So far we have covered motivations, challenges, different AI product categories available on the market and prerequisites for launching an automation project in the organization. While these may seem to be sufficient to get started with automation work, there is a need to understand one critical thing about these tools and technologies. Technologies like AI and Machine Learning are relatively new ideas on the technology market and, as with any novelty that comes into the public domain, not everything we hear is true. There is much ambiguity in the way AI concepts and capabilities are understood and there is a high potential and cost to falling prey to these misconceptions.  

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Here in our final part, we delve into some of these “AI Myths” and hope it helps you avoid some of these pitfalls.

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Misconceptions about using the AI Approach

When it comes to using AI for data extraction from documents, there are several misconceptions that are worth addressing. Here are some common ones:

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• AI can fully replace human involvement: While AI can automate and expedite the data extraction process, it is not intended to replace human involvement entirely. Human oversight is still crucial for validating and reviewing the extracted data, especially in cases where high accuracy is required or for complex document types. AI systems should be seen as tools to assist humans rather than as complete substitutes.
• AI can handle all document types with equal accuracy: AI systems may struggle with document types that deviate significantly from the training data or contain complex layouts, handwritten text, or specialized terminology. Each document type may require specific training and customization to achieve optimal accuracy. Not all AI models are equally suitable for all document types, and careful consideration is required to ensure compatibility.
• AI can perfectly extract data without errors: While AI can improve accuracy compared to manual data extraction, it is not infallible. AI models can still make mistakes or encounter challenges with ambiguous or poorly structured documents. Error rates can vary based on document complexity, image quality, or variations in data presentation. Regular monitoring, validation, and continuous improvement are necessary to mitigate errors.
• AI can replace the need for data preprocessing: Preprocessing documents, such as cleaning up noise, improving image quality, or standardizing formats, can significantly enhance the performance of AI systems. AI should be seen as a part of a larger data extraction workflow that may include preprocessing steps to improve results. Ignoring data preprocessing can lead to suboptimal accuracy or unreliable data extraction.
• AI can instantly provide accurate results without training: AI models require training on relevant data to learn patterns and extract information accurately. Training an AI model involves providing labeled data and iteratively refining the model's performance. Training and fine-tuning the AI system takes time and effort.Initial results may not be accurate until the model is adequately trained.
• AI can handle all languages equally well: While AI can support multiple languages, its performance may vary across different languages. Languages with more training data available or languages with simpler syntax may yield better results initially. Handling languages with complex structures, limited training data, or low-resource languages may require additional efforts to achieve acceptable accuracy levels.
• AI is a one-time implementation: Implementing AI for document data extraction is not a one-time process. It requires continuous monitoring, maintenance, and periodic retraining to adapt to changing document formats, new document types, and evolving data extraction requirements. Regular updates and ongoing optimization are necessary to ensure long-term effectiveness.
• AI is a plug-and-play solution: Implementing AI for data extraction requires expertise in machine learning, data science, system integration and domain knowledge. It involves tasks such as data preparation, feature engineering, model training, and integration with existing systems with potentially high degrees of complexity. Deploying AI successfully often requires collaboration between subject matter experts, data scientists, and IT professionals.
• AI can solve all data extraction challenges instantly: AI is a powerful tool, but it may not address all data extraction challenges instantly. Document variability, complex layouts, or specific industry requirements may demand customized solutions or additional manual intervention. AI should be viewed as a part of a broader data extraction strategy that may involve multiple technologies and techniques.
• AI guarantees 100% accuracy and perfection: AI systems can achieve high accuracy levels, but it's essential to acknowledge that they are not infallible. Achieving 100% accuracy may be challenging due to inherent document complexities, data inconsistencies, or system limitations. Businesses should set realistic expectations and implement appropriate quality control measures to ensure data accuracy and reliability.

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Understanding these misconceptions can help organizations make informed decisions about implementing AI for document data extraction and set realistic expectations regarding its capabilities and limitations.

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Misconceptions about the different AI products

Here are some common misconceptions about different types of AI products used for data extraction from documents:

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Optical Character Recognition (OCR) Software can accurately extract data from any document: 

• OCR software is effective at extracting text from printed or digital documents. However, the accuracy can be affected by document quality, variations in fonts, handwriting, or complex layouts. OCR may struggle with handwritten text or documents with low image quality, resulting in lower accuracy levels.

• OCR software can extract the sum total of all content from a document that has been scanned and stored as an image. However, it cannot extract specific data points or parts of the text based on needs of the user as they usually have no learning component in production.  Content extraction most aptly describes OCR functionality.

