How large could the AI automation market be by 2030?

Sizing depends on what’s included (RPA, IDP, process mining, AI agents, services), but triangulating segment forecasts and broader AI spending outlooks suggests the market could reach well into the tens of billions by 2030. Base-case scenarios imply a 22–28% CAGR from 2026 to 2030 as enterprise programs scale and governance improves.

Which vendors are shaping the competitive landscape?

Hyperscalers and specialist platforms are driving strategy. Enterprise buyers are gravitating toward integrated solutions from [Microsoft](https://microsoft.com), [Google](https://google.com), and [Amazon](https://aws.amazon.com), alongside automation-focused leaders like [UiPath](https://uipath.com) and [Automation Anywhere](https://www.automationanywhere.com).

How is generative AI changing automation programs?

Generative AI enhances automation by interpreting unstructured data, drafting communications, and triggering workflow steps with context, which raises ROI and expands use cases. As models integrate with orchestration layers, AI agents can execute multi-step tasks across applications while adhering to governance and audit policies.

What are the main risks to growth from 2026–2030?

Key risks include regulatory compliance uncertainty, data quality issues, and shortages in automation engineering and change management talent. Macroeconomic pressure can delay discretionary projects, but mission-critical workflow modernization tends to continue due to cost reduction and resilience mandates.

Where should enterprises start to capture value quickly?

Focus on high-friction workflows with clear KPIs—such as document-heavy processes in finance, customer operations, or supply chain—and leverage trusted platforms from [Microsoft](https://microsoft.com), [Google](https://google.com), [Amazon](https://aws.amazon.com), [UiPath](https://uipath.com), or [Automation Anywhere](https://www.automationanywhere.com). Establish governance early to manage risk, measure outcomes, and scale repeatably.

AI Automation Market Size, Growth and Forecast for 2026-2030

AI-driven automation is accelerating from pilots to enterprise-scale adoption, redefining workflows across sectors. This analysis maps the market’s trajectory through 2030, the forces behind demand, and the strategic moves by major vendors shaping the competitive landscape.

Published: November 13, 2025 By David Kim Category: Automation

AI Automation Market Size, Growth and Forecast for 2026-2030

Market Overview and Definitions

AI automation—an umbrella term covering robotic process automation (RPA), intelligent document processing (IDP), workflow orchestration, AI agents, and embedded machine learning—is entering a scale-up phase in large enterprises. Spending on AI solutions overall is set to surge, with global outlays projected to reach roughly $500 billion by 2027, according to industry analysts at IDC. Hyperautomation initiatives, which bundle these technologies into end-to-end digital workflows, continue to rank high on CIO agendas, as noted in Gartner’s overview of hyperautomation.

Parsing the market requires separating core RPA from the broader AI-enabled stack. RPA alone is forecast to expand significantly this decade, with long-term growth supported by use cases in finance, supply chain, and customer operations—an arc reflected in Grand View Research’s RPA market analysis. Layered atop RPA are IDP, process mining, and generative AI agents, which together transform static scripts into adaptive workflows capable of reasoning over unstructured data and triggering actions across enterprise systems.

Triangulating these segments suggests an AI automation market that could more than double between 2026 and 2030 as enterprises standardize platforms and migrate workloads to cloud-native stacks. While definitions vary, the market’s velocity is clear: consolidating pilots into enterprise-wide programs, converting labor savings into reinvestment, and embedding automation into core business processes alongside analytics.

Growth Drivers and Sector Adoption

Three forces underpin demand through 2030: the maturity of cloud automation platforms, the rise of generative AI, and operational urgency to remove manual bottlenecks in regulated industries. Generative AI’s ability to interpret documents, summarize knowledge, and draft business communications is already amplifying automation ROI—potentially adding $2.6–$4.4 trillion in annual value across functions, according to recent research from McKinsey. As AI models integrate with orchestration layers, line-of-business teams can automate multi-step processes that once required human judgment.

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