The research finds that this growth is being driven by the rapid commercialisation of foundation models, enterprise copilots, multimodal systems, and agentic AI across software development, customer operations, research, analytics, content creation, and business-process automation. Organisations are moving beyond small-scale pilots and embedding generative AI into core workflows, creating demand for computing infrastructure, model-development platforms, customisation, enterprise data integration, governance, cyber security, and managed services. Improvements in model reasoning, context handling, tool use, and inference efficiency are further expanding commercially viable use cases. However, high infrastructure costs, model reliability concerns, data-security risks, intellectual-property issues, regulatory uncertainty, and difficulty demonstrating returns on investment may slow adoption, particularly among smaller organisations and highly regulated industries.

Large-scale investment in accelerators and AI-optimised data centres

By offering, the infrastructure segment is expected to account for the largest share of the generative AI market in 2026, supported by sustained spending on accelerator chips, high-bandwidth memory, storage systems, and high-speed networking required for model training and inference. The growing size and complexity of foundation models, multimodal workloads, and long-running AI agents are increasing demand for dense computing clusters, optimised server architectures, and low-latency interconnects. Hyperscale cloud providers, model developers, sovereign AI programmes, and large enterprises are investing heavily in infrastructure to expand training capacity and support production-scale inference. Demand is also moving beyond GPUs toward integrated AI systems that combine accelerators, memory, networking, storage, and software optimisation. As generative AI adoption expands across cloud, on-premises, and edge environments, hardware vendors are positioned to benefit from both new infrastructure deployments and the continuing replacement of general-purpose systems with AI-optimised architectures. 

Highest end-user growth segment

The healthcare, pharmaceuticals & life sciences segment is projected to record the highest CAGR among end-user segments during the forecast period. Generative AI is increasingly being applied to drug discovery, clinical-document summarisation, medical knowledge retrieval, patient communication, regulatory documentation, trial design, scientific research, and administrative automation. Multimodal gen AI models are also creating opportunities to combine clinical text, medical images, laboratory information, genomic data, and research literature within integrated decision-support environments. Pharmaceutical companies are adopting generative AI to accelerate target identification, molecule design, evidence synthesis, and submission preparation, while healthcare providers are exploring its use in documentation, coding, care coordination, and patient engagement. Growth will be supported by demand for domain-specific models, secure deployment environments, explainability, data privacy, and human oversight. Although regulatory requirements and concerns regarding accuracy may moderate deployment, the high value of improved research productivity and reduced administrative burden is expected to sustain strong investment.

A dense concentration of hyperscalers, developers, and technology buyers in the US

North America is expected to account for the largest share of the generative AI market in 2026 because of its concentration of semiconductor companies, hyperscale cloud providers, foundation-model developers, enterprise software vendors, research institutions, startups, and large technology buyers. The region benefits from substantial data-centre investment, mature cloud infrastructure, strong venture funding, and early enterprise adoption across BFSI, healthcare, defence, professional services, media and entertainment, and IT. US-based companies occupy leading positions across the generative AI value chain, including accelerators, cloud compute, models, development platforms, governance systems, applications, and consulting services. Enterprises in the region are also progressing from isolated tools toward integrated deployments connected with organisational data, business applications, and automated workflows. Continued investment in AI infrastructure, energy capacity, cyber security, and workforce development is expected to reinforce North America’s leadership as generative AI adoption scales.

Major players

Some of the major players in the generative AI market and mentioned within the research include NVIDIA, Microsoft, Google, Amazon Web Services, IBM, Open AI, Anthropic, Meta, Mistral AI, and Cohers, among others. These companies compete across hardware, foundation models, development platforms, governance and security systems, agentic AI platforms, applications, and services. The report also examines specialist and emerging vendors that are expanding innovation in model development, inference optimization, multimodal generation, enterprise agents, AI governance, and industry-specific applications.