Document AI Market to Reach $27.6B by 2030 as Enterprises Embrace Task-Specific AI

The global Document AI market is projected to reach $27.62 billion by 2030, up from $14.66 billion in 2025, growing at a 13.5% CAGR, according to a new report by MarketsandMarkets™. The findings highlight a clear trend: enterprises are moving beyond generic large language models toward domain-tuned, task-specific AI agents that can read, reason, and act on documents with accuracy and compliance.

From General AI to Specialized Document Intelligence

The research attributes market growth to advances in intelligent automation, RAG-enabled architectures, and federated learning. These technologies allow organizations to train and deploy models that stay context-aware and privacy-safe, even when working across distributed data sources.

Unlike general AI chat systems, Document AI models specialize in extracting insights from structured and unstructured text, from loan documents to marketing proposals, while reducing the risks of hallucination and regulatory non-compliance.

Sector Spotlight: BFSI and Marketing Lead the Way

The Banking, Financial Services, and Insurance (BFSI) sector is forecast to grow the fastest, as financial institutions automate KYC, claims, loan processing, and regulatory reporting.Meanwhile, the marketing and sales segment is close behind, leveraging Document AI for proposal generation, campaign analytics, and contract management, with a projected 15.4% CAGR through 2030.

North America remains the largest market, driven by strong regulatory mandates, mature AI infrastructure, and high adoption rates in document-intensive sectors such as banking, healthcare, and government.

TheMarketAI.com Take

Early wide spread attention gravitated toward massive, general-purpose AI models like GPT, it seems that potential commercial traction is emerging in verticalized AI applications that can operate within strict regulatory and data-integrity frameworks.

Document AI sits squarely in that trend. Each use case, compliance summaries, policy audits, or sales proposals, effectively becomes a micro-agent with its own domain expertise. These agents are small enough to control, yet powerful enough to deliver measurable ROI.

It remains to be seen how reliable these task specific AI applications and if they manage to prevent/mitigate hallucinations. We've see that AI works great for low volume tasks with high tolerance for errors.