Originally published on Medium

This is an IDP blogpost covering aspects of what IDP is, what consists of an IDP workflow, the use of Generative AI, aspects of Agentic AI, top IDP use cases in the Retail and Consumer Packaging Goods (CPG) industries and the future outlook of what IDP and Agentic AI can do for Retail businesses. Intelligent document processing or IDP is one of the foremost AI ML solutions that is sought after by several organizations across the world. Over the past decade, AI has evolved across industries, but one recurring theme remains — Intelligent Document Processing (IDP). With businesses seeking automation, scalability, and compliance, IDP has emerged as a game-changer. Let's explore how AI, Generative AI, and Agentic Workflows are transforming document-heavy processes, particularly in Retail and Consumer Packaged Goods (CPG). ## What is IDP? Intelligent Document Processing or IDP is an advanced Artificial Intelligence (AI) powered approach to extract, process and analyze data from structured, semi and unstructured documents. It leverages several AI solutions such as Generative AI, Machine Learning, Natural Language Processing (NLP) and Optical Character Recognition (OCR) to automate document-heavy workflows, reducing manual effort and improving accuracy. That's a lot to digest. So let's unpack this one layer at a time. How is AI layered? The below image helps you to understand how AI and its successive solutions are intertwined: Artificial Intelligence & Machine Learning (AI/ML) Layers

Artificial Intelligence & Machine Learning (AI/ML) Layers

### Background Throughout history, humans have tried to capture and record history, culture and various ideas through documentation. This documentation has gone through several iterations while the core remained the same for a long time — hand written and stored. The advent of AI and the use of analytics for effective decision making, unlocked more opportunities for several organizations. Traditional document processing is manual, slow, error-prone and costly to maintain. There were also several inefficiencies and compliance issues that exposed organizations to be vulnerable to lawsuits and other regulatory challenges. This possibly made organizations to identify potential ways to overcome document management challenges while identifying the need to address, solve and cut down operational and capital expenses. Intelligent Document Processing (IDP) addressed several of these challenges through: - **Automation** - **Accuracy** - **Scalability** - **Compliance & Security** This unearths several business led benefits of using IDP: - **Cost savings:** Reduction in manual labor and processing time - **Faster decision-making:** AI-driven insights reduce approval and processing times - **Error reduction:** Automation minimizes manual entry errors and compliance risks - **Scalability:** Handling high volumes of documents - **Improved Customer Relationships:** Faster processing of invoices, claims leading to better partnerships ## IDP Components The Key components of IDP include — 1/Document Ingestion & Classification, 2/ OCR and NLP, 3/ Data Extraction & Validation, 4/ Workflow Automation and finally 5/ Integration. IDP Workflow

### Document Ingestion & Classification In this step, documents are first ingested to a specific location (Ex: Amazon S3) and Artificial Intelligence (AI) is used to sort, categorize, and tag documents (e.g., invoices, contracts, purchase orders, claims). The ingestion process doesn't have any limitations on the document format as it supports multi-format inputs like PDFs, images, scanned documents, emails, and handwritten forms. ### Optical Character Recognition (OCR) & NLP In this step, the documents and the associated images (if any) are scanned and converted into machine readable text. The succeeding Natural Language Process (NLP) process enables AI to understand the context, extract key entities, and summarize content. ### Data Extraction & Validation Post the processing steps, pre-trained or custom trained AI models (Ex: Large Language Models or LLMs like Anthropic Claude, Amazon Nova, etc.,) are used to extract structured data from documents. This extracted data is validated against knowledge bases, predefined rules (Bedrock Guardrails) and historical data. ## From Rule Based to Reasoning Agents: The Shift to Agentic AI Traditional AI systems rely on pre-defined rules and fixed task execution. These models are typically stateless, single-purpose, and operate within a narrow scope. Their design often requires human oversight for decision-making and supervision at key steps. Such systems work well for repetitive, predictable tasks — like document classification or rule-based chatbots — where logic doesn't change based on historical interactions or evolving context. However, this rigid approach quickly reveals its limitations in dynamic, real-world environments: - **No context-awareness:** Traditional systems cannot remember prior interactions or learn from them. Each session starts from scratch - **Single-task orientation:** They are built to perform isolated functions rather than coordinating across multiple steps or systems - **High reliance on human intervention:** Most traditional workflows need a human in the loop to manage exceptions or trigger transitions between steps Agentic AI breaks this mold by enabling autonomous, context-aware, and adaptive systems that can reason, plan, and execute multi-step workflows with minimal or no human input. Instead of relying on one monolithic AI model, Agentic AI solutions are composed of multiple specialized agents that: - Communicate and collaborate with each other - Access external tools and APIs - Remember past actions using persistent memory - Adapt their decisions based on changing goals or new inputs ### A Real World Example **Use Case:** Invoice processing is one of top relatable and key use cases in the Retail and Consumer Packaging Goods (Retail & CPG). Let us explore the solution through the lens of traditional AI vs Agentic AI approach. Document Processing — Traditional AI vs Agentic AI

