What is "Advanced Document Understanding?"

Intelligent Document Processing (IDP) is a category of artificial intelligence (AI) that is rapidly gaining popularity across many industries. As businesses continue to digitize their operations, IDP is becoming an essential tool for automating the processing of vast amounts of unstructured data from various sources.
IDP leverages technologies such as Machine Learning, Deep Learning, and expert systems to analyze, classify, and extract relevant data from unstructured documents like invoices, purchase orders, and contracts. In this infographic, we will explore these categories of AI and how they work together to power IDP. We will also use real estate predictive algorithms as an example to illustrate how IDP can help organizations make smarter decisions and improve efficiency.

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What is “Advanced Document Understanding”? <note for layout: title>
[Call Out: AI is making all the headlines, but is it ready for prime time?AI: Defined
Artificial intelligence
The ability of a computer or machine to learn from previous experiences
Able to understand and respond to:
Written prompts
Math problems
And more
Today, there are many different AI models in many forms:
Dynamic content suggestions on social media platforms
Self-driving cars
AI-powered Bing
Detecting security vulnerabilities
Writing and validating codeTypes of AIMachine learning (ML)
Enables machines to learn from data and experience without being manually programmed
Example: Snapchat and TikTok use machine learning to apply interactive filters
Deep learning (DL)
Deals with higher complexity patterns and datasets; notable for outperforming other machine learning data inputs such as images.
Like machine learning, DL consists of neural networks that learn to recognize patterns of input data
Example: Google’s MetNet-2 neural network predicts weather 12 hours ahead
Expert systems
Mimics human expert decision-making
Not self-aware, but can scale up human-level decision-making
Example: AI of chest X-rays can identify the kind and stage of lung cancer by taking the image of the upper body and measuring its shadow to determine type & severity
What happens when you unify machine learning, deep learning, and expert systems?
Let’s use Zillow Zestimate as an example
Expert System: “The smallest house on a block will usually sell higher than another house of the same size that is not the smallest one on the block.”
Machine Learning: “Homes in this zip code have been selling within a range of x% above or below the median for that state for the last 9 months, but anticipated changes in interest rates mean that we cannot place as much confidence in that number.”
Deep Learning: “Based on an image analysis of thousands of listed homes, houses with deciduous trees that are 8 meters from a house with larger leaves tend to boost sale prices by 0.09% over trees that are further away with smaller leaves.”[Call Out: In 2023, per IDC, global spending on artificial intelligence technology by governments and businesses will surpass US$500 billion]Categories of AI Applications