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Intelligent Document Processing (IDP) Platforms platforms can handle all document types equally well: 

• While IDP platforms are designed to handle various document types, their performance may vary depending on the complexity of the documents. Certain document types with unique layouts, structures, or handwriting styles may require additional customization or specific training to achieve accurate data extraction.

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Intelligent Data Capture (IDC) Systems can fully automate data extraction without human intervention: 

• IDC systems automate data extraction to a significant extent, but human involvement is often necessary to validate and ensure accuracy. Complex documents, ambiguous data, or exceptions may require human review and intervention for accurate extraction.

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Document Understanding AI can comprehend the full context of a document: 

• While Document Understanding AI can provide context and extract structured data from documents, it may not fully comprehend the semantic meaning or interpret the document content beyond predefined rules or patterns. Advanced understanding of complex documents or contextual information may require additional customization or specialized solutions.

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Data Extraction APIs can handle all data extraction requirements seamlessly: 

• Data extraction APIs provide pre-trained models and algorithms for specific data extraction tasks. However, they may have limitations in handling complex document structures, specialized domains, or industry-specific requirements. Additional customization or integration efforts may be necessary to meet specific data extraction needs.
• While APIs provide a cleaner way to get to a Straight-Through-Processing (STP) implementation in your automation flow, they typically lack the ability to annotate data effectively for a wide variety of document layouts and may be missing adaptive learning capabilities.  Any new variations encountered in your document layouts will require development effort to incorporate into your workflow.

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Robotic Process Automation (RPA) Tools can extract data from any document type with high accuracy: 

• RPA tools can automate data extraction tasks, but their accuracy may vary based on the document types and complexity. RPA tools are typically trained on specific document formats or templates and may require customization or adjustment for handling different document types accurately.
• Also RPA tools rely on manual effort to ensure all variations in document context are handled and may typically not present you with a high degree of adaptive learning capabilities.  Their performance may not increase with more documents going through.

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Industry-Specific Solutions can handle all document complexities in the respective industry: 

• While industry-specific solutions are designed to cater to specific document types or requirements in a particular industry, they may not handle all document complexities equally well. Fine-tuning or customization may still be necessary to achieve accurate data extraction, especially for highly specialized or complex document types.
• Industry specific solutions can typically handle only data points that are on publicly available documents as the vendor would need to build in the intelligence for this captive document segment.  If you have a proprietary set of documents which the solution has not encountered or if you need data that is not explicitly recognized by the solution, your cost of managing these exceptions may be significant.

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It's important to recognize that while AI products can significantly enhance data extraction capabilities, they have limitations and may require customization or additional human intervention to achieve optimal results. Understanding the specific strengths, limitations, and considerations for each type of AI product can help you make informed decisions and set realistic expectations when implementing data extraction solutions.

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In Summary

AI has revolutionized the process of data extraction from documents, offering businesses an efficient, accurate, and cost-effective solution to the challenge of extracting valuable insights from unstructured data. While there are still some challenges when it comes to using AI for data extraction, the technology has come a long way in recent years.There are now a wide range of products available to help businesses automate the data extraction process. 

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By leveraging AI technology, businesses can gain a competitive edge and make more informed decisions based on accurate, structured data.  But in doing so, businesses must be cognizant of the relevance of each AI approach to their specific business needs.  Making intelligent business decisions is paramount to the automation journey of an organization.  Knowing the complexities and pitfalls of this tech terrain helps avoid many of the pitfalls.  

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Future Focus

Looking at these ideas, understanding these key areas of focus and opportunity for businesses, a thought comes up.  What is the future of this segment, that seems so fragmented and complex at this time?  What would be a possible way to move forward with automation in a manner where a business is future-proofed and future-ready today?  These are some of the questions that drove the vision behind SageX.  It’s a product that was designed to help us navigate our way through all that AI has to offer today and in the coming years, without the fear of being locked up only with what we know today.

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Architecturally, the idea was not to create a product that solves every problem in the world (wouldn’t that be something!!!), but rather one that helps you get ready to face any problem in the management of data involving AI-centric approaches.  And to be able to do this quickly and efficiently, without needing to acquire extensive knowledge of technology, AI or data science.  Also there would need to be room to take in technological advancements that are yet to come, without disrupting your current workflows and business processes.  And convenience in extending your automation discovery to more of your business needs in a cost optimized manner.  With all this and the efficiency to integrate technology you might already have on hand, SageX may just be the one pitstop you need to make in this race.  

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