to leverage generative AI within their IaaS-backed IT service management platform. This offering automates routine IT tasks, predicts potential issues, and generates solutions, significantly enhancing operational efficiency and reducing manual intervention.
Oracle introduced 'Oracle Cloud Infrastructure (OCI) GenAI Enterprise Kit,' a comprehensive suite of tools and services tailored for large-scale GenAI deployments on their IaaS network. This kit includes specialized compute instances, data management solutions, and pre-built GenAI models to empower enterprises to rapidly build and deploy AI-driven applications.
IBM, Amazon Web Services, Microsoft, and Google Cloud dominate the IaaS Network Service market, offering comprehensive cloud platforms leveraging their extensive global infrastructure. Their roles involve providing scalable compute, storage, and networking for GenAI workloads, often integrating proprietary AI services like IBM WatsonX, AWS SageMaker, and Azure AI. NVIDIA is crucial for its specialized GPUs and CUDA platform, essential for training and inference. ServiceNow and Atlassian focus on workflow orchestration and integration, while Adobe and SAP embed GenAI into their enterprise applications, driving demand for underlying IaaS. Oracle's strong database presence and expanding cloud footprint also contribute. Strategic initiatives include developing robust APIs, expanding data center regions, and forging partnerships to enhance their GenAI offerings, fueling market growth.
| Report Component | Description |
|---|---|
| Market Size (2025) | USD 18.7 Billion |
| Forecast Value (2035) | USD 155.4 Billion |
| CAGR (2026-2035) | 18.7% |
| Base Year | 2025 |
| Historical Period | 2020-2025 |
| Forecast Period | 2026-2035 |
| Segments Covered |
|
| Regional Analysis |
|
Table 1: Global Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 2: Global Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Deployment Model, 2020-2035
Table 3: Global Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Industry Vertical, 2020-2035
Table 4: Global Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Service Type, 2020-2035
Table 5: Global Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Region, 2020-2035
Table 6: North America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 7: North America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Deployment Model, 2020-2035
Table 8: North America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Industry Vertical, 2020-2035
Table 9: North America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Service Type, 2020-2035
Table 10: North America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Country, 2020-2035
Table 11: Europe Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 12: Europe Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Deployment Model, 2020-2035
Table 13: Europe Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Industry Vertical, 2020-2035
Table 14: Europe Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Service Type, 2020-2035
Table 15: Europe Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035
Table 16: Asia Pacific Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 17: Asia Pacific Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Deployment Model, 2020-2035
Table 18: Asia Pacific Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Industry Vertical, 2020-2035
Table 19: Asia Pacific Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Service Type, 2020-2035
Table 20: Asia Pacific Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035
Table 21: Latin America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 22: Latin America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Deployment Model, 2020-2035
Table 23: Latin America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Industry Vertical, 2020-2035
Table 24: Latin America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Service Type, 2020-2035
Table 25: Latin America Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035
Table 26: Middle East & Africa Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 27: Middle East & Africa Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Deployment Model, 2020-2035
Table 28: Middle East & Africa Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Industry Vertical, 2020-2035
Table 29: Middle East & Africa Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Service Type, 2020-2035
Table 30: Middle East & Africa Accelerating Enterprise GenAI Workflow with IaaS Network Service Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035
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