Generative AI
Algorithms use existing data to create new content
Example: NovelAI uses existing materials to write ‘novels’ according to a prompt      
Predictive analytics
Data mining and statistical analysis can predict future events
Example: Google Maps uses predictive analytics to estimate travel time and time of arrival
Natural language processing
Enables computers to understand human language and respond
Example: Microsoft Translator translates written and spoken sentences into various formats
Computer vision
Ability to interpret and understand digital images
Example: Apple Face ID utilizes computer vision algorithms to unlock a mobile phone [Call Out: Per Ark Invest, AI will add US$200 trillion to global economic output by 2030]The State Of AI In Business
From a relatively meager 1.9 average number of AI capabilities used by businesses in 2018, the figure doubled by 2022 and will probably double again or more during 2023 for the next time when IDC and McKinsey report their annual stats  The average number of AI capabilities used by businesses today has doubled from 1.9 in 2018 to 3.8 in 2022
Most common AI capabilities:
Automating processes through robotics
Computer vision (meaning derive information from images or videos)
Natural language text understanding – talk to your data. How cool is that!Common AI use cases, by function:
Service operations optimization: 24%
Creation of new AI-based products: 20%
Customer service analytics: 19%
Customer segmentation: 19%
New AI-based enhancements of products: 19%
Customer acquisition and lead generation: 17%
Contact-center automation: 16%
Product feature optimization: 16%
Risk modeling and analytics: 15%
Predictive service and intervention: 14%
63% expect investment in AI to increase over the next 3 years[Call Out: Artificial intelligence is related to many other terms used to define and augment AI’s capabilities]Key Terms To Know
Machine learning
Computer ability to learn without being explicitly programmed
Computers identify patterns and make predictions
Application programming interface (API)
Set of defined rules that enable various applications to communicate with one another
Serves as an “intermediary layer” that processes data transfers between systems
For example, users can integrate chatGPT with existing systems to create a more powerful and intuitive chatbot
Data extraction
Identify and pull usable, targeted information from large, unconsolidated sources
JavaScript Object Notation (JSON)
Lightweight format for storing and transporting data
Used when data is transmitted from a server to a web page
Optical Character Recognition (OCR)
Text recognition or text extraction that allows users to extract printed or handwritten text from images and documents
Intelligent Document Processing (IDP)
Utilizes OCR to extract structure, relationships, key values, entities and other document details using an advanced machine learning-based AI service[Call Out: As businesses and governments explore responsible AI solutions, they will only reap clear and measurable benefits with a plan]The Benefits Of AI In Business and Government
Increased efficiency and productivity
AI handles menial tasks, allowing workers to complete higher-value tasks and minimizing costs and maximizing human capital
Greater speed
AI shortens development cycles and cuts time between design and commercialization, leading to quicker and higher ROI
Improved monitoring
AI processes large amounts of data, giving businesses real-time insights and even initiating solutions
Higher quality and increased accuracy
AI automates repetitive, rule-based tasks and helps reduce errors
Enhanced talent management
AI streamlines the hiring process by screening and identifying candidates and:
Measures employee sentiment
Helps identify high-performers
Suggests equitable pay[Call Out: AI isn’t a one-size-fits-all solution, putting the onus on businesses and governments to find and implement the right one for specific needs, info security, and other regulatory compliance]How To Successfully Incorporate AI Into Business Operations
Define business needs
What problems do AI need to solve?
How will you measure success?
Set near-term goals
What are the anticipated financial benefits of AI use?
Who is our economic buyer executive champion? What does she need?
How will the AI fit into daily operations?
Evaluate business capabilities
Determine which approach will work best for your business or public sector agency:
Build a new solution using internal resources
Buy a product
Collaborate with a partner to develop an AI solution
Outsource the AI development process
Prepare the data
Ensure the dataset is:
Free from incoherent data entries
Compatible with the algorithm to perform the task
Begin small
Use a small sample dataset to identify the value of the AI solution
Test AI performance before shifting to larger projects[Call Out: A common task many businesses can automate — document processing — can and should be handled using a next-gen AI solution like Lazarus AI]Intelligent Document Processing: Lazarus AIRikAI
Input agnostic language model
Works with no training or retraining required
Extracts data from any document regardless of type, format, or language
Contextualizes information, analyzes layouts, and finds answers to natural language questions
Understands 40+ languages and more every day
Recognizes human handwriting
Includes explainability metrics (confidence scores and bounding boxes)
RikAI is unique from other platforms on the market
While previous gen IDP tools struggle with one or more of the following important customer requirements:
Can't process handwriting
Need template building
Demand training and retraining
Drop in accuracy when new templates or document layouts are introduced
Charge an initial platform fee
Struggle to identify medical records and attending physician statements
RikAI goes a step beyond:
No need to gather or label heaps of training data
Powerful learning model is easy to integrate
Performance is better than previous-generation document processing, and can understand prior authorizations or physician’s notes
RikAI interprets typed text with 99.9% accuracy
Interprets physician handwriting with 92% accuracy
Options for price-performance for signatures and check boxes
RikAI doesn’t drop in accuracy when feeding it different document types
Teams don’t have to waste time collecting and labeling training data[Call Out: As more businesses and governments incorporate AI into daily operations, every day data science teams are increasingly challenged to develop the most sophisticated and precise solutions that deliver max value for exec business stakeholders, making it incredibly valuable to work with the leader in next-gen IDP Lazarus AI]CTA: Find your AI superpower for advanced document understanding