What is AI automation?

AI automation didn't emerge overnight—it evolved from decades of foundational technologies. The roots of AI automation can be traced back to early optical character recognition (OCR) solutions in the 1950s, but practical AI automation as we know it today began taking shape in the early 2010s, when several AI and automation technologies converged. 

The first significant wave came around 2015-2020, when advances in machine learning, natural language processing, and computer vision made it possible to build automated systems that could understand unstructured documents. When organizations saw they could go beyond the limits of traditional OCR to automate complex document workflows, intelligent document processing gained significant traction.  

But it was the 2020-2024 period that truly accelerated adoption. The intelligent document processing (IDP) market—a key component of AI automation—exploded from $1.05 billion in 2021 to $1.85 billion in 2023, with projections reaching $19-66 billion by 2032. This IDP growth reflects the broader AI automation momentum across document processing, email automation, and computer vision applications.  

Three key forces drove this unprecedented momentum: 

• Pandemic-driven digital transformation: COVID-19 forced organizations to rapidly digitize operations, creating an urgent demand for AI-powered solutions that could process documents from a distance. The IDP market grew at a 30%+ CAGR as businesses scrambled to streamline and maintain operations with distributed workforces. 

• AI technology maturation: advanced natural language processing and machine learning models became commercially viable and accessible. By 2024, over 50% of IDP solutions incorporated sophisticated AI and NLP features, enabling automation of complex documents like contracts and legal filings. And the emergence of generative AI (GenAI) in late 2022 and throughout 2023 further accelerated this trend, vastly improving NLP's ability to interpret, summarize, and generate human-like language across a wide range of business contexts. 

• Proven enterprise value: organizations using AI for document processing began reporting 50-70% reductions in processing time and up to 80% efficiency gains in invoice processing. With 75% of business data trapped in unstructured formats, the ROI became undeniable. 

Today's continued surge builds on this explosive growth foundation. The global IDP market alone is projected to grow at a 29-30% CAGR through 2032, demonstrating that AI automation has moved far beyond experimentation into large-scale enterprise deployment. 

RPA revolutionized enterprise automation by handling repetitive, rule-based tasks with speed, accuracy, and security. But as businesses encountered more variable, unstructured, and judgment-heavy workflows, RPA hit its limits. Business processes involving language, visual data, or contextual decisions remained out of reach. 

That changed with the rise of AI automation. 

By combining AI’s adaptability with RPA’s reliability, enterprises can now automate broader, more complex categories of work. Here’s how AI automation breaks through RPA’s old boundaries: 

• Machine learning enables systems to recognize patterns and make predictions—flagging fraud, anticipating equipment failure, or identifying high-value customers before sales even begin. 

• Natural language processing (NLP) allows automation to handle unstructured communication, including routing emails, extracting insights from contracts, or composing personalized responses. 

• Computer vision empowers software to interpret scanned documents, analyze product images, or validate visual inspections at a scale that rivals or exceeds human ability. 

• Generative AI (GenAI) enhances automation’s ability to generate human-like content, summarize complex documents, respond conversationally, and dynamically adapt to changing inputs, all of which has unlocked new frontiers in customer service, knowledge work, and content-heavy processes. 

The real power emerges when these AI technologies work hand-in-hand with RPA execution capabilities. For example, in invoice processing, AI doesn’t just extract fields.  It also interprets vendor context, flags anomalies, and validates data against historical patterns. RPA then updates multiple systems and triggers the appropriate workflow actions. 

Modern enterprise platforms now support this convergence natively. They provide unified development environments where AI handles unstructured inputs and decisions, and RPA manages integration and task execution—together unlocking massive new intelligent automation potential across the enterprise. 

As the world moves toward agentic automation—where AI agents act autonomously to make decisions, understand information, and take action across systems—AI automation still has an important role to play. 

That’s because, to perform effectively, AI agents need access to reliable tools and models—especially ones that can be trained quickly, learn continuously, and deliver high accuracy with repeatable outcomes. The “traditional AI” models used in AI automation, such as those powering IDP and computer vision, deliver these accurate, and repeatable results. Versus today’s large language models (LLMs), they offer high computational efficiency paired with a far lower tendency to hallucinate.  

While LLMs enable powerful new capabilities—particularly in language generation and reasoning—agents will require a mix of both traditional and generative AI to succeed across varied tasks. The ability to combine efficient, proven models with flexible generative techniques will be essential. 

AI automation provides that toolkit for agents, offering powerful, high-performing, and dependable capabilities that agentic systems need to operate effectively at scale. 

To move forward effectively with AI automation, it’s critical to select the right technology. 