Document Processing — Traditional AI vs Agentic AI

The challenges of Traditional AI are evident: - Slow turnaround due to manual checkpoints - Errors when unexpected formats arise - No memory of previous invoice patterns or recurring vendor issues The benefits from Agentic AI are clear. A scalable, intelligent workflow that handles thousands of invoices autonomously — learning, improving, and reducing human workload over time. The above example also showcases that Agentic Workflows enable automated approvals, fraud detection, and compliance checks. ## Integration with Enterprise Systems An important feature of an IDP process is the seamless connectivity between several enterprise systems for a smooth handover. IDP solutions can easily be connected with ERP, CRM, and cloud storage platforms (SAP, Salesforce, Amazon S3, SharePoint, etc.,). With integrations, the solution enhances decision-making by providing real-time insights into processed documents. Industries such as Insurance, Financial Services, Manufacturing, Healthcare and Life Sciences, etc., continue to see customers leveraging IDP solutions for a faster and more efficient mid stream processing for their businesses. While these industries have seen success, Retail and Consumer Packaging Goods are latching on to IDP solutions for several of their business involving supply chain, logistics, contracts processing, finance and compliance, brick and mortar stores, customer experience, and workflow automation. Let's see some potential use cases in Retail and CPG that can leverage IDP. ## Top Retail/CPG Use Cases ### Supply Chain & Logistics Automation Supply chain automation is one of the top use cases that could unearth a plethora of opportunities with IDP. This can involve automating invoice processing, purchase orders, and freight documentation. Enhance vendor management with AI-powered document classification and reducing errors in shipping and inventory records using AI-driven IDP. ### Retail Store & Customer Experience Enhancement Another area where customers are often looking to IDP agentic workflows involve retail store management and customer experience enhancement. Potential areas could involve: - Automating customer feedback processing from surveys, receipts, and forms - Enhancing loyalty programs by processing and analyzing purchase receipts and transactions - Improving customer onboarding by automating document verification (e.g., KYC for credit or membership programs) ### Contracts Processing In the consumer packaging goods industry, contracts processing is a high traffic use case that could involve automating contract extraction and compliance verification for retailer-supplier agreements, using Generative AI to summarize, analyze, and suggest contract optimizations and finally reducing revenue leakage by improving claims and deduction processing with AI-driven IDP. ### Finance & Compliance in Retail & CPG Regulatory and compliance regulation is key in making an organization adhere to the government and local laws. IDP with Agents can be used to automating tax filings, audits, and compliance documentation, streamlining financial reconciliations and fraud detection with AI-powered IDP and enhance regulatory compliance (e.g., FDA, GDPR) by automating document validation. ## IDP & Agents — The Future Outlook So let's recap on what IDP & Agents can do for Retail & CPG: **Intelligent Data Extraction & Workflow Automation** help understand how AI agents can extract structured and unstructured data from invoices, receipts, contracts, and claims by using LLMs like Amazon Nova, Anthropic Claude, or Meta's Llama for entity recognition, document summarization, and compliance checks. **Auto-Prioritization & Decision-Making with Agents** will help understand how AI Agents can intelligently route and escalate documents based on urgency, errors, or missing data while integration with ERP/CRM systems (SAP, Salesforce, Dynamics) to enable real-time decision-making. **Generative AI-Powered Knowledge Bases & Guardrails** help enhance contextual understanding of documents (e.g., understanding contracts, promotions, and supplier terms) while implementing Guardrails for AI-driven compliance checks, reducing risks in financial and legal document processing. Finally, **End-to-End Automation with LLM-Orchestrated Workflows** use Agentic Workflows to automate document approval chains while integrating Conversational AI for real-time document query and retrieval. This enables employees to ask AI-powered assistants about past contracts, invoices, etc. The all powerful agentic workflows, LLM-enhanced document intelligence, Integrations with IoT & Smart Retail (RFID Tracking, real time inventory) and a multi modal approach combining text, voice and image based document processing, makes IDP a game changer in the Retail & CPG industries. IDP and Agentic AI are revolutionizing Retail and CPG. Organizations adopting these solutions gain a competitive edge by automating workflows, reducing costs, and enhancing compliance. If you're exploring how IDP can transform your business, let's connect! ## References This blogpost is based on my research and experience using Amazon Web Services (AWS). The following references help understand IDP and associated services better. **AWS Services:** Amazon S3, Amazon Textract, Amazon Comprehend, Amazon SageMaker, Amazon Bedrock, Amazon DynamoDB, Amazon Step Functions, AWS Lambda, AWS IoT Core, Amazon QuickSight, Amazon OpenSearch Service, AWS CloudTrail **LLMs:** Amazon Titan, Amazon Nova, Anthropic Claude, Meta's Llama - [AWS for Retail](https://aws.amazon.com/retail/) - [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/) - [Deep dive on IDP at AWS](https://aws.amazon.com/solutions/intelligent-document-processing/) - [IDP Solution Library at AWS](https://aws.amazon.com/solutions/implementations/intelligent-document-processing/) --- *This blog was written by Ganesh Raam Ramadurai, reviewed for technical accuracy by Shsrams and images designed by Kosal.*

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