Harnessing the Full Spectrum of Input Documents: A Game-Changer in Automation

We talk a lot about Process Automation and harnessing the full spectrum of input documents, but that doesn’t mean everyone knows what that entails. Let’s clear some things up a bit… A process is initiated when a specific trigger event (end of another process, receiving an email, or a time-based event) occurs. Before the process can start, some input and information are required as a prerequisite. Often, this input is in the form of Word or PDF documents.

These documents can be classified in a spectrum that goes from highly structured to unstructured. Recent advancements in Artificial Intelligence (AI) have significantly broadened the capabilities of Digital Workers / Robotic Process Automation (RPA), enabling it to process a broader range of documents than ever before.

The Spectrum of Input Documents:

1. Structured Documents:

• Definition: Documents with a fixed format and predictable data layout, such as forms and spreadsheets.
• Example: financial reports, system-generated emails
• Processing: Traditionally handled using rule-based systems like Regular Expressions (Regex), which rely on predictable patterns.

2. Semi-structured Documents:

• Definition: These contain structured and unstructured elements without a predefined format.
• Example: Invoices, application forms, or PDFs with both forms and narrative text
• Processing: Previously challenging, but now more manageable with advanced parsing algorithms and machine learning techniques.

3. Unstructured Documents:

• Definition: Documents with no predefined data model, often in the form of free text.
• Example: Business correspondence, legal contracts, or industry-specific documents such as CAD drawings
• Processing: Requires AI models capable of understanding context, sentiment, and semantic relationships.

The traditional Digital Workers/RPA route has been to focus on structured/semi-structured documents. Unstructured documents are taken up only when the volume is high, and writing up a custom AI model is economically feasible. However, with recent improvements in AI with tools such as Microsoft Copilot and other Large Language Models (LLMs), the speed at which custom AI models can be built or questions asked on unstructured documents has significantly improved.

Implications for Businesses:

• Efficiency: Automating a broader range of document types leads to significant time and cost savings.
• Accuracy: Reduced human error in data processing.
• Scalability: Ability to handle increased volumes of complex documents.
• Insight: AI can provide deeper insights from unstructured data, aiding decision-making.

Conclusion:

Integrating AI with RPA has revolutionised how businesses handle document processing. By embracing these technologies, companies are efficiently harnessing the full spectrum of input documents, from structured to unstructured, paving the way for more intelligent, scalable, and insightful operations.

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