Generative AI & Agentic AI Market Report 2026-2032

By: Dimitris Paraskevopoulos, Joaquin Fernandez, Knud Lasse Lueth | Publisher: IOT Analytics | Year: 2026

ISBN: IOTANALYTICS34

You Pay: Rs. 487,500.00

Usually ships in 24 hours

Reward points: 4875 points

Description

A 283-page report sizing the generative & agentic AI market to 2032, including vendor market shares, enterprise use cases, adoption, and trends.

Market overview

IoT Analytics research indicates that the global Generative AI & Agentic AI market reached $308 billion in 2025. Based on enterprise adoption metrics and technology investment allocations, the market is forecast to reach $4.4 trillion by 2032, representing a compound annual growth rate (CAGR) of 46% between 2025 and 2032. Enterprise adoption reached mainstream levels in 2025, with 88% of surveyed organizations utilizing generative AI technologies. Market expansion correlates with an operational transition from human-prompted assistance toward autonomous agentic workflows that execute multi-step processes under human oversight.

This report outlines the five core segments comprising the technology stack:

AI Accelerators: GPUs, TPUs, LPUs, and custom ASICs used for model training and inference
AI Cloud & Platforms: rented compute infrastructure, model development environments, runtime operations, and agent orchestration layers
AI Services: strategy consulting, system integration, custom agent design, and managed services
AI Applications: end-user software including chatbots, coding tools, and domain-specific enterprise applications
Foundation Models: API usage, fine-tuning, and private model deployments

Key data snapshot

2025 market size: $308 billion
2032 forecast: $4.4 trillion
Key players: AMD, AWS, Accenture, Alibaba Cloud, Anthropic, Capgemini, Google, Intel, Microsoft, Mistral, Nvidia, OpenAI

Key trends & market leaders

Multi-step agentic workloads require repeated model calls, tool interactions, and memory retrieval, making inference the dominant cost driver across AI infrastructure. Data indicates that multi-step reasoning workflows consume up to 20x the amount of inference compute for every unit of training compute. Concurrently, open standards such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols are establishing common interfaces to connect AI applications, enterprise data sources, and independent software agents. Parallel to this, enterprises are increasing the deployment of specialized small language models (SLMs) to reduce latency and contain token consumption costs.

Regarding market share, infrastructure spending flows primarily to NVIDIA for AI accelerators and Microsoft for AI cloud and platforms. Within software and foundation models, leadership in 2025 was held by OpenAI for foundation models and AI applications, alongside Accenture for AI services. Data for H1 2026 indicates that Anthropic expanded its foundation model market share, driven by API adoption across coding and reasoning workloads.

What is in this report?

This 283-page analysis published in September 2026 details granular market sizing across 5 tracked segments and 15 industry verticals, an empirical analysis of 1,533 enterprise AI projects, competitive market shares across all stack layers, 7 enterprise case studies, and an evaluation of implementation blockers, security concerns, and financial bubble risks.

Key questions addressed

What is GenAI and agentic AI, how are they connected, and what are the technological components?
Which use cases are enterprises prioritizing, and how autonomous are deployments becoming?
What is the current market size, and how is the market expected to grow to 2032?
How large are the 5 tracked segments (AI accelerators, foundation models, AI cloud & platforms, AI applications, AI services), and how is the segment mix shift expected to develop?
What are the market shares of key players, and who leads in each of the 5 segments?
Which are the leading AI applications, and which application categories grow fastest?
How are leading enterprises deploying GenAI and agentic AI?
What are the important implementation considerations and adoption blockers?
What are the current and next trends — from inference costs to MCP/A2A and AI regulation?
Are we in an AI bubble? What do ROI, CapEx payback, and valuations say?

What's inside

1. Executive summary
2. Introduction — defining GenAI, the new paradigm of agentic AI, and how enterprise usage became mainstream
3. Fundamentals of agentic AI — how AI agents work, multi-agent systems, and what it takes to build agents
4. Market and competitive landscape overview — total market, segment mix shift, drivers and inhibitors, market share overview
5. AI accelerators — types and capabilities, training vs. inference chips, and leading companies including NVIDIA, AMD, Broadcom, Intel, and Cerebras
6. Foundation models — transformer architecture, open models, small language models, and leading companies including OpenAI and Anthropic
7. AI cloud & platforms — the platform stack, GPU cloud platforms, agentic development and orchestration platforms, and key players Microsoft, AWS, and GCP
8. AI applications — market size, top applications, horizontal categories and industry verticals
9. AI services — enterprise spending, market shares, and key players Accenture, IBM, and Deloitte
10. End-user adoption — analysis of GenAI & agentic AI projects by department, activity, industry, and autonomy, with 7 enterprise adoption examples
11. Market & technology trends — 8 key trends
12. Adoption challenges & AI bubble risk — implementation challenges and an assessment of enterprise ROI, capability expectations, CapEx payback, and equity valuations
13. Methodology & market definitions
14. About IoT Analytics