Traditional document processing methods require your workforce to manually read and extract vital information from invoices, POs and other relevant documents. It is repetitive, time-exhaustive and costly, which leads to several errors and burnout. According to Ardent Partners, the average cost of manually processing a document is approximately $11.
Without automation, document processing lacks in autonomy and scalability. It requires human intervention throughout the process for approvals and checks, adding to the cost and effort in your workflow.
To eliminate these issues AI-driven automation is a major driver and significant step for you to reduce cost and achieve unrealised productivity gains.
In the blog, we’ll focus on how AI agents can transform Intelligent Document Processing (IDP) to help you streamline your workflow more efficiently.
AI agents in Intelligent Document Processing
AI agents can assess, interpret and process documents by leveraging advanced technologies like natural language processing (NLP) and machine learning (ML). From data extraction to routing, agents can handle a wide range of document-related tasks. The focus is on freeing up manual labour with intelligent data capture so you can carry on with higher-priority tasks. AI agents in IDP shows significantly enhance processing time, reduce error rates and increase overall productivity through continuous learning and adaptive abilities.
Gaps in Traditional IDP
Rule-Based Limitations- Traditional IDPs can only process predictable formats, but they fail if documents deviate slightly from expected layouts. This rigidity limits accuracy since in the real world, variation is the norm.
Scalability Issues- As the volume of documents surges, rule-based systems fall behind in keeping pace, particularly with unstructured. It can cause delays and errors in your data processing systems.
Low Adaptability- Traditional methods cannot quickly learn from new format types; each change needs retraining or reconfiguration, delaying responsiveness in your existing workflows.
Human dependence- Rate of error and exception is high in manual data entry causing backlogs and slow decision making in document processing.
Integration challenges- Legacy methods lack the flexibility to seamlessly integrate with your ERP, CRM or cloud environment. It leads to fragmented workflow and data siloes in system.
How can AI agents fill these gaps?
Autonomous decision making- Automated document processing improves operational efficiency and decision-making up to 70%. Agents can learn from constant data feed and evolve handling complex data and generating actionable insights.
High scalability and seamless integration- AI agents easily handle large volumes of documents and integrate with your existing workflow, ensuring you achieve end-to-end automation.
Accurate data extraction- AI agents extract and validate data without any human intervention using ML and NLP, even if data is unstructured. Automation in IDP increases data extraction accuracy up to 94%.
Minimal human dependence- Agents automate tasks that are repetitive and handle exceptions, so that your system doesn’t rely on manual checks.
More time and cost-efficient-
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