Choose integrated platforms over point solutions

Modern AI automation requires multiple technologies working together, so look for platforms that seamlessly combine these capabilities rather than trying to integrate separate tools. Your platform should unify RPA and AI capabilities, as well as supporting agentic automation. Next, ensure it offers high-performing AI in critical areas like OCR, natural language processing, computer vision, intent analysis, and more. It should support integration with existing systems like CRMs, ERPs, and legacy apps through APIs and connectors. And it must provide robust orchestration capabilities. 

Prioritize fast, flexible model development that doesn’t require extensive training

The best modern solutions will be able to handle various document layouts without requiring extensive template creation or maintenance—and adapt to format variations automatically. You’ll want solutions that that can generate working models quickly from a small number of document samples instead of requiring laborious and time-consuming manual training. And seek out platforms that allow model customization to match your unique document types and business rules.

Look for integrated solutions that perform highly on all required capabilities

Take high-scale email processing, for example. Effective email automation should go beyond simple inbox triage. Look for a solution that can understand the intent of incoming messages, classify and route them accurately, and even generate appropriate responses in line with your brand and compliance needs. It should also handle attachments intelligently: extracting data and directing both the message and its contents to the right systems. To ensure timely attention to high-impact messages, advanced platforms should include sentiment and priority analysis to detect urgency and importance. 

Make sure the technology can deliver end-to-end automation across multiple systems

Your solution should be able to capture documents from multiple sources, including email attachments, cloud storage, APIs, mobile uploads, and direct integrations with business systems. Beyond that, you need a platform that doesn’t just extract data but can also validate it, route it to appropriate systems, trigger approval workflows, and handle exceptions intelligently.  

Look for enterprise-grade capabilities

Choose an AI automation platform that delivers high accuracy (95–99%), learns from user feedback, and scales effortlessly. It should support human-in-the-loop validation, offer transparency for trust and compliance, and integrate smoothly with systems like CRMs and ERPs. Security is essential so look for SOC 2 certification, encryption, access controls, and audit features.   

AI automation has established itself as the essential bridge between traditional RPA and emerging agentic systems. By combining machine learning, natural language processing, and computer vision with established automation frameworks, it addresses the critical gap in handling unstructured data, contextual decision making, and adaptive workflows. 

The technology's strengths are well-defined: document processing with 95-99% accuracy, email automation that handles thousands of communications daily, and computer vision applications that exceed human inspection capabilities, thereby reducing human error. Unlike RPA's structured execution or agentic automation's broad orchestration, AI automation excels at intelligent interpretation—understanding context, learning from patterns, and adapting to variations while maintaining the reliability businesses require. 

Modern enterprise automation platforms have evolved to seamlessly integrate AI automation with existing RPA infrastructure, creating hybrid workflows where each technology contributes to its core strengths. This collaborative approach, rather than replacement, has driven the measurable results organizations are achieving: 50-70% reductions in processing time, 80% efficiency gains in invoice processing, and 30% productivity improvements in email handling. 

AI automation has earned its place in the automation ecosystem not as a revolutionary disruption, but as a natural evolution that makes previously impossible processes practical, scalable, and economically viable. 

Q: What is AI automation? 

A: AI automation combines artificial intelligence (like machine learning, NLP, computer vision, and generative AI) with traditional automation. This allows it to automate business processes involving unstructured data, documents, and decision making. 

Q: What are the benefits of using AI automation in business? 

A: AI automation delivers faster workflows, fewer errors, lower costs, better compliance, scalability, and improved customer experiences. It frees employees from manual, repetitive tasks to focus on more strategic, creative, and high-value work. With less need for human intervention, there’s also a reduction in bottlenecks and human error. AI automation is ideal for manual processes spanning multiple systems: invoice processing, document review, customer onboarding, claims handling, email triage, and service requests. 

Q: What sets AI automation apart from other forms of automation? 

A: AI automation builds on traditional automation like RPA, which handles rule-based, structured tasks. What sets it apart is its ability to process unstructured data and make decisions based on context. Compared to agentic automation—which introduces AI agents that can reason, plan, and act autonomously—AI automation is more structured and deterministic. Today, these three types of automation work together symbiotically. RPA brings reliability and efficiency to structured processes. AI automation leverages intelligence to expand automation into a wider footprint. And agentic automation makes possible full end-to-end automation of complex, multi-system workflows. 

Q: What should I look for in AI automation technology? 

A: Look for a platform, not piecemeal AI and automation solutions. The platform should offer both RPA and AI capabilities—including machine learning (ML), NLP, document understanding, and GenAI. It should work with existing systems like CRMs, ERPs, and legacy apps, leveraging UI, API, and AI integration. Governance, role-based access, audit trails, and model transparency are critical, along with robust orchestration capabilities.