From Typewriters to AI Powerhouses: The Evolution of Business Process Automation
by Slavena Hristova, Director of Product Marketing
Document processing used to have a single goal: eliminate slow, error-prone manual data entry. OCR and data capture solved much of that decades ago.
Today, efficiency is no longer a differentiator; it’s expected. With generative AI and agentic systems entering the enterprise, leaders need confidence that their data, and their automation, can be trusted. Only then does the vision of autonomous automation become tangible.
Generative AI is powerful, but when applied to critical processes without preparation, it often produces answers that sound convincing but are simply wrong. This “hallucination” problem isn’t just a technical quirk—it’s inherent to the nature of LLMs, as OpenAI themselves have pointed out. More importantly, it creates business risks in compliance, cost, and reputation.
Once a tool for cutting manual work, intelligent document processing (IDP) has evolved into the engine that delivers the AI-ready data enterprises need as the foundation for automation. It turns messy, unstructured documents into clean, structured information that genAI can actually work with. Together, they enable automation that’s faster, safer, and scalable at enterprise level.
Let’s recap how we got here:
- The typewriter era: Manual data entry madness
In the 80s and 90s, armies of clerks typed data from invoices and contracts into business systems. OCR arrived to help, recognizing letters on a page, but offering no real understanding. - RPA: A step forward, but not quite there yet
In the 2010s, bots automated repetitive tasks and data transfers. But with 80%–90% of enterprise data locked in documents, RPA hit a wall. Without clean and structured inputs, the bots were stuck. They relied on tools like OCR to extract text but still couldn’t understand it. Progress? Yes. Perfect? Far from it. - The IDP breakthrough
Building on OCR and data capture, intelligent document processing arrived on the scene, harnessing advances in AI, machine learning, and natural language processing to take document automation further. IDP didn’t just read text; it interpreted and structured it. Contracts, invoices, and claims could be classified, validated, and transformed into usable data streams. This evolution made modern automation possible: faster, smarter, and enterprise-ready.
